具有电力需求预测更新的智能电网实时定价机制

智能电网环境下,基于需求侧管理的电力需求实时预测和定价机制对于维持电力供需平衡,削峰填谷至关重要。文中引入贝叶斯信息更新方法对智能电网中用户电力需求信息进行实时预测更新,然后将售电商与用户之间的实时电价与电力需求策略互动行为生成一主多从博弈模型并进行均衡分析。通过数值仿真分析,在与无电力需求预测更新下的智能电网实时定价机制对比以后,发现所提出的具有电力需求预测更新的实时定价机制可以提高用户用电满意度和参与需求侧管理的积极性,同时也增加了售电商利润。

Real-time Pricing Mechanism in Smart Grid with Forecasting Update of Power Demand

In the smart grid environment, the real-time forecasting and pricing of power demand based on demand side management(DSM)is crucial for maintaining supply-demand balance and reducing the peak of power. This paper adopts the Bayesian method to forecast and update the real-time power demand information of users in smart grid. Then, a leader-follower game model is established to model the real-time price and the interaction behavior of power demand strategy between one retailer and multiple customers, and the game equilibrium is analyzed. Numerical simulation analysis shows that, compared with the real-time pricing mechanism in smart grid without updating of electricity demand forecasting, the proposed real-time pricing mechanism with forecasting update of electricity demand is more advantageous to increase users'' satisfaction of electricity consumption and facilitate users to participate in DSM, while increase the profit of power retailer.