电力市场中微电网竞价优化的数据驱动方法

Rudai Yan, Yan Xu
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引用次数: 0

摘要

本文提出了一种基于深度强化学习的数据驱动解决方案,用于考虑备用市场报价的电力市场中的微电网投标。该框架基于马尔可夫决策过程,对微电网在不同阶段参与电力市场的情况进行建模,包括投标、市场清算和储备激活。该问题分为两个阶段:日前提交和实时市场期,所提出的方法主要集中在第一阶段。来自分布式能源的状态空间模型的状态信息用作策略网络的输入。采用深度确定性策略梯度来训练网络并产生确定性投标策略。然后,第二阶段可以基于第一阶段的结果来调整该策略。该方法通过真实世界的微电网系统和新加坡现货市场的数据进行了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A data-driven method for microgrid bidding optimization in electricity market

A data-driven method for microgrid bidding optimization in electricity market

This paper presents a deep reinforcement learning based data-driven solution to the microgrid bidding in the electricity market considering offers for the reserve market. The framework, based on the Markov decision process, models the microgrid's participation in the electricity market at different stages, including bidding, market-clearing, and reserve activation. The problem is split into two stages: day-ahead submission and real-time market period, and the proposed method mainly focus on the first stage. The state information from state-space models of distributed energy resources serves as input for the policy network. A deep deterministic policy gradient is employed to train the network and produce a deterministic bidding strategy. The second stage can then adjust this strategy based on the results from the first stage. The method is validated with real-world microgrid systems and data from the Singapore spot market.

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