Techno-socio-economic-environmental estimation of hybrid renewable energy system using two-phase swarm-evolutionary algorithm

A multi-objective optimal sizing model for an off-grid hybrid renewable energy system (HRES) consisting of solar photo-voltaic (PV), wind turbine (WT), diesel generator (DG) and lead-acid battery-based energy storage (ES) system is developed including techno-socio-economic and environmental considerations. The problem is formulated using six objectives; levelized cost of energy (LCOE), renewable fraction (RF), loss of power supply probability (LPSP), human development index (HDI), job creation (JC) and emission curtailment (EMC). To solve this real-world problem with complex practical constraints, a hybrid two-stage PSO-DE optimization (HPSODE) algorithm is constructed by connecting the improved variants of PSO and DE in series. The exploration capability of swarm intelligence is synthesized with the exploitation potential of classical evolutionary concepts, to achieve better performance with least dependence on algorithmic tuning parameters. A fuzzy satisfaction module (FSM) is appended to identify the solution with the highest possible attainment level for all the considered objectives. The selected optimization goals have a significant bearing on the optimal design, some are contradictory while others are similar in nature; therefore, impact analysis is presented. For the minimum cost solution, attainment of LCOE is 100%, HDI attainment is 0.27%, JC is 4.04%, emission curtailment is 6.29%, LPSP attainment is 91.25% and attainment of RF is 92.33%. On the other hand, when all six objectives are optimized, LCOE is attained by 68.61%, HDI by 92.97%, JC by 84.04% and EMC goal is satisfied by 78.98% while attainment of both LPSP and RF is 100%. For the selected location, the best configuration of HRES is PV-WT-ES, with total capacity of 175.44 kW. The levelized cost is 0.0779 $/kWh, human development index (HDI) is 0.6454, 0.2711 jobs are created and annual emission curtailment is 290147.28 kg, whereas unmet power is zero. Well distributed Pareto-optimal solutions are obtained, which offer multiple options to the designer. The results of the proposed HPSODE-FSM approach are compared and corroborated with results from literature and also with other state-of-the-art algorithms.