Optimal Allocation Method for a Fair Distribution of the Benefits in an Energy Community

Abstract

Since their introduction in the EU RED II Directive in 2018, energy communities are a key topic for distributed photovoltaic systems. However, the distribution of the economic benefit among participants and the evaluation of the best composition of the energy community are still to be fully understood. Herein, a method for the optimal distribution of the benefit among participants based on their own contribution to the system is proposed. This method will be compared to other possible allocation methods and to achieve this, an energy community model which considers energy exchanges and economic expenditures is used. This model is a linear programming model based on a single‐objective optimization approach. The user economic contribution to the community can be quantified through sequential optimizations. The composition of energy communities affects the result of the optimization, as well as the contribution of each user: the total effective contribution of the participants is higher when the composition is more heterogeneous and the overall payoff in the analyzed case study increases by 12% passing from the lowest to the highest possible heterogeneity. In this latter scenario, users contribute differently as well, and their contribution is measured and ranges between 10% and 97%.

A value sharing method for heterogeneous energy communities archetypes

“… 38 , 39 An optimal allocation method for a fair distribution of the benefits in an EC is proposed by Casalicchio et al. 40 The authors emphasized the marginal contribution VS method, fairness of the approach proposing a fairness index (FI, Gini), and several business models (BMs) to create simpler VS methods not involving optimization. These BMs are defined as 1) homogeneous distribution including investment, 2) excluding investments, 3) sharing according to the consumption, 4) per capita, and 5) sharing considering self-consumption.…”

Synergism of Fuzzy Leaky Bucket with Virtual Buffer for Large Scale Social Driven Energy Allocation in Emergencies in Smart City Zones

“…In this context, the fundamental question under scrutiny is how to allocate available electrical power within the various areas (zones) of a city in a manner that is efficient for the grid while also fostering a feeling of justice to the citizens [10]. The management of electricity at the zone level is still in its infancy, although there are ongoing efforts primarily leveraging artificial intelligence.…”

Business Models for Energy Community in the Aggregator Perspective: State of the Art and Research Gaps

“…What EC represents is a paradigm change, a switch from isolated consumers to active prosumers that participate in energy production, from a collaborative perspective. First of all, this would lead to an increase in efficiency and penetration of clean technologies, but also fight energy poverty and increase social inclusion through a new community spirit [24]. Moreover, EC is considered the most rapid way for citizens to take part in the energy supply chain.…”