Optimising rooftop photovoltaic adoption in urban landscapes: A system dynamics approach for sustainable energy transitions

•Rooftop agriculture for food production and photovoltaic (PV) panels for energy generation are two examples of how urban functional design presents a potential alternative to multi-function urban land-use that may give numerous ecosystem services. In order to find the optimal rooftop usage strategy that takes into account many choice criteria and to comprehend how rooftop solutions affect the layout of urban energy infrastructure, we provide a complete system modeling approach that demonstrates multi-objective optimization of energy systems.•The suggested PV size and cost factor, taking environmental conditions and shading effects into consideration, were determined using two methods: Quantum Particle Swarm Optimization (PSO) with Q-Learning System. Rooftop photovoltaics system sizing, economic feasibility, and energy efficiency are all affected by the results that are compared. University of Engineering & Technology (UET), a public sector institution, has its main campus in Taxila, where this research was conducted.•Situated in northern Pakistan, its appropriate position is advantageous for the research. The lifespan, performance ratio (PR), and decrease of the Rooftop Photovoltaics system’s carbon footprint are among the many additional criteria that are examined. Because of this, installing rooftop photovoltaic systems on government buildings is a more sensible and feasible solution. Rooftop agriculture for food production and photovoltaic (PV) panels for energy generation are two examples of how urban functional design presents a potential alternative to multi-function urban land-use that may give numerous ecosystem services. In order to find the optimal rooftop usage strategy that takes into account many choice criteria and to comprehend how rooftop solutions affect the layout of urban energy infrastructure, we provide a complete system modeling approach that demonstrates multi-objective optimization of energy systems. With a reduced levelized cost of electricity (LCOE), rooftop photovoltaics have gained considerable traction recently owing to technical, economical, and environmental benefits; this research aims to prove their viability. The suggested PV size and cost factor, taking environmental conditions and shading effects into consideration, were determined using two methods: Quantum Particle Swarm Optimization (PSO) with Q-Learning System. Rooftop photovoltaics system sizing, economic feasibility, and energy efficiency are all affected by the r