Techno-Economic Optimization and Stochastic Sizing of Solar-Wind-Biomass Hybrid Systems for Off-Grid Mountain Communities

Abstract

The electrification of remote, high-altitude communities presents extreme techno-economic challenges driven by rugged topologies, harsh winter climates, and the inherent intermittency of renewable energy resources. While solar and wind systems combined with battery storage dominate off-grid applications, their dependence on massive energy storage banks during extended winter deficits drastically inflates capital costs and lifecycle emissions. This paper proposes a robust stochastic optimization framework for a hybrid Solar Photovoltaic (PV), Wind Turbine (WT), and Biomass Gasifier (BM) microgrid system. By utilizing locally sourced biomass as a dispatchable buffer, the proposed mathematical framework minimizes the Levelized Cost of Energy (LCOE) and Net Present Cost (NPC) while strictly enforcing a 99% reliability threshold via the Loss of Power Supply Probability (LPSP). A high-fidelity numerical case study is conducted for an isolated settlement near Dushanbe, Tajikistan, analyzing the non-linear trade-offs between local bioenergy feedstock availability and chemical energy storage degradation in sub-zero climates. The Particle Swarm Optimization (PSO) coupled with Monte Carlo simulations demonstrates that the optimal PV-WT-BM configuration reduces the LCOE by 31.4% (\0.243/kWh) compared to standard PV-WT-Battery baseline topologies (\0.354/kWh). The integration of dispatchable bioenergy collapses necessary battery capacities by 81.0%, emphasizing that targeted policy subsidies for biomass logistics are highly economically superior to battery oversizing in extreme winter climates.