Data driven MCDM models for reliability-economic-environmental analysis of energy storage based autonomous micro-grid

Abstract

The decreasing cost trends and favorable government policies and incentives have led to adoption of renewable energy sources (RES) worldwide. However, these sources have meteorological limitations which leads to intermittency in generation thereby raising reliability issues. Therefore, many autonomous micro-grids relying on RES sources incorporate backup technologies. In this paper, nine configurations of three prominent backup technologies — battery storage system (BSS), hydrogen storage system (HSS), and diesel generator (DG) with RES based on ten economic-reliability-environmental criteria have been analyzed to select optimal backup configuration. The used data driven model presented in this work has been carried out using BFPSO-MCS-MCDM approaches. The ranking results of three MCDM approaches (TOPSIS, EDAS, and PROMETHEE II) are aggregated to obtain the final ranking result. Furthermore, in each approach, nine sets of ranking results (using nine different criteria weights) are aggregated. To determine the nine criteria weights, three objective-based weighting methods with three subjective methods have been combined using Bayes' theory. The results of the configuration ranking show that the PV, wind turbine, and BSS configuration followed by the PV, wind turbine, and HSS configuration provides the optimal solution based on the life cycle analysis considering reliability-economic-environmental criteria. The configuration of PV, wind turbine, and BSS offers lowest around 78 % lower LOLE and configuration of PV, wind turbine, and HSS offers around 65 % lower LCOE as compared to configuration of PV and DG which shows highest LCOE, along with moderate value of other performance criteria.