Optimal Electricity Generation Mix with Carbon Dioxide Constraint

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1 Optimal Electricity Generation Mix with Carbon Dioxide Constraint Zarina Ab Muis 1*, Haslenda Hashim 1, Zainuddin Abd. Manan 1, Faridah Mohd Taha 2 1 Department of Chemical Engineering, Faculty of Chemical Engineering and Natural Resources, Universiti Teknologi Malaysia, Skudai, Johor 2 Department of Electrical Power Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, Johor *Corresponding zarinamuis@fkkksa.utm.my Abstract Carbon dioxide is the major greenhouse gas contributor. In Malaysia, transportation, electricity and energy sector are identified as the major carbon dioxide emitter. Coal, natural gas, diesel, oil and hydro are the sources to generate electricity in Malaysia. Natural gas and coal generate 66% and 23.3% of the total electricity generation. In the 9 th Malaysia Plan, government of Malaysia encourage power producer shift from heavy reliance on natural gas and enhance use of alternative e.g. solar and nuclear. In this study, a simple MILP model has been developed to optimize fuel mix and meet CO 2 emission target. The model was developed and implemented in General Algebraic Modeling System (GAMS) for the fleet of electricity generation in Peninsular Malaysia only. New technology ie IGCC, NGCC and nuclear has been identified to reduce CO 2 emission by 50% and 60% from current CO 2 emission level. Keywords: GAMS, Solar PV, electricity generation, MILP severity, and sea level commonly referred to as climate change. Carbon dioxide is a main greenhouse gas (GHG) that is responsible for climate change. The usage of fossil fuel in energy use is the primary source that increases the concentration of carbon dioxide (CO 2 ) in the atmosphere. Energy use is largely driven by economic growth, as well as changes in the fuel used in electricity generation. Back in 1998, the United Nations Framework Convention on Climate Change, 1998 has already developed the Kyoto Protocol to stabilize the GHG emissions in the atmosphere by having industrialized countries commit to reduce their GHG emissions. The legal binding accord was signed by 165 countries to reduce GHG emissions. Among the South East Asia countries, Malaysia is the highest emitter for CO 2. Even worst, Malaysia, which has rapidly transformed from an agricultural economy to an industrialized one in the last four decade, is now ranked 26 th largest greenhouse gas emitter in the world [1]. Carbon dioxide emissions in Malaysia have increased by 221% since year 1990 to Fossil fuels contribute more than half of the total CO 2 increment. Figure 1 shows an increment of 153% of since 1990 to 2004 [2]. 1. Introduction Carbon dioxide, methane, nitrous oxide (NOx) and sulfur oxide (SOx) emission are categorized under greenhouse gas. Rising concentrations of greenhouse gases produce an increase in the average surface temperature of the earth over time and may, in turn, produce changes in precipitation patterns, storm

2 Malaysia recorded a growth of 33.4% from 60,492 GWh in 2000 to 80,701 GWh in 2005 [4] Figure 1 Carbon dioxide emissions in Malaysia from fossil fuel [2] During the Kyoto Protocol s first commitment period, developed-country signatories committed to reduce their greenhouse gas emissions by 5.2 percent from their 1990 levels within the period between 2008 and There are five major sectors in Malaysia emit CO 2. Transportation sector contributes the highest percentage of CO 2 emission which is 27 % from total CO 2 emission, million metric ton (MMt) in This is followed by electricity and energy sectors 25.7% [2] as indicated in Figure 2. Electricity generation in Malaysia is dominated by Tenaga Nasional Berhad (TNB) and Independent Power Producer (IPP). Current total installed generation capacity in Peninsular Malaysia is MW with TNB 48.1%, IPP, including IPP in Sabah, Sarawak, Sabah Electricity Sdn. Bhd. (SESB) and Syarikat SESCO Berhad (SESCO), own 46.9% and private generation, 5% [3]. Figure 2 CO 2 emissions by sectors in Malaysia [2] In Malaysia, natural gas, coal, diesel, fuel oil (distillate) and hydro was used to generate electricity. The share of natural gas as energy input in power station has decreased from 74.9 % in 2000 to 62.3% in The share of coal, however, increased tremendously from 9.7% in 2000 to 28.1% in Installed generation capacity is indicated in pie chart in Figure 3. The total electricity consumption for Figure 3 Malaysia s current installed generation capacity in percentage [4] This study aim to develop an optimization model to minimize cost of electricity with CO 2 emission limit. Current technologies such as Pulverized Coal (PC), Integrated Gasification Combined Cycle (IGCC), Natural Gas Combined Cycle (NGCC), solar Photovoltaic (PV) and nuclear was considered in the model. 2. Literature Review Several researchers have developed energy models for power generation technologies, such as PC, IGCC and NGCC in the context of carbon capture and sequestration. Rubin et al. [5], for instance, developed the Integrated Environmental Control Model (IECM) to provide an analytical tool to compare various environmental control options for fossil-fuel power plants. The model is built in a modular fashion that allows new technologies to be easily incorporated into an overall framework. A user can then configure and evaluate a particular environmental control system design. Current environmental control options include a variety of conventional and advanced systems for controlling SO 2, NOx, CO 2, particulates and mercury emissions for both new and retrofit applications. The IECM framework now is being expanded to incorporate a broader array of power generating systems and carbon management options (multi pollutant). A number of studies examined the prospects of incorporating new PC, IGCC and NGCC in the electricity generation sector. Narula et. al. [6] considered replacing existing coal plants with new plants such as NGCC, IGCC and PC and studied the impact of the incremental cost of CO 2 reduction on the

3 cost of electricity (COE) by implementing different technology options and compares COE. Genchi et al. [7] for instance, developed a prototype model for designing regional energy supply systems. Their model calculates a regional energy demand and then recommends the most effective combination of eleven different power supply systems to meet the required CO 2 emission targets at minimum cost. The new energy system to be installed includes co-generation systems, photo voltaic cell system, unused energy in sewage and garbage incineration, and solar energy water supply. Linares et al. [8] proposed a group decision multi-objective programming model for electricity planning in Spain based upon goal programming (GP). The objective was to minimize the total cost of the electricity generation, CO 2 emission, SO 2, NOx and radioactive waste. The model is capable of estimating the capacity to be installed for the year 2020 under four different social groups: regulators, academic, electric utilities and environmentalists. The preferences by the groups were expressed as weights in the model that affect the different main criteria in the objective function. Mavrotas [9], developed a mixed 0-1 Multiple Objective Linear Programming (MOLP) model and applied it to the Greek electricity generation sector for identifying the number and output of each type of power unit needed to satisfy an expected electricity demand. The first objective was to minimize the annual electricity production cost and the second objective dealt with the minimization of the total amount of SO 2 emissions. However, the model did not consider CO 2 mitigation. Bai and Wei [10] developed a linear programming model to evaluate the effectiveness of possible CO 2 mitigation options for the electricity sector in Taiwan. The strategies they considered included fuel alternatives, reduced peak load, energy conservation, improving power generation efficiency, and CO 2 capture. They found that the combination of reduced peak production and increasing power plant efficiency with CO 2 conservation was an effective strategy to meet significant CO 2 emission reductions. 3. Methodology The methodology of the project can be broken down into three main phases. The first phase of the project involves data gathering. Second phase involves superstructure development. Phase three, development of the model and model implementation. Phase 1 Data gathering Phase 1 focuses on gathering the necessary information of: 1. Existing plant data i.e. plant capacities, operational cost and CO 2 emission; 2. Capital and operational cost of solar and nuclear, capital and operating cost of PC, IGCC and NGCC. 3. Other data such as current electricity demand, fuel price. Phase 2 Superstructure development Superstructure representing all possible alternatives of fuel mix will be very complex. A simple superstructure is presented to illustrate the concepts. Three different scenarios are presented in Figures 4 to 6 to illustrate the impact of a CO 2 reduction strategy on the structure of energy supply. C i, NG i, D i, O i,and H i represents existing coal, natural gas, diesel, oil, and hydroelectric power plants respectively. Hypothetical new power plants are represented by PC i new, IG i new, NG i new, SO i new and N i new for pulverized coal, Integrated Gasification Combined Cycle (IGCC), Natural Gas Combined Cycle (NGCC), solar, and nuclear respectively. Three CO 2 mitigations strategies will be implemented, which include employing fuel balancing, fuel switching and enhanced use of alternative energy and advanced technologies. Fuel balancing is to adjust the operation of two generation stations to reduce CO 2 emissions. This strategy involves increasing electricity generation by non-fossil fuel plants. Therefore, fossil fuel plants will generate less electricity, hence less emission of CO 2. Superstructure for fuel balancing is shown in Figure 4. Fuel switching is to switch from carbonintensive fuels (e.g. coal) to less carbon-intensive fuels (e.g. natural gas). Existing generation stations must be retrofitted in order to use another fuel. Energy produced by alternative fuel (e.g. uranium and solar) emits no CO 2, and hence will reduce CO 2 emission. Superstructure for fuel switching is shown in Figure 5.

4 Optimization model consist of objective function and constraints. The model is formulated using an objective function that minimizes the net present value of the cost of electricity. The objective function consist of annualized cost for existing fossil and non-fossil fuel power plant, retrofit cost, capital cost for new power plant and annualized cost for new fossil and non-fossil fuel power plant. Objective function Figure 4 Fuel balancing for existing technologies Constraints 1. CO 2 emission limit 2. Optimal power generation must be less than current electricity generation 3. Logical constraint - lower bound of existing coal plant - upper bound for PC, IGCC and NGCC - Non negativity constraint Figure 5 Fuel switching from carbon intensive fuel to less intensive fuel Third mitigation strategy is increasing use of renewable energy and more efficient fossil fuel plant. In this case, superstructure will represents current and new technologies as illustrated in Figure 3.3. Existing technologies is represents by fossil fuel plants, such as gas turbine and conventional thermal consume coal, natural gas, diesel and fuel oil. Non fossil fuel plants are solar and nuclear. Where; Indices i power stations j fuels Binary variable Parameters =1 if coal-fired boiler i is operational using fuel j =0 otherwise =1 if power plant i is operational =0 otherwise Figure 6 Superstructure for existing and new technologies Phase 3 Model development and model implementation F ij C ij R cost S i V i Fixed and variable Operating & maintenance (O&M) cost for existing power stations (RM/MWh) Capacity of existing power plant (MW) Retrofit cost (RM/MW) Capital cost for new power plant (RM/MW) Fixed and variable Operating & maintenance (O&M) cost for new power stations (RM/MWh)

5 4. Case study The case study is electricity generation in Peninsular Malaysia. All data was tabulated in Table 1. Table 1. Data required in the optimization model Coal Power Plant: 1. Pelabuhan Klang 2. Janamanjung 3. Tg. Bin 4. Pasir Gudang 5. Prai 6. Jimah Capital cost O&M cost (RM/MW) (RM/MWh) - Coal NG Natural Gas Power Plant Hydroelectric Power Plant Solar PV Power Plant 20,000, Pulverized Coal (PC) 1,578, Integrated Gasification 2,121, Combined Cycle (IGCC) Natural Gas Combined 617, Cycle (NGCC) Total Capacity (MW) Results and discussion Optimal generation mix is shown in the Table 2 for CO 2 emission target for base case scenario (0%), 50% and 60%. Table 2 Results from GAMS for optimal generation mix with CO 2 emission target CO 2 Emission Target 0% 50% 60% Total CO x x x 10 7 Total Plant Capacity (MW) Total electricity generation in MWh K2 natural gas M2 coal x 10 5 PG1 coal x x x 10 5 PG2 coal x x PG2 ng P1 coal x x x 10 6 P2 coal x x x 10 6 P3 coal x x x 10 6 Natu Glugor x x x ral PKlang x x x 10 6 Gas CBridge x x 10 6 Serdang x x x 10 6 PGudang x x 10 6 Paka x x x 10 6 Kenyir x x x 10 6 Hyd Temenggor x x x 10 5 ro elect Bersia x x x 10 5 ric Kenering x x x 10 5 Chenderoh x x x 10 5 Jor x x x 10 5 Pergau x x x 10 5 Woh x x x 10 5 Piah & x x x 10 5 Odak PC x PC x PC x PC x PC x PC x IGCC x x IGCC x x IGCC x x IGCC x IGCC x IGCC x 10 6 NGCC x 10 6 NGCC x 10 6 NGCC x 10 6 NGCC x 10 6 Nuc x x 10 5 Nuc x x 10 5 Nuc x x 10 5 Total cost x x x 10 9 RM/MW-year CO 2 Emi ssio n (ton ne/y ear) PKlang (K) Janaman jung (M) x 10 5 Tg Bin (TB) Pasir x x x 10 5 Gudang (PG) Prai (P) x x x 10 6 Jimah (JH) Natural x x x 10 7 Gas Plant (NG) New PC, IGCC and NGCC (NP) x x x 10 6 The solver chose 4 PC boilers from 2 PC power plants, and 3 IGCC boilers from IGCC plant no. 1, for base case scenario. For 50% reduction of CO 2, five IGCC boilers from IGCC plant no. 1 and 2, and three nuclear reactor was chosen by the optimizer. Maximum CO2 reduction for this model is 60%. For maximum reduction, 3 IGCC boiler from IGCC plant no.2, 3 NGCC boiler from NGCC plant no 3 and 3

6 nuclear reactor was chosen for the optimum cost of electricity per year. 6. Conclusion From the results, it can be concluded that nuclear power station need to be built for higher CO 2 emission reduction. Solar power plant is not favor due to high capital cost and low efficiency. For each target, hydroelectric power station was fully utilized due to emission free technology and low operating cost. 7. References [1] Malaysian growth of carbon emissions highest in the world, says UN, (2007, November 29). International Herald Tribune. Retrieved on 30 June, 2008, from [2] Energy Information Administration (EIA) (2005), International Energy Annual 2005 CO 2 World Carbon Dioxide Emissions from the Consumption of Coal, (Million Metric Tons of Carbon Dioxide). USA: Government of US. Retrieved on 25 June, 2008, from [3] Energy Commission (2006) Annual Report 2006, Ministry of Energy, Water and Communications. Kuala Lumpur: Energy Commission [5] Rubin, E.S. et al., (2004) Comparative assessments of fossil fuel power plants with CO 2 capture and storage. Proceedings of the 7th International Conference on Greenhouse Gas Control Technologies, September 5-9, Vancouver [6] Narula R.G. et al., (2002) Incremental cost of CO 2 reduction in power plants. Proceedings of IGTI, ASME TURBO EXPO. June 3-6, 2002, Amsterdam, Netherlands [7] Genchi, Y., Saitoh, K., Arashi, N., Inaba, A. (2002), Assessment of CO 2 emissions reduction potential by using an optimization model for regional energy supply systems. 6th International Conference on Greenhouse gas Control Technologies, Oct 1-4, 2002, Kyoto, Japan [8] Linares, P., Romero, C., (2002) Aggregation of preferences in an environmental economics context: A goal programming approach. Int. J. Manage. Sci. 30, [9] Mavrotas, G. (1999) An energy planning approach based on mixed 0-1 multiple objective linear programming. International Transaction in Operational Research. 6, [10] Bai, H. and Wei, J., (1996) The CO 2 mitigation options for the electric sector. Energy Policy. 24, [4] Ministry of Energy, Water and Communication (2005) National Energy Balance Kuala Lumpur, Ministry Energy, Water and Communication