Efficient Shapley Value Approximation Methods

for Cost Redistribution in Energy Communities

Master Thesis (2022)
Author(s)

S.A. Cremers (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

Valentin Robu – Mentor (TU Delft - Algorithmics)

Han La la Poutré – Coach (TU Delft - Intelligent Electrical Power Grids)

Mathijs M. Weerdt – Coach (TU Delft - Algorithmics)

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2022 Sho Cremers
More Info
expand_more
Publication Year
2022
Language
English
Copyright
© 2022 Sho Cremers
Graduation Date
13-07-2022
Awarding Institution
Delft University of Technology
Programme
Electrical Engineering | Sustainable Energy Technology
Sponsors
Centrum Wiskunde & Informatica (CWI)
Faculty
Electrical Engineering, Mathematics and Computer Science
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

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.

Files

Scremers_thesis.pdf
(pdf | 3.7 Mb)
License info not available