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

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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.