Malaysia Power Analysis

Technische Universität MünchenLehrstuhl für Energiewirtschaft und Anwendungstechnik

Prof. Dr. rer. nat. Thomas Hamacher

Master’s Thesis

Analysis of the Power System of Malaysia

Melanie Maria Theresia MannhartMatr.No. 03635605

Supervisors:

Dr.-Ing. Tobias MassierProf. Dr. rer. nat. Thomas Hamacher

Abstract

With its growing economy, the demand for electricity in Malaysia is expected to triple in 2035as compared to 2012. Meeting this high electricity demand sustainably and environmental-friendly is a future challenge. This thesis analyses the actual and future power system inMalaysia, regarding not only the rapidly increasing electricity demand but also environmentand reliability aspects as well as total costs of the power system.Data about the actual power system from literature are implemented into the model URBS.These include the electricity demand, the transmission power grid and economic parametersof the energy market. By using this time step based model to find the most cost-effectivestructure of the future power system, impacts of intermittent energy sources on the powersystem are investigated. Moreover, the influence of reducing CO2 emissions and the expansionof the 2012 power grid in Malaysia are examined. A sensitivity analysis of fuel prices ispresented by varying gas prices.

In all cases, the expansion of transmission lines reduces the overall costs of the power system.As the potential of renewable energies (RE) and the demand for electricity are unequallydistributed, the construction of new transmission lines has a positive effect on the utilisationof RE, particularly on the exploitation of hydro power potential. The higher the CO2 emissionlimit, the higher the installed and used capacity of RE. However, due to the use of RE, back-up capacity has to be installed which results in higher investment costs. Considering lowergas prices, CO2 emissions are reduced by using cost-effective and low-emission gas-fired powerstations. Subsidies to fossil fuels restrain the development of RE. Hence, one can considerto diminish these subsidies while incentivising renewable energies to obtain a sustainable andreliable energy supply.

Statement of Academic Integrity

I,Last name: MannhartFirst name: Melanie Maria TheresiaMatr.No.: 03635605

hereby confirm that the attached thesis,

Analysis of the power system of Malaysia

was written independently by me without the use of any sources or aids beyond those cited,and all passages and ideas taken from other sources are indicated in the text and given thecorresponding citation.

Tools provided by the institute and its stuff, such as models or programs, are also listed.These tools are property of the institute or of the individual staff member. I will not use themfor any work beyond the attached thesis or make them available to third parties.

I agree to the further use of my work and its results (including programs produced andmethods used) for research and instructional purposes.

I have not previously submitted this thesis for academic credit.

Singapore, 01/04/2014

.................................................(Author: Mannhart, Melanie Maria Theresia)

Contents

1 Motivation 1

2 Data Research 122.1 Regional Distribution of Malaysia . . . . . . . . . . . . . . . . . . . . . . . . . . 122.2 Climate in Malaysia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.3 Energy Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.4 Demand for Electricity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.5 Power Stations and their Characteristics . . . . . . . . . . . . . . . . . . . . . . 202.6 CO2 Emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.7 Power Grid in Malaysia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3 Implementation of Data into the Model URBS 283.1 The Model URBS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.2 Definition of Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303.3 Spatial Resolution of Malaysia . . . . . . . . . . . . . . . . . . . . . . . . . . . 323.4 Classification of Model Input Commodities . . . . . . . . . . . . . . . . . . . . 323.5 Demand for Electricity by Region . . . . . . . . . . . . . . . . . . . . . . . . . . 343.6 Distribution of Potential of RE . . . . . . . . . . . . . . . . . . . . . . . . . . . 353.7 Economic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363.8 Further Assumptions Concerning Input Data . . . . . . . . . . . . . . . . . . . 363.9 Excel Macro ’filling table’ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

4 Scenarios 394.1 Definition of Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394.2 Proofing the Model by Evaluating the 2012 Reference Scenario . . . . . . . . . 424.3 Investigation of Future Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . 44

4.3.1 Verifying Political Targets by 2020 . . . . . . . . . . . . . . . . . . . . . 444.3.2 Long-Term View in 2035 . . . . . . . . . . . . . . . . . . . . . . . . . . . 494.3.3 Impact of Gas Price and CO2 Emission Restrictions in 2035 . . . . . . . 54

5 Conclusion and Outlook 58

Appendix 61

I

Contents

Acronyms and Symbols 90

List of Figures 92

List of Tables 93

Bibliography 96

II

1 Motivation

With endorsing the Bangkok Declaration on 8 August 1967 in Bangkok, Thailand, the As-sociation of South-East Asian Nations (ASEAN) was built by 5 founding members, namelyIndonesia, Malaysia, Philippines, Singapore and Thailand. They declared to establish an ’As-sociation for Regional Cooperation among the countries of South-East Asia to be known as theAssociation of South-East Asian Nations (ASEAN)’ in which they agreed on common object-ives and purposes [ASEAN.1967]. Furthermore, the Bangkok Declaration takes care of theway of accomplishing these aims. There are four institutions and organs founded which are theAnnual Meeting of Foreign Ministers (referred to as ASEAN Ministerial Meeting), a StandingCommittee which execute the work of ASEAN between ASEAN Ministerial Meetings, Ad-Hoc Committees and Permanent Committees of specialists and officials on specific subjectsand a National Secretariat in each member country which is responsible for the work of theAssociation on behalf of each member [ASEAN.1967]. Moreover, in the Bangkok Declarationis implied that ’the Association is open for participation to all States in the South-East AsianRegion subscribing to the aforementioned aims, principles and purposes.’ [ASEAN.1967].

For the time being, ASEAN includes ten member countries in South-East Asia (SEA) whichare Brunei Darussalam (since 1984), Cambodia (since 1999), Indonesia, Lao PDR (since 1997),Malaysia, Myanmar (since 1997), Philippines, Singapore, Thailand and Viet Nam (since 1995)[ASEAN.2014].

Due to the diverse structure of countries, there are differences in the stage of development.This includes the variety of energy use in every country concerning the dimension and patternsof the energy consumption. Moreover, related to the different energy uses and the endowmentsof energy sources, there are differences both in the economic growth as well as the growth of theenergy demand. According to [IEA.2013], Indonesia used the largest amount of primary energywith 36% of the total demand, followed by Thailand as the second-largest user with 21%in 2011. Moreover, Indonesia also consumed over 50 times more than Brunei Darussalamwhich consumes the minimal amount of energy of ASEAN. In total, the consumed energyper capita is 0.9 toe in SEA in 2011 which is relatively low compared to developed countries.Additionally, the growth rate of the primary energy demand varies by 2.3% to 3.5% per yearuntil 2035 [IEA.2013]. The prospective per-capita energy consumption is 1.4 toe/capita in2035.

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Table 1.1: Primary energy consumption of ASEAN by county in 2011 [IEA.2013].Share of primary energy consumption

Indonesia 35.7%Malaysia 13.5%Philippines 7.3%Thailand 21.5%Rest of ASEAN 21.7%

Total ASEAN 549 Mtoe

The previously mentioned diversity affects the demand for electricity.One of the key indicator of the electricity demand which is calculated as the per-capita elec-tricity demand varies by around 1,000 kWh/capita in Cambodia to around 9,000 kWh/capitain Brunei Darussalam. The total consumption of electricity in SEA came to 712TWh in 2011with an annual growth rate of 4.2% by 2035 [IEA.2013].Another indicator of the demand for electricity is the share of population without access toelectricity. It diversifies widely between the South East Asian region. The electrification ratein Brunei Darussalam, Malaysia, Singapore and Thailand came to almost 100%, whereas ashare of 66% of the population in Cambodia had no access to electricity. This was the lowestrate of ASEAN in 2011. In Myanmar, about one out of two lacked access to electricity. Al-though the electrification rate is still low and below the ASEAN average of 78%, Indonesiaachieved to extend the share of population with access to electricity from 53% in 2002 to 73%in 2011 [IEA.2013].

Regarding this widely spectrum of features in the particular countries and the high economicgrowth, it is likely that the electricity demand will increase rapidly. For the previous mentionedreasons, the focus on the overall energy consumption will shift from the already developedcountries towards Asia.

Thus, future challenges resulting from the rapidly growth have to be analysed and examined,especially regarding meeting the highly increasing demand for energy and electricity of theemerging countries in SEA. In the following, Malaysia as a developing country of the foundingcountries of ASEAN is considered more in detail.

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Profile of Malaysia

Malaysia, located in the middle of SEA, consists of two regions which are separated by theSouth China Sea, namely Peninsular Malaysia and Malaysian Borneo. Whereas PeninsularMalaysia is situated in the south of the Asian continent and at the boarder to Thailand,Malaysian Borneo is located in the north western area of the largest island of Asia. Thisisland is called Borneo, which itself is located in the east of Peninsular Malaysia. It consistsof three different countries, which are Brunei Darussalam, Sabah, Sarawak and Labuan whichbelongs to Malaysia, and Indonesian Kalimantan.

The total area of Malaysia comes to around 330,290 km2 [JPMy.2012] and consists of 58%lowland and 42% highland areas with high differences in altitude [Hussein.2010]. The lowestpoint of Malaysia is the Indian Ocean at sea level, whereas the highest point is the MountKinabalu at a height of 4,100m in Sabah [CIA.2014]. Furthermore, Malaysia holds the 29thlongest coast line in the world with a total length of around 4,675 km [Ahmad.2014b]. Thedistribution of the total Malaysian area is listed in Table 2.1. It shows that around 60% ofthe Malaysian area is part of Borneo, although 80% of the population is living in PeninsularMalaysia [JPMy.2012].

The Malaysian country is politically subdivided as follows: There are 13 federal statesand three federal territories, whose eleven states and two federal territories are in PeninsularMalaysia as well as two federal states and one federal territory in Malaysian Borneo, respect-ively. The capital Kuala Lumpur is in Peninsular Malaysia, which is one federal territory,although the seat of government is located in Putrajaya which is the second federal territoryin Peninsular Malaysia.

The total arable land came to a share of 5.44% [CIA.2014]. Given by [JPMy.2012], thecultivated area for main crops comes to 6,758.4 thousand hectares whose largest share wasoil palms with 74%. According to [DOA.2012], 97,275.19 hectares of the total land were idleland in 2012. It consisted of inland and paddy fields which has potential for cultivating butwere not planted for three years uninterruptedly. Thus, this land is considered as possiblelocations of, for instance, commercial solar farms without conflicts with the food industry.

Table 2.1 gives the population density and the population distribution by state. It isobvious that the two countries situated in Borneo, namely Sabah and Sarawak, have one ofthe lowest population density/km2 in overall Malaysia. In total, the Malaysian populationcomes to 29.34 Million in 2012 [JPMy.2012]. The labour force comes to 12.5 million peoplein 2012 from which there are distributed to the sectors with 52.7% to the service, 36.2% tothe industry and 11.1% in the agriculture sector [EPU.2012]. The unemployment rate comesto 3.2% which is relatively low [EPU.2012].

The structure of the production as a percentage of the Gross Domestic Product (GDP)in 2012 was composed of the largest share of the service sector with 54.6%. Additionally, the

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second largest share was contributed by the manufacturing sector with 24.9% and followedby the mining sector and the agriculture sector with 8.4% and 7.3%, respectively. Theconstruction sector added 3.5% to the GDP in 2012 [JPMy.2013].In total, the GDP in Malaysia came to 239.9 billion US$ [EPU.2012] at 2012 Purchasing PowerParity (PPP)-prices. Prospective outlooks predict an annual increase of the GDP by 5.0%until 2020 and by 3.4% from 2020 until 2035, respectively [IEA.2013]. The economic growthamounted to 5.6% in 2012 [JPMy.2013].

According to [Tang.2013], there is a causality between the electricity consumption andthe economic growth in Malaysia in the short-run as well as in the long term. The realincome affects the consumption positively, whereas the energy price and the technology in-novations contributes to it negatively. For instance, better developed products by technologyinnovations will increase the efficiency of the electricity utilisation and therefore reduce theelectricity consumption. Hence, one of the key findings of [Tang.2013] is that ’Malaysia is anenergy-dependent country.’. Accordingly, the energy sector is a key indicator for the country’seconomy and in the following it will be examined particularly.

National Energy Policy

With the oil crises in the 1970s, the awareness of limited resources of fossil fuels raised.Hence, the actual government launched the National Petroleum Policy in 1975 to guaranteeoptimal use of petroleum resources by controlling the industry regarding economic, socialand environmental coverage. In view of the global oil crises in 1973 and 1978, the economicgrowth was rigorously influenced by the increasing prices of oil. As Malaysia’s former energymix relied mostly on oil, the negative effects of the oil crises affected the Malaysian economy aswell. Thus, according to [Khor.2013] and [ASM.2013] which are literally identical in parts, theNational Energy Policy was framed in 1979 with the aim of providing energy cost-effectivelyboth from fossil and renewable sources. Furthermore, the use of energy was to be efficient andproductive as well. Additionally, the negative impact of the energy sector on the environmentwas to be reduced.

In 1980 the National Depletion Policy was formulated to prevent the limited energy re-sources from over-exploitation. Thus, the daily production of crude oil and natural gas wasrestricted to 630,000 barrels and 2,000MMscf, respectively, to extend the time frame of pos-sible future exploitation [Khor.2013].

Followed by the Four-Fuel Diversification Policy in 1981, the dependence on the mainenergy resource oil was to reduce further. The aim was to diversify the energy sources for theelectricity generation into oil, gas, hydro power and coal.

In 1990, the Electricity Supply Act (ESA) was adopted to administrate the MalaysianElectricity Supply Industry (MESI) [HAPUA.2013].

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With the local mining of fossil resources to ensure supply security, the National MineralPolicy was launched in 1998. This set of regulations formulated guidelines for the efficientmining of local coal resources by enhanced underground mining methods, advanced machineryand computerisation of the maintenance and administration of the mining companies.

In 2000, the Four-Fuel Diversification Policy was extended by renewable energies (RE) asa fifth energy source to the Five-Fuel Diversification Policy. This was a step forward into asustainable energy policy since RE were admitted as a possible fuel in addition to oil, gas,coal and hydro power.

When the ESA from 1990 had been completed with the Energy Commission Act (ECA) in2001, the Energy Commission (Suruhanjaya Tenaga) was enacted as the regulator of the MESIfor Peninsular Malaysia and Sabah. Particularly, the ESA and ECA authorise the EnergyCommission to govern the electricity tariff to the customers. However, the energy supplyindustry in Sarawak is controlled by the local utility according to the Sarawak ElectricitySupply Ordinance of 1992 which stays in contrast to the idea of an overall regulating authorityin Malaysia [HAPUA.2013].

With the growing recognition of RE, the programme of the Small Renewable EnergyPower (SREP) was launched in 2001. In the Renewable Energy Power Purchase Agree-ment (REPPA) the utilisation of RE as a source to generate power was approved. Withsigning this agreement, the national power utilities consent to purchase the generated elec-tricity by RE from independent Power Producers (IPP) for the upcoming 21 years in thenational power grid. Thereby, electricity which is generated by biomass, biogas (results fromwaste of palm oil mills and municipal landfills) as well as solar photovoltaic (PV) and wind isapplicable. However, capacity of those small power stations is restricted to 10MW. Thus, thetarget of RE capacity of 350MW could not be obtained as only less than 4% of this particularamount was installed.Regarding this failure, there were four different reasons. On the one hand, there were stillrelatively high subsidies to fossil fuels which compensate the small incentives of the SREPby far. On the other hand, the high investment costs of RE power stations combined withthe low incentives result in a long pay-off time and make investments unattractive. Further-more, long negotiations were conducted to sign REPPA’s with strictly terms. Uncertainty ofthe price and the future availability of biomass as a energy source enhanced the reluctanceof investments as well. Nevertheless, the SREP reinforced the government’s pledge of thedeveloping role of RE in the generation mix.

In addition, a further project in the Malaysian Palm Oil Industry, called Biomass-basedPower Generation & Co-generation (BioGen), was adopted in 2002 until 2010 to promote theimplementation of RE, which was also supported by the Global Environment Facility of theUnited Nations Development Programme (UNDP-GEF). Moreover, the Malaysian BuildingIntegrated Photovoltaic (MBIPV) in 2005 completed these initiatives which was to reduce the

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investment costs of solar PV. Furthermore, the installed capacity of solar PV utilisations inbuildings was to enlarge.

Followed by the National Green Technology Policy in 2009, the utilisation of environmental-friendly technologies was enacted. Related to this programme, the introduction of co-generationtechnologies which are based on biomass and the promotion of power generated by RE wereaccomplished. This also included the production and application of environmental-friendlymanufactures. Additionally, new markets were opened up by the favourable opportunities of’green’ products, buildings and the overall environmental management [Khor.2013].

Su

stainab

le En

ergy

Op

tions fo

r Electric P

ow

er Gen

eration in

Pen

insu

lar Malay

sia to 2

030

5

Figure 1. Timeline of energy-related policies and initiatives in Malaysia (1979–2015).

Figure 1.1: Overview of selected political frameworks from 1975 to 2015 [Khor.2013].

In 2010, the New Energy Policy completed the energy-related political framework so farand reaffirmed the government’s encouraging of the utilisation of RE for electricity generation.This programme considered economic efficiency, environmental and social aspects, especiallyto amplify the supply security by using RE. To accomplish this, five strategic key approacheswere defined. Firstly, the programme was been proactive to ensure and conduct an energysupply which is sustainable and solid. Additionally, there were measures in order to enhancethe role of RE and the implementation of a energy pricing which is based on current marketprices. Moreover, the controlling and management were to reinforce and finally to bring offthe policy shift [Khor.2013]. A further target was that 5.5% of the produced electricity isgenerated by RE by 2015 [Gan.2013].

The next step was to launch the Renewable Energy Act in 2011 to increase further thedevelopment of RE. It contains the scheme of Feed-In Tariff (FiT) for RE-based electricity.At the same time, the National Biomass Strategy 2020 was enacted to accredit the utilisationof biomass for the generation of biofuels [Khor.2013].

Additionally to those legislative frameworks, there are National Development Plans, calledMalaysia Plan (MP) which are defined every five years. These plans aim for enhancing thenational economic development and the rural electrification by implementing infrastructure

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development which are covering energy policies, strategies and initiatives. Thus, they are toenlarge the coverage of basic needs of people with deprived backgrounds. In every MP, specifictargets have been defined which are related to the lately adopted policies. In addition, Fig-ure 1.1 gives an overview of the policies and development plans from 1975 to 2015, accordingto [Khor.2013].

Structure of the Energy Sector in Malaysia

The composition of the power supply which is related to the analysis of the power system andthe energy sector is examined in the following.In Figure 1.2, the development of the primary energy supply in Malaysia is depicted from1980 until 2010 based on the periods of the MP [MEIH.2012b]. It is observed that thedifferent policies influence the primary energy supply considerably. The shift from crude oiland petroleum products to natural gas and coal is obvious. Solely, the development of otherRE is obscured.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1980 1985 1990 1995 2000 2005 2010

Shar

e o

f en

erg

y so

urc

es

in p

rim

ary

ener

gy s

up

ply

Coal and Coke Crude Oil and Petroleum Products Hydro Power Natural Gas

Figure 1.2: Primary energy supply in Malaysia in time steps based on the 5-year MalaysiaPlans (MP) [MEIH.2012b].

Additionally, the primary energy supply in 2012 is shown in Figure 1.3 [MEIH.2012b]. Itdepicts the high dependency on fossil fuels -formerly on oil, nowadays on natural gas and coal-as the main component of the primary energy supply. Natural gas contributed the largestshare of 46% to the primary energy supply. Followed by crude oil and coal with a share of 32%and 19%, respectively. Moreover, the primary energy supply consisted of 3% of hydro power.Biogas and biomass were still negligible.

Furthermore, the final energy demand per capita came to 1.59 toe in Malaysia in 2012.According to [IEA.2013], the per-capita energy consumption is estimated at 61% and willincrease up to 83% of the OECD average in 2035. The total amount of the final energydemand was 46,710 ktoe [MEIH.2012d, JPMy.2012]. Figure 1.4 depicts the distribution of the

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0 10000 20000 30000 40000

Biogas

Biomass

Coal and Coke

Crude Oil

Hydro Power

Natural Gas

ktoe

Primary EnergySupply (ktoe)

Figure 1.3: Primary energy supply in 2012 in ktoe [MEIH.2012b].

final energy demand by fuel type in 2012. Natural gas as the largest share contributed 33%,the second-largest parts are electricity and oil (both with a share of 21%), followed by dieselwith 19%. Coal and coke added only 4% of the final energy demand in 2012, although theymade a relatively huge part in the primary energy supply [MEIH.2012d]. Thus, the share ofcoal was already taken in account to the electricity share. Compared with the share of naturalgas in the primary energy supply as well, it is obvious that the generation of electricity ismainly based on fossil fuels.

4%

19% 21%

33%

2%

21%

Coal and Coke Diesel Electricity Gas Non-Energy Oil

Figure 1.4: Final energy demand (in total 46,710 ktoe) by fuel type in 2012 [MEIH.2012d].

In Figure 1.5, the electricity consumption by sector is depicted in 2012. Thereby, the indus-trial sector held the largest share of 45% of the total electricity consumption (10,011 ktoe).This was followed by the commercial sector with 33% and the residential sector with 21%,respectively. The agricultural and the transport sector made up a merely small part of finalelectricity consumption with 30 ktoe and 21 ktoe, respectively [MEIH.2012c].

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30 ktoe 3,325 ktoe

4,509 ktoe 2,126 ktoe

21 ktoe

Agriculture Commercial Industrial Residential Transport

Figure 1.5: Electricity consumption in Malaysia by sector in 2012 [MEIH.2012c].

As illustrated in Figure 1.6, the actual Malaysian generation mix of electricity in 2012 (intotal 122,535.6 GWh [JPMy.2012a]) was based on fossil fuels which resulted in high energy-related CO2 emissions [EPU.2012]. The mix was dominated by electricity of gas-fired powerstations with a share of 52.7%. Furthermore, the amount of electricity which was generatedby coal-fired power stations contributed the second largest share to the mix (38.9%). Hydropower stations generated a share of 7.3% of the total electricity, followed by a share of 1.0%of power stations fired by oil and petrochemicals. Generated electricity from other RE likesolar PV and wind was 0.2% in 2012.

38.9%

52.7%

7.3%

1.0%

0.2%

Coal Gas Hydro Power Oil and Petrochemicals Other RE

Figure 1.6: Electricity generation of 122.5TWh in Malaysia in 2012 [EPU.2012].

With its growing economy, the energy demand is also increasing in future. Due to thegrowing demand for electricity there will be future challenges. As already mentioned, thedemand for electricity will grow by 4.2% in Malaysia [IEA.2013]. Due to the combustionof fossil fuels to generate the needed electricity, the CO2 emissions related to the powergeneration will keep on rising in the near future in Malaysia unless the generation mix willchange.

At the 15th United Nations Framework on Climate Change Convention (UNFCCC) Con-ference of the Parties (COP) in Copenhagen in December 2009 (COP 15), the Malaysian

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government undertook voluntarily a 40% reduction of the emission intensity of 2005 by 2020,according to [ASM.2013]. This parameter is defined by the total CO2 emissions related to theGDP. Hence, there is a urgent need for acting.

Objective of This Thesis

The aim of this thesis is to analyse the power system of Malaysia particularly. It is tobe clarified how each part of the power system is linked to each other, namely the energyconsumption and demand situation, power generation, power transmission and distribution aswell as the energy market with respect to economic aspects. By means of a time-based model,the hourly coverage of the demand side and the supply side is considered by certain constraints.Hence, hourly time steps are regarded in which the demand for electricity has to meet by thegenerated electricity. One advantage of this approach over studies so far is that intermittentenergy sources and hourly load curves with possible peaks are taken into account. The usedmodel is based on a cost minimising algorithm with linear optimisation. Transferred to thepower system it is possible to simulate the future power station fleet and calculate the annualtotal costs of the power system to cover the future demand. Furthermore, energy-related CO2

emissions are derived and restricted. By limiting the energy-related CO2 emissions, the fleetof power stations has to be adjusted to power generating processes which are mostly low-emission processes. This is where renewable energy sources come in as electricity generationby RE is emission-free and a possible and sustainable alternative to fossil fuel-fired powerstations.

There are a few studies regarding the Malaysian power system so far. Most of them pickup just a part of the power system as e.g. the impact of RE on the power system. For in-stance, Ahmad and Mat Tahar [Ahmad.2014b] focus on selected RE for the development of asustainable electricity generation system in Malaysia. They use a analytic hierarchy processto make decision analysis based on multi-criteria. This approach is common in the energysector. The main criteria are technical, economical, social and environmental. They cometo the conclusion that Malaysia is endowed with potential of RE but its exploitation is alsolimited. Furthermore, the feed-in tariff, the efficiency and the lifetime of power stations affectthe future power system the most. They suggest to put the main stress on hydro power,biomass and solar PV as technologies to develop and generate electricity in Malaysia.Moreover, Koh and Lim [Koh.2010] did a case study in Sabah in 2010. In this paper theyevaluate alternative options to a planed coal-fired power station to meet the future demandin Sabah. They focus on solar PV, hydro power, biomass, wind turbines and ocean energy aswell as the construction of new transmission lines to Sarawak. These evaluations are based ontechnical, economic and environmental aspects compared to a 300 MW coal-fired power sta-tion. They found out that there are feasible options to the coal-fired power station which are

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hydro power, biomass and the construction of new transmission lines to Sarawak. Addition-ally, these alternatives are lower in CO2 emissions and therefore more environmental-friendly.Khor and Lalchand [Khor.2013] give recommendations on possible policies for a sustainableelectricity generation. Their results are based on the political framework and the economicand environmental development in Malaysia. The results are not model-based, even less timestep based. They conclude that there are a few strategical points to achieve a sustainableelectricity generation. Firstly, they suggest to enhance the supply security of coal as the gen-eration mix is mainly based on this energy resource. At the same time, subsidies to naturalgas are to reduce. Furthermore, RE are to promote more intensively and measures regard-ing energy efficiency has to be employed on. Additionally, Malaysia needs a common officialframework for coordinating the energy sector, according to [Khor.2013].Gan, Komiyama et al. [Gan.2013] did an analysis of the actual energy sector as well as of thefuture power system with low emissions in their paper about ’A Low Carbon Society Outlookfor Malaysia to 2035’. Their approach was to develop a model using the ordinary least squares(OLS) method based on historical data. By means of an econometric model combined with amacroeconomic sub-model and an energy-environment sub-model, they reached the result ofa macroeconomic and energy outlook in Malaysia to 2035. Using different scenarios like thereference and technologically advanced scenarios, they are able to predict the development ofthe economy and the energy sector on an econometrically basis. They find out that coun-termeasures have to be implemented to reduce the unsustainable development of fossil fuelsacross all sectors. However, their approach is not based on time steps as well.Modelling a time step based tool to analyse power systems has been done in other regions andcountries so far. Huber et al. [Huber.2012] gave results of a techno-economic model which op-timised the power system in Europe, the Middle East and North Africa (EUMENA) in 2050.The used model is based on hourly time steps and minimises the total annual costs of thepower system. In their study they also implemented a reduction of CO2 emissions as well aspossible extensions of the power grid between Europe, the Middle East and North Africa. Alsothe impact of intermittent energy sources were examined. They define four main scenarios inwhich they investigate the future power system in EUMENA under different constraints. Thisstudy results in the high potential of interconnected power grid regarding the minimising ofthe total system costs and offshore wind as the dominating renewable source for the electrictygeneration in future. Their work is the basis of the model which is used in this thesis.

The structure of this thesis is as follows. The current situation of the power system ofMalaysia is examined by literature research on available data regarding the energy sector.Additionally, economic data are taken into account as the used model solves the optimisationproblem under the main constraint of minimal total costs. These studies are described inChapter 2. In Chapter 3, the processing of those data and the implementation into the modelare shown. The actual generation mix in 2012 is modelled and future scenarios will be definedand examined in Chapter 4, based on the results of the linear optimisations calculated by themodel. Chapter 5 completes this thesis.

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2 Data Research

2.1 Regional Distribution of Malaysia

Malaysia consists of two main regions. Separated by the South China Sea, there are Penin-sular Malaysia and Malaysian Borneo. The country is political divided into 13 federal statesof which 11 states are located in Peninsular Malaysia and 2 states in Malaysian Borneo.Moreover, three federal territories exist which are W.P. Kuala Lumpur, W.P. Putrajaya inPeninsular Malaysia and W.P. Labuan in Borneo. The division by federal states and federalterritories is depcited in Figure 2.1. Most of the available information and data refers to thatclassification of the country.

The distribution of the total area of Malaysia is listed by regions in Table 2.1. Additionally,the population as well as the population density in 2012 are registered. The share of GDPin 2012 is shown in the fifth column of the table, related to a total GDP of 239.9 billion US$at 2012 PPP-prices.

Figure 2.1: Political map of Malaysia: Division into 13 federal states and 3 federal territories,namely Kuala Lumpur, Putrajaya and Labuan.

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2.2 Climate in Malaysia

Table 2.1: Distribution of area [JPMy.2012], population and share of GDP by re-gions [JPMy.2013].

Regions Area Population Population Density Share of GDP[km2] (’000) [/km2] [%]

Johor 19,016 3,439.6 180.88 9.2Kedah 9,425 1,996.8 211.86 3.4Kelantan 15,105 1,640.4 108.60 1.8Kuala Lumpur 243 1,713.4 7,051.03 15.2Melaka 1,652 842.5 509.99 2.9Negeri Sembilan 6,657 1,056.3 158.68 3.7Pahang 35,965 1,548.4 43.05 4.1Penang 1,031 1,611.1 1,562.66 7.0Perak 21,022 2,416.7 114.96 5.3Perlis 795 239.4 301.13 0.5Sabah1 73,951 3,463.3 46.83 6.3Sarawak 124,450 2,545.8 20.46 9.6Selangor2 8,022 5,730.2 714.31 23.5Terengganu 12,956 1,092.9 84.35 2.6Total 330,290 29,336.8 239.9 billion US$

1 including W.P. Labuan2 including W.P. Putrajaya


Due to the close position of Malaysia to the equator line (at 1° - 7° North latitude and 100° -120° East longitude [Mekhilef.2012]), it is characterised by an equatorial climate. High tem-peratures which are likely uniform the whole year, high humidity, relatively moderate windsand abundant rainfall throughout the year are characteristics for this kind of climate.

The range of temperatures comes to a minimal average of 22 ◦C - 24 ◦C per month anda maximum average of 29 ◦C - 33 ◦C. At the same time, the temperature does not changethroughout the year (merely differences around 2 ◦C) but it varies instead daily by a rangebetween 5 ◦C - 10 ◦C in coastal areas and 8 ◦C - 12 ◦C inland, respectively [McGinley.2011].

A further effect of the geographical location and topographical nature of Malaysia resultsin seasonal monsoon winds. Although they are generally light, there are some regular changesduring the year. Based on these, it is possible to categorise four different types of seasons,namely south-west monsoon season, north-east monsoon season and two inter-monsoon sea-sons. The south-west monsoon season appears around end of May until September, whereasthe north-east monsoon season lasts from the beginning of November to March. Accord-ing to [MMD.2014], the south-west monsoon causes south-western and moderate winds un-der 7ms−1. On the other hand, north-east monsoon winds are continually blowing fromeastern or north-eastern with a scale of around 5ms−1 to 10ms−1. However, they canreach 15ms−1 in the east coast states of Peninsular Malaysia at times of squally winds of

13


cold air from the north. Between these two seasons, the inter-monsoon seasons usually bringgentle and variable winds. Additionally, due to the maritime climate, there are sea breezes(usually on bright sunny afternoons) and land breezes (generally on clear nights) over thecoastal area [MMD.2014].

According to [MMD.2014], the seasonal wind patterns linked to the local topographicalnature define the rainfall distribution in Malaysia, while one distinguished between PeninsularMalaysia and Malaysian Borneo. The rainfall distribution in Peninsular is specified by threedifferent rainfall patterns, whereas there are five different scheme types in Borneo. The maindifferences between the patterns are the monthly distribution of minimal and maximal rainfall.

In Peninsular, the rainfall distribution of the east coast area is specified by a period with amaximum rainfall in November to January and and with a minimum rainfall in June and July.In the rest of Peninsular there are usually two maxima (October to November and April toMay) and two minima (June to July and February) of rainfall throughout the year. However,one minimum and one maximum of rainfall occur in the south-western coast area. That isbecause this coastal area is normally affected by lines of thunderstorms during early morning,called Sumatras, from May to August. Compared with the distribution in the east coastarea, the months of maximum and minimum rainfall are shifted to October and November(maximum) and February (minimum).

In Borneo, five different patterns are diversified as follows:The coastal areas of Sarawak and north-east areas of Sabah are characterised by rainfallwith one maximum and one minimum throughout the year. The maximal amount of rain isobserved in January. However, the months with the lowest rain vary in these to areas (forSarawak in June and July, for Sabah in April).The inland of Sarawak features almost homogeneously distributed rainfall with a annualamount of more than 5,000mm.The southern area of Sabah bears a resemblance to the pattern of the distribution of rainfallof the inland of Sarawak but there is usually less rainfall from February to April.Moreover, the central area of Sabah is also similar to the previously mentioned patternsbut two minima and two maxima are observed. May and October are the months withmaximal rainfall, whereas a minimum amount of rain is received during February and August.Nevertheless, the total amount of rainfall in this area is below the rainfall in the rest ofMalaysia.Furthermore, the north-west coastal area in Sabah resembles to the central area of this part ofBorneo. Differences occur in characteristic of the second minimum in August. The amount ofrainfall there is significantly higher than in the primary minimum. In some areas, the amountcomes to the fourfold compared to the minimal rainfall in August.

Caused by the equatorial location, the humidity is relatively high and lies between 70%to 90% [McGinley.2011]. It does not alter annually rather throughout the day time -like the

14

2.3 Energy Sources

temperature curve [McGinley.2011]. Additionally, complete days with an entirely clear sky areextremely rare [MMD.2014]. Thus, solar radiation fluctuates, especially during the monsoonseasons due to clouds which absorb sunshine and reduce the insolation. The daily averageof solar radiation comes to 4,500 kWh/m2 but it is generally lower during the south-westmonsoon and higher over the period of the north-east monsoon [Shafie.2011, Mekhilef.2012].Moreover, the daily average amount of hours with sunshine comes to six hours in Malaysia,but it is also dependent on the seasons and the location. It varies from an average of fivehours a day in Kuching, Malaysian Borneo, to seven hours a day in Alor Setar, PeninsularMalaysia [MMD.2014].

2.3 Energy Sources

To analyse the power system of Malaysia, information on the available energy sources forthe electricity generation are needed. As already mentioned in Chapter 1, the generation ofelectricity is based on fossil fuels, namely natural gas and coal. Oil and petrochemicals onlyplay a minor role in the power generation. Due to this dependence, the fuel price is an import-ant input which affects the electricity generation costs directly and significantly. Althoughthere might be a slight chance of occurring shortages in the supply of these commodities, itis assumed in the following that they are on stock.

Malaysia is endowed with fossil fuels. As natural gas dominated the energy mix in 2012, itis analysed initially.In total, Malaysia holds gas reserves of 7.3 trillion cubic meter in end-2012 which is a shareof 23.5% of ASEAN’s total gas resources [IEA.2013, p. 83]. Although this means the secondlargest amount of gas reserves after Indonesia in SEA, there are geographical imbalances,related to the location of gas reserves and the demand, as well as shortages in the supplyof natural gas in the energy sector. Power stations fired by more expensive fuels as oiland diesel fill in these supply lacks and also imported (and more expensive) LNG is usedto keep gas-fired power stations going. As a consequence of subsidies to natural gas bythe government, differences in the gas price are obvious. One agreed on an adjustment ofthe local gas price to market prices by 2016. Therefore, the gas price in 2012 is set tothe local price of 4.57US$/MMBtu which is 15.59US$/MWh, according to [Maybank.2012].Related to the discussed policy, the future gas price in Malaysia is based on the market priceof 12.21US$/MMBtu (41.66US$/MWh) in 2016, according to [Maybank.2012]. The annualgrowth rate of the gas price is set to 2.0% in the time period from 2011 to 2040 [EIA.2013d].

Beside gas, coal is the second basis of the generation of electricity. Malaysia’s coal re-serves come to 1,843million tons [ASM.2013]. Nevertheless, coal is a totally imported en-ergy resource. Imports mostly consist of Indonesian and Australian coal [OECD.2013, p.245]. Compared to the local fuel price of gas which is subsidised, the unsubsidised coal price

15

2.3 Energy Sources

comes to 103.6US$/metric ton, according to [TNB.2012]. Converted to the price per MWh,the fuel price amounts to 16.20US$/MWh, assuming a country specific net calorific valueof 5,500 kcal/metric ton. Thus, the price for coal in Malaysia was higher than the local gasprice in 2012. The share of coal in the generation mix was lower than the share of gas in 2012.With an annual growth rate of 1.0%, the coal price will increase moderately [EIA.2013d].Consequently, the future coal price will be lower than the gas price due to the future marketprice of gas from 2016 on. Hence, the share of coal in the generation mix will increase.

Although the capacity of power stations fired by oil and petrochemicals (mainly refer todiesel) is relatively high and the share of oil and diesel as a fossil fuel in the energy sector wasleading in the past, petrochemicals do not matter to the energy sector any more. The reasonfor the high installed generation capacity lies in the fact that Malaysia was abundant in oil. Forthe time being, Malaysia holds 17.9 billion barrels of oil resources but the production of crudeoil suffices only until 2020, when Malaysia’s role of an exporter of oil will change to an net im-porter [IEA.2013, p. 75], [Ali.2012]. Moreover, the consumption of oil and diesel in the energysector conflicts with the use in the transportation sector. The price for one barrel of crude oil inMalaysia comes to 111.26US$/barrel and to 63.84US$/MWh, respectively [EIA.2013d]. Thelocal diesel price amounts to 0.59US$/liter (converted to 60.24US$/MWh) [WorldBank.2014].Contrary to the extinction of oil from the generation mix, diesel will play an alternative op-tion, due to the use for generation sets e.g. with respect to off-grid solutions in rural areas.The overall availability of diesel at petrol stations is a remarkable advantage and is the reasonfor making diesel the favoured fuel particularly for generation sets. Prices of oil and dieselwill grow by an annual growth rate of 1.1% [EIA.2013d].

Biomass, biogas and landfill gas as renewable energy sources are also on stock and there areno temporal fluctuations of these energy sources. The price for biomass is 3.41US$/MWh,according to 1.00US$/MBtu from [Lazard.2013]. The higher production costs of gasificationof biomass to biogas is taken into account thus the price of an unit biogas is assumed tobe 10.00US$/MWh. The same applies to landfill gas, which is produced by municipal solidwaste (MSW), and the price is adopted to 10.00US$/MWh as well. Although biomass andbiogas are RE, the potential of them is limited due to the restricted area under cultivationand the conflict with the food industry. In total, the potential of these energy sources comesto 1,340MW of biomass and 410MW of biogas. The potential of landfill gas increases withthe population growth. By the year 2020, there will be approximately 9 million tons MSWper year with a heat content of around 2,200 kcal/kg [Ali.2012]. Based on this assumption thepotential of MSW amounts to 400MW by 2022 [ASM.2013].

As previously mentioned, Malaysia has coastal plains which are rising to commonly ar-boreous hills and mountains often covered by rainforests as well. Combining this fact with anannual average rainfall of about 3,540mm, Malaysia’s abundance of around 189 named riverswith a total length of 57,300 km is explained [Shafie.2011, Ahmad.2014b]. These steams ori-ginate in the mountainous areas in the country and flow to lower areas. Plenty of water and

16

2.3 Energy Sources

suitable terrain are advantageous for the potential of hydro power. Due to the fact that theannual rainfall in Borneo is almost twice the annual amount of rainfall in Peninsular Malaysia(5,080mm and 2,500mm, respectively), the potential in Borneo is higher than in PeninsularMalaysia [Mekhilef.2012]. Hydro power is by far the most important renewable energy sourcein Malaysia. Regarding the fluctuation of fuel prices, the costs of electricity which is gener-ated by hydroelectric power stations are less affected, as hydro power is a non-cost energysource. Thus, the costs of hydro electric power are only based on the construction and themaintenance costs. Nevertheless these costs can be relatively high. Regarding the difficileexploitation of hydro potential, higher construction costs of new hydroelectric power stationsare a problem as well. Building and utilisation of hydro power might frequently be a massiveimpairment of the nature such that the construction of more large-scale hydroelectric powerstations with an extensive dam is limited. Despite these problematic issues, micro-hydro sta-tions will become more attractive in future. However, investment costs are high, while theimpairment of the nature is moderate. The total potential of hydro is divided into large-scaleand micro-hydro power stations. It comes to 22,000MW and 500MW, respectively.

Further energy sources are RE, whose availability depends on weather conditions and sea-sonal variations. In case of intermittent sources the annual full-load hours and the normalisedhourly time series are important. Combining those two, it is possible to calculate the avail-able energy in every time step. The full-load hours as well as the time series are provided byNASA weather data and were prepared and edited by Karl Janker [Rienecker.2011]. Thereare historical data sets from 2000 to 2012 for all regions of solar PV, wind onshore and windoffshore. Thus, the data situation regarding intermittent renewable energies is very good andin great detail.

Table 2.2: Prices of fuels with giving respective references. The annual growth rates of fuelsare taken from [EIA.2013d].

FuelsPrice

ReferenceAnnual growth rate

[US$/MWh] [%]

Biogas 10.00 Assumption 0.0Biomass 3.41 [Lazard.2013] 0.0Coal 16.20 [TNB.2012] 1.0Diesel 60.24 [WorldBank.2014] 1.1Gas 15.59 [Maybank.2012] 2.0Landfill gas 10.00 Assumption 0.0Oil 63.84 [EIA.2013d] 1.1

17

2.4 Demand for Electricity


After launching a massive development project of the government called Rural ElectrificationProgramme to provide access to electricity in rural and isolated areas in the 1980s, Malaysia’selectrification rate of 99.4% was one of the highest rates in ASEAN in 2012 [OECD.2013,pp. 244, 246] and [APEC.2013]. Consequently, the demand per capita is relatively high andoverlies the average of other ASEAN. The demand for electricity is also fluctuating, accordingto regional and country-specific load curves. It depends on the time of the day as well ason seasonal variations. Moreover, public holidays affect the consumption of electricity. Dailyplanned unit commitment schedules of Peninsular Malaysia are provided by the MalaysianEnergy Commission (Suruhanjaya Tenaga). These figures reflect the load curve of the electri-city demand and are available in half hourly format on the website [SurTen.2012a]. However,a resolution of those data is not available by region.

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Figure 2.2: Hourly load curve of Peninsular Malaysia [SurTen.2012a].

There were illogical data about the absolute figure of the total consumed electricity in lit-erature in 2012 thus data of the Department of Statistics in Malaysia are used. Those datafit in the overall picture of the demand situation and are proven by other literature suchas [EPU.2012].Evaluating the data situation according to [JPMy.2012a], the actual demand for electri-city was 109,294GWh in 2012, which was split into 97,243GWh in Peninsular Malaysia,into 4,450GWh in Sabah and into 7,595GWh in Sarawak. In Malaysia, the industrial con-sumers held the largest share of 43.40% of the demand for electricity, followed by the com-mercial sector with 34.01% and the residential sector with a share of 21.34%. The sector ofpublic lighting consumed 1.25% of the overall amount in 2012 [JPMy.2012a].

18


Categories Peninsular MalaysiaSabah Sarawak

Residential 20301.4 1436 1584.1

Commercial 33218.1 1923 2026

Industrial 42488.7 1038 3912.1

Public lighting 1234.7 59 73

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Peninsular Malaysia

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Sabah

Residential Commercial Industrial Public lighting

21%

27%

51%

1%

Sarawak

Figure 2.3: Share of electricity consumption in Malaysia by region in 2012 [JPMy.2012a].

Figure 2.3 depicts the distribution of the total demand for electricity by sector and theregions Peninsular Malaysia, Sabah and Sarawak. It is remarkable that households in Penin-sular Malaysia and Sarawak held the same share of the respective demand for electricity. Theindustrial sector of these both regions possessed the largest share of 44% and 51%, respect-ively. They differed by only 7% which applied also to the commercial sectors. However, thecommercial sectors in Sabah held the largest share of electricity consumption, followed by theresidential (32%) and the industrial sector with a share of 23%. The sector of public lightingin all three regions had only a share of 1% and 2%, respectively [JPMy.2012a].

Due to a continual economic growth with an annual growth rate of GDP of 4.0% un-til 2035 [IEA.2013] and a higher urbanisation, the demand for electricity will grow increas-ingly [OECD.2013]. The future growth of the demand for electricity comes to 4.2% per yearin the period until 2040 and the total amount will almost double by 2030 [IEA.2013, p. 60].

19

2.5 Power Stations and their Characteristics

Figure 2.4: Demand for electricity in Malayisa in 2012, prepared by the means of [JPMy.2012a]and [JPMy.2013].


In Malaysia, there are three public energy utilities, namely Tenaga Nasional Berhad (TNB),Sabah Electricity Sendirian Berhad (SESB) and Sarawak Energy Berhad (SEB). Supported byIndependent Power Producers (IPPs), those operators of power stations ensure the electricitysupply in the respective regions. As a regulator Suruhanjaya Tenaga (Energy Commissionof Malaysia) was established in 2001 by the Malaysian government. Its aim is to advancethe potential of the energy supply industry. It warrants the dependable, reliable, safe andcost-effective supply of electricity and of piped gas, especially in Peninsular Malaysia andSabah [SurTen.2014].

Thus, one of the most relevant information which defines a power system are data aboutpower stations. A power station simply converts input energy sources to electricity. Thereare different technologies of generating electricity which also differ in their specific character-istics. Not only the capacity but also the specific type of power generation is crucial to makestatements about the power system.

The database of the World Electric Power Plants (WEPP) of [Platts.2010] is used to specifythe fleet of power stations in Malaysia. This database gives a detailed list of all (known) powerstations in Malaysia and it provides the basis for the analysis of the fleet of power stations.The data of power stations are listed by region and itemised by technology which defines the

20


type of electricity generation. In detail, the provided data of the power stations are the unit,the name and the operator of the power stations. The operators are mostly the local utilitiesand IPPs, as already mentioned. The relative capacity in MW for each unit which can beused for generating electricity is listed as well. Moreover, it is specified by which (alternative)fuels the power station is fired. The actual status of the station is registered as well as theyear of construction, year of retire and year of coming on line, respectively. Furthermore, themanufacturer of the turbine and generator is listed. Last, the data set is completed by theinformation of the location of the power station, regarding the city, country, area and thegeo-location ID.

Research on investment costs have also been conducted. Moreover, operational costs ofpower stations are researched. These consist of fixed and variable costs. Seven differentreferences -in this case [BV.2012, Chang.2013, EIA.2013c, IEA.2010, IEA.2013, Lazard.2013,Schaber.2012]- are analysed and listed in Table 2.3. However, none of the references coversthe required information of the power stations in Malaysia completely. Thus, a combinationof sources which are properly combinable and complementary are inevitable to complete dataabout costs of the electricity generation technologies.

Table 2.3: Analysis of References: Decision making.

Reference + -

[Chang.2013] Data for SEA related to linear optim-isation

Quoted literature from 2008 and 2005partly, not explicable

[EIA.2013c] Latest data from 2013, nearly complete World prices (referred to U.S. market)

[IEA.2013] Costs explicit for SEA Only 5 different technologies

[IEA.2010] Regional costs of power stations Not complete, in combination withother reference

[Lazard.2013] Almost every technology, for each com-plete data, on an unsubsidised basis

Referred to U.S. prices and power sta-tions

[Schaber.2012] Data related to linear optimisation Explicit for Europe in Euro; no datae.g. for diesel generator sets

[BV.2012] Projected data for 2015 Related to U.S. market, not complete

According to [IEA.2010], future investment costs are set to the given investment costs of In-dia which are adapted to 2012. Costs of technologies which are not considered in this referenceare provided by [EIA.2013c]. Fixed and variable costs are mainly given by [EIA.2013c] andcompleted by [Lazard.2013]. A development of the investment costs is given by [IEA.2010]in time steps of 2020 and 2035, whereas the development of the fixed and variable costs arenot considered. An overview of investment costs as well as fixed and variable costs is listedin Table 2.4.

21


Table 2.4: Overview of investment costs in 2012, 2020 and 2035 of selected electricity gen-eration technologies by [IEA.2010], [EIA.2013c] and [Lazard.2013]. All costs referto 2012 US$.

Generation technology Investment costs [US$/MW] Fixed costs Variable costs2012 2020 2035 [US$/MW/yr] [US$/MWh/yr]

Biogas engine 500,000 500,000 500,000 15,000 0Biomass 2,263,778 2,169,015 2,053,194 105,630 5.26Supercritical coal 1,579,380 1,579,380 1,579,380 31,180 4.47Gas turbine 421,168 421,168 421,168 7,040 10.37CCGT 737,030 737,030 737,030 15,370 3.27OCGT 973,000 973,000 973,000 7,340 15.45Hydro 1,968,960 2,105,840 2,190,074 14,130 0Micro-Hydro 3,137,702 3,169,289 3,221,935 14,130 0Solar PV 2,737,592 1,968,960 1,495,146 24,690 0Wind onshore 2,695,475 1,895,256 1,768,906 74,000 0Wind offshore 1,526,734 1,505,676 1,484,617 39,550 0

Additionally, it is important to know how much of an energy source is needed to generateelectricity. Thus, the efficiency has to be stated. Losses of energy conversion from an inputsource to electricity are dependent on the process which are specified by [Platts.2010]. Theselosses are taken into account by the efficiency of power stations. Data about the efficiencyof generation units are not provided by [Platts.2010]. Thus, references have to be combined.Information especially about fossil fuel-fired power stations are provided by the annual re-port of the Energy Commission in Malaysia [SurTen.2012b]. It gives the efficiency of powerstations operated by TNB and IPPs. The average efficiency of those generation technologiesare listed in Table 2.5. However, there are missing data about power stations which are firedby MSW and biogas. Those figures are provided by [Zahoransky.2013]. Moreover, efficienciesof generator sets which are fired by gas and oil have to be added by [EIA.2013e]. Addition-ally, [Kaltschmitt.2009] gives the efficiency of generation units powered by biomass. Thesedata are also listed in Table 2.5.

Moreover, [IEA.2010] provides data about future efficiencies of energy conversion technolo-gies. In this reference, the data are given by different regions in the world. Future data aboutthe efficiencies of Indian power stations are chosen. Those information are filled up by datagiven by [EIA.2013e]. Table 2.6 lists the information about future efficiencies in Malaysia.

The lifetime of the technical equipment is a further parameter of power stations. The life-time affects the period of depreciation. Thus, it is a significant information for calculatingannual investment costs of power stations. Those data are given by [Lazard.2013]. As thereis no lifetime of hydro power stations given, an assumption about the depreciation periodhas to be made. Although hydro power stations might be on line for more than 50 years, thedepreciation period is assumed to be 30 years.

22


Table 2.5: Overview of 2012 efficiencies of electricity generation technologies given by differentreferences.

Generation technology Efficiency [%][SurTen.2012b]Coal thermal power station 35.2Conventional thermal power stations 29.5Diesel engine 33.0CCGT 44.2OCGT 26.5[Zahoransky.2013]Biogas engine 25.0MSW 25.0[EIA.2013e]Gas engine 30.0Oil engine 34.0[Kaltschmitt.2009]Biomass 15.0

Furthermore, [TNB.2012] provides data about the Equivalent Availability Factor (EAF)of power stations which are operated by this utility. There are four different generationtechnologies which are specified and listed in Table 2.7. Open Circle Gas Turbines (OCGT)have the highest availability to generate electricity. Their factor is almost 97% per year.Combined Circle Gas Turbines (CCGT) are 88% of a year on line, whereas coal-fired powerstations have an availability of 83% per year. Conventional thermal power stations whichare powered by oil or gas possess an annual availability of only 81%. Information about theremaining power stations and their availability are unpublished.

In March 2011, an earthquake shook Japan and ended in a nuclear disaster due to ra-diation leakages of the nuclear power station Fukushima Daiichi [Khor.2013]. This nuclearcatastrophe showed the disadvantages of nuclear power evidently. Thus, the undertaking ofbuilding nuclear power stations in 2020 in Malaysia was deferred by the government indef-initely, according to [EIA.2013b, p.17] and [Ali.2012]. Although the utilisation of nuclearpower seems to be an option to cover the increasing electricity demand cost-effectively andemission-freely, the social, economical and environmental issues prevail. Hence, constructingnuclear power stations is not in the scope of this thesis.

23

2.6 CO2 Emissions

Table 2.6: Overview of future efficiencies of electricity generation technologies given by differ-ent references.

Generation technology Efficiency [%][IEA.2010]Biomass 35.0Biogas engine 30.0Supercritical coal 39.0CCGT 60.0Gas turbine 40.0MSW 30.0[EIA.2013e]Diesel engine 36.0Gas engine 30.0Oil engine 36.0

Table 2.7: Availability of specified power stations [TNB.2012].Generation technology EAF [%]CCGT 87.6OCGT 96.6Coal thermal power station 83.0Conventional thermal power stations 80.8

2.6 CO2 Emissions

Energy-related emissions as CO2 are a significant criterion of the power system as it repres-ents the environmental sustainability of a power system. Hence, the electricity-specific CO2

emission factor is defined as the total energy-related CO2 emissions per kWh from electricitygeneration.

In the energy sector the energy-related emissions emerge from combustion of fossil fuels togenerate electricity. The amount of CO2 emissions, which is released, depends on the fossilfuel and its specific characteristics. Table 2.8 lists the specific emission characteristics of fossilfuels referred to the net heat of combustion from [UBA.2003].

Table 2.8: Specific emission characteristics of fossil fuels related to the net heat of combustion[UBA.2003].

Fossil fuels Emission characteristicin kg CO2 /MWh

Coal 342.0Diesel 266.4Gas 201.6Oil 273.6

24

2.7 Power Grid in Malaysia

The total amount of CO2 emission which stems from the electricity generation are calcu-lated by the specific emission characteristic of fossil fuels combined with the efficiency of theelectricity generation technology. Thus, the efficiency of fossil fuel-fired power stations affectsthe electricity-specific CO2 emission factor as well. The higher the efficiency of a generationtechnology, the lower the energy-related CO2 emissions.

In 2011, the electricity-specific CO2 emission factor amounted to 688 g/kWh in Malay-sia [IEA.2013a].


The transmission grid is separated into three main parts: Peninsular Malaysia, Sabah andSarawak. A connection between those individual parts did not exist in 2012. In futureconsiderations, the benefits of such connections are examined.

The transmission grid data in Malaysia are fragmentary, especially information about thecapacity of specific transmission lines are missing. However, combining references and makingassumptions indicated the actual situation with sufficient precision in 2012.

Peninsular Malaysia An overview of the electricity network is given by the official utilityof Peninsular Malaysia in their annual report of 2012 [TNB.2012, p. 188]. Existing trans-mission lines in 2012 are depicted based on different voltage levels. The voltage levels rangefrom 500 kV to 132 kV. It is worth mentioning that there are a high voltage DC line fromthe North of Peninsular Malaysia to Thailand. However, this reference gives no data aboutthe installed capacity of each line. Furthermore, [PMGSO.2012] provides actual figures ofthe transmitted electricity for specific lines in Peninsular Malaysia. Peninsular Malaysia issubdivided into five parts and consists of the Northern, Eastern, Central, South Central andSouthern part. Information about the capacity are only available for those connections on aactual basis. Hence, the determination of the HV transmission capacity is based on those data.Thereby, figures of three different days and different daytimes were collected as snap-shot ofthe inter-regional transfer. The maximum value is taken and increased by 10%, assuming thatthe maximum capacity of these connections are not reached in operation and there is back-upcapacity. However, [PMGSO.2012] does not define different voltage levels. Thus, by combingthose assumptions with the data of the existing transmission lines of Peninsular in 2012, thepower grid is defined sufficiently accurately regarding the available data.

Sabah As TNB also operates the power grid in Sabah, fragmentary data are available in thesame annual report [TNB.2012]. It is obvious that there was no connection to the borderingSarawak in 2012 and information about the capacity of the transmission and distribution grid

25


of this region are not given as well. Thus, it is assumed that the power grid is perfectlyconnected within the region.

Sarawak Information about the transmission grid in Sarawak exist hardly. The official utilitydoes not provide any data about the installed capacity of transmission lines and the distribu-tion gird. All assumptions regarding the transmission grid of Sarawak are based on data ofPeninsular Malaysia if it is required.

Although there are more than one voltage level, the consideration of transmission lines isdone only for a general high voltage level due to the fragmentary data situation. Furthermore,it is assumed that there is only one submarine cable possible between Borneo and PeninsularMalaysia.

Length of transmission lines were defined by evaluation GIS data from [GADM.2012] com-bined with the location of power stations by [TNB.2012]. The transmission nodes are notnecessarily in the centre of a region but there where the most of power stations are located.This is depicted in Figure 2.5 where the future power supply system is illustrated.

Regardless of the region, there are universal specific investment costs and fixed costs.Moreover, the same specific loss factor applies to all lines. Those information are givenby [Schaber.2012a].Converted the figures to US$ by assuming an exchange rate of 1.00Euro = 1.348US$, thespecific investment costs amount to 540US$/MW/km for overhead transmission lines. Ad-ditionally, the specific investment costs of submarine cables come to 3,370US$/MW/km. Tocalculate the total investment costs of a cable, the specific investment costs are multiplied bythe length of each transmission line. Furthermore, the fixed costs come to 9,440US$/MW/yrfor both line and cable types (assuming an exchange rate of 1.00Euro = 1.348US$ as well).A development of costs and prices regarding the power grid are not taken into account.

Additionally, transmission losses are also depending on the length of each line/cable. Thespecific losses are 4%/1000 km [Schaber.2012a]. Consequently, the efficiency of cables differsregarding the length of the transmission line and cable.

Table A.2 gives an overview of the defined transmission capacities, calculated lengths andthe resulting investment costs and efficiency based on the founded length. Investment costsoccur if capacity is added to the acutal installed capacity in 2012 which are also based on thelength of the connection.

Moreover, the depreciation time is a interest, due to the annuity resulting from high in-vestment costs and the related profitability of expanding the transmission grid in Malaysia.According to [Schaber.2012a] the depreciation period is set to 40 years for every new trans-mission line/cable.

26


1:7,500,000

0 250 500 750 km

Power Grid

Regions

PerlisKedahPenangPerakKelantanTerengganuPahangSelangorKuala LumpurNegeri SembilanMelakaJohorSarawakSabah

Legend

Power Grid in Malaysia

Project: ASEAN/Malaysia

Data: Rienecker.2011 Map Source: GADM version 2.0

Publisher: TUM CREATE Author: Melanie MANNHART Date: 13.03.2014

Figure 2.5: Map of Malaysia. Red marked the possible future power grid which is defined asinput data.

27

3 Implementation of Data into the ModelURBS

3.1 The Model URBS

The main task of a power system is to constantly cover the demand for electricity by theutilisation of power generation sources. The model URBS which is used in this thesis toanalyse the power system was developed by [Heitmann.2005] and modified by [LfE.2013].

URBS uses linear optimisation to solve energy-economic problems. The model is written inGeneral Algebraic Modeling System (GAMS). It is further divided into sub-models which aredefined by linear equations which represent constraints. These equations are solved by Cplexwhich is a solver of GAMS. Input data regarding demand situation, fleet of power stations,power grid as well as environmental and economic aspects are organised in Excel files.

The main objective is to minimise the total costs of the power system while keeping con-straints. The result is the most cost-effective configuration of a power system under givenconstraints. According to [Heitmann.2005], the objective function is formulated as

min∑

i

cinviCnewi +∑

i

cfixiCi +

∑i,t

cvariEini,t . (3.1)

In it, cinvi represents the specific annual investment costs of every power station and trans-mission line i. The new constructed power generation and transmission capacities are Cnewi .cfixi

stands for the specific fixed costs of every of every power station and transmission line i

and Ci stands for the installed generation and transmission capacities. Furthermore, the spe-cific annual variable costs cvari for every power station and transmission line i is considered.Eini,t represents the total generated electricity in every time step t. The sum of those factorsis minimised by the model.

28

3.1 The Model URBS

Moreover, there are constraints which are met in every time step:

DemandIn every time step, the demand for electricity has to be covered in every region. This isachieved either by generating electricity in the region itself or by transporting electricitywhich is generated in the bordering regions.

EfficiencyThe amount of generated electricity depends on the efficiency of a power station. Additionally,electricity transport by transmission lines results in losses as well. In order to meet thepredetermined demand for electricity, losses by generation and transmission have to be coveredby the total generated electricity.

Expansion of capacityIf the installed generation and transmission capacity are not enough to cover the demand, themodel will construct new power stations and new transmission lines. However, upper-boundsmight apply for limits of potential of RE or energy resources.

CO2 emissionsDue to the combustion of fossil fuels, electricity generation by conventional thermal powerstations results in CO2 emissions. The total amount of produced CO2 emissions can be re-stricted. The power generation mix and/or the generation capacity will have to be changedto meet the predetermined restrictions on CO2 emissions. It is possible to restrict those byregion or country.

Figure 3.1: Overview of the structure of the model URBS.

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3.2 Definition of Processes

By implementing hourly based time-steps, it is possible to evaluate impacts of intermittentenergy sources on the power system.

In the thesis at hand, a further development of this model is used. The model is run byusing Matlab as interface. It reads the excel input files and calls GAMS which solves theequations using the Cplex solver, as already mentioned. The results are given back to Matlaband are evaluated.

The further developed model URBS is successfully used by previous studies, for exampleby [Huber.2012].

The following section presents a brief overview of the used input data. This is mainlyconsisted by information about energy sources, electricity demand, data of power stations,transmission lines and market trends for the actual state and future scenarios as well.


The detailed data about the individually types of generation technologies from [Platts.2010]are simplified by defining seven generation process types for Malaysia. This aggregationis based on the same type of generation technology and the description of the databasefrom [Platts.2010]. The defined process types are listed in Table 3.1.

As specific characteristics of power stations depend on the fired fuel, those process typeshave to be combined with 11 energy sources. Not all compositions make sense and some arenot relevant in practice so there are 19 uniquely defined combinations of processes as inputdata for URBS. Based on the process types combined with energy sources, the fleet of powerstations is characterised as the model basis.

Besides the conventional power stations such as CCGT, Gas Turbines (GT) and conven-tional steam power stations, some specific adoptions have to be made regarding GeneratorSets (GS) as well as biomass and biogas fired power stations. By means of integrating pro-cesses which represent GS, the actual situation in regions of SEA is reflected. Small andbackup generator sets are a common solution in off-grid systems in SEA. As these units areoften diesel-fired due to getting this fuel easily at petrol stations, the process type ’GS’ iscombined with ’Diesel’. Moreover, processes of biomass were redefined to distinguish solidbiomass from biogas and their individual production methods. The additional expendituresof the gasification process to biogas are taken into account by the biogas fuel price.

As already mentioned, the Platts database also provides information about planned powerstations and their expected year of going on line. This means that power stations which areplanned to be connected to the power grid by 2012 are set to operating power stations inthis thesis instead of their planning status given by [Platts.2010]. As a consequence, their

30


Table 3.1: Aggregation of generation technologies based on data of [Platts.2010] to 7 genera-tion processes.

Defined process typeGeneration tech-

Description given by [Platts.2010]nology givenby [Platts.2010]

CC Combined-cycleCombined Cycle Gas CCSS Combined-cycle single shaft configurationTurbine (CCGT) GT/C Gas turbine in combined-cycle

ST/C Steam turbine in combined-cycle

Gas Turbine (GT)GT Gas/combustion turbine

GT/H Gas turbine with heat recoveryGT/S Gas turbine with steam send out

Generator Set (GS) IC Internal combustion (reciprocating diesel) engineIC/H Internal combustion engine with heat recovery

Steam power ST Steam turbinestation ST/S Steam turbine with steam send outHydro HY Hydroelectric turbine generatorSolar PV PV Photovoltaic cellsWind WTG Wind turbine generator

capacity is added to the fleet of power stations in Malaysia. Thus, the 2012 capacities definedby processes are derived. Capacities of the same process type and the same powering aresummarised to virtual power stations which are implemented into the model URBS. Thetotal capacity of the power station fleet in 2012 in Malaysia is listed by energy source inTable A.5.

Based on the defined processes, the investment costs are given by the International EnergyAgency (IEA). Those costs were adjusted to prices in 2012 by considering a cumulated inflationrate of 5.292% [BLS.2014]. Fixed and variable costs of the defined processes are taken from theAssumptions to the Annual Energy Outlook 2013 of the Energy Information Administration(eia) and are given in 2012 US$.

In Table A.1, an overview of the defined processes as well as of investment, fixed and variablecosts is given. The 2012 efficiency by each process is registered in the same Table A.1. Afuture development of costs and efficiency is described in Section 2.5 and is also implementedinto the model for future considerations.

As mentioned in Section 2.5, data about the availability (EAF) of a power station of Pen-insular Malaysia are provided by [TNB.2012]. However, SEB and SESB do not give anyinformation about the availability of their power stations. Hence, it is assumed that the avail-ability figures of power stations in Peninsular Malaysia also apply for power stations in Sabahand Sarawak. The EAF is only implemented for generation technologies which do not haveany time series and the respective annual full-load hours. The EAF is shown in Table A.1.

31

3.3 Spatial Resolution of Malaysia

3.3 Spatial Resolution of Malaysia

In Section 2.1, the political division of Malaysia is described. This subdivision of Malaysia isrelated to federal states and federal territories due to the detailed data which are provided byliterature such as [EPU.2012, JPMy.2012, JPMy.2012a, JPMy.2013, SurTen.2012b].

Due to its influence on the Malaysian economy and the high electricity demand, KualaLumpur is considered as a separate region. W.P. Labuan as well as W.P. Putrajaya are addedto the relative states Sabah and Selangor. Data of Putrajaya and Labuan are factored intothe region Selangor and Sabah, respectively. Hence, a resolution of 13+1 regions is defined topreserve and keep the given accuracy of detailed data, according to the political map.The 14 regions are listed in Table 2.1 in Chapter 2.

3.4 Classification of Model Input Commodities

In- and output data regarding energy sources, electricity and CO2 emissions are defined ascommodities in the model framework. The commodity type describes the properties of thecommodity. There are four different types, which are demand and environmental as an outputdata, stock and intermittent commodities as input data.

The electricity demand is set to demand which means that the model has to cover thesefigures by generating electricity in every time step. As the electricity demand fluctuatesthroughout the day and the year, the load curve is implemented into the model. This isrealised by a normalised time series and the total amount of the demand for electricity byregion.

Data about the daily load curve of Peninsular Malaysia are provided by [SurTen.2012a]. Tosimulate a whole year, data of every fourth week of 2012 are chosen, starting from the firstweek in January. Thus, 13 different weeks are defined as the model simulation time frame.After normalising the load curve by dividing the hourly values by the total sum, the timeseries of the electricity demand is been pre-processed for the model. With multiplying thevalues of the time series by the total respective demand for electricity of each region, the localload curve is defined on an hourly basis. Because those load curve data are not available byregions, the time series applies to all regions of Peninsular Malaysia. Moreover, as there areno data about the load curve of Sabah and Sarawak available, it is assumed that they aresimilar to the time series of Peninsular Malaysia. Hence, the same time series is implemented.

Values of the CO2 emissions are specified as env (environmental) to express their impact onthe environment. This parameter is one of the most important constraints since the model hasto change the power generation mix, if the actual CO2 emissions resulting from the electricitygeneration are higher than the implemented limit. Although it is possible to define CO2

32

3.4 Classification of Model Input Commodities

emission restrictions by region in the model, the total amount of CO2 emissions is consideredin this thesis regardless the origin of CO2 emissions.

The energy sources have to be specified based on the availability. Energy sources such ascoal or gas are on stock whereas hydro power, solar insolation and wind are intermittent.

The distinction between energy sources is necessary to represent the fluctuation of thecommodity. While stock commodities assumed to be always and unlimited available, theavailability of intermittent commodities fluctuates like the electricity demand. Hence, thetemporal course of the availability is implemented into the model by adding a time serieswhich are provided by [Rienecker.2011] and further prepared by Karl Janker. Additionally,the total amount of annual full-load hours of intermittent commodities are included basedon data given by [Rienecker.2011] and prepared by Karl Janker as well. Those data existfrom 2000 to 2012. The methodology which is used for the selection of the annual full-loadhours is as follows: The averages of the annual full-load hours in 2000 until 2012 are calculatedfor every region. Next, the value of the year whose figure is the closest to the calculated averageof a region is set to be the input data of the model. An example of this selection method isregistered in Table 3.2. The input data of the other intermittent energy sources are listed inTable 3.4.To calculate the electricity generated by intermittent energy sources in each time step, thevalue of the annual full-load hours are multiply by the value of the normalised time series inthis time step.

Moreover, overviews of the distribution of full-load hours of solar PV, wind onshore andoffshore by region are depicted in Figures A.1, A.2 and A.3.

Table 3.2: Example of the methodology used for the selection of the annual full-load hours ofsolar PV in Malaysia.

Regions Average full-load Reference year Annual full-loadhours [h](2000-2012) hours of PV [h]

Johor 1,103.48 2000 1,099.29Kedah 1,221.03 2000 1,233.53Kelantan 1,176.43 2001 1,157.88Kuala Lumpur 1,134.62 2000 1,140.65Melaka 1,075.69 2000 1,080.98Negeri Sembilan 1,052.25 2000 1,082.05Pahang 1,078.11 2006 1,035.53Penang 1,221.48 2001 1,126.04Perak 1,154.50 2011 1,169.75Perlis 1,184.09 2001 1,197.10Sabah 1,293.93 2010 1,354.93Sarawak 1,176.01 2001 1,182.64Selangor 1,134.62 2000 1,140.65Trengganu 1,218.47 2005 1,231.78

33

3.5 Demand for Electricity by Region

3.5 Demand for Electricity by Region

In total the consumption of electricity was 109.3TWh in 2012 [JPMy.2012a]. This is brokendown as follows: In Peninsular Malaysia, the demand for electricity amounts to 97.2TWh,whereas the electricity demand equals 12.1TWh in Borneo.

For Borneo, a further allocation is given in the literature. The total electricity demand ofBorneo splits into 4.5TWh in Sabah and into 7.6TWh in Sarawak. However, as there are nofigures of the electricity demand by region in Peninsular Malaysia, the demand for electricityhas to be determined by making assumptions.

According to [Tang.2013] which studies the nexus between the energy consumption andthe GDP, a bi-directional causality exists between those two factors in Malaysia. Based onthat finding, the actual and the future demand for electricity is allocated by the share ofGDP in Peninsular Malaysia. This approach results in the distribution of the demand forelectricity which is listed in Table 3.3. Corresponding to this approach, the future electricityis allocated related to an annual growth rate of 4.2%, according to [IEA.2013, p. 60]. Thefuture distributions are also shown in Table 3.3.

Table 3.3: Distribution of demand for electricity in 2012, 2020 and 2035 [JPMy.2012a,IEA.2013].

Region 2012 [TWh] 2020 [TWh] 2035 [TWh]Johor 11.3 15.7 29.0Kedah 4.1 5.8 10.7Kelantan 2.2 3.1 5.7Kuala Lumpur 18.7 26.0 48.2Melaka 3.6 5.0 9.3Negeri Sembilan 4.5 6.3 11.7Pahang 5.0 7.0 13.0Penang 8.6 12.0 22.2Perak 6.5 9.0 16.7Perlis 0.6 0.8 1.5Sabah 4.5 6.2 11.5Sarawak 7.6 10.6 19.6Selangor 28.9 40.1 74.4Terengganu 3.2 4.5 8.3

109.3 151.9 281.5

Remarkable is the high demand for electricity in Kuala Lumpur and Selangor. A shareof 44% of the total electricity demand was used in both regions in 2012. The approach thatKuala Lumpur is considered as an own region, is validated by this finding.

34

3.6 Distribution of Potential of RE

3.6 Distribution of Potential of RE

It is assumed that only 50% of the overall potential which is given in the literature is exploitedby 2035. Utilisation of RE potential will become more difficult in future. Hence, the firstshare of the potential has an easier access than the rest of the potential.

As hydro power is already exploited in Peninsular Malaysia, the potential is distributed toSabah and Sarawak, according to [WEC.2010]. As a consequence, 81% of the potential ofhydro power is hold by Sarawak, whereas Sabah is endowed with a share of 19% of hydropower potential.

The allocation of micro-hydro power stations is done by a study of Hussein [Hussein.2010].This paper defines possible locations for micro-hydro stations based on specific criteria suchas elevations and stream flow velocity. According to the findings by Hussein, the distributionof the potential of micro-hydro power is obtained.

Related to biomass and biogas, the utilisation of energy crops contradicts available foodsources partially. With a growing population the share of cultivated area which is possiblyused for energy plants will be rare. Hence, half of the in [Khor.2013] given potential is assumedas well. The potential is distributed by the total area of each region.

Solid waste as an energy source has also a limited potential. This energy source resultsfrom municipal solid waste (MSW). Thus, the higher the population of a region is, the higheris the potential for solid waste. Hence, the potential is distributed by the population in eachregion.

Solar PV is restricted to 5GW in Kuala Lumpur due to the limited area and locations forPV cells. In all other regions, it is assumed that the potential is unlimited. It is possibleto install as much solar PV generation capacity as needed. However, the profitability oflocations is reflected by the different full-load hours of NASA data. Those data are registeredin Table 3.4.

Due to limited average wind speed resulting from the equatorial location of the country, thepotential of wind is low. However, the capacity is set to unlimited due to the implementationof the full-load hours and the resulting profitability. The full-load hours of on- and offshorewind which are listed in Table A.3 show that the utilisation of wind as an energy source isnot profitable. As a consequence, wind power systems do not play a role in the future powersystem in Malaysia. The distribution of renewable energy sources is listed in Table A.3.

35

3.7 Economic Data

3.7 Economic Data

To consider the economic depreciation of investments, the rate of interest is to be defined.Thus, investments are more attractive and reflect the usual approach. In this thesis the annualrate of interest is set to 7%. Additionally, all quoted prices and costs in this thesis refer toreal 2012US$. Hence, the cumulated inflation rate of 5.292% is taken into account to adjustprices from 2010 to 2012 [BLS.2014].

3.8 Further Assumptions Concerning Input Data

Due to high complexity of the power systems several assumptions have to be made.

Generally, the availability of hydroelectric power stations depends on the fluctuation ofwater head caused by rain fall and it also depends on seasons, a time series is includedin the model. However, a fluctuation of hydro power is not considered yet. Instead, anevenly distributed time series is assumed. Moreover, the annual full-load hours are assignedfrom 3,200 h to 4,000 h for each region based on the basis scenario 2012. The distribution ofthe full-load hours are listed in Table 3.4.

Table 3.4: Full-load hours of selected renewable energies [h].

Region Hydro SunGlobal Windonshore offshore

Johor 3,200 1,099.29 226 449Kedah 4,000 1,233.53 528 346Kelantan 4,000 1,157.88 186 181Kuala Lumpur 4,000 1,140.65 423 130Melaka 4,000 1,080.98 186 50Negeri Sembilan 3,200 1,082.05 186 32Pahang 4,000 1,035.53 245 87Penang 4,000 1,126.04 245 127Perak 3,200 1,169.75 724 248Perlis 3,200 1,197.10 724 236Sabah 4,200 1,354.93 849 297Sarawak 3,200 1,182.64 849 71Selangor 3,200 1,140.65 423 130Terengganu 4,000 1,231.78 552 416

This thesis assumes that the intra-regions are perfectly connected with general losses ofdistribution and transmission of 10% which the model takes into account by a 10% higherelectricity demand. That means basically that there is no lack in the power distributionand transmission within a region. Losses of transmission lines which connect two regions areconsidered separately as already mentioned.

One further assumption relates to the scenario with the possibility of extension of trans-

36

3.9 Excel Macro ’filling table’

mission lines. As Malaysia is one country and the federal states collaborate very closely, itis assumed that there is no restriction on the possible import and export of the the state’selectricity demand, respectively. This means that it is allowed to cover the demand for elec-tricity of one region merely with imported energy from the contiguous states. This appliesfor example in Kuala Lumpur which is almost entirely supplied by the neighbouring regionSelangor.

Additionally, the first results showed that the high future demand in Kuala Lumpur resultsin installing fossil fuel-fired power stations there. Due to air pollution issues, the capacity offossil fuel-fired power stations in Kuala Lumpur is restricted. As a consequence, the future gridconnection between Kuala Lumpur and Selangor are followed up to the needed transmissioncapacity to provide the required electricity in Kuala Lumpur. The investment costs for addingcapacity to this line are neglected due to the generation capacity restrictions. This furtherassumptions as the first results are also discussed later on in Chapter 4.3.

Data about energy sources are specified in literature inconsistently. They differ especiallywith respect to the heat content of fossil fuels. Hence, conversion factors are used to convertunits to the model input data format. Those factors are listed in Table 3.5.

Table 3.5: Conversion factors related to the input data format.Data Unit Unit in MWh ReferenceEnergy MMBtu 0.2931 MWh/MMBtu [IEA.2013]Gas MMscfd 1 cf = 0.02832 m3 276.12 MWh/MMscfd [UNFCCC.2007]Coal metric ton 1 kcal = 4186.8 J 6.39650 MWh/t [IEA.2013]Biogas MMscfd 1 cf = 0.02832 m3 0.177 MWh/MMscfd [Ahmad.2011]Oil barrel 1 barrel = 5.9466 MMBtu 1.7429 MWh/barrel [EIA.2013d]

Auxiliary factors and assumptions:

Gas: 35.1·106 J/Sm3

Coal: 5.5 ·106 kcal/tBiogas: 22.5 ·106 J/Sm3


The model needs detailed data by process and commodity for each region. As a consequence,there are contingency tables as for example a process has same key information as for instanceinvestment costs which apply for each process and region. The installed capacities of processesdiffer between the regions, so there is another contingency table for this information in theused Excel worksheet. Thus, redundancy is avoided by using contingency tables for the inputdata. However, the input data which are given to model must be organised in data sets whichinclude all informations of one process in one region. So the contingency tables are split todata sets including the data by region, processes and commodities. Hence, the more regions

37


are considered, the more data sets are inevitable. Due to the quantity of data, there is a macroimplemented to facilitate the providing of the data for the model. It is entirely necessary to fillthe data-related table which are orange marked contingency tables and the macro transfersthe data into the model-related tables which are marked green. This macro simply copiesthe data of the contingency tables of commodities and processes by site together into themodel-related tables. Thus, changes in the figures of the input data are easily implementedand do not make any efforts. It is merely necessary to fill the data-related tables and to runthe macro. Thus, the changes will be adopted to the model-related tables automatically.

38

4 Scenarios

4.1 Definition of Scenarios

The next step to investigate the power system of Malaysia is to consider the impacts ofcertain constraints of the power system by defining scenarios. A scenario is characterised byspecific restrictive conditions regarding the input data of the power system. By changing theinput data, the model resolves linear equations according to the actual circumstances andconstraints. For instance, the results differ if gas prices are manipulated or energy-relatedCO2 emissions are limited. Starting from the first week in 2012, the simulation time frameis defined by 13 weeks by choosing every fourth week. One of those weeks is depicted inFigure 2.2.

The 2012 reference scenario is defined to validate the model. In this scenario, all implemen-ted constraints are based on the year 2012. All costs and expenses reflect the prices observedin 2012. Thus, the reference scenario is regarded as the status quo in this thesis. Hence,in the context of a growing energy demand in Malaysia, the generation of electricity willincrease in the next years. In this thesis, future scenarios demonstrate the impact of the elec-tricity generation on climate change and air pollution due to energy-related CO2 emissions.Thus, they are defined by assuming future input data as described in Chapter 2. For thesefuture scenarios the model will install more generation capacity to meet the future demandcost-effectively, if it is required. Furthermore, more expensive fuel prices and a negative de-velopment of investment costs of power stations are taken into account. All prices and costsrefer to real 2012 US$.

There are two future years which will be examined. To demonstrate how political targetscan be achieved by 2020, the modelled year is set to 2020 for the first scenarios. To extendthese studies, a long-term case is defined in 2035 to show future challenges in general. In thesescenarios, the impact of new transmission lines and CO2 restrictions as well as alterations inthe gas price in 2035 will be demonstrated.

Due to the data situation and complexity of the power system, further assumptions haveto be made.

39


Evaluating the first scenarios, the model suggests to build coal-fired power stations inKuala Lumpur of around 4.4GW. However, it is obvious that this result is not attractive anddesirable due to air pollution issues. Hence, the capacity of fossil fuel-fired power stationshas to be restricted. The maximum capacity of coal-fired power station is set to 0 sincethere was no installed unit powered by coal in 2012. Whereas there were some gas, oil anddiesel power stations, the maximum capacity of power stations fired by gas, oil and dieselis set to 50MW. Due to these assumptions it is clear that the required electricity must begenerated in bordering regions. Moreover, it is assumed that the capacity of the transmissionline between Kuala Lumpur and the neighbouring region Selangor is perfectly followed up.Investment costs of this transmission line are neglected due to the forced capacity limits offossil fuel-fired power stations in Kuala Lumpur.These assumptions apply to all following scenarios.

Additionally, there are short terms defined to describe the main constraints of the followingscenarios. The use of appropriate short cuts facilitates the evaluation and the naming ofscenarios in the following part. Those short codes are listed in Table 4.1. An overview of allscenarios which are considered in this thesis are registered in Table 4.2.

Table 4.1: Overview of used scenarios short terms and their definition.Short code of scenarios Definition

2020BAU Business as usual scenario in 2020 and no re-strictions on energy-related CO2 emissions

2020TrOFF Scenarios in 2020 without expansion of trans-mission grid

2020TrON Scenarios 2020 with construction of new trans-mission lines

2035BAU Business as usual scenario in 2035 and no re-duction of energy-related CO2 emissions

2035TrOFF Scenarios in 2035 without expansion of trans-mission gird

2035TrON Scenarios in 2035 with construction of newtransmission lines

40


Table 4.2: Overview of scenarios which are considered.

Scenario Target Expansion of Reduction of Reduction Gas priceyear 2012 power grid CO2 emissions compared to [US$/MWh]

01: 2012Reference 2012 no no 15.5902: 2020BAU 2020 no no 44.2103 2020 no 26.50% 2020BAU 44.2104 2020 yes no 44.2105 2020 yes 26.50% 2020BAU 44.2106: 2035BAU 2035 no no 60.6907 2035 no 25% 2035BAU 60.6908 2035 no 50% 2035BAU 60.6909 2035 no 75% 2035BAU 60.6910 2035 yes no 60.6911 2035 yes 25% 2035BAU 60.6912 2035 yes 50% 2035BAU 60.6913 2035 yes 75% 2035BAU 60.6914 2035 yes no 24.5815 2035 yes 25% 2035BAU 24.5816 2035 yes 50% 2035BAU 24.5817 2035 yes 75% 2035BAU 24.5818 2035 yes no 47.8919 2035 yes 25% 2035BAU 47.8920 2035 yes 50% 2035BAU 47.8921 2035 yes 75% 2035BAU 47.89

41

4.2 Proofing the Model by Evaluating the 2012 Reference Scenario


The actual generation capacity in 2012 on the basis of [Platts.2010] is listed in the appendix,Table A.5. As mentioned in Section 3.2, the data base is modified to represent the actual stateof 2012. The following considerations of scenarios are built upon this generation capacity andare assumed to be installed. Additionally, a possible deconstruction of power stations is notallowed in 2012 to represent the actual situation.

As previously mentioned in Chapter 1, the generation mix of the Malaysian power sys-tem was dominated by fossil fuels in 2012. Figure 1.6 shows the distribution by fuel type.This generation mix is re-modelled by the model URBS. To reflect the actual generation mix,the commodity supply is restricted since the 2012 power system is not optimised regardingthe minimal total costs. The Annual Report of TNB [TNB.2012] gives reference values, butsome adjustments have to be done, as these figures are only valid for power stations whichare located in Peninsular Malaysia. The total supply of gas and coal was 960MMscf per dayand 20million tons per year, respectively [TNB.2012]. Distributed by the total installed capa-city of each power station, the power generation mix is re-built. In the scenario 2012Reference,the model is not allowed to build new generation capacities, although this configuration ofthe power system may not be cost-effective. However, the aim of rebuilding the status quo isto reflect the actual power generation mix and the power system. By simulating the referencescenario with the actual constraints, the generation mix of 2012 is derived as depicted inFigure 4.1.

38.9%

52.7%

7.3%

1.0%

0.2%

Coal Gas Hydro Power Oil and Petrochemicals Other RE

Figure 4.1: Modelled electricity generation mix in Malaysia in 2012 [EPU.2012].

Key results which characterise the power system are listed in Table 4.3.One main figure is the total generated electricity of 121.73TWh. Compared to the actualgenerated electricity of 122.54TWh in Malaysia, the model reflects reality very well by calcu-lating the generated electricity of the given demand situation. Consequently, this observationis a valid proof for the input data and the used model.

Moreover, the annual total costs sum up 5.94 billion US$. The costs of generating electricity

42


hold a share of 96.6%, while the remaining part belongs to operational costs of the highvoltage power grid for transmission (costs of the distribution grid is neglected as previouslymentioned). Additionally, the CO2 emission factor for the electricity generation representsthe impact on environmental sustainability. The higher the share of fossil fuels to the powergeneration mix, the higher the electricity-specific CO2 emission factor. Thus, the lower thevalue is, the higher the penetration of low-emission processes to generate electricity. In thescenario 2012Reference, the power generation mix is dominated by fossil fuels. Hence, theelectricity-specific CO2 emission factor is relatively high. The simulated result is overallconsistent with the actual emission factor of 688 g/kWh [IEA.2013a, p. 112], although thedata reference is taken from the year 2011.

Furthermore, the levelised cost of electricity (LCOE) is one of the decisive factors of thepower system.

Table 4.3: Summary of key results of the scenario 2012Reference.

Reference Scenario 2012

Generated electricity 121.73TWhGeneration costs 5.71 billion US$Transmission costs 0.23 billion US$Electricity-specific CO2 emission factor 622.90 g/kWhLCOE 4.88US$ct/kWh

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4.3 Investigation of Future Scenarios


4.3.1 Verifying Political Targets by 2020

As mentioned in Chapter 2, the input data in 2020 are modified by taking into account theannual growth rate of the electricity demand as well as the future development of costs andprices. In contrast to the reference case, deconstruction of generation capacity is allowed inthe following scenarios.

As in December 2009 at the UNFCCC Conference in Copenhagen the government pledgedto reduce the overall CO2 emission intensity by 40% compared to 2005, the target year 2020is investigated. This target has an effect on the overall CO2 emissions, although only apart is caused by power generation. Thus, this indicator is adjusted to the CO2 emissionsrelated to the energy sector. According to [Theseira.2012], about 60% of the overall emissionsin the BAU case is reducible in the energy, industry and transportation sector in 2020 toachieve the political target. In the energy sector, the potential of CO2 reductions accountsto 28.85Mt CO2 in 2020 [Theseira.2012, p. 12]. Thus, on the basis of the total emissions inthe scenario 2020BAU, the energy-related CO2 emissions are reduced by 26.5% in the powergeneration in 2020. The limit of CO2 emissions is the key input data to verify the feasibilityand additional costs of the political target. In coming scenarios the impacts of the promisedCO2 restrictions on the power system and the total costs are examined.

In the following section, it will be demonstrated how this target can be reached cost-effectively.

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4.3.1.1 Influences of CO2 Emission Limits Related to Political Targets on the FuturePower System without Construction of New Transmission Lines in 2020

The scenarios without construction of new transmission lines (2020TrOFF) are subdivided asfollows.

First, a scenario without any CO2 emission restrictions is simulated. Then the feasibilityof the political target of reducing CO2 emissions is evaluated which has been announced bythe government at the UNFCCC conference in Copenhagen.

By meeting the demand of 151.9TWh in 2020, an expansion of the generating capacity iscalculated by minimising the total costs of the power system in 2020.An overview of the generating capacity by energy resource is given in Table A.6 without anyCO2 emission restrictions. Table A.7 shows the generating capacity under the constraints ofthe political target to reduce CO2 emissions. Compared to the generation capacity in 2012,the expansion of the future power station fleet is noticed. Further capacity of hydro powerstations accounts to about 4GW, followed by power stations fired by biomass and biogaswith 450MW which applies for both scenarios.

Regarding the scenario without CO2 restrictions, the main share of new generating capacityis contributed by coal-fired power stations of almost 12GW. This is mainly due to the low fuelcosts of coal and their low operational costs. However, the use of coal-fired power stationsresults in high energy-related CO2 emissions. In this scenario, the electricity-specific CO2

emission factor is 797.4 g/kWh. This equals an increase of 28% compared to the modelledfactor of the scenario 2012Reference.

As a consequence, the generation capacity regarding the scenario with restricted CO2 emis-sions has to be adapted to meet the limit of CO2 emissions. In this scenario, around 4GW ofgas-fired power stations are added to the capacity of 2012 by the model, whereas the capacityof gas-fired power stations regarding the scenario without restrictions of CO2 emissions wasnot extended. The capacity of coal-fired power stations is extended by almost 2GW com-pared to 2012. It is obvious that the CO2 emission limit is reached by shifting the electricitygeneration from coal-fired to gas-fired power stations. This is also proven by evaluating thepower generation mix. The resulting power generation mix is depicted in Figure 4.2.Regarding the scenario 2020BAU, the share of electricity which is generated by coal-firedpower stations is 80%, followed by hydro power with 14%. The proportion which is con-tributed by gas-fired power stations accounts to only 4%. Electricity generated by biogasand biomass sum up to 2%. Compared to the power generation mix of the scenario withrestrictions on CO2 emissions, there is one main difference. That is that the power generationmix consists of 43% electricity generated by gas and 40% by coal-fired power stations. Dueto this shift from coal to gas, the political target is achieved in 2020.

To discuss the feasibility, the costs are decisive. An overview of them are listed in Table 4.4.

45


Another finding is that the enforcement of CO2 restrictions results in higher costs. The mainreason for this is the inverse balance between costs and the environmental-friendly aspect, forexample of coal. On the one hand the fuel price of coal as an energy resource is very low, buton the other hand coal is the fuel with the highest CO2 emission factor. Another example isthe installation of solar PV. On the one hand, the investment costs are relatively high but onthe other hand there are no fuel price and low operational costs.

The total costs of the BAU scenario are 11.64 billion US$. The total costs regarding thescenario with CO2 limits are 17.9% higher which also results in higher LCOE. In this scen-ario, the LCOE increases by 1.19US$ct/kWh compared to the LCOE in the scenario 2020BAUof 6.89US$ct/kWh. There is also an increase of 66% compared to the LCOE of the referencescenario in 2012 to achieve the political targets. Moreover, the electricity-specific CO2 emis-sion factor decreases to 583.4 g/kWh regarding the scenario with restrictions of CO2 emissions.Additionally, the investment costs of new power stations regarding the scenario with CO2 lim-its are 38.2% less compared to the investment costs of the scenarion 2020BAU, although thetotal costs of the power system are higher. This is mainly because the installed generationcapacity of gas-fired power stations existed in 2012. However, due to a cost-minimising solu-tion, it is cheaper to build new coal power stations to meet the demand for electricity in2020. Nonetheless, operational costs to generate electricity increase by 18.2% compared tothe scenario 2020BAU. Thus, there are higher total costs of the power system.

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Verifying Political Targets by 2020 (2/3)

Melanie Mannhart | 17/03/2014 23

Cost-minimised Power Generation Mix:

Impact of Transmission Lines and CO2 Limits

169.01 TWh

169.80 TWh

168.98 TWh

169.48 TWh

Figure 4.2: Overview of modelled structures of the power generation mix in Malaysia fordifferent scenarios in 2020.

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4.3.1.2 Impacts of Political Targets regarding CO2 Emission Reduction on the PowerSystem in 2020 with Expansion of the Transmission Power Grid

Compared to the previously described scenarios without considering the construction of newtransmission lines (2020TrOFF), the following section presents the scenarios in 2020 witha cost-effective construction of new high voltage transmission lines (2020TrON ). The modeltakes new transmission lines into account whenever and wherever it is cost-effective to thetotal costs of the power system. The feasibility study of political targets in 2020 applies inthese scenarios as well. Hence, the energy-related CO2 emissions are reduced by 26.5%, asalready mentioned.

The generation capacity regarding the scenario with new transmission lines is listed inTable A.8 without CO2 emission limits and in Table A.9 with reduction of CO2 emissions.There is no significant difference between the generation capacity of the scenario withoutCO2 restrictions and the BAU scenario. Additionally, the power generation mix is the samein those two scenarios. Moreover, the costs which are shown in Table 4.4 are similar.

In contrast to that, differences are observed between the scenario with and without CO2

restrictions. A shift from coal-fired power stations to hydro power stations and power stationspowered by biomass is noticed, regarding the scenario with CO2 emission restrictions. Thecapacity of biomass-fired power stations is extended to 670MW in total. The capacity ofcoal power stations amounts to 12.4GW. This contribution reflects the power generation mixwhich is depicted in Figure 4.2. The share of coal decreases to 51% but is still higher than itsshare in the power generation mix compared to the scenario 2020TrOFF with CO2 emissionlimits. In the scenario 2020TrON, gas contributes only 18% to the power generation mix andelectricity generated by biomass increases to 3%. The increase of hydro power of the powergeneration mix from 14% to 27% is remarkable. This shift arises from the installation of asubmarine cable from Malaysian Borneo to Peninsular Malaysia which has a positive effecton the overall costs. Although the length of the cable is around 790 km and its constructionmeans high investment costs, this solution is cost-minimal.

Hydro power is emission-free but relatively expensive to construct, whereas coal has a lowfuel price but its combustion results in high CO2 emissions. By combining hydro power andcoal in the scenario 2020TrON, the limit of CO2 emissions is exploited by a higher share of coalcompared to the scenario 2020TrOFF as hydro power does not causes CO2 emissions. Hence,the cost-effective advantage of this submarine cable is obvious. Without a transmission linefrom Borneo to Peninsular Malaysia, the demand in Borneo is too low to exploit the wholehydro power potential of this region which is in contrast to a connection between Borneo andPeninsular. Thus, electricity which is generated by hydro power in Borneo covers the demandin Peninsular Malaysia in the case with a submarine cable. Borneo itself contributes morethan 82% of the total electricity generated by hydro power. This result shows the urgent needto construct the grid connection between these two regions of the country which was already

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planned in 2009 [TNB.2012]. The project was launched in 2010 by the Minister of Energy,Green Technology and Water (KeTTHA) but the cable is not in operation yet [TNB.2012].

Regarding the scenarios with limited CO2 emissions, the total costs without constructionof new transmission lines are lower than the costs without any new constructed transmis-sion lines. To compare these two figures, the generation costs are about 17% higher com-pared to the scenario 2020TrOFF and CO2 restrictions. An increase of the transmissioncosts of 1.07billion US$ compared to the scenario 2020TrOFF is compensated regarding thetotal costs by lower generation costs in the scenario 2020TrON. This is also proven by theLCOE of the scenario 2020TrON with CO2 emission restrictions. Thus, the LCOE amountsto 7.57US$ct/kWh and is around 7% under the LCOE of the scenario 2020TrOFF with limitsof CO2 emissions.

In the case with CO2 restrictions, investment costs increase by 10.2% related to the scen-ario 2020BAU. In contrast to the scenario without construction of new transmission lines butwith the restrictions on CO2 emissions, the higher share of hydro power stations as well asof coal-fired power stations results in higher investment costs. But at the same time, theoperational costs are 25% lower than in the case with CO2 emission reduction and withoutexpansion of the power grid.

All these aspects proof that building new high voltage transmission lines are cost-attractivein the consideration of the mentioned political targets. Although the aim of reducing theoverall CO2 emission intensity by 40% seems to be a tough target, the share of this reductionin the generation sector is relatively low. However, due to this political target, the electricityprice for consumers will increase in practice.

Table 4.4: Summary of key results of the scenarios in 2020.Scenarios in 2020

2020TrOFF 2020TrONRestrictions on Restrictions on

/ CO2 / CO2

emissions emissions

Generated electricity in TWh 169.01 169.80 168.98 169.48Generation costs in billion US$ 11.39 13.47 11.35 11.50Transmission costs in billion US$ 0.25 0.25 0.27 1.32Electricity-specific CO2 emission factor in g/kWh 797.40 583.40 797.10 584.50LCOE in US$ct/kWh 6.89 8.08 6.88 7.57

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4.3.2 Long-Term View in 2035

As a long-term consideration, the future power system of 2035 will be investigated. Themodel calculates the capacity which has to be installed to meet the future demand on thebasis of the year 2012. Due to air pollution issues, the capacity of fossil fuel-fired powerstations in Kuala Lumpur is restricted, as previously mentioned. The development of costsand prices as well as the demand for electricity in 2035 is taken into account. A deconstructionof generation capacities by the model is possible but without any costs in all scenarios. Inthis long-term approach the reduction of energy-related CO2 emissions is set to 25%, 50%and 75%, respectively.

4.3.2.1 Influences of CO2 Emission Restrictions on the Power System withoutConstruction of New Transmission Lines in 2035

The power generation capacities regarding these long-term scenarios are listed in the appendixin Tables A.10 to A.13. The scenario 2035BAU demonstrates how the future demand can becovered in 2035. Compared to the power generation capacity in 2012, a huge expansion ofcoal-fired power stations regarding this scenario is observed which comes to 39.2GW. Lowoperational costs of this generation technology are arguments for an expansion. The requiredcapacity of gas-fired power stations in 2035 was already installed in 2012, so that there is nocapacity added. The generation capacity of hydro power stations grows from around 7.1GWto 9.6GW. Moreover, the capacity of power stations fired by biogas and biomass amountsto 0.9GW in 2035 which is constant for all following long-term scenarios. Regarding thescenarios with restrictions on CO2 emissions, the fleet of power stations is changed by themodel due to the constraints of limited CO2 emissions. More gas-fired power stations arebuilt, whereas the capacity of coal-fired power stations regresses related to the capacity in theBAU scenario. The higher the CO2 emission restrictions, the higher this shift from coal to gas.Additionally, capacity of hydro power and also solar PV is added to the fleet of power stationsby the model. New installed capacity of solar PV amounts to about 15.1GW, whereas thegeneration capacity of hydro power stations regarding the scenario with a reduction of 25%CO2 emissions increases by 0.8GW related to the BAU scenario. Furthermore it stays almostat this level in the remaining scenarios. Regarding the scenario with restrictions on the CO2

emissions of 50% and 75%, the capacity of coal decreases to the capacity which was alreadyinstalled in 2012. However, the generation capacity of gas-fired power stations raises to themaximum of 43.5GW compared to the scenario with a reduction of 75% of the 2035BAU CO2

emissions. In this scenario, the capacity of solar PV increases rapidly to 98.2GW. It is alsoobserved that with a higher share of electricity generated by intermittent energy resourcesthe power generation capacity is oversized in total. Due to the fluctuation of the generatedelectricity it is required to have backup generation capacity to compensate such fluctuations.

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As the development of the power station fleet already showed, the power generation mixregarding these scenarios demonstrates the shift from coal to gas with increasing reduction ofCO2 emissions. The higher the restrictions on CO2 emissions, the higher the penetration ofRE, in this case solar PV. This impact on the structure of the generated electricity results ina balance between the investment costs and operational costs combined with the restrictionson CO2 emissions. Regarding the scenario without any restrictions, the share of coal in thepower generation mix is reduced from 83.3% to 52.8%, to 19.3% and to 0% with increasingreductions of CO2 emissions by every 25%. However, the share of electricity generated by gas-fired power stations develops from 3.9% to 27.4%, to 59.6% and to 51%, respectively. Theshare of hydro power is almost constant of around 10.8% to 11.7%. With higher reductionsof CO2 emissions, the share of solar PV amounts to 6.1%, related to the scenario with 25%CO2 reductions, followed by 7.4% and 35.7% with increasing restrictions on CO2 emission,respectively.

Table 4.5: Summary of key results of the scenarios in 2035 without construction of new trans-mission lines.

Scenarios in 2035 without expansion of the power grid

Reduction of CO2 emissions compared to the 0% 25% 50% 75%emissions of the scenario 2035BAU

Generated electricity in TWh 312.94 313.39 313.39 338.80Generation costs in billion US$ 22.95 26.29 30.40 39.64Transmission costs in billion US$ 0.32 0.32 0.32 0.32Energy-related CO2 emissions in g/kWh 744.00 557.20 371.50 171.80Abatement costs in US$/t CO2 0.00 57.47 64.00 95.59LCOE in US$ct/kWh 7.44 8.49 9.80 11.79

The key results of the power system under the specific constraints are presented in Table 4.5.The table shows the generated electricity in each case based on the demand situation in 2035.Additionally, the costs of generation and transmission, the CO2 emission factor of electricityas well as the abatement costs of CO2 emissions and LCOE are listed.According to the findings of the power generation capacity and the power generation mix,the figures reflect the more cost-intensive reduction of CO2 emissions by implementing REto reach a low-emission power system. Although the operational costs first increase withhigher restrictions on CO2 emissions, the operational costs decrease regarding the scenariowith 75% CO2 emission limits related to the scenario with a 50% reduction. This is because ofthe installation of solar PV to the fleet of power stations which was described in Section 4.3.1.Combined with the fact that RE do not contribute any fuel cost, the operational costs decreasewith a higher penetration of solar PV. At the same time the investment costs increase threefold in the scenario with 75% CO2 emission restrictions.

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In the end, the higher the restrictions on CO2 emissions, the higher the total costs of thepower system. This is also proven by the LCOE which raise from 7.44US$ct/kWh in thescenario 2035BAU to 11.79US$ct/kWh compared to the scenario with the highest emissionlimit.

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Construction of new

transmission lines No Yes No Yes No Yes No Yes

Generated electricty

in TWh (=100 %) 312.94 312.97 313.39 313.12 313.39 313.12 338.80 324.42

Figure 4.3: Overview of modelled power generation mix in Malaysia in 2035.

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4.3.2.2 Effects of CO2 Emission Limits on the Power System with Expansion of thePower Grid in 2035

In this section, the results of the scenarios 2035TrON and additional constraints are described.In this case, it is remarkable that the capacity of hydro power stations is higher than in the casewithout construction of new transmission lines, regardless of restrictions on CO2 emissions.This is the case because of the already mentioned impact of the transmission line from Borneoto Peninsular Malaysia. The hydro potential of Borneo is better exploited due to the electricitytransport to Peninsular Malaysia. Thus, the capacity of hydro power stations stays at thesame level of around 13.2GW. With a higher share of electricity generated by emission-freeprocesses, the capacity of coal- and gas-fired power stations is higher than in the scenarioswithout expansion of the existing power grid. Nevertheless, the shift from coal to gas is alsoobserved as pointed out in the previous section. Additionally, the capacity of solar PV is lowerthan in the scenarios 2035TrOFF. Due to the higher capacity of hydro power which is emissionfree, it is more cost-attractive to build gas-fired power stations to meet the demand, whilerespecting the CO2 emission limit. Hence, the capacity of solar PV amounts to only 79.5GWregarding the scenario with a reduction of 75% of the CO2 emissions compared to the scenario2035BAU. The generation capacities of the scenarios 2035TrON with increasing CO2 emissionrestrictions are listed in Tables A.14 to A.17.

The power generation mix is derived from the structure and development of the generationcapacities in these scenarios accordingly. An overview of the specific power generation mixesis depicted in Figure 4.3. Compared to the scenarios without expansion of the existing powergrid, the power generation mix differs for following reasons: With the construction of newtransmission lines (in particular the submarine cable from Borneo to Peninsular Malaysia) thepotential of hydro power is better exploited due to the higher demand situation in Borneo.By installing more hydro power, the share of solar PV decreases by 5.1% due to the highinvestment costs and the share of gas increases slightly.

Moreover, the investment costs related to the power stations of these scenarios are higherthan the costs related to the scenarios 2035TrOFF. However, with a CO2 emission reductionof 75% compared to the scenario 2035BAU, the investment costs of the generation capacityare lower in the scenario 2035TrON (17.76 billion US$ vs. 15.30billion US$). This is becauseof the higher share of hydro power stations and the lower share of solar PV. At the same time,the operational costs of the power stations regarding the scenario 2035TrOFF with increasingrestrictions on CO2 emissions have the same development but are lower in all scenarios. Theadded transmission lines lead to additional investment and operational costs related to thepower grid, but are lower than the differences between the savings according to the scenarios2035TrOFF and 2035TrON. This is also proven by the total costs of the power system. Intotal, the minimised costs of the power system are lower than without expansion of the existingpower grid, but show the same cost trends.

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All figures including the abatement costs are listed in Table 4.6. Compared to the scenariowithout new transmission lines, the abatement costs are lower with new transmission lines,partly up to 15% of the abatement costs without expansion of the power grid. In the casewithout any CO2 emission restrictions, the expansion of the power grid results in a moreeffective operation of the power generation capacity with the result of lower CO2 emissionsand negative abatement costs. With higher reduction of CO2 emissions, the LCOE increase,ranging from 7.43US$ct/kWh to 11.66US$ct/kWh. These figures are also listed in Table 4.6.

In conclusion, the higher the reduction of CO2 emissions, the more cost-attractive theconstruction of transmission lines.

Table 4.6: Summary of key results of the scenarios in 2035 with construction of new trans-mission lines.

Scenarios in 2035 with expansion of the power grid

Reduction of CO2 emissions compared to the 0% 25% 50% 75%emissions of scenario 2035BAU

Generated electricity in TWh 312.97 313.12 313.12 324.42Generation costs in billion US$ 22.93 25.45 29.55 36.76Transmission costs in billion US$ 0.33 0.78 0.78 1.05Energy-related CO2 emissions in g/kWh 741.30 557.70 371.80 179.40Abatement costs in US$/t CO2 −6.72 50.90 60.68 83.29LCOE in US$ct/kWh 7.43 8.38 9.69 11.66

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4.3.3 Impact of Gas Price and CO2 Emission Restrictions in 2035

In the following scenarios, the first results of the preceding studies are applied. The advantagesof constructing new transmission lines are already shown in the previous section as the mostcost-attractive solution. Hence, the scenarios 2035TrOFF are not considered and evaluated.Thus, the allowance of expanding the power grid (2035TrON ) is set in the coming study ofall scenarios.

As mentioned in Chapter 2.3, the price of gas is adjusted to the market price by reducingthe subsidies to natural gas by 2016. By assuming that the government may keep the gasprice low by further subsidies, the development of the gas price in the first case is based on theprice in 2012 with an annual growth rate of 2.0% [EIA.2013d]. Thus, the gas price amountsto 24.58US$/MWh in 2035. In the second consideration, a higher development of the gasprice is assumed. In this scenario, the gas price is set to 47.89US$/MWh based on the gasprice in 2012 with an assumed annual growth rate of 5.0%.

Implementing those assumptions, the different power generation mixes are depicted in Fig-ure 4.4. In the first case, the power generation capacity as well as the generation mix are dom-inated by gas. Due to the lower fuel costs of gas, it is cost-attractive to install around 40.1GWgas-fired power stations. Regarding the scenario with a CO2 emission reduction of 75% ofthe compared to the scenario 2035BAU, the generation capacity of solar PV increases rapidlyto 79.5GW, also the capacity of gas-fired power stations raises slightly which means again aoversized power generation capacity and higher investment costs. This is shown by the figureswhich are listed in Table 4.7.

The structure of the power generation mix consists of about 86% electricity generated bygas. Coal and hydro power contribute a share of 7% and 6% to the generated electricity,respectively. Electricity which is generated by biomass and biogas amounts to around 1%. Asthis configuration of the power generation capacity and its planning of generating electricityis under the CO2 emission limit of 25% and 50%, the power system of those scenarios is notchanged by the model. By increasing the CO2 emissions to 75%, coal as a energy resourcefor the electricity generation disappears almost entirely from the power generation mix. Theutilisation of gas decreases to 53% of the power generation mix, whereas the share of elec-tricity produced by solar PV raises to 31%. In this scenario, hydro power contributes 14%.Moreover, generated electricity by biogas and biomass amounts to 2%.

A summary of key indicators of the future power system is listed by scenario in Table 4.7,including total generated electricity, the generation and transmission costs, the electricity-related emission factor as well as the abatement costs per saved t CO2 and the LCOE. Thesefigures show that the utilisation of power stations is not changed, although the reduction ofCO2 emission is more than 50%. This is because the limit of CO2 emissions is already fulfilled

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4.3 Investigation of Future ScenariosLong-Term View 2035 (1/3)



Impact of Gas Price and CO2 Limits

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Generated electri-

city in TWh (=100 %) 312.95 313.00 312.95 313.15 312.95 313.13 324.40 324.37

Figure 4.4: Overview of modelled power generation mix in Malaysia in 2035 with modifyingthe gas price.

in these scenarios due to the low fuel costs of gas and the resulting relatively low CO2 emissions.This fact also influences the electricity-related CO2 emission factor which is negative for thescenario up to a reduction of CO2 emission of 50% compared to the scenario 2035BAU. Alsoon the basis of the low gas price, the LCOE are lower (5.38US$ct/kWh and 8.45US$ct/kWh)compared to previous scenarios.

For a higher gas price, the power generation capacity of coal power stations is extendedto 34.6GW and decreases to 7.9GW with increasing CO2 emission reductions. Additionally,the capacity of gas-fired power stations increases from the basis value in 2012 of 14.1GWto 22.7GW, to 36.4GW and to 41.5GW, respectively, while increasing the restrictions onCO2 emissions by every 25%. The potential of biomass and biogas is fully exploited by a totalcapacity of 0.9GW regardless of the development of the CO2 emission limits. Moreover, witha limit of 75% CO2 emission, a capacity of 79.4GW solar PV is added to the power generationcapacity which also results in an oversized power station fleet. Hence, the investment costsare higher in this case. Additionally, the power generation mix is similar to to the structureof the power generation capacity. In the scenario 2035TrON without any restrictions on CO2

emissions but with a higher gas price, the power generation mix is dominated by coal with ashare of 78%. Gas and hydro powered stations contribute 10% and 11% to the generationmix, respectively. The higher the reduction of CO2 emissions, the lower the share of electricity

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Table 4.7: Summary of key results of the scenarios in 2035 with construction of new trans-mission lines and a modified gas price of 24.58US$/MWh.

Scenarios in 2035 with expansion of the power gridand a gas price of 24.58US$/MWh


Generated electricity in TWh 312.95 312.95 312.95 324.40Generation costs in billion US$ 16.53 16.53 16.53 26.35Transmission costs in billion US$ 0.32 0.32 0.32 1.05Electricity-related CO2 emissions in g/kWh 356.40 356.40 356.40 179.40Abatement costs in US$/t CO2 −52.93 −52.93 −52.93 23.70LCOE in US$ct/kWh 5.38 5.38 5.38 8.45

generated by coal. At the same time, the share in the power generation mix of gas developswith higher reduction of CO2 emissions from 33% to 67% and then down to 53% regardingthe scenario with 75% CO2 emission limit. The share of hydro power rises slightly to aconstant value of 14%. The same applies for electricity generated by biomass and biogason a 2% share in the power generation mix. Regarding the last scenario with CO2 emissionrestrictions of 75%, the share of solar PV of the power generation mix jumps up to 31%.

To compare the key results of the scenarios, especially costs and emission factors, Table 4.8shows these figures for a higher gas price. As already mentioned, the leap of the share of solarPV is also observed for the rapid increase of the investment costs of the scenario with a 75%reduction of CO2 emissions.

The costs of transmission lines are not affected by modifying the gas price, especially byincreasing the price for gas. Those costs are the same as in the scenarios with a lower gas price.The generation costs are higher due to the higher fuel costs of gas. However, the incrementof generation costs amounts to 37% with an CO2 emission limits increase from 50% to 75%regarding the scenarios with a lower gas price, an increase by 24% is observed in the scenariowith a higher gas price.

The abatement costs related to the BAU scenario in 2035 are also influenced by a highergas price. Compared to the abatement costs with a gas price of 24.58US$/MWh, the abate-ment costs are higher due to the higher total costs of the power system. Accordingly, theprice for electricity is more cost-intensive due to higher total costs. The LCOE rangesfrom 7.29US$ct/kWh to 10.52US$ct/kWh with greater restrictions on CO2 emissions.

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Table 4.8: Summary of key results of the scenarios in 2035 with construction of new trans-mission lines and gas price of 47.89US$/MWh.

Scenarios in 2035 with expansion of the power gridand a gas price of 47.89US$/MWh


Generated electricity in TWh 313.00 313.15 313.13 324.37Generation costs in billion US$ 22.49 23.44 25.30 33.08Transmission costs in billion US$ 0.33 0.73 0.73 1.05Energy-related CO2 emissions in g/kWh 712.40 557.70 371.80 179.50Abatement costs in US$/t CO2 −45.77 15.53 23.68 62.19LCOE in US$ct/kWh 7.29 7.72 8.31 10.52

Comparing the results of these two scenarios with different gas prices, it is obvious thatmanipulating the fuel price affects the costs as well as the power generation mix.The higher the fuel costs, the higher the operational costs of the power system and the higherthe penetration of RE. As the model solves the equations of the power system by minimisingthe total costs, the lower operational costs (due to the fact that RE do not have fuel costs)compensate for the higher investment costs of RE. In the end, lower total costs are observed.

Hence, subsidies to fossil fuels have to be reduced to hold the balance between the opera-tional costs of fossil fuels and investment costs of RE. With the appropriate balance betweenthose two parameters it is possible to have a cost-effective sustainable power system in Malay-sia.

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5 Conclusion and Outlook

The rapid increase of the electricity demand in Malaysia is a major future challenge for thecountry. This includes the sustainable, reliable and environmental-friendly future electricitysupply to meet the higher demand for electricity. In Malaysia, the demand for electricity willalmost triple by 2035, compared to 2012.

For analysing the actual and the future power system of Malaysia, the time step based modelURBS is used. By means of the model URBS, impacts and influences of certain parameters onthe power system are derived. While meeting the demand and at the same time minimisingthe overall costs in hourly time steps, the power system is modelled.

Planing the use and control of fossil fuel-fired power stations is time-independent and relat-ively straightforward. In contrast, the utilisation of intermittent energy sources is dependenton their availability. Implemented time series of fluctuating electricity demand and renewableenergies, such as solar PV and wind, represent that availability to a power system. The modelcovers the fluctuating demand for electricity by using the available energy sources. Thus, itis analysed how the power system reacts to intermittent energy sources and the demand loadcurve.

The increasing demand situation in the future is a key parameter which affects the powersystem. But also effects of expanding the 2012 power grid and CO2 emission restrictions onthe system are examined. Moreover, a sensitivity analysis of fuel prices are added. In thisthesis, the variation of the gas price is taken into account.

Since the model solves linear problems of the power system by minimising the total costs,the high influence of input data regarding costs is obvious. Although the data situation ispatchy in parts and available data contradict themselves, it was possible to re-model theMalaysian power system in 2012. Verifying the model by comparing the results to historicaldata ensures that the model presents the actual power system and its behaviour.

Without any restrictions on the power system, the future power generation mix as wellas the power generation capacity is dominated by fossil fuels, due to the their low totalcosts. A further advantage of fossil fuel-fired power stations is their constant availability togenerate electricity. But this aspect results in a high dependency on fossil energy resources(which are limited), particularly on imports. In Malaysia, this applies especially to coalwhich is currently imported from Indonesia and Australia. A high dependency of limited and

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imported fossil fuels would be the result of the most cost-effective solution in future withoutany restrictions. However, this does not reflect the official position of the government. In thelast decades, particular political frameworks were launched to decrease the reliance on onlyone fossil energy resource. Thus, a diversification of the power generation mix is striven fora reliable and secure energy supply in future, although it will be more expensive. Furtherconstraints are examined to give an idea of possible alternatives to a sustainable and lowemission power system.

In all scenarios, the positive effect of expanding the power grid on total costs is demon-strated. Constructing new transmission lines is the future foundation of an affordable elec-tricity supply in Malaysia. Although there are investment costs regarding the power grid inthe first step, the overall costs of the power system are reduced compared to the scenarioswithout expanding the power grid. This is the fact due to a more efficient utilisation of thepower stations.

Moreover, restrictions on CO2 emissions have a strong impact on the structure of the powersystem, especially the penetration of renewable energies are affected. The higher the CO2

limits are, the higher is the share of renewable energies in the power generation mix. Onefinding is that wind does only play a minor role in Malaysia due to the equatorial location ofthe country. The wind potential in the Malaysian region is very low and does not representan economical alternative to fossil fuels. Instead, solar PV as an energy source for generatingelectricity is more important. But the cost-effective utilisation is dependent on several factors.On the one hand, with higher prices of fossil fuels, the installed capacity of solar PV increases.Thus, restrictions on CO2 emissions affect the increase of using solar PV as an energy sourcepositively. On the other hand, additional back-up capacity, for instance of gas-fired powerstations, has to be installed due to inconstant availability of solar PV. The reason is that back-up capacity will have to generate electricity if there is no solar radiation but a high demand.Installing back-up capacity results in higher investment costs which have to be compensateby the lower operational costs.

Additionally, the fuel prices influence the structure of the future power system as well.The higher fuel costs are, the higher the operational costs. Thus, the use of technologieswith high investment costs but without any fuel costs such as renewable energies becomemore cost-attractive for generating electricity. A relief to fossil fuels to diminish the fuelcosts distort economic market mechanism. As a consequence, subsidies to fossil fuels restrainthe development of renewable energies. This is mainly due to man-made imbalance betweenhigher investment costs of RE and the operational costs of power stations fired by fossil fuels.

The input data of the upper bound of renewable energies are based on a literature researchon the potential in Malaysia. Studies regarding the determination and constraints of RE po-tential support a more detailed discussion of the power system and its energy sources. Thisapplies also to hydro power. Its potential is given in research studies and is implemented in

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the URBS model. However, the respective time series is not inserted into the model yet. Thefluctuation of hydro power related to the weather conditions would give more insight on theimpact of hydro power to the power system.Implementing tidal and wave energy would increase the presence of renewable energies butthose technologies are still in the early stages of their development and profitability. Addi-tionally, research on the potential of tidal energy are needed in the future.

Furthermore, impacts of energy storage on the power system would be an interesting con-sideration. It should be decided which storage technologies have a promising future in ASEANand which of them should be implemented into the model. Data about the specific storagepower stations are also needed. As a first approach, water storage power stations could bedefined, especially in Malaysia and its high potential of hydro power. Feasibility studies re-lated to economic and social aspects should be conducted as construction of the necessarydams are always an impairment of nature.

Moreover, adding more voltage levels of the transmission power grid increases the accuracyof the analysis of the power system. But these additional efforts will be only reasonable ifproper data are available.

Additionally, the fleet of power stations consists of power stations of different characteristics.In this thesis power stations of the same power generation technology are pooled to a virtualpower station with specific characteristics. But in reality, the bundled power stations vary forexample in the efficiency or maintenance costs. A more precise approach would therefore beto define more types of the same generation technology to reflect the real characteristics ofeach power station. The additional expenditure on data research to provide the specific datasets and a longer simulation time should be weighed against the gained accuracy.

Lastly, a modelling approach which is able to optimise the power system by including futuretrends and iterative considerations would reflect the actual behaviour of the power systemmore in detail. This would be interesting especially when it comes to extended long-termevaluations.

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Appendix

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Appendix

Table A.1: Overview of input data regarding power stations after combining data of [Platts.2010] and informations of costs. Additionally,costs for transmission lines and submarine cables, losses and depreciation out of [Schaber.2012a] are shown.

Power stations

Technology Energy sourceInvestment costs Fixed costs Variable costs Efficiency Load Depreciation

cinv cfix cvar ηfactor [yr][US$/MW] [US$/MW/yr] [US$/MWh/yr] af

Steam Biomass 2,263,778 105,630 5.26 0.150 0.808 20power station Coal 1,579,380 31,180 4.47 0.352 0.830 40

Gas 973,000 7,340 15.45 0.265 0.808 20Oil 973,000 7,340 15.45 0.265 0.808 20

GT Diesel 421,168 7,040 10.37 0.295 0.966 20Gas 421,168 7,040 10.37 0.295 0.966 20Oil 421,168 7,040 10.37 0.295 0.966 20

CCGT Gas 737,030 15,370 3.27 0.442 0.876 20Oil 737,030 15,370 3.27 0.442 0.876 20

GS Biogas 500,000 15,000 0.00 0.250 0.966 20Diesel 500,000 15,000 0.00 0.330 0.966 20Gas 500,000 15,000 0.00 0.300 0.966 20Landfill gas 8,312,000 392,820 8.75 0.250 0.966 20Oil 500,000 15,000 0.00 0.340 0.966 20

Hydro power Conventional 1,968,960 14,130 0.00 1.000 1.000 30Micro-hydro 3,137,702 14,130 0.00 1.000 1.000 30

Solar PV Sun 2,737,592 24,690 0.00 1.000 1.000 20Wind turbine Wind offshore 2,695,475 74,000 0.00 1.000 1.000 20

Wind onshore 1,526,734 39,550 0.00 1.000 1.000 20

Transmission

TypeSpecific Fixed costs Losses Depreciation

investment costs [US$/MW/yr] [%/1000 km] [yr][US$/MW/km]

Overhead line 540 9,440 4 40Submarine cable 3,370 9,440 4 40

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Table A.2: Overview of capacity, lengths and efficiency of the power grid in 2012 in Malaysia by each connection by [PMGSO.2012].Investment costs based on the length of connection apply if capacity is added by the model.

Regions 2012 modelled Investment costs Length of Efficiency

From To capacity transmission lines submarine cable[MW] [US$/MW] [km] [km]

Perlis Kedah 340 39,299.58 72.8 0 0.997Kedah Penang 0 41,478.48 76.8 0 0.997Kedah Perak 153 72,911.34 135.0 0 0.995Penang Perak 950 46,737.54 86.6 0 0.997Perak Kelantan 34 83,314.44 154.3 0 0.994Perak Selangor 948 98,458.20 182.3 0 0.993Kelantan Terengganu 50 75,050.28 139.0 0 0.994Pahang Terengganu 1,138 91,068.30 168.6 0 0.993Pahang Selangor 672 59,848.74 110.8 0 0.996Selangor Kuala Lumpur 1,978 10,776.24 20.0 0 0.999Selangor Negeri Sembilan 2,981 46,995.66 87.0 0 0.997Melaka Negeri Sembilan 235 27,607.50 51.1 0 0.998Johor Pahang 470 135,805.68 251.5 0 0.990Johor Negeri Sembilan 500 120,802.32 223.7 0 0.991Johor Melaka 744 95,619.96 177.1 0 0.993Sabah Sarawak 0 298,361.88 552.5 0 0.978Pahang Kuala Lumpur 500 95,619.96 177.1 0 0.993Negeri Sembilan Kuala Lumpur 500 95,619.96 177.1 0 0.993Sarawak Johor 0 2,382,883.90 273.6 789.8 0.989

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Appendix

Table A.3: Upper bound of power generating capacities of renewable energies by region in 2020 and 2035 in MW Malaysia [ASM.2013].Capacity of solar PV and wind is set to unlimited.

Distribution of Potential in MW

Region Biomass Biogas Hydro Micro-hydro MSW

Johor 39 12 20 23Kedah 19 6 8 14Kelantan 31 9 17 11Kuala Lumpur 12Melaka 3 1 6Negeri Sembilan 14 4 29 7Pahang 73 22 44 11Penang 2 1 0 11Perak 43 13 57 16Perlis 2 2Sabah 150 46 2,056 30 24Sarawak 252 77 8,944 37 17Selangor 16 5 3 39Terengganu 26 8 5 7

Totoal 670 205 11,000 250 200

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Appendix

Figure A.1: Full-load hours of solar PV in Malaysia in [h].

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Appendix

Figure A.2: Full-load hours of onshore wind in Malaysia in [h].

66

Appendix

Figure A.3: Full-load hours of offshore wind in Malaysia in [h].

67

Appendix

Table A.4: Overview input data: installed capacity by region in MW in 2012 by [Platts.2010].Power stations

Indonesia Malaysia

Technology Java Kalimantan East Sumatra Borneo Peninsular Singapore

Steam Biomass 6.10 27.93 19.00 898.00 129.33 39.35 23.09Coal 15,216.80 273.63 526.50 1,865.00 480.00 7,449.00 0.00Gas 20.00 16.00 0.00 25.00 20.00 6.00 1,250.00Oil 2,436.00 34.50 53.20 263.20 0.00 1,371.00 3,450.00

Gas turbine Gas 2,222.65 286.01 73.50 1,939.04 572.18 3,327.20 733.73Oil 1,653.00 508.00 461.71 1,312.30 373.90 168.20 168.00Diesel 1,106.75 14.00 0.00 0.00 0.00 0.00 0.00

Combinedcycle

Gas 4,258.71 50.00 90.00 662.40 574.90 9,454.51 6,030.00

Oil 1,762.23 10.00 60.00 312.00 0.00 948.32 848.00

Engine Diesel 456.00 228.37 1,031.18 96.47 196.24 17.72 0.00Gas 25.63 25.40 0.00 156.45 0.00 0.00 1.00Oil 0.00 0.00 0.00 0.00 599.63 197.35 80.91

Hydroelectric Hydro 2,361.00 30.17 689.20 1,486.48 490.30 1,982.81 0.00Geothermal Heat 1,064.00 0.00 45.00 0.00 0.00 0.00 0.00Solar PV Insolation 0.00 0.00 0.14 0.00 0.00 0.01 0.02Wind turbine Wind 0.00 0.00 0.00 0.00 0.15 0.00 0.00

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Table A.5: Modelled generation capacity in the scenario 2012Reference based on [Platts.2010].Unit: (MW) Biogas Biomass Coal Diesel Gas Hydro LGas Oil SunGlobal Wind-onJohor - - 2,244 - 1,249 0 - 252 - -Kedah - 3 - - 228 1 - 109 - -Kelantan - - - - - 621 - 59 - -Kuala Lumpur - - - 1 14 - - 6 - -Melaka - - - 2 1,615 - - - - -Negeri Sembilan - - 1,505 1 1,995 - - - - -Pahang 10 1 - - 108 11 - 126 - -Penang - - - - 690 - - 433 - -Perak - 10 2,100 - 1,958 946 - 0 - -Perlis - - - - 710 - - - - -Sabah - 116 - 42 576 80 - 802 - 0Sarawak - 2 480 154 690 430 - 172 - -Selangor - 2 1,600 14 2,672 3 2 653 0 -Terengganu - - - - 1,610 400 - 1,042 - -Total 10 133 7,929 214 14,116 2,493 2 3,653 0 0

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Table A.6: Modelled generation capacity in scenario 2020BAU.Unit: (MW) Biogas Biomass Coal Diesel Gas Hydro LGas Oil SunGlobal Wind-onJohor 12 39 2,342 - 1,249 0 - 252 - -Kedah 6 19 783 - 228 9 - 109 - -Kelantan 9 31 119 - - 637 - 59 - -Kuala Lumpur 0 0 - 1 14 - - 6 - -Melaka 1 3 550 2 1,615 - - - - -Negeri Sembilan 4 14 1,505 1 1,995 - - - - -Pahang 32 73 1,294 - 108 55 - 126 - -Penang 1 2 1,034 - 690 - - 433 - -Perak 13 43 2,100 - 1,958 946 - 0 - -Perlis 0 2 121 - 710 - - - - -Sabah 46 116 - 42 576 1,497 - 802 - 0Sarawak 77 2 480 154 690 2,946 - 172 - -Selangor 5 16 9,880 14 2,672 3 2 653 0 -Terengganu 8 26 400 - 1,610 405 - 1,042 - -Total 215 385 20,607 214 14,116 6,500 2 3,653 0 0

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Table A.7: Modelled generation capacity by region and process in scenario 2020TrOFF and political target of CO2 emission reduction.Unit: (MW) Biogas Biomass Coal Diesel Gas Hydro LGas Oil SunGlobal Wind-onJohor 12 39 2,244 - 1,249 20 - 252 - -Kedah 6 19 - - 400 9 - 109 - -Kelantan 9 31 - - 82 637 - 59 - -Kuala Lumpur 0 0 - 1 64 - - 6 - -Melaka 1 3 - 2 1,615 - - - - -Negeri Sembilan 4 14 1,505 1 1,995 29 - - - -Pahang 32 73 24 - 560 55 - 126 - -Penang 1 2 - - 995 - - 433 - -Perak 13 43 2,100 - 1,958 1,004 - 0 - -Perlis 0 2 - - 710 - - - - -Sabah 46 116 - 42 576 1,555 - 802 - 0Sarawak 77 2 480 154 690 3,622 - 172 - -Selangor 5 16 3,385 14 5,566 7 2 653 0 -Terengganu 8 26 - - 1,610 405 - 1,042 - -Total 215 385 9,738 214 18,071 7,343 2 3,653 0 0

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Table A.8: Modelled generation capacity in the scenario 2020TrON and no CO2 emission restrictions.Unit: (MW) Biogas Biomass Coal Diesel Gas Hydro LGas Oil SunGlobal Wind-onJohor 12 39 2,342 - 1,249 0 - 252 - -Kedah 6 19 783 - 228 9 - 109 - -Kelantan 9 31 119 - - 637 - 59 - -Kuala Lumpur 0 0 - 1 14 - - 6 - -Melaka 1 3 550 2 1,615 - - - - -Negeri Sembilan 4 14 1,505 1 1,995 - - - - -Pahang 32 73 1,294 - 108 55 - 126 - -Penang 1 2 1,034 - 690 - - 433 - -Perak 13 43 2,100 - 1,958 946 - 0 - -Perlis 0 2 121 - 710 - - - - -Sabah 46 116 - 42 576 2,056 - 802 - 0Sarawak 77 2 480 154 690 2,186 - 172 - -Selangor 5 16 9,880 14 2,672 3 2 653 0 -Terengganu 8 26 400 - 1,610 405 - 1,042 - -Total 215 385 20,607 214 14,116 6,299 2 3,653 0 0

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Table A.9: Modelled generation capacity in the scenario 2020TrON and limited CO2 emissions defined by political targets.Unit: (MW) Biogas Biomass Coal Diesel Gas Hydro LGas Oil SunGlobal Wind-onJohor 12 39 2,244 - 1,249 20 - 252 - -Kedah 6 19 221 - 228 9 - 109 - -Kelantan 9 31 84 - - 637 - 59 - -Kuala Lumpur 0 0 - 1 14 - - 6 - -Melaka 1 3 - 2 1,615 - - - - -Negeri Sembilan 4 14 1,505 1 1,995 29 - - - -Pahang 32 73 - - 108 55 - 126 - -Penang 1 2 250 - 690 - - 433 - -Perak 13 43 2,100 - 1,958 1,004 - 0 - -Perlis 0 2 - - 710 - - - - -Sabah 46 150 - 42 576 2,086 - 802 - 0Sarawak 77 252 480 154 690 8,944 - 172 - -Selangor 5 16 5,498 14 2,672 7 2 653 0 -Terengganu 8 26 - - 1,610 405 - 1,042 - -Total 215 670 12,382 214 14,116 13,196 2 3,653 0 0

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Table A.10: Modelled power generation mix of the scenario 2035BAU.Unit: (MW) Biogas Biomass Coal Diesel Gas Hydro LGas Oil SunGlobal Wind-onJohor 12 39 4,494 - 1,249 0 - 252 - -Kedah 6 19 1,660 - 228 9 - 109 - -Kelantan 9 31 602 - - 637 - 59 - -Kuala Lumpur 0 0 - 1 14 - - 6 - -Melaka 1 3 1,326 2 1,615 - - - - -Negeri Sembilan 4 14 1,624 1 1,995 - - - - -Pahang 32 73 2,089 - 108 55 - 126 - -Penang 1 2 3,399 - 690 - - 433 - -Perak 13 43 2,100 - 1,958 946 - 0 - -Perlis 0 2 214 - 710 - - - - -Sabah 46 150 415 42 576 2,086 - 802 - 0Sarawak 77 252 480 154 690 5,474 - 172 - -Selangor 5 16 19,905 14 2,672 3 2 653 0 -Terengganu 8 26 918 - 1,610 405 - 1,042 - -Total 215 670 39,227 214 14,116 9,617 2 3,653 0 0

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Table A.11: Modelled power generation capacity of the scenario 2035TrOFF and a CO2 reduction of 25% compared to the scenario2035BAU.Unit: (MW) Biogas Biomass Coal Diesel Gas Hydro LGas Oil SunGlobal Wind-onJohor 12 39 3,570 - 1,707 20 - 252 - -Kedah 6 19 299 - 892 9 - 109 2,150 -Kelantan 9 31 294 - 273 637 - 59 - -Kuala Lumpur 0 0 - 1 64 - - 6 5,000 -Melaka 1 3 206 2 1,615 - - - - -Negeri Sembilan 4 14 1,505 1 1,995 29 - - - -Pahang 32 73 1,147 - 589 55 - 126 - -Penang 1 2 2,349 - 690 - - 433 - -Perak 13 43 2,100 - 1,958 1,004 - 0 - -Perlis 0 2 - - 710 - - - - -Sabah 46 150 - 42 619 2,086 - 802 164 0Sarawak 77 252 480 154 690 6,188 - 172 - -Selangor 5 16 11,218 14 7,842 7 2 653 5,257 -Terengganu 8 26 - - 1,610 405 - 1,042 2,571 -Total 215 670 23,168 214 21,253 10,440 2 3,653 15,142 0

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Table A.12: Generating capacity of the scenario 2035TrOFF and 50% CO2 emission reduction compared to 2035BAU.Unit: (MW) Biogas Biomass Coal Diesel Gas Hydro LGas Oil SunGlobal Wind-onJohor 12 39 2,244 - 2,965 20 - 252 - -Kedah 6 19 - - 1,179 9 - 109 2,199 -Kelantan 9 31 - - 551 637 - 59 - -Kuala Lumpur 0 0 - 1 64 - - 6 5,000 -Melaka 1 3 - 2 1,776 - - - - -Negeri Sembilan 4 14 1,505 1 1,995 29 - - - -Pahang 32 73 - - 1,678 55 - 126 - -Penang 1 2 726 - 2,210 - - 433 - -Perak 13 43 2,100 - 1,958 1,004 - 0 - -Perlis 0 2 - - 710 - - - - -Sabah 46 150 - 42 619 2,086 - 802 164 0Sarawak 77 252 480 154 690 6,190 - 172 - -Selangor 5 16 1,600 14 16,863 7 2 653 8,300 -Terengganu 8 26 - - 1,610 405 - 1,042 2,641 -Total 215 670 8,655 214 34,868 10,442 2 3,653 18,303 0

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Table A.13: Generating capacity of the scenario 2035TrOFF and with reduction of CO2 emissions by 75% compared to the scenario2035BAU.Unit: (MW) Biogas Biomass Coal Diesel Gas Hydro LGas Oil SunGlobal Wind-onJohor 12 39 2,244 - 5,093 20 23 252 13,856 -Kedah 6 19 - - 1,375 9 14 109 4,066 -Kelantan 9 31 - - 516 637 11 59 1,327 -Kuala Lumpur 0 0 - 1 64 - 12 6 5,000 -Melaka 1 3 - 2 1,910 - 6 - 2,105 -Negeri Sembilan 4 14 1,505 1 2,421 29 7 - - -Pahang 32 73 - - 1,981 55 11 126 77 -Penang 1 2 - - 3,584 - 11 433 7,640 -Perak 13 43 2,100 - 2,156 1,004 16 0 6,605 -Perlis 0 2 - - 710 - 2 - 741 -Sabah 46 150 - 42 623 2,086 24 802 1,000 0Sarawak 77 252 480 154 690 7,171 - 172 - -Selangor 5 16 1,600 14 20,750 7 39 653 51,096 -Terengganu 8 26 - - 1,610 405 7 1,042 4,736 -Total 215 670 7,929 214 43,483 11,423 183 3,653 98,248 0

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Table A.14: Generating capacity of the scenario 2035TrON without CO2 emissions limits.Unit: (MW) Biogas Biomass Coal Diesel Gas Hydro LGas Oil SunGlobal Wind-onJohor 12 39 4,494 - 1,249 0 - 252 - -Kedah 6 19 1,660 - 228 9 - 109 - -Kelantan 9 31 602 - - 637 - 59 - -Kuala Lumpur 0 0 - 1 14 - - 6 - -Melaka 1 3 1,326 2 1,615 - - - - -Negeri Sembilan 4 14 1,624 1 1,995 - - - - -Pahang 32 73 2,089 - 108 55 - 126 - -Penang 1 2 3,399 - 690 - - 433 - -Perak 13 43 2,100 - 1,958 946 - 0 - -Perlis 0 2 214 - 710 - - - - -Sabah 46 150 263 42 576 2,086 - 802 - 0Sarawak 77 252 480 154 690 5,817 - 172 - -Selangor 5 16 19,905 14 2,672 3 2 653 0 -Terengganu 8 26 918 - 1,610 405 - 1,042 - -Total 215 670 39,075 214 14,116 9,960 2 3,653 0 0

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Table A.15: Modelled generating capacity in the scenario 2035TrON and reduction of 25% emissions compared to the scenario 2035BAU.Unit: (MW) Biogas Biomass Coal Diesel Gas Hydro LGas Oil SunGlobal Wind-onJohor 12 39 2,244 - 1,607 20 - 252 - -Kedah 6 19 439 - 667 9 - 109 2,166 -Kelantan 9 31 321 - 253 637 - 59 - -Kuala Lumpur 0 0 - 1 64 - - 6 5,000 -Melaka 1 3 240 2 1,615 - - - - -Negeri Sembilan 4 14 1,505 1 1,995 29 - - - -Pahang 32 73 1,362 - 390 55 - 126 - -Penang 1 2 2,350 - 690 - - 433 - -Perak 13 43 2,100 - 1,958 1,004 - 0 - -Perlis 0 2 - - 710 - - - - -Sabah 46 150 - 42 576 2,086 - 802 - 0Sarawak 77 252 480 154 690 8,981 - 172 - -Selangor 5 16 12,699 14 6,486 7 2 653 3,976 -Terengganu 8 26 - - 1,610 405 - 1,042 2,375 -Total 215 670 23,739 214 19,312 13,233 2 3,653 13,517 0

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Table A.16: Modelled generation capacity in the scenario 2035TrON and CO2 emission reductions of 50% compared to the scenario2035BAU.Unit: (MW) Biogas Biomass Coal Diesel Gas Hydro LGas Oil SunGlobal Wind-onJohor 12 39 2,244 - 1,609 20 - 252 - -Kedah 6 19 - - 1,092 9 - 109 2,223 -Kelantan 9 31 93 - 464 637 - 59 - -Kuala Lumpur 0 0 - 1 64 - - 6 5,000 -Melaka 1 3 - 2 1,779 - - - - -Negeri Sembilan 4 14 1,505 1 1,995 29 - - - -Pahang 32 73 - - 1,678 55 - 126 - -Penang 1 2 1,170 - 1,783 - - 433 - -Perak 13 43 2,100 - 1,958 1,004 - 0 - -Perlis 0 2 - - 710 - - - - -Sabah 46 150 - 42 576 2,086 - 802 - 0Sarawak 77 252 480 154 690 8,981 - 172 - -Selangor 5 16 1,729 14 16,753 7 2 653 7,995 -Terengganu 8 26 - - 1,610 405 - 1,042 2,641 -Total 215 670 9,320 214 32,760 13,233 2 3,653 17,858 0

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Table A.17: Modelled generation capacity in the scenario 2035TrON and with reduction of CO2 emissions of 75% compared to the scenario2035BAU.Unit: (MW) Biogas Biomass Coal Diesel Gas Hydro LGas Oil SunGlobal Wind-onJohor 12 39 2,244 - 3,732 20 23 252 8,775 -Kedah 6 19 - - 1,357 9 14 109 9,271 -Kelantan 9 31 - - 434 637 11 59 516 -Kuala Lumpur 0 0 - 1 64 - 12 6 5,000 -Melaka 1 3 - 2 1,882 - 6 - 1,194 -Negeri Sembilan 4 14 1,505 1 2,399 29 7 - - -Pahang 32 73 - - 1,872 55 11 126 - -Penang 1 2 - - 3,467 - 11 433 - -Perak 13 43 2,100 - 2,089 1,004 16 0 6,849 -Perlis 0 2 - - 710 - 2 - - -Sabah 46 150 - 42 576 2,086 24 802 780 0Sarawak 77 252 480 154 690 8,981 17 172 - -Selangor 5 16 1,600 14 20,519 7 39 653 39,201 -Terengganu 8 26 - - 1,610 405 7 1,042 7,886 -Total 215 670 7,929 214 41,401 13,233 200 3,653 79,472 0

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Table A.18: Power generation capacity in the scenario 2035TrON with a adjusted gas price of 24.58US$/MWh and no restrictions on CO2emissions.Unit: (MW) Biogas Biomass Coal Diesel Gas Hydro LGas Oil SunGlobal Wind-onJohor 12 - 2,244 - 3,677 0 - 252 - -Kedah 6 3 - - 1,267 1 - 109 - -Kelantan 9 - - - 585 621 - 59 - -Kuala Lumpur 0 - - 1 64 - - 6 - -Melaka 1 - - 2 1,805 - - - - -Negeri Sembilan 4 - 1,505 1 1,995 - - - - -Pahang 32 1 - - 1,788 11 - 126 - -Penang 1 - - - 2,972 - - 433 - -Perak 13 10 2,100 - 1,958 946 - 0 - -Perlis 0 - - - 710 - - - - -Sabah 46 116 - 42 719 2,056 - 802 - 0Sarawak 77 2 480 154 2,742 430 - 172 - -Selangor 5 2 1,600 14 18,215 3 2 653 0 -Terengganu 8 - - - 1,610 400 - 1,042 - -Total 215 133 7,929 214 40,108 4,469 2 3,653 0 0

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Table A.19: Power generation capacity in the scenario 2035TrON, according to the scenario with an adjusted gas price of 24.58US$/MWh.The CO2 emissions are limited to 25% compared to the scenario 2035BAU.Unit: (MW) Biogas Biomass Coal Diesel Gas Hydro LGas Oil SunGlobal Wind-onJohor 12 - 2,244 - 3,677 0 - 252 - -Kedah 6 3 - - 1,267 1 - 109 - -Kelantan 9 - - - 585 621 - 59 - -Kuala Lumpur 0 - - 1 64 - - 6 - -Melaka 1 - - 2 1,805 - - - - -Negeri Sembilan 4 - 1,505 1 1,995 - - - - -Pahang 32 1 - - 1,788 11 - 126 - -Penang 1 - - - 2,972 - - 433 - -Perak 13 10 2,100 - 1,958 946 - 0 - -Perlis 0 - - - 710 - - - - -Sabah 46 116 - 42 719 2,056 - 802 - 0Sarawak 77 2 480 154 2,742 430 - 172 - -Selangor 5 2 1,600 14 18,215 3 2 653 0 -Terengganu 8 - - - 1,610 400 - 1,042 - -Total 215 133 7,929 214 40,108 4,469 2 3,653 0 0

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Table A.20: Power generation capacity in the scenario 2035TrON, a restriction on the CO2 emissions by 50% compared to the scenario2035BAU and an adjusted gas price of 24.58US$/MWh.Unit: (MW) Biogas Biomass Coal Diesel Gas Hydro LGas Oil SunGlobal Wind-onJohor 12 - 2,244 - 3,677 0 - 252 - -Kedah 6 3 - - 1,267 1 - 109 - -Kelantan 9 - - - 585 621 - 59 - -Kuala Lumpur 0 - - 1 64 - - 6 - -Melaka 1 - - 2 1,805 - - - - -Negeri Sembilan 4 - 1,505 1 1,995 - - - - -Pahang 32 1 - - 1,788 11 - 126 - -Penang 1 - - - 2,972 - - 433 - -Perak 13 10 2,100 - 1,958 946 - 0 - -Perlis 0 - - - 710 - - - - -Sabah 46 116 - 42 719 2,056 - 802 - 0Sarawak 77 2 480 154 2,742 430 - 172 - -Selangor 5 2 1,600 14 18,215 3 2 653 0 -Terengganu 8 - - - 1,610 400 - 1,042 - -Total 215 133 7,929 214 40,108 4,469 2 3,653 0 0

84

Appendix

Table A.21: Power generation capacity in the scenario 2035TrON with a modified gas price of 24.58US$/MWh and a CO2 emission limitof 75% compared to the scenario 2035BAU.Unit: (MW) Biogas Biomass Coal Diesel Gas Hydro LGas Oil SunGlobal Wind-onJohor 12 39 2,244 - 3,758 20 23 252 8,772 -Kedah 6 19 - - 1,374 9 14 109 9,276 -Kelantan 9 31 - - 473 637 11 59 516 -Kuala Lumpur 0 0 - 1 64 - 12 6 5,000 -Melaka 1 3 - 2 1,878 - 6 - 1,263 -Negeri Sembilan 4 14 1,505 1 2,418 29 7 - - -Pahang 32 73 - - 1,980 55 11 126 - -Penang 1 2 - - 3,476 - 11 433 - -Perak 13 43 2,100 - 2,115 1,004 16 0 6,875 -Perlis 0 2 - - 710 - 2 - - -Sabah 46 150 - 42 576 2,086 24 802 780 0Sarawak 77 252 480 154 690 8,981 17 172 - -Selangor 5 16 1,600 14 20,516 7 39 653 39,132 -Terengganu 8 26 - - 1,610 405 7 1,042 7,843 -Total 215 670 7,929 214 41,637 13,233 200 3,653 79,457 0

85

Appendix

Table A.22: Overview of the generation capacity in 2035 under certain constraints which are following: an expansion of the power grid isallowed, the gas price is set to 47.89US$/MWh and no restrictions on CO2 emissions are made.Unit: (MW) Biogas Biomass Coal Diesel Gas Hydro LGas Oil SunGlobal Wind-onJohor 12 39 4,164 - 1,249 0 - 252 - -Kedah 6 19 1,467 - 228 9 - 109 - -Kelantan 9 31 558 - - 637 - 59 - -Kuala Lumpur 0 0 - 1 14 - - 6 - -Melaka 1 3 887 2 1,615 - - - - -Negeri Sembilan 4 14 1,505 1 1,995 - - - - -Pahang 32 73 1,951 - 108 55 - 126 - -Penang 1 2 2,687 - 690 - - 433 - -Perak 13 43 2,100 - 1,958 946 - 0 - -Perlis 0 2 - - 710 - - - - -Sabah 46 150 - 42 576 2,086 - 802 - 0Sarawak 77 252 480 154 690 5,599 - 172 - -Selangor 5 16 18,341 14 2,672 3 2 653 0 -Terengganu 8 26 447 - 1,610 405 - 1,042 - -Total 215 670 34,587 214 14,116 9,742 2 3,653 0 0

86

Appendix

Table A.23: Power generation capacity in the scenario 2035TrON with reduction of CO2 emissions of 25% compared to the scenario2035BAU and a modified gas price of 47.89US$/MWh.Unit: (MW) Biogas Biomass Coal Diesel Gas Hydro LGas Oil SunGlobal Wind-onJohor 12 39 2,244 - 1,737 20 - 252 - -Kedah 6 19 1,100 - 263 9 - 109 - -Kelantan 9 31 183 - 395 637 - 59 - -Kuala Lumpur 0 0 - 1 64 - - 6 - -Melaka 1 3 75 2 1,615 - - - - -Negeri Sembilan 4 14 1,505 1 1,995 29 - - - -Pahang 32 73 1,257 - 756 55 - 126 - -Penang 1 2 1,305 - 1,720 - - 433 - -Perak 13 43 2,100 - 1,958 1,004 - 0 - -Perlis 0 2 - - 710 - - - - -Sabah 46 150 - 42 576 2,086 - 802 - 0Sarawak 77 252 480 154 690 8,944 - 172 - -Selangor 5 16 11,898 14 8,603 7 2 653 0 -Terengganu 8 26 - - 1,610 405 - 1,042 - -Total 215 670 22,146 214 22,691 13,196 2 3,653 0 0

87

Appendix

Table A.24: Generation capacity in the scenario 2035TrON with a gas price of 47.89US$/MWh and a reduction of CO2 emissions of 50%compared to the scenario 2035BAU.Unit: (MW) Biogas Biomass Coal Diesel Gas Hydro LGas Oil SunGlobal Wind-onJohor 12 39 2,244 - 1,901 20 - 252 - -Kedah 6 19 - - 1,307 9 - 109 - -Kelantan 9 31 - - 569 637 - 59 - -Kuala Lumpur 0 0 - 1 64 - - 6 - -Melaka 1 3 - 2 1,689 - - - - -Negeri Sembilan 4 14 1,505 1 1,995 29 - - - -Pahang 32 73 - - 1,956 55 - 126 - -Penang 1 2 - - 2,961 - - 433 - -Perak 13 43 2,100 - 1,958 1,004 - 0 - -Perlis 0 2 - - 710 - - - - -Sabah 46 150 - 42 576 2,086 - 802 - 0Sarawak 77 252 480 154 690 8,944 - 172 - -Selangor 5 16 1,600 14 18,395 7 2 653 0 -Terengganu 8 26 - - 1,610 405 - 1,042 - -Total 215 670 7,929 214 36,381 13,196 2 3,653 0 0

88

Appendix

Table A.25: Overview of the power generation capacity in 2035 with following constraints: the expansion of the power grid is allowed, thegas price is set to 47.89US$/MWh and the CO2 emissions are limited to 75% compared to the scenario 2035BAU.Unit: (MW) Biogas Biomass Coal Diesel Gas Hydro LGas Oil SunGlobal Wind-onJohor 12 39 2,244 - 3,761 20 23 252 8,775 -Kedah 6 19 - - 1,365 9 14 109 9,254 -Kelantan 9 31 - - 440 637 11 59 516 -Kuala Lumpur 0 0 - 1 64 - 12 6 5,000 -Melaka 1 3 - 2 1,887 - 6 - 1,372 -Negeri Sembilan 4 14 1,505 1 2,406 29 7 - - -Pahang 32 73 - - 1,884 55 11 126 - -Penang 1 2 - - 3,472 - 11 433 - -Perak 13 43 2,100 - 2,098 1,004 16 0 6,910 -Perlis 0 2 - - 710 - 2 - - -Sabah 46 150 - 42 576 2,086 24 802 777 0Sarawak 77 252 480 154 690 8,981 17 172 - -Selangor 5 16 1,600 14 20,567 7 39 653 38,989 -Terengganu 8 26 - - 1,610 405 7 1,042 7,835 -Total 215 670 7,929 214 41,531 13,233 200 3,653 79,427 0

89

Acronyms and Symbols

Acronyms and Their Definitions

ASEAN Association of Southeast Asian NationsBAU Business as usualCCGT Combined Circle Gas TurbineEAF Equivalent Availability FactorECA Energy Commission ActESA Electricity Supply ActFiT Feed-In TariffGAMS General Algebraic Modeling SystemGDP Gross Domestic ProductGS Generator SetGT Gas TurbineHV High VoltageIPP Independent Power ProducerLCOE Levelised Cost of ElectricityLNG Liquefied Natural GasMESI Malaysian Electricity Supply IndustryMBIPV Malaysia Building Integrated PhotovoltaicMP Malaysia PlanMSW Municipal Sold WasteNG Natural GasOCGT Open Circle Gas TurbinePPP Purchasing Power ParityPV PhotovoltaicRE Renewable EnergiesREPPA Renewable Energy Power Purchase AgreementSEA Southeast AsiaSEB Sarawak Energy BerhadSESB Sabah Electricity Sendirian BerhadSREP Small Renewable Energy PowerTNB Tenaga Nasional BerhadUNDP-GEF United Nations Development ProgrammeUNFCCC United Nations Framework on Climate Change ConventionW.P. Wilayah Persekutuan (Federal Territory)

90

Acronyms and Symbols

Symbols and Units

cf cubic footMMBtu Million british thermal unitMMscf Million standard cubif foottoe tons of oil equivalent

91

List of Figures

1.1 Overview of selected political frameworks from 1975 to 2015 [Khor.2013]. . . . . . . . . 61.2 Primary energy supply in Malaysia in time steps based on the 5-year Malaysia Plans

(MP) [MEIH.2012b]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.3 Primary energy supply in 2012 in ktoe [MEIH.2012b]. . . . . . . . . . . . . . . . . . . . 81.4 Final energy demand (in total 46,710 ktoe) by fuel type in 2012 [MEIH.2012d]. . . . . . 81.5 Electricity consumption in Malaysia by sector in 2012 [MEIH.2012c]. . . . . . . . . . . . 91.6 Electricity generation of 122.5TWh in Malaysia in 2012 [EPU.2012]. . . . . . . . . . . . 9

2.1 Political map of Malaysia: Division into 13 federal states and 3 federal territories, namelyKuala Lumpur, Putrajaya and Labuan. . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.2 Hourly load curve of Peninsular Malaysia [SurTen.2012a]. . . . . . . . . . . . . . . . . . 182.3 Share of electricity consumption in Malaysia by region in 2012 [JPMy.2012a]. . . . . . . 192.4 Demand for electricity in Malayisa in 2012, prepared by the means of [JPMy.2012a]

and [JPMy.2013]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.5 Map of Malaysia. Red marked the possible future power grid which is defined as input

data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.1 Overview of the structure of the model URBS. . . . . . . . . . . . . . . . . . . . . . . . 29

4.1 Modelled electricity generation mix in Malaysia in 2012 [EPU.2012]. . . . . . . . . . . . 424.2 Overview of modelled structures of the power generation mix in Malaysia for different

scenarios in 2020. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464.3 Overview of modelled power generation mix in Malaysia in 2035. . . . . . . . . . . . . . 514.4 Overview of modelled power generation mix in Malaysia in 2035 with modifying the gas

price. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

A.1 Full-load hours of solar PV in Malaysia in [h]. . . . . . . . . . . . . . . . . . . . . . . . . 65A.2 Full-load hours of onshore wind in Malaysia in [h]. . . . . . . . . . . . . . . . . . . . . . 66A.3 Full-load hours of offshore wind in Malaysia in [h]. . . . . . . . . . . . . . . . . . . . . . 67

92

List of Tables

1.1 Primary energy consumption of ASEAN by county in 2011 [IEA.2013]. . . . . . . . . . . 2

2.1 Distribution of area [JPMy.2012], population and share of GDP by regions [JPMy.2013]. 132.2 Prices of fuels with giving respective references. The annual growth rates of fuels are

taken from [EIA.2013d]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.3 Analysis of References: Decision making. . . . . . . . . . . . . . . . . . . . . . . . . . . . 212.4 Overview of investment costs in 2012, 2020 and 2035 of selected electricity generation

technologies by [IEA.2010], [EIA.2013c] and [Lazard.2013]. All costs refer to 2012 US$. . 222.5 Overview of 2012 efficiencies of electricity generation technologies given by different

references. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232.6 Overview of future efficiencies of electricity generation technologies given by different

references. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.7 Availability of specified power stations [TNB.2012]. . . . . . . . . . . . . . . . . . . . . . 242.8 Specific emission characteristics of fossil fuels related to the net heat of combustion

[UBA.2003]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.1 Aggregation of generation technologies based on data of [Platts.2010] to 7 generationprocesses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.2 Example of the methodology used for the selection of the annual full-load hours of solarPV in Malaysia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.3 Distribution of demand for electricity in 2012, 2020 and 2035 [JPMy.2012a, IEA.2013]. . 343.4 Full-load hours of selected renewable energies [h]. . . . . . . . . . . . . . . . . . . . . . . 363.5 Conversion factors related to the input data format. . . . . . . . . . . . . . . . . . . . . 37

4.1 Overview of used scenarios short terms and their definition. . . . . . . . . . . . . . . . . 404.2 Overview of scenarios which are considered. . . . . . . . . . . . . . . . . . . . . . . . . . 414.3 Summary of key results of the scenario 2012Reference. . . . . . . . . . . . . . . . . . . . 434.4 Summary of key results of the scenarios in 2020. . . . . . . . . . . . . . . . . . . . . . . 484.5 Summary of key results of the scenarios in 2035 without construction of new transmission

lines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504.6 Summary of key results of the scenarios in 2035 with construction of new transmission

lines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 534.7 Summary of key results of the scenarios in 2035 with construction of new transmission

lines and a modified gas price of 24.58US$/MWh. . . . . . . . . . . . . . . . . . . . . . 564.8 Summary of key results of the scenarios in 2035 with construction of new transmission

lines and gas price of 47.89US$/MWh. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

93

List of Tables

A.1 Overview of input data regarding power stations after combining data of [Platts.2010]and informations of costs. Additionally, costs for transmission lines and submarinecables, losses and depreciation out of [Schaber.2012a] are shown. . . . . . . . . . . . . . 62

A.2 Overview of capacity, lengths and efficiency of the power grid in 2012 in Malaysia byeach connection by [PMGSO.2012]. Investment costs based on the length of connectionapply if capacity is added by the model. . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

A.3 Upper bound of power generating capacities of renewable energies by region in 2020 and2035 in MW Malaysia [ASM.2013]. Capacity of solar PV and wind is set to unlimited. . 64

A.4 Overview input data: installed capacity by region in MW in 2012 by [Platts.2010]. . . . 68A.5 Modelled generation capacity in the scenario 2012Reference based on [Platts.2010]. . . . 69A.6 Modelled generation capacity in scenario 2020BAU. . . . . . . . . . . . . . . . . . . . . . 70A.7 Modelled generation capacity by region and process in scenario 2020TrOFF and political

target of CO2 emission reduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71A.8 Modelled generation capacity in the scenario 2020TrON and no CO2 emission restrictions. 72A.9 Modelled generation capacity in the scenario 2020TrON and limited CO2 emissions

defined by political targets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73A.10 Modelled power generation mix of the scenario 2035BAU. . . . . . . . . . . . . . . . . . 74A.11 Modelled power generation capacity of the scenario 2035TrOFF and a CO2 reduction

of 25% compared to the scenario 2035BAU. . . . . . . . . . . . . . . . . . . . . . . . . . 75A.12 Generating capacity of the scenario 2035TrOFF and 50% CO2 emission reduction com-

pared to 2035BAU. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76A.13 Generating capacity of the scenario 2035TrOFF and with reduction of CO2 emissions

by 75% compared to the scenario 2035BAU. . . . . . . . . . . . . . . . . . . . . . . . . . 77A.14 Generating capacity of the scenario 2035TrON without CO2 emissions limits. . . . . . . 78A.15 Modelled generating capacity in the scenario 2035TrON and reduction of 25% emissions

compared to the scenario 2035BAU. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79A.16 Modelled generation capacity in the scenario 2035TrON and CO2 emission reductions

of 50% compared to the scenario 2035BAU. . . . . . . . . . . . . . . . . . . . . . . . . . 80A.17 Modelled generation capacity in the scenario 2035TrON and with reduction of CO2

emissions of 75% compared to the scenario 2035BAU. . . . . . . . . . . . . . . . . . . . 81A.18 Power generation capacity in the scenario 2035TrON with a adjusted gas price of

24.58US$/MWh and no restrictions on CO2 emissions. . . . . . . . . . . . . . . . . . . . 82A.19 Power generation capacity in the scenario 2035TrON, according to the scenario with an

adjusted gas price of 24.58US$/MWh. The CO2 emissions are limited to 25% comparedto the scenario 2035BAU. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

A.20 Power generation capacity in the scenario 2035TrON, a restriction on the CO2 emissionsby 50% compared to the scenario 2035BAU and an adjusted gas price of 24.58US$/MWh. 84

A.21 Power generation capacity in the scenario 2035TrON with a modified gas price of24.58US$/MWh and a CO2 emission limit of 75% compared to the scenario 2035BAU. 85

A.22 Overview of the generation capacity in 2035 under certain constraints which are follow-ing: an expansion of the power grid is allowed, the gas price is set to 47.89US$/MWhand no restrictions on CO2 emissions are made. . . . . . . . . . . . . . . . . . . . . . . . 86

A.23 Power generation capacity in the scenario 2035TrON with reduction of CO2 emissionsof 25% compared to the scenario 2035BAU and a modified gas price of 47.89US$/MWh. 87

94

List of Tables

A.24 Generation capacity in the scenario 2035TrON with a gas price of 47.89US$/MWh anda reduction of CO2 emissions of 50% compared to the scenario 2035BAU. . . . . . . . . 88

A.25 Overview of the power generation capacity in 2035 with following constraints: the ex-pansion of the power grid is allowed, the gas price is set to 47.89US$/MWh and theCO2 emissions are limited to 75% compared to the scenario 2035BAU. . . . . . . . . . . 89

95

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Malaysia Power Analysis