With the emergence of energy communities, where a number of prosumers 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 has attracted increasing interest for redistribution in energy settings - however, computing it exactly is intractable beyond a few dozen prosumers. In this paper, we examine a number of methods for approximating the Shapley value in realistic community energy settings, and propose a new one. To compare the performances of these methods, we also design a novel method to compute the Shapley value exactly, for communities of up to several hundred agents by clustering consumers into a smaller number of demand profiles. We compare the methods in a large-scale case study of a community of up to 200 household consumers in the UK, and show that our method can achieve very close redistribution to the exact Shapley values but at a much lower (and practically feasible) computation cost.

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ACM International Conference on Future Energy Systems
Intelligent and autonomous systems

Cremers, S., Robu, V., Hofman, D., Naber, T., Zheng, K., & Norbu, S. (2022). Efficient methods for approximating the Shapley value for asset sharing in energy communities. In e-Energy '22: Proceedings of the Thirteenth ACM International Conference on Future Energy Systems (pp. 320–324). doi:10.1145/3538637.3538861