Re-scheduling the unit commitment problem in fuzzy environment
Re-scheduling the unit commitment problem in fuzzy environment
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DOI：
10.1109/fuzzy.2011.6007313
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发表时间：
2011-06
期刊：
影响因子：
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通讯作者：
Bo Wang;You Li;J. Watada
中科院分区：
文献类型：
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作者：
Bo Wang;You Li;J. Watada
对未来电力需求的传统预测总是基于历史数据。然而，实际电力需求受到天气、温度和意外紧急情况等许多其他因素的影响。仅使用历史信息并不能很好地预测未来的真实需求。本研究参考了相关领域专家的意见。为了应对历史数据的不确定性和专家意见的不精确，我们采用模糊变量来更好地表征预测的未来电力负荷。此处通过考虑模糊环境中的旋转储备成本来更新传统的机组组合问题（UCP）。作为解决方案，我们提出了一种称为局部收敛厌恶二元粒子群优化（LCA-PSO）的启发式算法来求解 UCP。所提出的模型和算法用于分析多个测试系统。所提出的算法与传统方法的比较表明，LCA-PSO 在寻找最优解方面表现更好。
The conventional prediction of future power demands are always made based on the historical data. However, the real power demands are affected by many other factors as weather, temperature and unexpected emergencies. The use of historical information alone cannot well predict real future demands. In this study, the experts' opinions from related fields are taken into consideration. To deal the uncertainty of historical data and imprecise experts' opinions, we employ fuzzy variables to better characterize the forecasted future power loads. The conventional unit commitment problem (UCP) is updated here by considering the spinning reserve costs in a fuzzy environment. As the solution, we proposed a heuristic algorithm called local convergence averse binary particle swarm optimization (LCA-PSO) to solve the UCP. The proposed model and algorithm are used to analyze several test systems. The comparisons between the proposed algorithm and the conventional approaches show that the LCA-PSO performs better in finding the optimal solutions.