Multi-objective optimal peak load shaving strategy using coordinated scheduling of EVs and BESS with adoption of MORBHPSO

With increase of electrical vehicles (EVs), carbon emissions and dependence on fossil energy would be reduced. However, uncoordinated charging may further raise the peak load and magnify the load imbalance which will pose a key challenge to the system reliability. Hence, the peak load shaving whereby coordinated optimal scheduling of EVs and energy storage systems (ESS) has attracted more and more attention. And challenges arise in terms of multiple objectives, algorithms with better global searching performance and constraints handling methods. In this paper, the proposed multi-objective optimal peak load shaving strategy aims to achieve the best peak load shaving effect with the minimum electricity cost whereby coordinated scheduling of EVs and battery energy storage systems (BESS). Especially, load balance constraint, charging/discharging power limits consid-ering state of charge (SOC) as well as capacity limits of EVs and BESS, vehicles to grid (V2G), time-of-use (TOU) price and driving behavior of EVs with different types are all considered. The multi-objective random black-hole particle swarm optimization algorithm (MORBHPSO) with adjustable power redundancy method is adopted. Four case studies have been carried out on a regional distribution network with 130 EVs and 20 BESS. And satisfactory results were obtained in terms of better peak load shaving effect (a 70.6 % decrease in load fluc-tuation level) and better economic benefit (a 40.56 % reduction in electricity cost). Moreover, MORBHPSO performs better than multi-objective particle swarm optimization algorithm (MOPSO) in terms of a 41.47 % decline in load fluctuation level and a 5.44 % decrease in electricity cost with only half the iterations. Furthermore, it is also found that different from the impact of EVs' driving behavior, TOU price is conducive to obtaining smoother load curve and lower electricity cost.

Automated summary

The increase of electric vehicles (EVs) can lead to a reduction in carbon emissions and dependence on fossil energy. However, uncoordinated charging can cause peak load issues and load imbalance, posing challenges to system reliability. This paper proposes a multi-objective optimal peak load shaving strategy that aims to achieve the best peak load shaving effect with minimal electricity cost through coordinated scheduling of EVs and battery energy storage systems (BESS). The strategy considers various factors such as load balance constraint, charging/discharging power limits, EV and BESS capacity limits, vehicles to grid (V2G), time-of-use (TOU) price, and EVs' driving behavior. The results show significant improvements in load fluctuation level and electricity cost reduction.