Optimal Battery Scheduling with and without Renewable Energy Sources for Efficient Home Energy Management

Battery energy storage system (BESS) is the key component for integrating a smart home with renewable energy sources. This energy storage system is limited by the state-of-charge (SoC) of the battery. The meta-heuristic algorithms effectively determine the feasible charging and discharging schedules that effectively cater home energy management. This paper presents a day-ahead scheduling strategy for a battery in a smart home using two meta-heuristic evolutionary algorithms; antlion optimization (ALO) and salp swarm optimization (SSA) algorithms. This scheduling strategy aims to minimize the electricity usage cost by consumers subjected to day-ahead pricing (DAP) and the associated battery charging and discharging constraints. This paper does not consider any restrictions on the usage of appliances in the smart home. Renewable energy sources (RES) such as wind and solar power generation are also integrated. The suggested algorithms are subjected to over 20 trial runs and the simulation results are compared with the existing techniques such as grey wolf optimization (GWO), particle swarm optimization (PSO) algorithms, and without scheduling operation. The simulation (MATLAB-R2017a) results demonstrate that the SSA technique provides a better optimal charging and discharging action of batteries than ALO, GWO, PSO algorithms and without scheduling operation.