An optimal scheduling strategy of hybrid photovoltaic BESS and charging in smart factories

Abstract

Integration of photovoltaic (PV), Battery Energy Storage System (BESS) and electric vehicles (EV) charging is an upward trend in modern smart factories to achieve energy conservation and efficiency targets. In this regard, an optimal scheduling strategy for BESS and EV charging is proposed in this paper, aiming to achieve the power demand shifting and cost saving for smart factories. Firstly, typical industrial loads and PV generation are characterised and forecasted by a BP neural network that is trained with a practical operational data set. Then, an EV charging modelling is developed in Monte Carlo simulation. Finally, an EV charging station with integration of PV- BESS is developed in an elaborated energy management system, in which the optimal scheduling strategy of BESS and EV charging process is solved using a multi-objective genetic algorithm (NSGA-II). The purpose of the scheduling is to minimise the variance of the power curve and energy cost with the consideration of satisfying power balance constraints on both energy generation and usage sides. The results of the case study demonstrate that optimized scheduling model can release the peak shaving capacity and reduce costs.