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（以及其他形式或市场干预）使人们对市场以社会有效方式（规模、技术和时机）提供新产能的能力产生怀疑。我们建议开发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阐明重组辩论中的一些有争议的问题。例子包括： “繁荣与萧条”周期平衡的可能性、对环境产生有害影响的潜在技术偏差以及纳入容量市场的必要性。在我们提案的第二部分中，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、适应性的（可能是次优的）规则时，从长远来看，重复的互动可能会导致均衡结果。尽管许多“基于代理”的模拟模型被提倡用于分析电力市场，但它们在收敛和/或均衡本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将电力市场的竞争表示为具有特殊结构的游戏，即“潜在游戏”。对于此类游戏，“虚拟游戏”学习算法类已被证明以概率一收敛。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类的实用性。