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.

增加间歇性可再生能源、分布式能源和消费者参与的数量。因此，市场代理人和系统运营商将面临一个更加复杂、波动和不可预测的电力市场。与此同时，欧盟的透明度法规正在提供越来越多的公开市场和物理系统数据库。人工智能（AI）工具，如算法交易和逆向工程算法，为市场参与者和系统运营商提供了一个有希望的途径，以增加毛利率，减少交易努力，并增加更广泛的市场参与者的可访问性。尽管有这些潜在的好处，但市场动态变化的影响尚未得到充分理解，在其他行业，类似的变化导致了合谋的结果。最近一些串通算法定价的例子包括无意中的价格上涨，以及亚马逊上卡特尔的创建和执行。鉴于电力市场的独特性质，即供应和需求必须在任何时候都匹配，市场操纵的可能性很大。虽然竞争和市场管理局最近的调查强调了与这种潜在的串通行为有关的不确定性和风险是限制因素。特别是，通过逆向工程深入了解竞争对手的投标策略可以使代理人行使市场支配力。逆向工程其他市场参与者的交易策略也提出了关于人工智能道德及其对知识产权影响的重要问题。然而，逆向工程这些投标策略也有助于发现串通行为。例如，纳斯达克证券交易所在2019年7月宣布，它将试用人工智能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（如电力市场管理局）可以从了解市场参与者的投标做法中获益匪浅，从而提高辨别串通或操纵情况的可能性。为了了解这些对电力市场的影响，在现有的文献中的一个主要差距必须加以解决。在这种情况下，这个博士研究生提出回答的问题“在多大程度上公开可用的数据能源系统可以用来逆向工程交易策略？“，“在与当前市场条件不同的市场中，代理人可能如何表现”，“拥有此类信息将对市场的运作产生什么影响？以及“如何制定法规以促进人工智能的应用”。为了做到这一点，学生将研究最先进的人工智能技术在能源市场的应用，开发多智能体评估框架，量化人工智能应用的市场影响，并为市场和监管框架的发展提供信息。