负载预测更新下的实时电价Stackelberg博弈模型

A Stackelberg Game-theoretic Approach to Real-time Pricing under Load Forecasting Update

智能电网环境下,在实时电价Stackelberg博弈模型的基础上引入负载预测,以匹配实时负载和预测负载为目标,设计售电商与用户之间的主从博弈模型以及负载预测更新下的实时定价机制,得到双方的最优实时电力价格和最优用电行为。通过将当日实时电价机制均衡状态下的负载时间序列融入电力供应商电力价格权重时间序列向量,得到进一步优化的日前定价实时电价下的均衡负载时间序列,构成整体不断推进不断优化的闭环。同时,给出实时负载与预测负载序列的匹配程度评价指标与判断标准。通过数值仿真分析,在与未优化的实时定价机制对比以后,发现所提出的负载预测更新下的实时定价机制能够在提高电网运行效率的同时显著降低电力用户用电成本。

In the smart grid environment,load prediction is introduced based on the real-time price Stackelberg game model.In order to match the real-time load and the predicted load,the master-slave game model between the seller and the user is designed.By establishing the real-time pricing mechanism under load forecast update,the optimal real-time power price and optimal power consumption behavior of both sides are obtained.By integrating the load time series under the equalization state of the day-ahead real-time electricity price mechanism into the power supplier power price weight time series vector,the further optimized load-balancing time series under the day-ahead real-time electricity price mechanism is obtained,finally forming a closed-loop that is constantly advancing and optimizing.At the same time,the matching degree evaluation index and judgment criterion of real-time load and predictive load sequence are presented.Through numerical simulation analysis and comparison with the unoptimized real-time pricing mechanism,it is found that the proposed real-time pricing mechanism based on load prediction and update can significantly reduce the power consumption cost of power users while improving the operation efficiency of the power grid.