Reliability-resilience-economic–sustainability-based off-grid hybrid energy system for remote communities in the Indian Himalayan region

• Proposes a framework for identifying optimal hybrid energy systems. • Salp Swarm Algorithm-based Reliability-Resilience-Economic–Sustainability analysis. • Suggests strategies for utilizing dump energy. • Identifies the most optimal system configuration (C-19) based on MCDA. • Sensitivity analysis suggests that the LCC & COE are sensitive to load demand and the interest rate. Reliable energy remains a pressing challenge in the remote, rural, and hilly regions of developing nations. Harsh climatic conditions, scattered settlements, and rugged terrain often render grid extension unfeasible, resulting in frequent outages or complete access denial. These constraints directly limit education, healthcare, and economic opportunities. Off-grid hybrid energy systems (HESs) that integrate renewable and conventional technologies can address these challenges by offering a cost-effective, environmentally friendly, and resilient alternative. This study presents a multi-criteria decision analysis (MCDA) framework for identifying the optimal, sustainable, and resilient HES configuration for rural communities. The framework considers four battery technologies—lead-acid, lithium-ion, sodium-sulfur, and nickel–cadmium—operated at 80 % depth of discharge under a 0 % loss of power supply probability. Ten villages in Pauri Garhwal, Uttarakhand, have been selected as the case study. Twenty-four HES configurations are simulated and optimized using the Salp Swarm Algorithm. The MCDA systematically evaluates all the configurations against lifecycle cost (LCC), renewable fraction (RF), annual greenhouse gas (GHG) savings, Shannon-Wiener Diversity Index (SWDI), and dumped energy. Results identify configuration C-19 as the most robust option, achieving an LCC of $4,918,123, cost of energy (COE) of $0.234/kWh, and GHG savings of 865,498.77 kgCO 2 -eq/year. The analysis further explores strategies for managing excess dumped energy, ensuring better utilization of renewable generation. Sensitivity analysis reveals that LCC and COE are most sensitive to variations in load demand and the nominal interest rate. The proposed framework offers a pathway for reliable and sustainable energy access, directly supporting resilient livelihoods in rural, hilly communities.