This paper proposes an aggregator for the optimal scheduling of a Electric Vehicle (EV) charging station. Assuming that the station charging can consume energy from the power grid and also from a renewable source at no cost, Day-Ahead and Real-Time strategies are developed for the station, considering uncertainty in the local renewable generation source. First, a scheduler decides the energy to buy in the day ahead market from the power grid considering a renewable source forecast and an EVs schedule. An autoregressive model developed from 5 years of historical solar radiation data is applied. In real time, a model predictive control strategy is designed to follow the scheduled power from the grid, compensating variations in renewable generation by exploiting flexibility in the EVs charging process. The resulting optimization problems are convex programming problems that can be solved efficiently. Simulation analysis show the effectiveness of the strategy in absorbing the variability of the renewable source, minimizing the deviations between the day ahead schedule and the actual real time consumption from the grid.

Optimal Operation Strategy for Electric Vehicles Charging Stations with Renewable Energy Integration / Diaz-Londono, Cesar; Ruiz, Fredy; Mazza, Andrea; Chicco, Gianfranco. - ELETTRONICO. - 53:(2020), pp. 12739-12744. ((Intervento presentato al convegno 21st IFAC World Congress tenutosi a Virtual nel 12-17 July 2020 [10.1016/j.ifacol.2020.12.1899].

Optimal Operation Strategy for Electric Vehicles Charging Stations with Renewable Energy Integration

Diaz-Londono, Cesar;Ruiz, Fredy;Mazza, Andrea;Chicco, Gianfranco
2020

Abstract

This paper proposes an aggregator for the optimal scheduling of a Electric Vehicle (EV) charging station. Assuming that the station charging can consume energy from the power grid and also from a renewable source at no cost, Day-Ahead and Real-Time strategies are developed for the station, considering uncertainty in the local renewable generation source. First, a scheduler decides the energy to buy in the day ahead market from the power grid considering a renewable source forecast and an EVs schedule. An autoregressive model developed from 5 years of historical solar radiation data is applied. In real time, a model predictive control strategy is designed to follow the scheduled power from the grid, compensating variations in renewable generation by exploiting flexibility in the EVs charging process. The resulting optimization problems are convex programming problems that can be solved efficiently. Simulation analysis show the effectiveness of the strategy in absorbing the variability of the renewable source, minimizing the deviations between the day ahead schedule and the actual real time consumption from the grid.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2899512