Determination of optimal battery utilization to minimize operating costs for a grid-connected building with renewable energy sources

This paper proposes strategies to optimize the daily charge and discharge schedule of a battery bank, in order to minimize the operating cost of a building that uses renewable energy sources. The schedule was optimized using a range of battery charge and discharge rates over a 24 h period. These rates were controlled using a genetic algorithm (GA) and a particle swarm optimization algorithm (PSO), which utilized day-ahead prediction data for electricity consumption and electricity price, as well as electricity output from a photovoltaic system and a wind turbine. The results showed that the building operating costs decreased as the number of available charge and discharge rates was increased. The average daily operating cost was reduced by up to 31% using the GA and by up to 28% using the PSO, compared to the scenario where no battery was used. Furthermore, the reduction in average daily operating costs began to plateau as the number of charge and discharge rates reached 12. It was also shown that the scaling of irradiance, wind speed and electricity price inputs impacted the optimized daily operating cost of the building. A sensitivity analysis was conducted to investigate how this scaling of inputs affected the overall performance of the GA. It was found that the optimized daily operating costs were almost unchanged after numerous scaling percentages were applied to the electricity price, with additional cost reductions of up to 3% compared to the scenario where no scaling percentages were applied. In contrast, scaling percentages applied to weather data were found to have a more significant impact on the optimized operating costs, with additional cost reductions of up to 17% compared to the scenario where no scaling percentages were applied. Moreover, a non-linear relationship was observed between the weather data scaling percentage and optimized daily cost.