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

在我们的建议的第一部分中，我们研究了价格上限下的电力市场的长期可靠性（或供应的安全性）。这种做法（以及其他形式的市场干预）使人怀疑市场是否有能力以社会有效的方式（规模、技术和时机）提供新的能力。我们建议建立一个投资的动态博弈模型。对均衡投资的全面描述将有助于澄清重组辩论中的一些有争议的问题。例子包括;“繁荣和萧条”周期的可能性，在平衡，一个潜在的技术偏见与有害影响的环境，并需要纳入容量markets.In我们的建议的第二部分，我们研究学习算法作为潜在的强大的计算工具，电力市场。电力市场的复杂性要求在建模工作中引入某种形式的有限理性。然而，当参与者使用简单的，适应性（可能次优）的规则，重复的互动可能会导致长期的均衡结果。虽然，许多“代理为基础的”仿真模型已被提倡用于分析电力市场，他们缺乏坚实的理论支持的问题，如收敛和/或均衡的性质。在这个建议中，我们将代表竞争的电力市场下的某些拥塞管理协议，作为一个特殊的结构，即“潜在的游戏”的游戏。对于这类游戏，“虚拟游戏”学习算法已被证明以概率1收敛。大规模模型的计算测试将用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.