基于神经网络的微电网参与上层电网实时优化调度策略

微电网优化调度策略除要解决风电、光伏就地消纳及其自身稳定运行问题外,还应具备调用分布式电源、具有需求响应能力的负荷等灵活性资源向电网提供辅助服务,参与上层电网实时调度的能力。基于此,文章提出一种基于BP神经网络的微电网资源优化调度策略。结合微电网运行成本和需求响应容量收益建立日前阶段经济最优调度策略;日内模拟阶段模拟预测功率波动以及上层电网实时需求,通过神经网络学习,得到日内阶段调度模型,为日内调度做准备;日内阶段通过上层电网的需求响应信号,将联络线功率输入到神经网络训练模型当中,得到日内阶段各个分布式电源实时功率。所提策略既能保障微电网的经济运行,又能满足上层电网的实时调度要求。最后以日后最优调度算例结果验证了策略的经济性和有效性。

Strategy Based on Neural Network for Microgrid Participating in Real-Time Optimal Scheduling of Upper Grid

In addition to solving the problems of on-site photovoltaic and wind power and its stable operation, the optimization scheduling strategy for microgrid should also have flexible resources such as distributed power and loads with demand-response capabilities to provide auxiliary services to the grid and capabilities to participate in real-time scheduling of the upper-level grid. This paper proposes a microgrid resource optimization scheduling strategy based on BP neural network. This paper combines the microgrid operating costs and demand-response capacity gains to establish an economic optimal scheduling strategy for the day-to-day stage. The intra-day simulation stage simulates the forecast of power fluctuations and the real-time demand of the upper-level grid, and learns through the neural network to obtain the intra-day scheduling model to prepare for intra-day scheduling. In the daytime phase, through the demand-response signal of the upper-layer power grid, the power of the contact line is input into the training model of neural network, and the real-time power of each distributed power source in the daytime phase is obtained. The strategy proposed in this paper can not only guarantee the economic operation of the microgrid, but also meet the real-time dispatching requirements of the upper-level grid. Finally, the economics and effectiveness of the strategy are verified by the results of day-after optimal scheduling examples.