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%.
Optimal Allocation Method for a Fair Distribution of the Benefits in an Energy Community
Casalicchio V.;Manzolini G.;
2021-01-01
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%.File | Dimensione | Formato | |
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