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A New Combinatorial Meta-heuristic Algorithm for Stochastic Electric Power System Production Costing and Operations Planning

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8 Author(s)
Baozheng Liu ; School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110004, CHINA ; Ping Ren ; Liqun Gao ; Nan Li
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This paper presents a new method - modified particle swarm optimization (PSO) for stochastic electric power system production costing and operations planning. In this method we formulate a stochastic in which unit availability and system load demand are random parameters with known statistics. Unit outages are modeled as Markov processes. The unit commitment status variables are (0,1) integers while the unit dispatch loading levels take on decimal values. The unit commitment status variables together with the unit dispatch loading levels are random processes satisfying appropriately derived deterministic equivalent differential equations. A case on stochastic electric power system production costing and operations planning problem is presented to show the methodology's feasibility and efficiency, and the modified particle swarm optimization algorithm should require less computational burden and time compared to trial and error approaches

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2006 6th World Congress on Intelligent Control and Automation  (Volume:2 )

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