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• W3048907571 abstract "As the power systems in some large developing and developed countries are getting bigger, solving large-scale unit commitment (UC) is an urgent need and significant task to ensure their economic operation and contribute green energy consummation to society. In this article optimization models covering economy and environmental protection are established, and an improved binary artificial fish swarm algorithm (IBAFSA) is presented to solve the large-scale UC problems. The parameters of IBAFSA are improved by Lévy flight and adaptive average visual distance to search space more actively, and a double threshold selection strategy is used to enhance the effectiveness of population evolution in the optimization. Meanwhile, a heuristic greedy search algorithm among the best individuals of all generations in the iterative process of the optimization is proposed, which is beneficial to improve computation convergence and reach the optimum solution. A fast constraints processing mechanism based on the heuristic modifying strategy of unit violation is established to handle the coupling between system spinning reserve constraint and unit minimum up and down time constraint. The effectiveness of the proposed approach is verified by the UC simulations of test systems of 10-1000 units, the IEEE 118-bus system, and a large-scale power system of 270 units. The numerical simulating results show that the proposed UC solution method can achieve the near-optimal solutions in a reasonable time, improve the economic and environmental benefits of a large-scale power system, and is a general method to adapt to the changes of the objective function and constraints of a UC optimization." @default.
• W3048907571 created "2020-08-18" @default.
• W3048907571 creator A5037644703 @default.
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• W3048907571 date "2020-01-01" @default.
• W3048907571 modified "2023-10-18" @default.
• W3048907571 title "Improved Binary Artificial Fish Swarm Algorithm and Fast Constraint Processing for Large Scale Unit Commitment" @default.
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• W3048907571 doi "https://doi.org/10.1109/access.2020.3015585" @default.
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