Multi-objective smart charging strategy of plug-in electric vehicles in distribution system

• The paper proposes a methodology for modelling PEVs charging demand in a public charging station. • Due to the stochastic nature of PEVs mobility, probability distribution functions (PDFs) are used to model the PEVs charging start time, initial state of charge and daily driving distance. • Then a multi-objective smart charging model of PEVs in a centralized framework based on time of use (TOU) price in public charging station is proposed. • The weighted sum method is used to convert the multi-objective optimization to single objective optimization problem and two Meta heuristic algorithms, ant lion optimization (ALO) and grey wolf optimization (GWO) is used to solve the optimization problem. • The objective functions considered here is charging cost minimization, load variance minimization and power loss minimization. • The proposed approach is tested on IEEE 33- bus distribution network. The simulation results suggest that the proposed approach improves grid performance and provide economic benefits to customers. Plug-in Electric Vehicles (PEVs) are getting increasingly popular since they offer a low carbon emission and highly efficient alternative for transportation. Public charging infrastructure, including charging stations (CS) and PEV enabled parking lots has been constructed in response to the increasing number of PEVs. However, integrating a large of PEVs to the current distribution system will put additional strain on it and increases the risk of power loss, frequent overloads and voltage swings. Smart charging schemes in which the distribution system operator (DSO) overseas PEV charging with specific technical or financial goals can lessen these difficulties. This paper first suggests a methodology to model the load demand due to PEV charging at a public charging station. Probability distribution functions (PDFs) are utilised to model the PEVs charging start time, initial state of charge (SOC) and daily driving distance due to stochastic character of PEV mobility. Next, a centralised framework based on time of use (TOU) pricing for PEVs using public charging stations is proposed for a multi-objective smart charging model. Ant lion optimisation (ALO) is utilised to solve the optimisation problem and the results obtained is validated with grey wolf optimisation (GWO) and nondominated sorting genetic algorithm (NSGA-II). The multi-objective optimisation is reduced to a single objective optimisation problem using the weighted sum approach. Here, the objective functions of power loss minimisation, load variance minimisation, and charging cost minimisation are taken into consideration. The suggested method is tested on an IEEE 33 & 69 bus distribution network. According to the simulation results, the suggested strategy enhances grid performance and offers consumers financial advantages.