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Increased demand for productive uses of energy is a driver of energy consumption. Low-income households in the off-grid market have a budget constraint. By understanding the energy priorities of low-income households in the off-grid market, utilities can develop innovative solutions for servicing the market. This paper builds on recent literature on productive uses of electricity for increased investment and pay back for micro-grid investment across the eight productive user categories that were developed in Rwanda. We ask: how can the range, success, and benefits of productive uses of energy be expanded in resource-constrained settings? To answer the research question, we address the need for supporting infrastructure of business development services, energy services bundling, and utility policy instruments that support productive use of energy. We created metrics for prioritizing productive uses of energy in the off-grid market. In the face of a budget constraint, low-income households that prioritize domestic over business and services uses of energy need integrated services that support the consumptive-productive-service link. Realistic cost-benefit analyses are needed. Solar irrigation as a small industry case in Rwanda can achieve at best a 3-year payback period if it can increase productivity by 54%. We offer recommendations to increase energy demand from productive uses of energy in the off-grid market: work with local cultural norms to support business development; develop realistic cost-benefit analyses based on local data, and partner with business training service providers. 

A classic multi-period stochastic energy system expansion planning (ESEP) model aims to address demand uncertainty by requiring immediate demand satisfaction for all scenarios. However, this approach may result in an expensive system that deviates from the planner’s long-term goals, especially when facing unexpectedly high demand scenarios. To address this issue, we propose a chance-constrained stochastic multi-stage ESEP model that allows for a portion of demand to remain unmet in specific periods while still ensuring complete demand satisfaction during most of the planning horizon, including the final period. This approach provides more time flexibility to build infrastructure and assess needs, ultimately reducing costs and allowing for a broader view of infrastructure planning options. To solve the chance-constrained stochastic model, we introduce a binary- search-based progressive hedging algorithm heuristic, which is particularly useful for large-scale models. We demonstrate the effectiveness and benefits of implementing the chance-constrained model through a case study of Rwanda using real-world data.