AI applications in future energy markets: Market implications and regulatory requirements

未来能源市场中的人工智能应用：市场影响和监管要求

• 批准号: 2453725
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2453725
• 关键词: AI; applications; future; energy; markets

increasing amounts of intermittent renewables, distributed energy resources and consumer participation. As a result, market agents and system operators will face a more complex, volatile and unpredictable electricity market. Simultaneously, the EU's transparency regulations are providing a growing repository of publicly accessible market and physical system data. Artificial intelligence (AI) tools, such as algorithmic trading and reverse-engineering algorithms, present a promising avenue for market participants and system operators to increase gross margin, reduce trading efforts, and increase accessibility to a broader range of market participants. Despite these potential benefits, the impact of the changing market dynamics is not fully understood and in other industries similar changes have led to collusive outcomes. Some recent examples of collusive algorithmic pricing include inadvertent price rises, and creation and enforcement of cartels on Amazon. Given the unique nature of the electricity market where supply and demand must be matched at all times, the potential for market manipulation is great. Although recent investigations by the Competition and Market Authority (CMA) emphasize the uncertainty and risk associated with such potential collusive action as limiting factors. In particular, having in depth knowledge of competitors' bidding strategies through reverseengineering could allow an agent to exercise market power.Reverse-engineering other market actors' trading strategies also raises important questions regarding the ethics of AI and its implications for intellectual property rights.However, reverse-engineering these bidding strategies can also help detect collusive behaviour. For example, the NASDAQ stock exchange announced in July 2019 that it will be trialling AI to detect cases of market manipulation and abuse. It is conceivable that the electricity system operator and regulatory authorities (e.g. Ofgem) could benefit greatly from knowledge of market participants' bidding practices to improve the likelihood of discerning instances of collusion or manipulation. In order to understand these impacts on electricity markets, a major gap in the existing literature must be addressed.In this context, this PhD studentship proposes to answer the questions "to what extent publicly available data on the energy system can be used to reverse-engineer trading strategies?", "how agents may behave in a market with varying conditions to today's market", "what impact having such information would have on the functioning of the market?" and "how regulation needs to be developed to facilitate the applications of AI". In order to do so, the student will investigate the state-of-the-art AI techniques for energy market applications, develop a multi-agent assessment framework, quantify the market impact of AI applications and inform the development of market and regulatory framework.

越来越多的间歇性可再生能源，分布式能源和消费者参与。结果，市场代理商和系统运营商将面临更复杂，波动和不可预测的电力市场。同时，欧盟的透明度法规正在提供越来越多的公共市场和物理系统数据的存储库。人工智能（AI）工具（例如算法交易和反向工程算法）为市场参与者和系统运营商提供了有希望的途径，以提高毛利率，减少交易工作，并提高对更广泛的市场参与者的可访问性。尽管有这些潜在的好处，但不断变化的市场动态的影响尚未完全理解，在其他行业中，类似的变化导致了综合结果。联合算法定价的最新示例包括无意价格上涨，以及在亚马逊上创建和执行卡特尔。鉴于电力市场的独特性质必须始终及时匹配，因此市场操纵的可能性很大。尽管竞争和市场管理局（CMA）的最新调查强调了与限制因素这样的潜在犯罪行动相关的不确定性和风险。尤其是，通过逆转工程深入了解竞争对手的投标策略，可以使代理商行使市场能力。逆向工程其他市场参与者的交易策略还提出了有关人工智能道德及其对知识产权的影响的重要问题。但是，这些竞标策略也可以帮助检测犯罪行为。例如，纳斯达克证券交易所于2019年7月宣布，它将试用AI来检测市场操纵和滥用案件。可以想象，电力系统运营商和监管机构（例如OFGEM）可以从了解市场参与者的竞标实践中受益匪浅，以提高辨别勾结或操纵实例的可能性。为了理解这些对电力市场的影响，必须解决现有文献的主要差距。在这种情况下，该博士学位学生建议回答“在何种程度上可以使用有关能源系统的公开可用数据，可以用来逆转工程贸易策略？以及“需要如何制定法规来促进AI的应用”。为此，学生将调查用于能源市场应用的最新AI技术，开发多代理评估框架，量化AI应用的市场影响并为市场和监管框架的开发提供信息。