Comprehensive energy system optimization using developed coyote algorithm for effective management of battery, heat source, and thermal storage

The modern building sector faces the challenge of meeting energy demands while minimizing environmental impacts and promoting energy efficiency. This research paper presents a comprehensive approach to optimizing building energy systems using a modified metaheuristic, the Developed Coyote Algorithm (DCO). The increasing importance of storage equipment in energy organizations, driven by changes in peak-load demand and the growing adoption of renewable energy sources, necessitates efficient storage solutions. Battery Energy Storage (BES) and Thermal Energy Storage (TES) are commonly used to store excess energy generated from renewable sources and supply it during peak demand. By applying the DCO algorithm, the operational plans of energy systems comprising BES, air-source heat pumps, and TES can be efficiently optimized with minimal computational requirements. The proposed method aims to enhance the productivity and sustainability of energy systems while providing valuable insights for policymakers and stakeholders involved in renewable energy development. The discharge of batteries and thermal energy storage occurs during low electricity prices, with charging power fluctuating between 100 and 150 kW. GWO and IWWO results show that charging is done at night with 150 kW, with a reduced amount used during other times. Thermal energy storage can store energy from 500 to 800 kW during night time, while DCO generates 700–900 kW. The results of this research contribute to the effective management of batteries, heat sources, and thermal storage in building energy systems, further advancing the utilization of renewable energy resources