The future power system will be characterised by increasing amounts of intermittent renewables, distributed resources and consumer participation. As a result, market agents and 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. 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. 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. In particular, having in depth knowledge of competitors’ bidding strategies through reverse engineering 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.
In order to understand these impacts on electricity markets, 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”. The student will investigate the 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.
