考虑负荷季节特性的电价型需求响应最优定价策略

Optimal Pricing Strategy of Electricity Price Demand Response Considering Seasonal Characteristics of Load

实施峰谷分时电价策略能够有效降低负荷峰谷差同时节约电网投资,但不同季节的负荷特性具有显著差异性,其影响峰谷分时电价最优策略的制定,因此文章提出了一种考虑负荷季节特性的峰谷分时电价定价策略及时段划分模型。首先,描述所提出的需求响应架构;其次,采用k均值方法获取各季节典型日的负荷曲线,并采用改进的移动边界技术对各季节典型日负荷曲线进行时段划分,通过设置时段划分约束因子,并采用戴维森堡丁指数(Davies-Bouldindex, DBI)作为目标函数建立峰谷时段划分优化模型;然后,构建考虑负荷季节特性的需求价格弹性矩阵,以及考虑负荷季节特性的峰谷分时电价优化模型,并采用粒子群优化(particle swarm optimization, PSO)算法求解模型。采用RTS测试系统提供的负荷序列样本对所提算法和模型进行验证分析,验证了所提方法和模型的有效性以及正确性。

The implementation of peak-valley time-of-use(TOU) price strategy can effectively reduce the peak-valley difference of load and save investment for power grid, but the load characteristics in different seasons are quite different, which affects the formulation of the optimal peak-valley TOU price strategy. Therefore, this paper mainly studies the peak-valley TOU price pricing strategy and period partitioning model considering multiple seasonal characteristics. Firstly, the demand response architecture proposed in this paper is described in combination with the main innovations of this paper. Secondly, the k-means method is adopted to obtain the load curve of typical days in each season, and the improved moving boundary technology is adopted to partition the load curve of typical days in each season. The optimization model for peak-valley period partitioning is established by setting the period partitioning constraint factors and adopting the Davies-Bouldin index(DBI) as the objective function. Then, the price elasticity of demand considering seasonal characteristics and the pea-valley TOU price optimization model considering multiple seasonal characteristics are established, and the particle swarm optimization(PSO) algorithm is used to solve the model. RTS is used to verify and analyze the algorithm and model, which verifies the effectiveness and correctness of the method and model proposed in this paper.