Two-Stage Multi-objective Unit Commitment Optimization under Future Load Uncertainty
Two-Stage Multi-objective Unit Commitment Optimization under Future Load Uncertainty
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DOI：
10.1109/icgec.2012.147
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发表时间：
2012-08
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
机组组合问题是在满足未来电力需求的前提下尽可能降低总发电成本。因此，必须根据对未来需求的正确预测进行优化。然而，各种不确定因素影响这些负荷，使准确的预测不成功。本研究借由模糊集理论的应用，建立一个两阶段多目标模糊规划模型。为了有效地定义供电可靠性，基于模糊可信度理论，提出了最大停电时间的概念。另外，设计了一种改进的两层多目标粒子群优化算法作为求解方法。最后，本研究的性能进行了讨论，从几个测试系统的实验结果进行比较。
The unit commitment problem is to reduce the total generation cost as much as possible while satisfying future power demands. Therefore, optimization must be performed based on correct predictions of future demands. However, various uncertain factors affect these loads making an exact forecasting unsuccessful. This study mitigates this difficulty by applying fuzzy set theory and the objective is to build a two-stage multi-objective fuzzy programming model. to define the supply reliability effectively, we propose a new concept of maximal blackout time based on the fuzzy credibility theory. in addition, an improved two-layer multi-objective particle swarm optimization algorithm is designed as the solution. Finally, the performance of this study is discussed in comparison with experimental results from several test systems.