EPNES: Security of Supply & Strategic Learning in Restructured Power Markets

在我们提案的第一部分，我们研究了价格上限下电力市场的长期可靠性（或供应安全）。这（以及其他形式的市场干预）使人们对市场以社会有效的方式（规模、技术和时机）提供新产能的能力产生了怀疑。我们建议建立一个动态的投资博弈模型。对均衡投资的全面描述，将有助于揭示重组辩论中一些有争议的问题。例子包括;平衡的“繁荣与萧条”周期的可能性，对环境有害的潜在技术偏差，以及纳入产能市场的必要性。在我们提案的第二部分，我们研究了学习算法作为电力市场潜在的强大计算工具。电力市场的复杂性要求在建模工作中加入某种形式的有限理性。然而，当玩家使用简单的、适应性的（可能是次优的）规则时，从长远来看，重复的互动可能会产生平衡结果。尽管许多“基于智能体”的模拟模型被提倡用于分析电力市场，但它们在收敛和/或均衡性质等问题上缺乏坚实的理论支持。在本提案中，我们将在某些拥塞管理协议下代表电力市场中的竞争，作为具有特殊结构的游戏，即。“潜在的游戏”。对于这类游戏，“虚拟游戏”学习算法的收敛概率为1。大规模模型的计算测试将有助于验证和评估所提出的策略学习模型的实用性。

In the first part of our proposal, we study the long run reliability (or security of supply) of electricity markets under price caps. This (and other forms or market intervention) cast doubts on the market ability to provide new capacity in a socially efficient manner (scale, technology and timing). We propose to develop a dynamic game model of investment. A full characterization of equilibrium investment will shed light on a number of contentious issues in the restructuring debate. Examples include; the possibility of "boom and bust" cycles in equilibrium, a potential technology bias with harmful effects on the environment, and the need to incorporate capacity markets.In the second part of our proposal we study learning algorithms as potentially powerful computational tools for electricity markets. The complexity of electricity markets calls for the incorporation of some form of bounded rationality in the modeling efforts. However, when players use simple, adaptive (possibly sub-optimal) rules, repeated interaction may induce equilibrium outcomes in the long run. Although, many "agent based" simulation models have been advocated for analyzing electricity markets, they lack solid theoretical support on issues such as convergence and/or the nature of equilibrium. In this proposal we will represent competition in electricity markets under certain congestion management protocols, as games with a special structure, i.e. "potential games'. For this class of games, the class of "fictitious play" learning algorithms has been proven to converge with probability one. Computational tests a large-scale model will serve to validate and assess the practicality of the class of strategic learning models proposed.