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.

本文提出一种使用智能电表数据和功能，同时确定投标和购买策略，基于历史招标经验学习的双代理深度确定性策略梯度方法来优化招标和购买决策，并通过在线学习确保趋势跟踪和自适应，结果表明该模型可以通过离线 / 在线学习投标规则并稳健地进行适当的出价，在面对多种情况时获得最优利润。

基于数据驱动的需求响应在线互动竞标策略

Data-Driven Online Interactive Bidding Strategy for Demand Response

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