Reliability constrained multi-period generation expansion planning of electrical energy resources using MILP
SUMMARY The main goal of Generation Expansion Planning (GEP) is to minimize total costs associated with new power generating units’ installation subject to technical and economical constraints. This paper addresses the GEP with probabilistic reliability criteria. The Loss‐of‐Load Probability reliabi...
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| Veröffentlicht in: | International transactions on electrical energy systems Jg. 23; H. 7; S. 961 - 974 |
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01.10.2013
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| Abstract | SUMMARY
The main goal of Generation Expansion Planning (GEP) is to minimize total costs associated with new power generating units’ installation subject to technical and economical constraints. This paper addresses the GEP with probabilistic reliability criteria. The Loss‐of‐Load Probability reliability index is explicitly augmented as a new constraint which takes into account the reserve requirements. The outage cost is represented by the Expected Energy Not Served index. Due to nonlinear nature of these reliability indices, the GEP optimization problem with reliability criteria is very complicated to solve. Accordingly, the focus of this work is to deal with the reliability constrained multi‐period GEP problem as a Mixed Integer Linear Programming (MILP). The results in the case study indicate the effect of reliability considerations on decision‐making process. The simulation results also show the superiority of the proposed MILP‐based method in comparison with the well‐known metaheuristic algorithms and Dynamic Programming approach in the viewpoints of the accuracy and computational speed. Copyright © 2012 John Wiley & Sons, Ltd. |
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| AbstractList | SUMMARY
The main goal of Generation Expansion Planning (GEP) is to minimize total costs associated with new power generating units’ installation subject to technical and economical constraints. This paper addresses the GEP with probabilistic reliability criteria. The Loss‐of‐Load Probability reliability index is explicitly augmented as a new constraint which takes into account the reserve requirements. The outage cost is represented by the Expected Energy Not Served index. Due to nonlinear nature of these reliability indices, the GEP optimization problem with reliability criteria is very complicated to solve. Accordingly, the focus of this work is to deal with the reliability constrained multi‐period GEP problem as a Mixed Integer Linear Programming (MILP). The results in the case study indicate the effect of reliability considerations on decision‐making process. The simulation results also show the superiority of the proposed MILP‐based method in comparison with the well‐known metaheuristic algorithms and Dynamic Programming approach in the viewpoints of the accuracy and computational speed. Copyright © 2012 John Wiley & Sons, Ltd. |
| Author | Akbari, Mohammad Amin Roosta, Alireza Aghaei, Jamshid Rabiee, Abdorreza Gitizadeh, M. |
| Author_xml | – sequence: 1 givenname: Jamshid surname: Aghaei fullname: Aghaei, Jamshid email: Correspondence to: Jamshid Aghaei, Electronic and Electrical Department, Shiraz University of Technology, Modares Blvd. Shiraz, Iran. P.O.71555-313. ;, aghaei@sutech.ac.irj_aghaei@yahoo.com organization: Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran – sequence: 2 givenname: Alireza surname: Roosta fullname: Roosta, Alireza organization: Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran – sequence: 3 givenname: Mohammad Amin surname: Akbari fullname: Akbari, Mohammad Amin organization: Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran – sequence: 4 givenname: Abdorreza surname: Rabiee fullname: Rabiee, Abdorreza organization: Department of Electrical Engineering, Islamic Azad University, Damavand Branch, Tehran, Iran – sequence: 5 givenname: M. surname: Gitizadeh fullname: Gitizadeh, M. organization: Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran |
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| References_xml | – reference: Wang X, McDonald JR. Modern Power System Planning. McGraw Hill International Limited: Singapore, 1994; 208-219. – reference: Stremel JP. Production costing for long-range generation expansion planning studies. IEEE Transactions on Power Apparatus Systems 1982; 101(3):526-536. – reference: Sirikum JS, Techanitisaward A. Power generation expansion planning with emission control: a nonlinear model and a GA-based heuristic approach. Energy Research 2006; 30:81-99. – reference: Stremel JP. Production costing using the cumulated method of representing the equivalent load curve. IEEE Transactions on Power Apparatus Systems 1980; 99(5):1947-1955. – reference: Park JB, Park YM, Won JR, Lee KY. An improved genetic algorithm for generation expansion planning. IEEE Transactions on Power Systems 2000; 15(3):916-922. – reference: Mavrotas G, Diakoulaki D, Papayannakis L. An energy planning approach based on mixed 0-1 multiple objective linear programming. Transaction on operation Research 1999; 6(2):231-244 – reference: Antunes CH, Martins AG, Brito IS, A multiple objective mixed integer linear programming model for power generation expansion planning. Energy 2004; 29:613-27. – reference: Murgan P, Kannan S, Baskar S. NSGA-II algorithm for multi-objective generation expansion planning Problem. Electric Power Systems Research 2009; 79:622-628. – reference: Bufford F, Galliana FD. An electricity market with a probabilistic spinning reserve criterion. IEEE Transactions on Power Systems 2004; 19(1):300-307. – reference: Park YM, Park JB, Won JR. A hybrid genetic algorithm/dynamic programming approach to optimal long-term generation expansion planning. International Journal on Electric Power Energy Systems 1998; 20(4):295-303. – reference: Masse P, Gilbert R. Application of linear programming to investments in the electric power industry. Management Science 1975; 3(2):149-166. – reference: Storn R, Price K. Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 1997; 11:341-359. – reference: Kannan S, Slochanal SMR, Subbaraj P, Padhy NP. Application of particle swarm optimization technique and its variants to generation expansion planning problem. Electric Power Systems Research 2004; 70:203-210. – reference: Kannan S, Slochanal SMR, Padhy NP. Application and comparison of metaheuristic techniques to generation expansion planning problem. IEEE Transactions on Power Systems 2005; 20(1):466-475. – reference: Zhu L, Chow MYA. Review of emerging techniques on generation expansion planning. IEEE Transactions on Power Systems 1997; 12(4):1722-1728. – reference: Kannan S, Baskar S, McCalley JD, Murgan P. Application of NSGA-II Algorithm to Generation Expansion Planning. IEEE Transactions on Power Systems 2009; 24(1):454-561. – reference: Nakamura S. A review of electric production simulation and capacity expansion planning programs. Energy Research 1984; 8:231-240. – reference: Michalewich Z. Genetic algorithm + Data structures = Evolutionary programs. Springer: Berlin, 1996. – reference: Floudas CA. Nonlinear and Mixed-Integer Optimization: Fundamentals and Applications. Oxford Univercity Press: New York, 1995. – reference: Sirikum J, Techanitisawad A, Kachitivichyanukul V. New Efficient GA-Benders' Decomposition Method: For Power Generation Expansion Planning With Emission Controls. IEEE Transactions on Power systems 2007; 22(3):1092-1100. – reference: Wong KP, Wong, YW Combined genetic algorithm/ simulated annealing /fuzzy set approach to short-term generation schedule with take-or-pay fuel contract. IEEE Transactions on Power Systems 1996; 11(1):128-136. – reference: Bertoldi O, Salvaderi L, Scalcino S. Monte Carlo Approach in Planning Studies: an application to IEEE RTS. IEEE Transactions on Power Systems 1998; 3:1146-1154. – reference: Billinton R, Allan RN. Reliability Evaluation of Power Systems, 2nd edn. Plenum: New York, 1996. – reference: Moon PY, Ryul WJ, Bae PJ, Gee KD. Generation expansion planning based on an advanced evolutionary programming. IEEE Transactions on Power Systems 1999; 14(1):299-305. – reference: David AK, Zhao R. An expert system with fuzzy sets for optimal planning. IEEE Transactions on Power Systems 1991; 6(1):59-65. – reference: Khodr HM, Gomez JF, Barnique L, et al. A linear programming methodology for the optimization of electric power-generation schemes. IEEE Transactions on Power Systems 2002; 17(3):864-69. – volume: 8 start-page: 231 year: 1984 end-page: 240 article-title: A review of electric production simulation and capacity expansion planning programs publication-title: Energy Research – volume: 101 start-page: 526 issue: 3 year: 1982 end-page: 536 article-title: Production costing for long‐range generation expansion planning studies publication-title: IEEE Transactions on Power Apparatus Systems – volume: 11 start-page: 341 year: 1997 end-page: 359 article-title: Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces publication-title: Journal of Global Optimization – volume: 19 start-page: 300 issue: 1 year: 2004 end-page: 307 article-title: An electricity market with a probabilistic spinning reserve criterion publication-title: IEEE Transactions on Power Systems – volume: 30 start-page: 81 year: 2006 end-page: 99 article-title: Power generation expansion planning with emission control: a nonlinear model and a GA‐based heuristic approach publication-title: Energy Research – year: 2001 – volume: 99 start-page: 1947 issue: 5 year: 1980 end-page: 1955 article-title: Production costing using the cumulated method of representing the equivalent load curve publication-title: IEEE Transactions on Power Apparatus Systems – volume: 79 start-page: 622 year: 2009 end-page: 628 article-title: NSGA‐II algorithm for multi‐objective generation expansion planning Problem publication-title: Electric Power Systems Research – year: 1996 – volume: 17 start-page: 864 issue: 3 year: 2002 end-page: 69 article-title: A linear programming methodology for the optimization of electric power‐generation schemes publication-title: IEEE Transactions on Power Systems – volume: 3 start-page: 1146 year: 1998 end-page: 1154 article-title: Monte Carlo Approach in Planning Studies: an application to IEEE RTS publication-title: IEEE Transactions on Power Systems – volume: 14 start-page: 299 issue: 1 year: 1999 end-page: 305 article-title: Generation expansion planning based on an advanced evolutionary programming publication-title: IEEE Transactions on Power Systems – year: 2010 – volume: 3 start-page: 149 issue: 2 year: 1975 end-page: 166 article-title: Application of linear programming to investments in the electric power industry publication-title: Management Science – year: 1982 – volume: 70 start-page: 203 year: 2004 end-page: 210 article-title: Application of particle swarm optimization technique and its variants to generation expansion planning problem publication-title: Electric Power Systems Research – volume: 11 start-page: 128 issue: 1 year: 1996 end-page: 136 article-title: Combined genetic algorithm/ simulated annealing /fuzzy set approach to short‐term generation schedule with take‐or‐pay fuel contract publication-title: IEEE Transactions on Power Systems – volume: 20 start-page: 466 issue: 1 year: 2005 end-page: 475 article-title: Application and comparison of metaheuristic techniques to generation expansion planning problem publication-title: IEEE Transactions on Power Systems – start-page: 208 year: 1994 end-page: 219 – year: 2004 – volume: 6 start-page: 59 issue: 1 year: 1991 end-page: 65 article-title: An expert system with fuzzy sets for optimal planning publication-title: IEEE Transactions on Power Systems – year: 1997 – volume: 22 start-page: 1092 issue: 3 year: 2007 end-page: 1100 article-title: New Efficient GA‐Benders’ Decomposition Method: For Power Generation Expansion Planning With Emission Controls publication-title: IEEE Transactions on Power systems – year: 1974 – year: 1995 – volume: 15 start-page: 916 issue: 3 year: 2000 end-page: 922 article-title: An improved genetic algorithm for generation expansion planning publication-title: IEEE Transactions on Power Systems – volume: 12 start-page: 1722 issue: 4 year: 1997 end-page: 1728 article-title: Review of emerging techniques on generation expansion planning publication-title: IEEE Transactions on Power Systems – volume: 24 start-page: 454 issue: 1 year: 2009 end-page: 561 article-title: Application of NSGA‐II Algorithm to Generation Expansion Planning publication-title: IEEE Transactions on Power Systems – volume: 29 start-page: 613 year: 2004 end-page: 27 article-title: A multiple objective mixed integer linear programming model for power generation expansion planning publication-title: Energy – volume: 20 start-page: 295 issue: 4 year: 1998 end-page: 303 article-title: A hybrid genetic algorithm/dynamic programming approach to optimal long‐term generation expansion planning publication-title: International Journal on Electric Power Energy Systems – volume: 6 start-page: 231 issue: 2 year: 1999 end-page: 244 article-title: An energy planning approach based on mixed 0–1 multiple objective linear programming publication-title: Transaction on operation Research |
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The main goal of Generation Expansion Planning (GEP) is to minimize total costs associated with new power generating units’ installation subject to... |
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| SubjectTerms | Expected Energy Not Served (EENS) Generation Expansion Planning (GEP) Loss-of-Load Probability (LOLP) Mixed Integer Linear Programming (MILP) Reliability Metrics |
| Title | Reliability constrained multi-period generation expansion planning of electrical energy resources using MILP |
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