Advanced geospatial analytics and multi-objective optimization framework for national rooftop solar assessment: Cuba case study

This study presents a comprehensive assessment of Cuba's rooftop solar potential through an innovative methodological framework that integrates advanced geospatial analysis, big data analytics, and multi-objective optimization. Our approach specifically addresses the critical methodological gap between microscale precision and macroscale applicability, enabling granular analysis scalable to large regions without compromising computational efficiency. The framework employs the electrical storm optimization algorithm to process over 2.8 million rooftops, identifying 1424,789 suitable locations for photovoltaic (PV) installations across Cuba. The results demonstrate a potential capacity of 1375 MWp under current technological capabilities, which could meet 39.6 % of Cuba's projected 2050 midday peak electricity demand and generate 2175 GWh yearly (11.1 % of the country's projected annual energy demand). Through scenario-based logistic modelling incorporating technological, economic, and social variables, we simulate six distinct scenarios from "Economic Stagnation" to "Disruptive Innovation" for the period 2024–2050, quantifying installed capacity, energy production, self-consumption patterns, and economic viability metrics. Regional analysis reveals southeastern provinces as optimal for PV deployment because of superior solar irradiance (6.2 kWh/m²/day), whereas urban areas such as Havana show the highest feasibility because of dense rooftop availability. This research provides evidence-based strategies for policymakers by identifying critical success factors, such as technological advancements (elasticity coefficient of 3.1 for installed power), economic incentives (ROI averaging of 21.42 %), and regionalized deployment approaches prioritizing high-irradiance areas.