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

在我们的提案的第一部分，我们研究了在价格上限下电力市场的长期可靠性(或供应安全)。这种(以及其他形式或市场干预)令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。我们建议建立一个动态的投资博弈模型。对均衡投资的充分描述将揭示重组辩论中的一些有争议的问题。这些例子包括：“繁荣和萧条”均衡循环的可能性，对环境有害的潜在技术偏向，以及纳入容量市场的必要性。在我们的提案的第二部分，我们研究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纳入某种形式的有限理性。然而，当玩家使用简单的、自适应的(可能是次优的)规则时，从长远来看，重复的互动可能会导致均衡结果。虽然已有许多“基于主体”的仿真模型被用于电力市场分析，但在收敛和均衡性质等问题上缺乏坚实的理论支持。在该方案中，我们将在一定的阻塞管理协议下将电力市场中的竞争表示为具有特殊结构的博弈，即“潜在博弈”。对于这类博弈，“虚拟博弈”学习算法类已被证明以概率1收敛。计算测试一个大规模模型将用于验证和评估所提出的这类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.