Capacity planning for fossil fuel and renewable energy resources power plants

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2 ** Corresponding author. Tel.: ; fax: ** address: * address: *** address: Mixed method of Analytical Hierarchy Process (AHP) with linear programming (LP) To inspect the replacement of fossil power plant with renewable energy resources to produce electricity, we use (AHP) Analytical Hierarchy Process and Linear programming. At first, we apply AHP to gain total weights of power plants from net data and generalization (by the use of Expert Choice software). Then we use linear programming for maximization of power plants capacity considering total weights got from AHP and as a result the determined power plants will be chosen by their capacities (the use of Lingo software)..1. Analytical Hierarchy Process (AHP) AHP is a decision making method which we can make decision related to various criteria or multiple decisions. This method was presented by Professor Saaty and provided great field for decision making. Main structure of the process consists of objective, criteria and items which are hierarchical to each other. In this method judgments are done by low quantitative amount 1 to 9 (according to table 1) and by even comparisons (these numbers are presentable by divisions). At first, criteria importance toward objective and then items toward each criterion in even comparison matrix will be presented and finally criteria weight will be determined. The advantages of this method are: simplicity, comprehensiveness, judgments incompatibility calculation possibility, structure of Analytical Hierarchy process and qualitative criteria [1]. One of the reasons for selecting this method is provision of group cooperation. To mix different judgments of people, all the even comparisons matrixes were gathered and mixed the judgments by geometrical average and get relative weights of criteria and items []. Table1. Saaty spectrum Numeral Values Important degree of Pair-Wise Comparison 1 Equal Equal until comparative 3 comparative comparative until 6 until 7 Very 8 Very until inordinate 9 inordinate - Total Algorithm of energy production assessment technology (AHP) Analytical Hierarchy process drawing of inspected case To do even comparison of items in each surface of hierarchy process structure and matrix of even comparison form ( to determine criteria weights ) Relative of criteria toward each other Relative of each power plant toward each criteria Extract of weights for criteria and finally gaining the total value of each power plant and their ranking by the usage of Export Choice software -3- Even comparison and priorities extraction In this section, according to AHP algorithm, final weights of items are calculated by Export Choice software. We should form the even comparison matrix of items toward all criteria toward objective. In this case, economical and technical information table () and (3) which is collected by Tavanir production planning office organization [3] is used and also considering senior manager and industry experts points of view related to power plant energy, industry and numerous seniority meetings and expertise executive panels and using hierarchical analysis method, special weights are specified according to their importance. Table. Technical and economical information of fossil fuel power plants Power plants Time setup (year) Life time (year) Emergency exit rate Internal consumption Efficiency Plant factor Euro/KW Capital cost Ral/KW Operating and maintenance cost Fixed annual variable Euro Ral/KW cent/kwh Ral/Kwh Gas (big) Steam Combine cycle

3 Table 3. Technical and economical information of renewable energy resources power plants Power plants Solar thermal Photovoltaic Wind Geothermal Biomass Fuel cell (natural gas) Small hydro Micro turbine Life time (year) 10 0 Capital cost Efficiency Operating and maintenance annual cost \$/KW Plant factor Capacity Factor Final price of electricity cent/kwh Even comparisons of this paper are done during three phases and according views of some experts in this field. First phase: Hierarchical construction for first phase is made. Between fossil and renewable energy resources power plants according to defined criteria there was a Pair- Wise Comparison and the result of this phase was tables of even comparison. Among defined criteria, there `s a pair comparison and their weights are identified. After entering all even comparisons matrixes in Expert Choice software, the considered model was run and each weight of each power plant gained. Total weight of each fossil and renewable energy resources power plants are 0.19, Second phase: In this phase like first phase we run four stages for fossil fuel power plants and finally final weight of each fossil fuel power plant is gained. In this structure, total operation and capital cost criteria will be respectively gained which required investment for establishing 1(KW) of each power plants is calculated and along years (Economical period) we make equivalent these cost. It is essential that if two investment criteria and annual operation cost are individually considered, creating network probability and non-linear dependence among options and criteria are increased, usually AHP process leads to incorrect answers []. In this article, applying mixture creativity of these two criteria and equivalence of these two types of investment and annual operation cost prevent this problem. In this phase, after entering all even comparison matrixes in Export Choice software, model which were mentioned in second phase were performed and final weights of each power plants gained. Total weight of each steam, gas and compound cycle power plants are 0.16, 0.0, 0.9. Third phase: In this phase like first phase, we run four stages for renewable energy resources power plants and at last we gain final weight of each power plant. In this phase, among defined criteria there `s a pair comparison and weight of each criterion is identified. Finally for third phase of renewable energy resources power plants final weights are identified according to mentioned criteria. So for wind, small hydroelectric, solar thermal, photovoltaic, biomass, micro turbine, geothermal and fuel cell power plants are 0.17, 0.13, 0.18, 0.117, 0.11, 0.11, and are gained. 3. Capacity determination process of power plants by mathematics linear programming After final weight determination of items (in part - 3), in this part determination of linear programming of optimized combination of power plant to determine load up to 1 is planned. The rate of throughout net increasing load up to 1 will be almost 1 (MW). Considering to percent as circulatory storage, necessary production rate to fill the load will be between 1810 to (MW) whose 6000 (MW) will be produced by water electrical power plant. So necessary production rate will be estimated 110 to 1876 (MW) until that year []. 3

4 To optimize mathematic linear programming, we do definition and symbolism as below: P F : Fossil fuel power plants capacity P R : renewable energy resources power plants capacity P g : Capacity of gas power plant P s : capacity of steam power plant P cc : capacity of combined cycle P Fc : capacity of fuel cell power plant P pv : capacity of photovoltaic power plant P Solar : capacity of solar thermal power plant P W : capacity of wind power plant P Ge : capacity of geothermal power plant P M : capacity of micro turbine power plant P Sh : capacity of small hydro power plant P B : capacity of biomass power plant g, PFg,W g : Efficiency, plant factor and weight of gas power plant s, PF s, W s : Efficiency, plant factor and weight of steam power plant cc, PF cc, W cc : Efficiency, plant factor and weight of combined cycle power plant pv, PFpv, W pv : Efficiency, plant factor and weight of photovoltaic power plant Solar,PF Solar,W Solar : Efficiency, plant factor and weight of solar thermal power plant W, PF W, W W : Efficiency, plant factor and weight of wind power plant Fc, PF Fc,W Fc : Efficiency, plant factor and weight of fuel cell power plant Ge, PF Ge, W Ge : Efficiency, plant factor and weight of geothermal power plant B, PF B,W B : Efficiency, plant factor and weight of biomass power plant M, PF M,W M : Efficiency, plant factor and weight of micro turbine power plant Sh, PF Sh,W Sh : Efficiency, plant factor and weight of small hydro power plant W F : fossil fuel power plant final weight of first phase W R : renewable energy resources power plant final weight of first phase L u1 : the upper required load equals 1876 L L1 : lowest required load equals 110 Linear programming for first phase is as below: Max Z 1 = W F P F + W R P R S.T. L L1 <= P F + P R <= L u1 (1) P F <=0.19*1876 () P R <=0.81*1876 (3) () P R, P F >= 0 The objective function represents the fact that total weights of projects which total weights W i are gained by AHP should be maximized. The first limitation represents total capacity of selected projects should be among required predicted load according to to percent circulatory storage. Restriction () and (3) states the highest point of technologies. restriction shows non-negative capacity of power plants. Linear programming for second phase is as below: L u : upper required load is 668/6 L L : Lowest required load is 638/1 Max Z = W g P g + W s P s + W cc P cc s.to L L <= P g + P s + P cc <= L u (1) P g <=0.9*668.6 () P s <=0.16*668.6 (3) P cc <=0.0*668.6 () P s + P cc >= 0. * ( P g + P s + P cc ) () g P g + s P s + cc P cc >= 0.3*( P g + P s + P cc ) (6) 0.6*( P g + P s + P cc ) <= PF g * P g +PF s * P s +PF cc * P cc (7) P g, P s, P cc >= 0 (8) The objective function represents the fact that total weights of projects which total weights W i are gained by AHP should be maximized. The first limitation represents total capacity of selected projects should be among required predicted load according to to percent circulatory storage. Fifth limitation represents that almost 0 percent of throughout net peak load will be filled by power plants with high output and plant factor (PF). So this programming determination is supposed by steam and combined cycle power plants. To change optimized fuel to electrical energy and minimum decrease of environmental pollution due to electricity production in power plants, the efficiency limitation in this programming is considered 3 percent and applied which is shown limitedly (6). According to plant factor (PF) of power plants which defines the function period of that power plant during this year. It is needed to have enough energy to be produced by gas, steam and combined cycle power plants in circuit should be more than required throughout net. As throughout net load factor (LF) is estimated 6 percent [] so limitation of produced energy of power plants are shown by limitation (7). Restriction (8) indicates non-negative power plants capacity.

5 Linear programming model for third phase is as below: L u3 : Top required charge is 6193/36 L L3 : Lowest required charge is 883/688 Max Z 3 = W W P W + W B P B + W Fc P Fc + W pv P pv + W Solar P Solar + W Ge P Ge + W M P M + W Sh P Sh s.to L L3 <= P W + P B + P Fc + P pv + P Ge + P M + P Sh + P Solar <= L u3 (1) W P W + B P B + Fc P Fc + pv P pv + GeP Ge + M P M + Sh P Sh + Solar P Solar >= 0.3*( P W + P B + P Fc + P pv + P Ge + P M + P Sh + P Solar ) () 0.6*( P W + P B + P Fc + P pv + P Ge + P M + P Sh + P Solar ) <= PF W * P W +PF B * P B +PF M * P M +PF Ge * P Ge +PF Sh * P Sh +PF Solar * P Solar +PF pv * P pv +PF Fc * P Fc (3) P W, P Ge, P M, P pv, P Sh, P Solar, P B, P Fc >= 0 () Now, by the gained total weights from firsts, second and third phases and tables () and (3) data, problems of linear programming are as below and by its solution capacity of power plants foundation will be obtained. By solving the first phase linear programming model by software Lingo, amount of capacity of each two types of fossil fuel and renewable energy resources power plants and objective function are as below: power plants for the sake of change in resources restriction criteria ranking, and three gas, steam and combined cycle power plants for the sake of change in capital and operation cost of power plants considering between the distance of zero to one. According to figure (1), by increasing the criterion importance resources restriction, choices importances respectively are: renewable energy resources and fossil fuel power plants. According to figure (), by increasing the criterion importance capital and operation cost, choices importance respectively are: combined cycle, gas and steam power plants. Fig. 1. Renewable energy resources and fossil fuel power plants sensitivity according to resource scarcity. Z 1 = 67/96, P R = 6193/36, P F =668/6 By solving the second phase linear programming model by software Lingo, amount of capacity of each three types of fossil fuel power plants and objective function are as below: Z =73.1, Pg =183/63, P cc =367/.80, P s = 10/9 Results represent main share of combined cycle power plant to supply the load in 1 year. By solving the third phase linear programming model by software Lingo, amount of capacity of each eight renewable energy resources power plants are as below: Z = , P Fc =1/, P W =806/171, P Ge =1866/660, P M =0, P pv =0, P Sh =0, P Solar =0, P B =0. Sensitivity Analysis to determine changes limitation optimal solution In sensitivity analysis by changing weight or significance of a criterion, variation in items ranking will be assessed. Although there is lack of certainty to determine rate (weights) of criteria, it is so useful for decision making to select items. Cost and fuel resources scarcity criteria of power plants because of high importance and priority toward other criteria are more influential to select better item and sensitivity analysis are done toward these two criteria. figures (1) and () respectively show variation method in ranking of two fossil fuel and renewable energy resources Fig.. Projects sensitivity toward total operation cost criteria and power plant capital cost. Conclusion One of the main issues in electrical generation planning is to find optimized combination of power plants to supply secure and sustain load for future. Generally in generation planning, we can optimize the mono criteria objective function by WASP for changing some important criteria to cost criteria apart from no important criteria. On the other hand, this problem follows different qualitative and quantitative factors, so solving a mono criterion function can not be a suitable trend to consider all effective factors and criteria. An appropriate solution is to consider all qualitative and quantitative criteria by using decision making techniques along with multiple criteria. By sensitivity analysis we can observe the variation of response when the importance weight in each criterion is changed as well as optimized combination of power plants. As a result, decision maker is able to inspect effect of all factors on final response under uncertain circumstances. According to sensitivity analysis it is clarified that if fossil fuel resources scarcity criterion of

6 power plants has important weight, renewable energy resources power plants will be competitive with fossil fuel power plants. References 1- L. Saaty Multicriteria Decision Making the Analytic Hierarchy Process University of Pittsburg.(1998) - H Jin Son & K. Jomin (1998). Capital Budjeting Process for Electric Power Utilities an analytic hierarchy process approach. International Journal of Energy Research Economical and technical information of Iran s power plants, production planning office, Tavanir organization R.Ramanathan, L.S.Ganosh, Theory and methodology using AHP for resource allocation problem, European Journal of Operation Research, States simultaneous load estimation report, macro load ovservation group, Tavanir organization Ahmaed, K., 199, Renewable Energy Technologies, A Review of the Status and Costs of Selected Technologies. 7- Bezdek, R.H., et al., 01, National Goals for Solar Energy: Economic and Social Implications, Nat. Resour. J., Vol: :. 8- Cantor, R., 1991, the External Costs of Fuel Cycles: Background Document to the Approach and Issue Prepared for the Energy by resource for the Future, Washington Department, DC. 9- Colle, et al., 0, Economic Evaluation and Optimization of Hybrid/Disel/Photovoltaic Systems Integrated to Utility Grids, Solar Energy, No. 16, P.P Federal Ministry for The Environment, Nature Conservation and Nuclear Safety Germany, 0, Concentrating Solar Power for The Mediterranean Region. 11- Lesourd, J.B., 01, Solar Photovoltaic System: the Economic of a Renewable Energy Resource, Environmental Modeling and Software, No.16, P.P Saif-ul-Rehamn, M., 01, Economic Competitiveness of Solar Energy With Conventional Fuels and Electricity, Solar Energy, Vol: 18: Shababi, SH., Hosseini, M.S., 1997, Economic and Technical Evaluations of Solar Photovoltaic Power Plants in Iran, Electricity Magazine, No., P.P

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