Smart Charging and Discharging of Electric Vehicles Based on Multi-Objective Robust Optimization in Smart Cities

The combination of low-carbon electricity and electric vehicles brings considerable economic and environmental benefits but also introduces challenges due to the complexity and uncertainty of system synergies in smart cities. To take full advantage of photovoltaic power generation and release the potential of electric vehicles as distributed energy storage facilities, we propose a smart charge and discharge scheduling strategy based on a convergent demand response strategy to simultaneously improve economic and environmental benefits. Specifically, we develop a multi-objective robust optimization framework that accounts for the benefits of multiple parties of smart charging and discharging systems and formulate a bounded uncertainty set based on partial statistical information from real data on solar radiation intensity. We transform the original model into an equivalent nonlinear robust formulation using max-ordering scalarization and the robust augmented weighted Tchebycheff method. Then, we apply the dual method to further transform into the corresponding robust linear counterpart. We use the McCormick method to process the nonlinear terms to obtain the mixed integer programming counterpart. A case study, based on real data from Car2go and its solar radiation intensity in Portland, Oregon, shows that (1) our methodological framework effectively addresses the smart charging and discharging scheduling of electric vehicles containing the complexity and uncertainty of system synergies in smart cities, and exhibits good performance. (2) The designed convergent demand response strategy prompts photovoltaic power generation and charging demand synergy for demand-side management, achieving regulation effects such as peak shaving and valley filling. (3) The proposed method and strategy yields good results in multiple regards, such as charging cost, load regulation, and clean energy utilization, for example, when the scale of EVs is 50, the reduction in load fluctuation reaches 69.97%