Optimal Regulation of Prosumers and Consumers in Smart Energy Communities

TL;DR

This paper introduces stochastic, distributed algorithms for regulating prosumers and consumers in smart energy communities, ensuring privacy and achieving social welfare optimization through probabilistic decision-making and feedback signals.

Contribution

It presents novel algorithms that coordinate heterogeneous prosumers and consumers privately, optimizing community social welfare in a distributed manner.

Findings

Average activity levels reach optimal values over time

Community asymptotically achieves social optimum

Algorithms are effective with heterogeneous energy sources

Abstract

In smart energy communities, households of a particular geographical location make a cooperative group to achieve the community's social welfare. Prosumers are the users that both consume and produce energy. In this paper, we develop stochastic and distributed algorithms to regulate the number of consumers and the number of prosumers with heterogeneous energy sources in the smart energy community. In the community, each prosumer has one of the heterogeneous energy sources such as solar photovoltaic panels or wind turbines installed in their household. The prosumers and consumers decide in a probabilistic way when to be active. They keep their information private and do not need to share it with other prosumers or consumers in the community. Moreover, we consider a central server that keeps track of the total number of active prosumers and consumers and sends feedback signals in the community at each time step; the prosumers and consumers use these signals to calculate their probabilistic intent. We present experimental results to check the efficacy of the algorithms. We observe that the average number of times prosumers and consumers are active reaches the optimal value over time, and the community asymptotically achieves the social optimum value.