叶宇剑, 袁泉, 汤奕, STRBACGoran. 抑制柔性负荷过响应的微网分散式调控参数优化[J]. 中国电机工程学报, 2022, 42(5): 1748-1759. DOI: 10.13334/j.0258-8013.pcsee.210565
引用本文: 叶宇剑, 袁泉, 汤奕, STRBACGoran. 抑制柔性负荷过响应的微网分散式调控参数优化[J]. 中国电机工程学报, 2022, 42(5): 1748-1759. DOI: 10.13334/j.0258-8013.pcsee.210565
YE Yujian, YUAN Quan, TANG Yi, STRBAC Goran. Decentralized Coordination Parameters Optimization in Microgrids Mitigating Demand Response Synchronization Effect of Flexible Loads[J]. Proceedings of the CSEE, 2022, 42(5): 1748-1759. DOI: 10.13334/j.0258-8013.pcsee.210565
Citation: YE Yujian, YUAN Quan, TANG Yi, STRBAC Goran. Decentralized Coordination Parameters Optimization in Microgrids Mitigating Demand Response Synchronization Effect of Flexible Loads[J]. Proceedings of the CSEE, 2022, 42(5): 1748-1759. DOI: 10.13334/j.0258-8013.pcsee.210565

抑制柔性负荷过响应的微网分散式调控参数优化

Decentralized Coordination Parameters Optimization in Microgrids Mitigating Demand Response Synchronization Effect of Flexible Loads

  • 摘要: 基于价格的微网分散式调控相较于集中式调控具有扩展性和保密性优势,但存在柔性负荷在低电价时段大量聚集产生新峰值的过响应问题,影响系统安全高效运行。现有研究在主参数电价之外引入辅助参数以缓解过响应,但未进行辅助参数取值的优化,且未考虑微网网络约束对不同节点辅助参数最优取值的影响。为此该文提出缓解负荷过响应的微网分散式调控辅助参数优化方法,为不同节点的柔性负荷制定参数最佳取值以最小化微网总运行成本。首先建立微网分散式调控优化模型,以及电动汽车、智能家电的柔性负荷需求响应模型;进而提出基于深度强化学习的辅助参数优化方法,采用多维连续状态及动作空间学习各节点参数的取值。最后仿真结果验证了所提优化方法的有效性。

     

    Abstract: Despite its scalability and privacy advantages over centralized coordination schemes, decentralized price-based coordination in microgrids suffers from the demand response concentration effect, transferring flexible loads to low-price periods and yielding new demand peak, which hampers the efficient and secure operation of the system. Previous works have introduced auxiliary coordination parameters beyond electricity price to mitigate this effect. However, uniform values of these coordination parameters have been applied to all flexible loads, despite the effects of network constraints. To this end, this paper proposed an auxiliary parameters optimization for decentralized coordination in microgrids, applying optimal value for flexible loads in different nodes to mitigate demand response synchronization effect and minimize the total operational cost of the microgrid. Firstly, the decentralized coordination optimization model of microgrid and the demand response model of flexible loads, i.e. electric vehicles and smart appliances were established. Then a DRL-based approach to select the optimal values of auxiliary parameters was proposed, posing the parameter optimization problem in multi- dimensional continuous state and action spaces. Finally, simulation results demonstrated the effectiveness of the proposed optimization method.

     

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