考虑负荷发展和用户行为的分时电价优化研究

分时电价作为需求侧管理的一种重要经济手段，其在国内的全面实施势在必行，但电力需求的快速增长导致分时电价对用户的激励效果缺乏时效性。针对此问题，提出一种考虑负荷发展的分时电价优化方法，利用BP神经网络预测和灰色预测法预测出未来2年的典型日负荷曲线，将未来负荷曲线代入分时电价优化模型的结果作为电价约束，再对当年的典型日负荷曲线进行优化计算，得到合理的分时电价。算例将仅考虑当年典型日负荷曲线的优化结果与考虑负荷发展的优化结果进行对比，验证该优化方法延长分时电价时效的有效性。

Research on Optimization of TOU Considering Load Development and User Behavior

As an important economic means of demand-side management, TOU (time of use) is imperative for full implementation in the country. However, the rapid growth of electricity demand lead to TOU ineffective for user's incentive. Aiming at this problem, this paper puts forward a TOU optimization method considering load development, and uses BP neural network prediction and grey prediction to forecast the typical daily load curve of the next two years. The reasonable TOU is obtained through substituting the future load curve into the TOU optimization model as electricity price constraint, and optimizing the typical daily load curve of the year. The optimization results with sole consideration of the typical daily load curve are compared with that with consideration of load development, which verifies the effectiveness of the proposed optimization method for extending the TOU timeliness.