基于数据驱动的需求响应在线交互竞价策略
Data-Driven Online Interactive Bidding Strategy for Demand Response
摘要：
需求响应（DR）作为未来电网的重要能源之一，以响应周期短、成本低的特点提供调峰服务，提高可再生能源的利用效率。建立了各种DR类别，例如自动DR、激励DR、紧急DR和按需投标。然而，由于对居民和商业用户效用模型认识不足的现实问题，电力市场中需求竞价聚合器的研究还处于起步阶段。在此问题上，投标报价和投标数量是两个必要的决策变量，同时考虑到由于市场和参与者的不确定性。在本文中，我们确定投标和采购策略同时利用智能电表的数据和功能。提出了一种双智能体深度确定性策略梯度方法，通过学习历史投标经验来优化决策。在线学习进一步利用每日获得的最新投标经验，以确保趋势跟踪和自适应。采用两个环境模拟器验证了模型的鲁棒性。仿真结果表明，该模型在面对不同情况时，通过离线/在线学习报价规则，鲁棒地进行报价，能够获得最优收益。
英文摘要：
Demand response (DR), as one of the important energy resources in the future's grid, provides the services of peak shaving, enhancing the efficiency of renewable energy utilization with a short response period, and low cost. Various categories of DR are established, e.g. automated DR, incentive DR, emergency DR, and demand bidding. However, with the practical issue of the unawareness of residential and commercial consumers' utility models, the researches about demand bidding aggregator involved in the electricity market are just at the beginning stage. For this issue, the bidding price and bidding quantity are two required decision variables while considering the uncertainties due to the market and participants. In this paper, we determine the bidding and purchasing strategy simultaneously employing the smart meter data and functions. A two-agent deep deterministic policy gradient method is developed to optimize the decisions through learning historical bidding experiences. The online learning further utilizes the daily newest bidding experience attained to ensure trend tracing and self-adaptation. Two environment simulators are adopted for testifying the robustness of the model. The results prove that when facing diverse situations the proposed model can earn the optimal profit via off/online learning the bidding rules and robustly making the proper bid.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
