Outline
This project aims to apply the theory of signalling in games to local energy markets, with simulations on real-world distribution grids.
Motivation
Local Energy Markets are a framework to integrate small-scale energy resources at the household level towards the wider grid. Local Energy Markets typically consist of a market operator, who is responsible for pricing and information communication, and prosumers (producers+consumers) at the residential scale. The objective of the market operator is to coordinate prosumers for fair and efficient market outcomes. On the other hand, the prosumers may have selfish objectives (profit maximization).
Prosumers typically have stochastic production sources (e.g. solar panels), whose realization depends on the weather and is unknown to the prosumers; and flexible demand (e.g. Electric Vehicle charging), which can be controllable. The profits for prosumers may depend on the combination of their stochastic production and controllable consumption. Prosumers may choose to deploy their demand side flexibility in order to achieve selfish objectives, like profit maximization, which may come at the expense of fairness and efficiency for the larger market.
The goal of the project is to investigate whether the market operator can strategically communicate forecasts of weather and/or renewable production [1] to prosumers in order to achieve more efficient and fair market outcomes. The project will involve applying existing techniques from information design in games [2] and conducting simulations on real-world grid and consumption data.
Milestones
- Weeks 1-4: Literature review and problem formulation.
- Weeks 5-8: Implementation of code for signalling in local energy markets.
- Weeks 9-12: Experiments on real world distribution grids.
- Weeks 13-14: Writing and evaluation of results.
Requirements
We look for self motivated students with a strong background in coding in Python (cvxpy) and familiarity with convex optimization, probability and game theory. If interested, please send a short paragraph on your background and fit for the project along with your Bachelor and Master transcripts to: [email protected]
References
[1] Rene Aid, Anupama Kowli, and Ankur A. Kulkarni. Signalling for Electricity Demand Response: When is Truth Telling Optimal? arXiv:2302.12770. July 2023.
[2] Emir Kamenica and Matthew Gentzkow. “Bayesian Persuasion”. en. In: American Economic Review 101.6 (Oct. 2011), pp. 2590–2615. issn: 0002-8282.