Nature-Inspired Search Method for Cost Optimization of Hybrid Renewable Energy Generation at the Edge

The global deficient in energy generation has called for innovative ways of energy generation. Currently, renewable energy is one of such innovation which is based of producing energy from wind and/or solar. However, generating and harvesting renewable energy is challenged in terms of the economic cost of energy generation to meet demand of users. In this study, we consider the problem of renewable energy generating as a multi-objective optimization problem which is subject to constraints such as cost. The study attempts to solve this problem by proposing a novel nature-inspired Social Spider-Prey (SSP) hunting technique to optimize the economic cost of hybrid renewable energy generation systems. The experimental results show a reduction cost of approximately 0.05 US Dollars in renewable energy generation.