Multi-objective stochastic optimization of off-grid hybrid renewable energy systems in fragile states

Fragile states face low electrification rates, acute vulnerability to climate change, and a dependence on imported diesel, which can cause violence related to fuel transport. To address these unique challenges, we develop a two-stage multi-objective stochastic optimization model for planning off-grid hybrid renewable energy systems (O-HRES) that jointly optimizes upfront investment, diesel consumption, community coverage, and supply reliability. The first stage determines the technology mix and installed capacities, whereas the second stage determines hourly dispatch. This complex, large-scale optimization problem is solved by leveraging an augmented ϵ -constraint (AUGMECON) approach, whose results are compared to the true optimum and a multi-objective metaheuristic benchmark (NSGA-II). Uncertainty in renewable availability is captured using multiyear historical weather data and a new iterative extension of an existing integer programming-based (IP-based) scenario reduction method. Our scenario reduction method provides 4000-fold runtime savings with a negligible (0.23%) hypervolume loss. Moreover, AUGMECON obtains 4.2% higher hypervolume than NSGA-II and requires only 10% of the runtime of NSGA-II. Overall, our combination of iterative IP-based scenario reduction and AUGMECON-based multi-objective stochastic optimization successfully scales to real-world problem sizes in fragile states. Five case studies across three sub-Saharan African countries with site-specific data demonstrate the generalizability of our approach and provide actionable insights into key trade-offs for planners and humanitarian agencies. Compared with the status quo, our solutions reduce diesel consumption by up to 63 million liters, generate emissions benefits of up to $6 million, and deliver community benefits worth up to $13 million through expanded local electricity access over the 10-year planning horizon at a single site.