Hierarchical multi-objective planning for city-scale rooftop solar deployment

Urban rooftop solar deployment can reduce building-sector emissions, but planning decisions must balance rooftop availability, hourly demand matching, technology configuration, investment cost, and grid export constraints. This study develops a hierarchical multi-objective planning framework for city-scale rooftop solar deployment. Building-level rooftop solar potential, hourly electricity demand from an urban building energy model, rooftop photovoltaic systems, solar water heating systems, battery storage options, operating modes, techno-economic performance, and grid export capacity limits were integrated into an integer linear programming-based portfolio optimization model. The framework compares net-present-value (NPV)-led, life-cycle-energy-saving (LCES)-led, and cost-led planning priorities through controlled tolerance relaxation of the primary objective. The case study included 7,674 buildings, with 2.32 km² of available rooftop area, 1.40 km² of practically installable photovoltaic area, and 308.59 MW of maximum photovoltaic capacity. Results showed that grid export capacity strongly reshaped deployment outcomes, but the benefits saturated beyond a threshold. Under the NPV-led scenario, increasing grid export capacity raised NPV by 44.12% and LCES by 51.53% before saturation at 270 MW. Under the LCES-led scenario, storage-intensive portfolios under tight grid constraints were progressively replaced by export-enabled configurations as grid capacity increased. Under the cost-led scenario, grid expansion mainly reduced initial investment by 0.25 billion CNY and improved NPV by 0.21 billion CNY, with limited additional energy-saving gains. Marginal analysis showed that modest tolerance relaxation captured most co-benefits, whereas larger relaxations produced diminishing returns. The framework provides interpretable planning evidence for city-scale rooftop solar deployment under competing priorities and grid constraints.