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

EPNES：供应安全

• 批准号: 0224747
• 负责人: Alfredo Garcia
• 金额: $ 30万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0224747&HistoricalAwards=false
• 关键词: EPNES; Security; Supply; Strategic; Learning

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.

在提案的第一部分中，我们研究了价格上限下电力市场的长期可靠性（或供应安全性）。这种（以及其他形式或市场干预）对市场以社会有效的方式（规模，技术和时机）提供新能力的市场能力提出了怀疑。我们建议开发一种动态的投资模型。均衡投资的全面表征将阐明重组辩论中的许多有争议的问题。例子包括；平衡中“繁荣与胸围”周期的可能性，对环境有害影响的潜在技术偏见，并需要整合产能市场。 。电力市场的复杂性要求将某种形式的有限合理性纳入建模工作。但是，当玩家使用简单，自适应（可能是最佳）规则时，从长远来看，重复互动可能会引起平衡结果。尽管已经提倡许多用于分析电力市场的“基于代理”的仿真模型，但它们在收敛和/或平衡性质等问题上缺乏稳固的理论支持。在此提案中，我们将在某些拥堵管理协议下代表电力市场的竞争，作为具有特殊结构的游戏，即“潜在的游戏”。对于此类游戏，已被证明“虚拟游戏”学习算法可以与概率之一。