Multi-objective optimal peak load shaving strategy using coordinated scheduling of EVs and BESS with adoption of MORBHPSO. (1st August 2023)

• Record Type: Journal Article
• Authors: Liu, Jing; Wang, Hongyu; Du, Yanping; Lu, Yilan; Wang, Zhenghang
• Is Part Of: Journal of energy storage. Volume 64(2023)
• Publication Date: 2023-08-01
• Subjects: Peak load shaving -- Electric vehicles (EVs) -- Battery energy storage systems (BESS) -- Multi-objective optimization -- Multi-objective random black-hole particle swarm optimization algorithm (MORBHPSO); Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2023.107121
• Languages: English
• ISSNs: 2352-152X

Abstract

Abstract: 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 considering 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 fluctuation 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. Highlights: A multi-objective optimal peak load shaving strategy whereby coordinated scheduling of EVs and BESS is proposed. Detailed constraints handling methods for capacity limits and power limits of EVs and BESS are given. MORBHPSO is adopted and performance comparisons between MORBHPSO and MOPSO are analyzed.