Fair Management of Vehicle-to-Grid and Demand Response Programs in Local Energy Communities

Electric Vehicles (EV) are emerging in electricity grid, where the Vehicle-to-Grid (V2G) feature is a major flexibility opportunity for Demand Response (DR) programs. Optimized and fair management of EVs flexibility activation is then required. In the present paper it is proposed a methodology to deal with the complex management of the Local Energy Communities (LEC) with such resources. The method allows the fair selection of DR and V2G participants. Focusing on the EVs, it is compared two approaches: performance rate and clustering groups (considering extrinsic and intrinsic characteristics using modeling of preferences). The method was tested in five office buildings with a shared EV parking lot. Four different events were studied, and the results show that the performance rate might not be enough to have the best results from both the local manager and V2G participants. According to the numerical results, using the performance method the total reduction obtained was 60.20 kW confronting with the 105.13 kW reduced using the clustering method.

Automated summary

This paper proposes a methodology to deal with the complex management of Local Energy Communities (LEC) with electric vehicles (EVs), and compares two approaches: performance rate and clustering groups. The results show that the clustering method is more effective in achieving the best results.