Optimization of hybrid renewable energy generation using a nature-inspired algorithm with advanced IoT analytics

Abstract

A stable and cost-effective power supply in an autonomous hybrid energy system requires an efficient design process for renewable energy technologies. Accordingly, the best design of a standalone hybrid renewable energy system (HRES) should consider several factors such as renewable energy data, load profile, technical and economic analysis of the renewable technologies, ideal location for the power system, etc. Different data from renewable energy sources are modelled into an optimization problem which incorporates the crucial point, in HRES, of the correct sizing of the various power components, which directly affect the cost and power security/reliability of the system. This thesis proposes an innovative meta-heuristic optimization algorithm called Social Spider-Prey (SSP) that mimics the foraging behaviour of social spiders and prey(s) on the social web. By examining the foraging behavioural traits of social spiders and prey(s), a global optimization algorithm was developed to solve a hybrid renewable energy optimization problem of correct sizing, minimal cost, and highest reliability. In SSP, artificial spiders are considered search agents. On the one hand, every spider can freely roam the social web, a hyperdimensional search space, to implement an exploratory search scheme. On the other hand, nearby spiders relative to a captured prey search the neighbourhood, which is implemented as an exploitative search mechanism. These two search strategies are harmonized in SSP to solve the multi-source renewable power generation optimization problem effectively. Four different power generation scenarios were analysed to determine optimal power generation using experimental real-time environment data collected with sensors and secondary data retrieved from a benchmark dataset, National Renewable Energy Laboratory (NREL). The optimization algorithms inspired by nature, namely Social Spider-Prey (SSP), Particle Swarm Optimization (PSO), Teaching-Learning Based Optimization (TLBO) algorithm and Social Spider Algorithm (SSA), were used in a comparative study to search for a near-optimal result for the hybrid system configuration that satisfies the optimization problem. The results show the economic and reliable implications of different system configurations that meet the specified combined criteria, as indicated in the HRES optimization problem, to make the best investment decision. The SSP guaranteed optimal annualized system costs and met the reliability constraints for all the case scenarios: wind/biomass/battery (ZAR 3,431,512.26 and LPSP of 0.011), PV/wind/ biomass (ZAR 2,549,792.71 and LPSP of 0, 0011), PV/biomass/battery (ZAR1, 638,628.82 and LPSP of 0.00021) and PV/wind/biomass/battery (ZAR1, 412,142.80 and LPSP of 0.0141). Based on this result, the study proposes the SSP as an optimization approach for the solar PV/wind/biomass/battery hybrid system, as it ensures 99.98% power reliability. In addition, a Kruskal-Wallis test was performed to determine the significant differences among the comparison algorithms.