多时间尺度下主动配电网源-储-荷协调经济调度

电力市场细分众多,主动配电网内资源的性能经济性各不同,且不平衡功率惩罚较重,主动配电网能否有效消纳可再生能源,减少不平衡功率惩罚,很大程度上依赖于运行调度是否合理。基于此,提出日前主辅市场下主动配电网多时间尺度优化经济调度模型。该模型考虑市场竞争以及时间尺度间的调度衔接优化,通过协调调度可再生分布式电源、储能以及负荷侧各类需求响应,在促进可再生能源消纳、减小不平衡功率的同时提升主动配电网运行经济性。首先,分析了主动配电网多源协调调度并网方式与辅助服务市场性能划分。其次,综合考虑储能的快速调节与能量时移特性以及需求侧响应时间尺度特性,以日前调度收益最大以及日内运行成本最小为目标,建立多时间尺度协调调度模型。然后,基于二进制扩充法与拉丁超立方采样将模型转化为混合整数线性规划问题进行求解。最后通过算例验证了模型的正确性与有效性。

Source-storage-load coordinated economic dispatch of an active distribution network under multiple time scales

There are many subdivisions in the electricity market, the performance and economics of active distribution network resources are different, and the imbalance power penalty is heavy. Whether the active distribution network can effectively absorb renewable energy and reduce the imbalance power penalty depends largely on whether the operation schedule is reasonable. Based on this, a multi-time scale optimal economic dispatch model of an active distribution network in the day-ahead main and auxiliary market is proposed. This model considers market competition and optimization of scheduling connections between time scales. By coordinating and scheduling renewable distributed power, energy storage, and load-side demand responses, it promotes the consumption of renewable energy and reduces unbalanced power while increasing the initiative for economical operation of the distribution network. First, it analyzes the way of multi-source coordinated dispatching of an active distribution network and the performance division of the ancillary service market. Secondly, it comprehensively considers the rapid adjustment of energy storage and the characteristics of energy time shift and the characteristics of demand side response time scale. The goal is to minimize the operating cost within a day, and establish a multi-time scale coordinated scheduling model. Then, based on the binary expansion method and Latin hypercube sampling, the model is transformed into a mixed integer linear programming problem for solution. Finally, an example is used to verify the correctness and effectiveness of the model. This work is supported by the Project of Sichuan Science and Technology Department (No. 2019YFG0152).