Abstract This article focuses towards agent based implementation of restructured power market with learning capabilities for generators. The market model considered for the analysis is wholesale market and through learning capability of the generator will confront self-sufficient smart generator to perform optimal bidding for a long term. The Agent Based Modeling for Electricity Systems (AMES) permits dynamic testing with learning traders. The whole electricity market is managed by the Independent System Operator (ISO) which computes the hourly Locational Marginal Price (LMP) and commitments of power exchange for day-ahead market operation. The bidding strategy of generators is trained using stochastic reinforcement learning algorithm (JReLM) developed under Java platform. For the analysis, agents are classified as market traders and Independent System Operator (ISO) linking to the IEEE 5-Bus system. The analysis is further extended to an IEEE-30 Bus system and the results demonstrate great potential of the agent based computational ability in the electricity market to help the generators to exercise more market power with optimal bidding than normal generators.

中文翻译：

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Roth-Erev 强化学习方法使用基于代理的动态建模智能发电机竞标以实现长期电力市场运营

摘要 本文侧重于具有发电机学习能力的重组电力市场的基于代理的实现。分析考虑的市场模型是批发市场，通过发电机的学习能力，将面对自给自足的智能发电机，以长期进行最优竞价。基于代理的电力系统建模 (AMES) 允许对学习交易者进行动态测试。整个电力市场由独立系统运营商 (ISO) 管理，该运营商计算每小时的位置边际价格 (LMP) 和日前市场运营的电力交换承诺。生成器的投标策略使用在 Java 平台下开发的随机强化学习算法（JReLM）进行训练。对于分析，代理被归类为市场交易者和独立系统运营商 (ISO)，它们与 IEEE 5-Bus 系统相连。该分析进一步扩展到 IEEE-30 总线系统，结果证明了基于代理的计算能力在电力市场中的巨大潜力，可以帮助发电机比普通发电机以最佳投标方式行使更多的市场力量。