Supply Reliability and Generation Cost Analysis Due to Load Forecast Uncertainty in Unit Commitment Problems
Supply Reliability and Generation Cost Analysis Due to Load Forecast Uncertainty in Unit Commitment Problems
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DOI：
10.1109/tpwrs.2013.2238561
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发表时间：
2013-08-01
影响因子：
6.6
通讯作者：
Watada, Junzo
中科院分区：
文献类型：
--
作者：
Wang, Bo;Li, You;Watada, Junzo
机组组合优化问题的目标是在满足未来电力需求的同时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分析。然而，各种不确定因素影响着这些负荷，使得准确的预测不成功。本研究将模糊集理论应用于未来不确定负荷的评估，从而缓解了这一困难。本研究的目的是建立一个基于24小时不确定负荷预测的两阶段多目标模糊规划模型。第一阶段是对不精确负荷区间数据进行决策的过程，第二阶段是机组组合调度的优化过程，它通过最大化供电可靠性和最小化总发电成本来同时寻找最优解。为了定义不确定预测下的供电可靠性，提出了基于模糊可信度理论的成功运行最大停电时间的概念。此外，在前人研究的基础上，设计了一种改进的两阶段多目标粒子群优化算法作为该模型的求解方法。最后，通过与几个测试系统的实验结果的比较，讨论了该算法的性能。
The goal of a unit commitment optimization problem is to reduce the total generation cost as much as possible while satisfying future power demands. Thus, analysis must be performed based on correct predictions of future demands. However, various uncertain factors affect these loadsmaking an exact forecasting unsuccessful. This study mitigates this difficulty by applying fuzzy set theory to evaluate the future uncertain loads. The objective of this research is to build a two-stage multi-objective fuzzy programming model based on 24-hour uncertain load forecasting. The first stage is a decision-making process on the interval data of the imprecise power loads, whereas the second stage pursues the optimization of the unit commitment scheduling, which can help find both optima simultaneously by maximizing power supply reliability and minimizing total generation cost. To define the supply reliability under uncertain forecasting, we propose a new concept of maximal blackout time during successful operation, which is based on the fuzzy credibility theory. Furthermore, as a solution approach to this model, an improved two-stage multi-objective particle swarm optimization algorithm is designed based on our previous studies. Finally, the performance of this algorithm is discussed in comparison with experimental results from several test systems.