Co-Optimizing Technical Performance and Community Affordability in Smart Solar Microgrids for Underserved Regions
Solar microgrids represent a technically viable and increasingly cost-competitive pathway to electricity access in underserved regions of sub-Saharan Africa, South Asia, and the Pacific, yet a disproportionate number of deployed systems experience abandonment or financial collapse within five years of commissioning. This paper argues that such failures reflect not primarily technical inadequacy or affordability barriers in isolation, but a co-optimization failure arising from frameworks that treat these two dimensions as sequential rather than jointly determined design problems. A systematic conceptual synthesis was conducted across 168 peer-reviewed studies, technical reports, and institutional assessments published between 2010 and 2020, following a structured PRISMA-adapted protocol and drawing on multi-criteria optimization theory, energy poverty literature, distributed generation engineering, smart-grid control architectures, and community-level financial modeling. Four primary failure modes emerge from sequential optimization: the tariff-reliability paradox, stranded capacity, technology-community mismatch, and governance vacuum. To address these, the paper develops and theoretically validates a co-optimization framework comprising four integrated modules: a Smart Performance Core, a Community Affordability Engine, a Bidirectional Feedback Mechanism, and an Adaptive Governance Layer. Validation against 23 empirical case studies across 14 countries demonstrates convergent construct validity and internal consistency, with co-optimized systems achieving average operational lifespans 3.2 years longer and revenue collection rates 28 percentage points higher than sequentially optimized counterparts. The framework provides operationalizable principles for system designers, energy planners, and development finance institutions seeking to reduce system abandonment and accelerate equitable energy access in underserved regions.