The adoption of the smart and sustainable charging infrastructures for electric vehicles (EVs) represents a key-point to accelerate the electro-mobility uptake. This paper presents an optimal power flow management for a smart charging micro-grid that minimizes the cost and reduces the impact on the grid of EVs requests. This goal is achieved by means of an optimal integration and control of renewable energy, stationary energy storage system, and V2G into the charging infrastructure. In particular, the optimal power flow management solves a stochastic multilayer optimization problem based on the definition of different priority levels representing the coordination of the ESS with the other sources. The output of the algorithm is the day-ahead prediction of the energy generation and, thus, the optimization of energy flow during the entire day. Possible variation of energy sources during the operation are also taken into account by means of a real time control adaptation.
Optimal energy control for smart charging infrastructures with ESS and REG / Abronzini, Umberto; Attaianese, Ciro; Di Monaco, Mauro; Tomasso, Giuseppe; Matilde, D'Arpino; Giovanni, Pede; Antonino, Genovese. - (2016), pp. 1-6. (Intervento presentato al convegno International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles and the International Transportation Electrification Conference – ESARS ITEC 2016 tenutosi a Toulouse nel November 2-4, 2016).
Optimal energy control for smart charging infrastructures with ESS and REG
Attaianese Ciro;
2016
Abstract
The adoption of the smart and sustainable charging infrastructures for electric vehicles (EVs) represents a key-point to accelerate the electro-mobility uptake. This paper presents an optimal power flow management for a smart charging micro-grid that minimizes the cost and reduces the impact on the grid of EVs requests. This goal is achieved by means of an optimal integration and control of renewable energy, stationary energy storage system, and V2G into the charging infrastructure. In particular, the optimal power flow management solves a stochastic multilayer optimization problem based on the definition of different priority levels representing the coordination of the ESS with the other sources. The output of the algorithm is the day-ahead prediction of the energy generation and, thus, the optimization of energy flow during the entire day. Possible variation of energy sources during the operation are also taken into account by means of a real time control adaptation.| File | Dimensione | Formato | |
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