Power System Planning and Cost Forecasting Using Hybrid Particle Swarm-Harris Hawks Optimizations

Hybrid renewable-based power production has emerged as a crucial development in the electrical power system because to its ability to supply consumers with cost-effective and carbon-free electricity. Incorporating several renewable energy sources and storage systems with a grid-connected system necessitates extra design requirements, which raising the renewable system's overall cost. Therefore, optimizing the size of the components is essential to minimizing the cost of the system and limiting its negative impacts. This paper presents a hybrid of Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO) known as PSHHO technique-based economic analysis of smart grid (SG) Hybrid Renewable Energy Systems (HRES). Here, the hybrid PSHHO approach is presented to tackle the problem of HRES cost analysis. The major purpose of this study is to minimize the system cost and increase overall system efficiency. Finally, the proposed approach is tested in the MATLAB tool, and the performance of the proposed method is compared with other existing methods such as HHO, PSO, and WOA. The result demonstrates the proposed system can meet the load demand with low Cost of Energy (COE) (21.5 Rs/kWh) than existing methods.