Integrated socio-environmental and techno-economic factors for designing and sizing of a sustainable hybrid renewable energy system.

• Socio-environmental and techno-economic factors are proposed for system sizing. • Grasshopper Optimization Algorithm outperforms Particle Swarm optimization. • 15.9% of the energy cost is incurred due to Socio-environmental factor. • Kurtosis value indicates that the proposed algorithm can escape local minima. • Grasshopper optimization algorithm is effective in reducing the system cost. Hybrid renewable energy systems (HRESs) are penentrating into the distributed energy market, which is being proved to be more economical than extending grid-electricity through long distance thereby incurring transmission losses. But, the major concern is the accountability issues resulted from socio-environmental factors that are still the hindrace to the sustainability of the HRES. In this paper, two major socio-environmental factors, viz. land cost and carbon emission penalty are integrated with the important techno-economic factors for designing and sizing of an HRES that comprises PV, biogas generator, and pumped hydro storage (PHS) system. The approach is applied for remote area electrification of an Indian town. The component level modeling of the HRES is performed considering the ambient conditions of the location. In this work, for the first time GOA based meta -heuristic optimizer is developed for solving the optimization problem. The simulation results obtained using GOA are compared with the famous meta -heuristic PSO algorithm to understand the robustness of the former optimizer in terms of computational efforts and statistical performance based on skewness and kurtosis of the total net present cost (TNPC). It is found that the contribution of the socio-environmental factors in the present case is 6% of the TNPC as compared to 94% in regard to techno-economic factors. Comparision of the two optimizers demonstrate shorter computational time and lower cost in respect of GOA. The higher kurtosis value of the TNPC obtained through GOA optimizer indicates the capability of this optimizer to escape from local minima with a greater capability to approximate the global optimum. From GOA, the optimal size of the HRES is obtained as 57.5 kW (341 m2) of PV system, 8 kW of BG plant, 378.4 kWh of energy storage and 33 kW of converter size. On comparing the optimized cost of the present HRES with a very recent work under similar load condition, the present design is found to exhibit lower value of cost of energy. [ABSTRACT FROM AUTHOR]