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
Hauptverfasser: Aghaei, Jamshid, Roosta, Alireza, Akbari, Mohammad Amin, Rabiee, Abdorreza, Gitizadeh, M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Blackwell Publishing Ltd 01.10.2013
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ISSN:2050-7038, 2050-7038
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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.
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.
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  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
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  organization: Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
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  givenname: Mohammad Amin
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  organization: Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
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  givenname: Abdorreza
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  organization: Department of Electrical Engineering, Islamic Azad University, Damavand Branch, Tehran, Iran
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  surname: Gitizadeh
  fullname: Gitizadeh, M.
  organization: Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
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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.
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.
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.
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Masse P, Gilbert R. Application of linear programming to investments in the electric power industry. Management Science 1975; 3(2):149-166.
Murgan P, Kannan S, Baskar S. NSGA-II algorithm for multi-objective generation expansion planning Problem. Electric Power Systems Research 2009; 79:622-628.
Bufford F, Galliana FD. An electricity market with a probabilistic spinning reserve criterion. IEEE Transactions on Power Systems 2004; 19(1):300-307.
Nakamura S. A review of electric production simulation and capacity expansion planning programs. Energy Research 1984; 8:231-240.
David AK, Zhao R. An expert system with fuzzy sets for optimal planning. IEEE Transactions on Power Systems 1991; 6(1):59-65.
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.
Zhu L, Chow MYA. Review of emerging techniques on generation expansion planning. IEEE Transactions on Power Systems 1997; 12(4):1722-1728.
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References_xml – reference: Wang X, McDonald JR. Modern Power System Planning. McGraw Hill International Limited: Singapore, 1994; 208-219.
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– 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.
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  publication-title: IEEE Transactions on Power Systems
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  publication-title: IEEE Transactions on Power Systems
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  publication-title: IEEE Transactions on Power Systems
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  publication-title: Transaction on operation Research
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Snippet SUMMARY 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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istex
SourceType Publisher
StartPage 961
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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