Planning and operation of solar-hydrogen-storage integrated electric vehicle charging stations in smart city

Abstract

The global push towards carbon neutrality by 2050 has intensified the need for sustainable, energy-efficient electric vehicle charging infrastructure. Traditional charging stations rely heavily on the conventional grid, which presents challenges for integrating renewable energy sources and supporting the widespread adoption of electric vehicles (EVs). This thesis addresses these challenges by developing innovative strategies for energy-efficient electric vehicle charging stations (EVCSs) that incorporate renewable energy sources, enhance energy exchange capabilities, and improve the infrastructure’s overall contribution to social welfare and carbon emission reduction. Although prior research has made strides in enhancing EV charging efficiency and incorporating renewable energy, significant gaps remain. Many existing studies overlook comprehensive models that optimize both energy management and economic viability across EVCS networks. There is a need for solutions that facilitate effective integration of renewable energy sources, such as solar hydrogen and battery storage systems, with minimal reliance on traditional distribution networks. Furthermore, limited attention has been given to optimizing energy transfers between stations and implementing real-time pricing models to balance supply and demand in variable conditions. This thesis addresses these gaps by presenting a comprehensive model for integrating renewable energy into EVCSs, including solar hydrogen and storage-integrated EVCSs (SHS-EVCSs), supported by advanced simulation and optimization techniques such as the Particle Swam Optimization (PSO) Algorithm, the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D). These methods facilitate the identification of optimal solutions for energy management and cost-effectiveness. Additional contributions include a novel peer-to-peer (P2P) energy dispatch strategy based on game theory, a hierarchical model to enhance driver welfare and operational efficiency, and a Markov decision process with Monte Carlo simulations for accurate demand prediction and realtime pricing. Together, these innovations provide a robust framework for designing future EVCS infrastructure aligned with global carbon neutrality goals, offering practical insights into renewable energy integration, network optimization, and economic impacts on urban transportation systems.