Economic Reliability of Deregulated Power Markets: A Machine Learning Model for Design and Assessment

电力市场设计缺陷背后的主要原因之一是缺乏考虑重要市场特征的全面量化模型。这一缺陷，加上缺乏计算可行的方法来解决随机博弈(例如，获得纳什均衡)，可以用来模拟市场行为，导致电力市场的经济可靠性较低。本研究的目标是：1)建立一个随机博弈论模型框架，包括能源竞价和价格结算政策、FTR拍卖、需求弹性和其他不断演变的市场规则等特征；2)制定基于强化学习(RL)的求解方法并进行收敛分析；3)利用电力市场行业数据进行测试和对标；以及4)制定培养学生的计划，并为电力市场专业人员提供继续教育。所提出的模型及其求解方法将使我们能够识别市场的均衡状态，该均衡状态的特征是市场参与者的稳态竞价策略以及由此产生的能源价格。因此，可以在实施之前评估替代市场设计的经济可靠性。基于RL的解决方法也可以推广到其他经济部门的问题。更广泛的影响将包括1)通过传播从研究中获得的知识来提高电力市场的经济可靠性，2)由于更好的市场设计而导致的竞争加剧，从而导致流程创新和价格降低，以及3)发展一批能够设计和分析放松管制的电力市场的专业人员。

One of the main reasons behind power market design flaws is the lack of comprehensive quantitative models that take into account important market features. This deficiency coupled with an absence of computationally viable methods for solving stochastic games (e.g., obtaining Nash Equilibrium) that can be used to model market behavior has resulted in lower economic reliability for power markets. The objectives of this research are: 1) to develop a stochastic game theoretic modeling framework consisting of features like energy bidding and price settlement policies, FTR auction, demand elasticity and other evolving market rules, 2) to formulate a reinforcement learning (RL) based solution methodology and develop its convergence analysis, 3) to conduct testing and benchmarking with power market industry data, and 4) to develop plans to train students and also offer continuing education for power market professionals. The proposed model and its solution methodology would allow us to identify the equilibrium state of a market, which is characterized by steady state bidding strategies of the market participants and the resulting energy prices. Thus the economic reliability of alternative market designs can be assessed before implementation. The RL based solution methodology can also be extended to problems in other sectors of economy. The broader impacts would include 1) enhanced economic reliability of power markets through dissemination of knowledge gained from the research, 2) increased competition due to better market design resulting in process innovation and price reduction, and 3) development of a diverse group of professionals capable of designing and analyze deregulated power markets.