Lithium-ion battery: Battery sizing with charge scheduling for load levelling, ramp rate control and peak shaving using metaheuristic algorithms

Abstract

Renewable energy sources (RES) are considered the most promising alternatives to fossil fuels, especially solar photovoltaic (PV). However, the variation in solar irradiance leads to power mismatching with load demand based on the required power distribution. Hybrid solar PV battery is a clean and reliable technology for compensating the load demand during peak hours. In this research, the solar PV is modelled by Nasik Wani substation datasets, and the lithium-ion battery is considered for power scheduling. The proposed work used the emperor penguin optimization (EPO) for battery scheduling to provide the required power to the load during increasing demand and to store the excess energy during lower demand. This proposed work schedules the battery by considering peak shaving and load levelling constraints. Optimum sizing of the battery is an important factor in achieving cost-effective units based on battery operation. In this regard, battle royale optimization (BRO) is used for optimum battery sizing to consider the capital cost, operating cost and maintenance cost. The proposed work is modelled through the MATLAB platform, and the results will be compared using existing Binary Particle Swarm Optimization (BPSO), Teach and Learn Based Optimization (TLBO) and Chimp Optimization Algorithm (COA) for battery scheduling; moreover, the PSO and simulated annealing (SA) approaches for optimum sizing. The capital cost of the proposed method is 821,400$, and the operational and maintenance cost is 11,625.5($/year). The comparative analysis shows that the proposed method outperforms existing approaches for proper scheduling and sizing the battery to meet the load demand.