Efficient Shapley Value Approximation Methods

Efficient Shapley Value Approximation Methods: for Cost Redistribution in Energy Communities

Cremers, Sho (TU Delft Electrical Engineering, Mathematics and Computer Science)

Robu, Valentin (mentor)
la Poutré, J.A. (graduation committee)
de Weerdt, M.M. (graduation committee)

Delft University of Technology

2022-07-13

With the emergence of energy communities, where a number of prosumers (consumers with their own energy generation) invest in shared renewable generation capacity and battery storage, the issue of fair allocation of benefits and costs has become increasingly important. The Shapley value, a solution concept in cooperative game theory initially proposed by Nobel prize-winning economist Lloyd Shapley, has attracted increasing interest for redistribution in energy settings. However, due to its high time complexity, it is intractable beyond communities of a few dozen prosumers. This study proposes a new deterministic method for approximating the Shapley value in realistic community energy settings and compares its performance with existing methods. To provide a benchmark for the comparisons of these methods, we also design a novel method to compute the exact Shapley value for communities of up to several hundred agents by clustering consumers into a smaller number of demand profiles. Experimental analyses with large-scale case studies of a community of up to 200 household consumers in the UK show that the newly proposed method can achieve very close redistribution to the exact Shapley values but at a much lower (and practically feasible) computation cost. Furthermore, it performed similarly to the probabilistic, state-of-the-art approximation method while having smaller time complexity as well as other desirable characteristics for cost redistribution in energy communities.

Energy Community
Shared assets
Shapley value
Approximation algorithms
Smart grid
Stratification
Cost allocation

http://resolver.tudelft.nl/uuid:1f670359-539d-4a09-9186-bbbe8f696e13

Student theses

master thesis

© 2022 Sho Cremers