Evolving robust policies for community energy system management

Cardoso, Rui; Hart, Emma; Pitt, Jeremy

Abstract

Community energy systems (CESs) are shared energy systems in which multiple communities generate and consume energy from renewable resources. At regular time intervals, each participating community decides whether to self-supply, store, trade, or sell their energy to others in the scheme or back to the grid according to a predefined {\em policy} which all participants abide by. The objective of the policy is to maximise average satisfaction across the entire CES while minimising the number of unsatisfied participants.
We propose a multi-class, multi-tree genetic programming approach to evolve a set of specialist policies that are applicable to specific conditions, relating to abundance of energy, asymmetry of generation, and system volatility. Results show that the evolved policies significantly outperform a default handcrafted policy. Additionally, we evolve a generalist policy and compare its performance to specialist ones, finding that the best generalist policy can equal the performance of specialists in many scenarios. We claim that our approach can be generalised to any multi-agent system solving a common-pool resource allocation problem that requires the design of a suitable operating policy.

Citation

Cardoso, R., Hart, E., & Pitt, J. (2019, July). Evolving robust policies for community energy system management. Presented at GECCO '19, Prague, Czech Republic

• Presentation Conference Type: Conference Paper (published)
• Conference Name: GECCO '19
• Start Date: Jul 13, 2019
• End Date: Jul 17, 2019
• Acceptance Date: Mar 21, 2019
• Publication Date: Jul 13, 2019
• Deposit Date: Apr 29, 2019
• Pages: 1120-1128
• Series Title: GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference
• Book Title: GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion
• ISBN: 978-1-4503-6748-6
• DOI: https://doi.org/10.1145/3321707.3321763
• Keywords: Genetic Programming, Multi-agent system, Community energy system management,
• Public URL: http://researchrepository.napier.ac.uk/Output/1758240