The reliable operation of a power system requires a real-time balance between supply and demand. However, it is difficult to achieve this balance solely by relying on supply-side regulation. Therefore, it is necessary to cooperate with effective demand-side management, which is a key strategy within smart grid systems, encouraging end-users to actively engage and optimize their electricity usage. This paper proposes a novel bi-level optimization model for integrating solar, hydrogen, and battery storage systems with charging stations (SHS-EVCSs) to maximize social welfare. The first level employs a non-cooperative game theory model for each individual EVCS to minimize capital and operational costs. The second level uses a cooperative game framework with an internal management system to optimize energy transactions among multiple EVCSs while considering EV owners’ economic interests. A Markov decision process models uncertainties in EV charging times, and Monte Carlo simulations predict charging demand. Real-time electricity pricing based on the dual theory enables demand-side management strategies like peak shaving and valley filling. Case studies demonstrate the model’s effectiveness in reducing peak loads, balancing energy utilization, and enhancing overall system efficiency and sustainability through optimized renewable integration, energy storage, EV charging coordination, social welfare maximization, and cost minimization. The proposed approach offers a promising pathway toward sustainable energy infrastructure by harmonizing renewable sources, storage technologies, EV charging demands, and societal benefits.

The importance of electric vehicle charging stations (EVCS) is increasing as electric vehicles (EV) become more widely used. EVCS with multiple low-carbon energy sources can promote sustainable energy development. This paper presents an optimization methodology for direct energy exchange between multi-geographic dispersed EVCSs in London, UK. The charging stations (CSs) incorporate solar panels, hydrogen, battery energy storage systems, and grids to support their operations. EVs are used to allow the energy exchange of charging stations. The objective function of the solar-hydrogen-battery storage electric vehicle charging station (SHS-EVCS) includes the minimization of both capital and operation and maintenance (O&M) costs, as well as the reduction in greenhouse gas emissions. The system constraints encompass the power output limits of individual components and the need to maintain a power balance between the SHS-EVCSs and the EV charging demand. To evaluate and compare the proposed SHS-EVCSs, two multi-objective optimization algorithms, namely the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), are employed. The findings indicate that NSGA-II outperforms MOEA/D in terms of achieving higher-quality solutions. During the optimization process, various factors are considered, including the sizing of solar panels and hydrogen storage tanks, the capacity of electric vehicle chargers, and the volume of energy exchanged between the two stations. The application of the optimized SHS-EVCSs results in substantial cost savings, thereby emphasizing the practical benefits of the proposed approach.

As subsidies for renewable energy are progressively reduced worldwide, electric vehicle charging stations (EVCSs) powered by renewable energy must adopt market-driven approaches to stay competitive. The unpredictable nature of renewable energy production poses major challenges for strategic planning. To tackle the uncertainties stemming from forecast inaccuracies of renewable energy, this study introduces a peer-to-peer (P2P) energy trading strategy based on game theory for solar-hydrogen-battery storage electric vehicle charging stations (SHS-EVCSs). Firstly, the incorporation of prediction errors in renewable energy forecasts within four SHS-EVCSs enhances the resilience and efficiency of energy management. Secondly, employing game theory’s optimization principles, this work presents a day-ahead P2P interactive energy trading model specifically designed for mitigating the variability issues associated with renewable energy sources. Thirdly, the model is converted into a mixed integer linear programming (MILP) problem through dual theory, allowing for resolution via CPLEX optimization techniques. Case study results demonstrate that the method not only increases SHS-EVCS revenue by up to 24.6% through P2P transactions but also helps manage operational and maintenance expenses, contributing to the growth of the renewable energy sector.

This paper presents an optimisation method for the direct energy exchange between two electric vehicle (EV) charging stations located in the UK. Each EV charging station consists of solar panels, hydrogen and battery energy storage systems (SHBS). The stations are interconnected through a energy exchange system that enables the transfer of excess energy from one station to the other. The objective function of SHBS charging stations is to minimize the capital and operation and maintenance costs of the stations. The system constraints are the power output of individual components, as well as the power balance between SHBS charging stations and EV charging demand. Genetic Algorithm is used to optimize the system, considering various factors such as the size of the solar panels and hydrogen storage tanks, the capacity of the electric vehicle chargers, and the amount of energy exchanged between the two stations. The optimized system yields substantial cost savings.

This paper proposes the novel design and operation of solar-hydrogen-storage (SHS) integrated electric vehicle (EV) charging station in future smart cities, with two key functionalities: 1. super-fast and off-grid charging; 2. multi-energy charging system using solar, hydrogen and energy storage. The integrated system design and modelling of SHS-EV charging station include hydrogen fuel cell generator to conduct off-grid and high-density power generation, a local solar power generation facility, a power-to-gas electrolysis for hydrogen production from power grid and solar power, and hydrogen and battery storage facilities to conduct local energy balancing. The operation model of SHS-EV charging station is established through a nonlinear optimization problem with complex objective function. Objective function of SHS-EV charging station is to minimize the operation costs including hydrogen fuel and electricity purchase costs. The system constraints are the technical limits of individual energy devices, as well as the system-level balancing of energy supply with the EV charging demand. The simulation results show that the proposed SHS-EV charging station can meet the EV charging demand by super-fast and off-grid charging from multi-energy sources. The SHS-EV charging station can also provide CO2 free power generation at point of charging. By using particle swarm optimization (PSO) to solve the SHS-EV charging system optimization model in MATLAB, the operation costs of SHS-EV charging station are greatly reduced.