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- W2369891092 abstract "With the increasing integration of wind farms,the randomness and uncontrollability of wind power cause some difficulties in system generation planning and scheduling.Power system dispatching with consideration of interval forecasting information of wind power with a certain probability is helpful to ensure power system reliability.This paper proposes a day-ahead unit commitment model based on interval forecasts of wind power and simultaneously presents an advanced quantum-inspired binary particle swarm optimization(QBPSO) algorithm.The proposed method used the advanced QBPSO for the unit on/off problem and the primal-dual interior point method for load economic dispatch problem.Based on the QBPSO,this paper adopts partially greedy mutation strategy to make particles easily jump out of the local optimal solutions.The paper also deploys heuristic adjusted regulations to correct cross-border individuals,thus the efficiency of the algorithm and the accuracy of solutions were improved.The simulation examples of systems composed of 10-to 100-units proved the validity of the model and algorithm." @default.
- W2369891092 created "2016-06-24" @default.
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- W2369891092 date "2013-01-01" @default.
- W2369891092 modified "2023-09-27" @default.
- W2369891092 title "Solutions to Unit Commitment Problems in Power Systems With Wind Farms Using Advanced Quantum-inspired Binary PSO" @default.
- W2369891092 hasPublicationYear "2013" @default.
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