Cost Optimization of Hybrid Renewable Energy System Based on Nature-Inspired Search Method

Two popular renewable energy sources of solar irradiation and wind speed usually offer amiable intervention, especially for rural electrification. They are useful in rural areas where the supply of electricity by the national grid infrastructure is not a viable option economically. By the gift of nature, multiple renewable energy sources are often available in those areas. Optionally, these renewable energy sources can be combined to help minimize the cost of energy production contingent on the cost of operation, the amount of energy produced, the load demand, and the environmental factors. The objective of this research task is to propose a framework for meeting the power load demand of consumers while optimizing the operational costs of hybrid renewable energy from solar and wind power. A nature-inspired/meta-heuristic optimization method is proposed in this framework, to minimize the cost of the hybrid energy subject to the required constraints from the renewable energy system. The proposed algorithm was applied to solve a hybrid energy problem. Experimentation with empirical data is conducted, and KSA is evaluated against other nature-inspired algorithms such as BAT and WSAMP with minus previous steps. The real-life data were collected in Ghana from energy farms in Accra, Kumasi and Navrongo. The efficacy of the energy optimization is found to be sensitive to the meta-heuristic algorithms (KSA, BAT and WSAMP with minus previous step). The experiment result shows that by using KSA algorithm in hybridizing solar and wind energy, the cost of electricity could be minimized and adequately meet the demand of consumers.