EAGER: Design of Distribution-Level Electricity Markets: Demarginalization and Decentralized Learning

EAGER：配电级电力市场的设计：去边缘化和去中心化学习

• 批准号: 2129631
• 负责人: Andrew Liu
• 金额: $ 22万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2129631&HistoricalAwards=false
• 关键词: EAGER; Design; Distribution; Level; Electricity

This NSF EAGER project aims to design new electricity markets for distribution-level power systems, which can enable small prosumers with renewable generation to directly trade energy at the distribution level. Such distribution-level markets face significant new challenges due to low marginal cost and high uncertainty of renewable generation, as well as decentralized decision making of a large number of prosumers. This project will bring transformative changes by developing new market structures that are radically different from the existing transmission-level markets, which greatly reduce price-volatility, eliminate price-fixing, encourage long-term investment, and improve grid reliability. This will be achieved by considering new energy products that are tailored to resources with low marginal costs and significant generation uncertainty. The intellectual merits of the project include (i) novel distribution-level market designs that will produce much more stable market prices and encourage investment despite uncertain long-run outlook of future operating conditions; and (ii) learning algorithms that enable small and inexperienced prosumers to learn how to bid in the market. The broader impacts of the project include (i) contributing to the increasing adoption of renewable energy, which benefits our society as a whole and is essential for the evolution towards a sustainable future; and (ii) training of students interested in future energy systems and dissemination of results to both academic and industry.While electricity markets at the transmission level are well-established, markets at the distribution level are still at their infancy. Replicating the market principles from the transmission-level market to the distribution level will likely produce highly volatile market prices, increase the financial risks of both consumers and suppliers, and may even incentivize price-manipulation. To address these challenges, this project will design novel plan-ahead usage-right markets for both energy and ancillary services that naturally lower price-volatility and greatly eliminate the potential for market-power abuse and price-fixing. Further, in view of the limited expertise and resources of prosumers, the project will design bidding-by-learning strategies to automate the bidding processes for rationality-bounded bidders who may not even know their own valuation of electricity consumption.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF EAGER项目旨在为配电级电力系统设计新的电力市场，使可再生能源发电的小型生产者能够直接在配电级进行能源交易。由于可再生能源发电的边际成本低、不确定性高，以及大量产消者的决策分散，这种分销层面的市场面临着重大的新挑战。该项目将通过开发与现有传输级市场截然不同的新市场结构带来变革性变化，从而大大降低价格波动，消除价格固定，鼓励长期投资并提高电网可靠性。这将通过考虑针对边际成本低和发电不确定性大的资源的新能源产品来实现。该项目的智力优势包括：（i）新颖的分销层面市场设计，将产生更稳定的市场价格，并鼓励投资，尽管未来经营状况的长期前景不确定;（ii）学习算法，使小型和缺乏经验的生产消费者能够学习如何在市场上投标。该项目的更广泛影响包括：（i）促进越来越多地采用可再生能源，这有利于我们整个社会，对迈向可持续未来至关重要;及（ii）培训对未来能源系统感兴趣的学生，并向学术界及工业界推广研究成果。虽然输电层面的电力市场已建立完善，分销层面的市场仍处于起步阶段。将市场原则从传输级市场复制到分销级，可能会导致市场价格高度波动，增加消费者和供应商的财务风险，甚至可能刺激价格操纵。为了应对这些挑战，该项目将为能源和辅助服务设计新颖的提前计划使用权市场，自然降低价格波动，并大大消除滥用市场力量和价格操纵的可能性。此外，鉴于产消者的专业知识和资源有限，该项目将设计学习型投标策略，以自动化投标过程，为理性有限的投标人提供服务，这些投标人甚至可能不知道自己对电力消耗的估值。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

Distribution-level markets under high renewable energy penetration

• DOI: 10.1145/3538637.3538846
• 发表时间: 2022-06
• 期刊: Proceedings of the Thirteenth ACM International Conference on Future Energy Systems
• 作者: Peizhong Ju;Xiaojun Lin;Jianwei Huang

Comparisons of Auction Designs Through Multiagent Learning in Peer-to-Peer Energy Trading

• DOI: 10.1109/tsg.2022.3190814
• 发表时间: 2023-01
• 期刊: IEEE Transactions on Smart Grid
• 影响因子: 9.6
• 作者: Zibo Zhao;Chengzhi Feng;Andrew L. Liu