Multi criteria decision making for the most suitable combination of energy resources for a decentralized hybrid energy solution with green hydrogen as the storage option

Abstract

Decentralized hybrid renewable energy systems are emerging as possible solutions for remote locations not feasible for grid power. However, such systems always need a storage device to bridge the gap between demand and supply during its operation. Electrochemical storage systems are currently the most common devices for this purpose. An alternative novel process to meet this gap between the demand and supply of power may be to generate hydrogen with excess renewable power during off-peak load. Produced green hydrogen may be used by fuel cells to generate electricity subsequently to compensate for the gap during peak load. However, practical suitability of acceptance of these alternative options will depend on economic feasibility including risk in return on investment. This is explored through comparative evaluation of these options with relative assessment of a few indicative parameters including uncertainties. These parameters are fuel consumption, cost of electricity, net present cost, financial risk and renewable fraction with full load meeting. As projects with new technologies have generally associated risk with return on investment, a Monte Carlo Simulation is also done to assess and compare the uncertainties of different options. As the five different evaluation criteria do not converge to the same best solution, finally a multi-criteria decision-making approach is adopted to decide the acceptable solution. The sensitivity analysis is also performed to examine the robustness of the decided solution. The analysis results show that the combination of photovoltaic-wind-generator-Lithium-ion is techno-economically acceptable with a moderate risk and a significant renewable share. Corresponding values of different parameters are: cost of electricity-$0.159/kWh, net present cost - $4,24,568, fuel consumption- 2628L/year, renewable fraction- 96.5%, and standard deviation–0.45% respectively. Though the methodology is generic, the study is demonstrated with data of a remote Indian village.