Risk-Sensitivity in Mean Field Games and Energy Markets

平均场博弈和能源市场的风险敏感性

• 批准号: RGPIN-2022-05337
• 负责人: Firoozi, Dena
• 金额: $ 1.89万
• 依托单位: HEC Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765256
• 关键词: Risk; Sensitivity; Mean; Field; Games

Motivation: Due to global warming, it is necessary to reduce carbon emissions worldwide. In this light, regulatory acts have been imposed by requiring the regulated firms to generate a specific percentage of their electricity via an eligible renewable energy resource. There is a tracking system via issuing certificates for every 1 MWh renewable energy generation. In case of non-compliance, the firms face a monetary penalty that must be paid to the regulator. These certificates can be traded on markets and their price is driven via supply and demand. Due to the uncertainty associated with intermittent renewables, the price could be very volatile and trading in such markets could incur a higher risk to firms. Moreover, transaction costs in such markets are in general higher than in voluntary markets. Hence, to facilitate the regulation, which in turn contributes to a smoother transition to a low-carbon future, an efficient market design is crucial. Objective: Mean field game (MFG) theory studies a class of large-population games, where agents are not only impacted by their individual behaviour but also by the mass behaviour of all the other agents in the system. The theory provides mathematical characterizations of the Nash equilibria of such games. The objective of this proposal is to introduce a fundamental study of risk-sensitivity in MFGs and to employ it for optimal design of compliance REC markets. Therefore, the first part of this proposal investigates equilibrium pricing and contract design in markets where participants are risk sensitive and there is an influential market participant. In all analyses it is assumed that all market parameters are known as is usually the case in games. However, the latter assumption does not hold in practice and some form of learning is required. For that reason, in the second part, risk-sensitive learning of the individual and the population parameters in MFGs is investigated. This paves the way for extensive data-driven analysis proposed in each case for compliance markets case studies, which ultimately will be used for market design purposes. The setups considered in this proposal are not addressed in the literature. More specifically, this, among others, includes considering risk-sensitivity in MFGs for equilibrium pricing, contract design, modeling an influential market participant (major agent), and learning and adaptation. Methodology: The set of projects in this proposal addresses different aspects of risk-sensitivity in MFGs and is designed in a way that they can be investigated simultaneously and independently of each other. The main analytical tool that is used in all projects is MFG methodology which has proven useful in modeling markets with a large number of interacting participants and a few influential ones. We extend the methodology to account for risk-sensitivity of agents in markets with a clearing condition, a risk-sensitive major agent, and risk-sensitive learning and adaptation.

动机：由于全球变暖，有必要在全球范围内减少碳排放。有鉴于此，监管法案要求受监管的公司通过合格的可再生能源生产一定比例的电力。有一个跟踪系统，通过对每1兆瓦时的可再生能源发电颁发证书。如果不遵守规定，这些公司将面临罚款，必须支付给监管机构。这些证书可以在市场上交易，其价格由供求关系决定。由于间歇性可再生能源的不确定性，价格可能非常不稳定，在这样的市场上交易可能会给企业带来更高的风险。此外，这种市场的交易成本一般高于自愿市场。因此，为了促进监管，进而促进向低碳未来的平稳过渡，有效的市场设计至关重要。目的：平均场博弈（MFG）理论研究一类大群体博弈，其中的主体不仅受到其个体行为的影响，还受到系统中所有其他主体的群体行为的影响。该理论提供了这种博弈的纳什均衡的数学特征。本提案的目的是介绍mfg风险敏感性的基础研究，并将其用于合规REC市场的优化设计。因此，本文的第一部分研究了参与者对风险敏感且存在有影响力的市场参与者的市场中的均衡定价和契约设计。在所有分析中，我们都假设所有市场参数都是已知的，就像游戏中的情况一样。然而，后一种假设在实践中并不成立，需要某种形式的学习。因此，在第二部分中，研究了mfg中个体和群体参数的风险敏感学习。这为在合规市场案例研究的每个案例中提出的广泛数据驱动分析铺平了道路，最终将用于市场设计目的。本建议中所考虑的设置在文献中没有提及。更具体地说，这包括考虑mfg中均衡定价的风险敏感性、合同设计、对有影响力的市场参与者（主要代理人）建模以及学习和适应。方法：本提案中的一组项目涉及mfg风险敏感性的不同方面，其设计方式可以同时且彼此独立地进行调查。在所有项目中使用的主要分析工具是MFG方法，该方法已被证明对具有大量相互作用的参与者和少数有影响力的参与者的市场建模非常有用。我们将该方法扩展到具有清算条件、风险敏感的主要代理人以及风险敏感的学习和适应的市场中代理人的风险敏感性。