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首页> 外文会议>International Conference on Soft Computing for Problem Solving >Variant Roth-Erev Reinforcement Learning Algorithm-Based Smart Generator Bidding as Agents in Electricity Market
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Variant Roth-Erev Reinforcement Learning Algorithm-Based Smart Generator Bidding as Agents in Electricity Market

机译：基于Variant Roth-Erev加强学习算法的智能发电机竞标作为电力市场的代理

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摘要

The dynamically changing deregulated electricity market involves different entities and the aim of each entity is to achieve maximum profit while performing electricity price and power bidding. The agent-based modeling of electricity systems was used to model the market entities under whole sale electricity market operation. This paper discusses about the strategic learning ability of generators in an IEEE 30 bus system using Variant Roth-Erev learning algorithm. It also analyzes the variation in the generator commitments through the implemented learning algorithm during the present day schedule and helps the generator to perform smart bidding in the next electricity market operation. The results presented show that the smart generators are able to bid strategically in the electricity market and which will reflect in its net earnings in a market scheduled on a day-ahead basis.
机译：动态变化的解除管制电力市场涉及不同的实体，每个实体的目的是在执行电价和电力竞标时实现最大利润。基于代理的电力系统建模用于在整个销售电力市场运行下进行市场实体。本文讨论了使用变体Roth-erev学习算法IEEE 30总线系统中发电机的战略学习能力。它还通过在当前计划期间通过实现的学习算法分析了发电机承诺的变化，并帮助发电机在下一次电力市场运行中执行智能竞标。结果表明，智能发电机能够在电力市场中策略性地竞标，这将在市场上的市场净收入中反思其净收入。

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