Ensuring the reliability and stability of standalone microgrids (MGs) is fundamental to the effective integration of renewable energy sources, which are inherently uncertain. This work presents a stochastic optimization model using mixed-integer linear programming (MILP) to determine the optimal operation of electric vehicle charging stations (EVCS) with transactive control, emphasizing the balance between economic efficiency and system reliability. As a result, deploying EVCS will become a vital strategy for integrating renewable energy. An innovative method for supplying electric power from EV fleets involves using transportation networks as additional infrastructure. This article proposes that transportation networks, EVCS, and MGs can be optimally scheduled under uncertain photovoltaic (PV) generation using transactive control. The stochastic optimization problem is formulated as a mixed-integer nonlinear program and implemented in a moving-horizon framework for real-time onboard operation. The framework is tested on the IEEE 30-bus transmission network. The results show the efficiency of the proposed framework as an improvement tool for economic performance and operational stability in renewable-integrated power markets, and it reduces peak loads through the coordinated charging and discharging of vehicles.
This study presents the techno-economic optimization of a hybrid backup system integrated within an off-grid microgrid framework with electric vehicle (EV) grid-interaction capability. A real-world case study from a remote region in Egypt is used to evaluate system performance under realistic operating conditions. The optimization problem is formulated to minimize the net present cost (NPC) while ensuring system reliability using a penalty-based loss of power supply probability (LPSP). The system integrates photovoltaic (PV), wind turbines (WT), battery energy storage systems (BESS), hydrogen energy storage systems (HESS), and EVs. The novelty of this work lies in the development of a coordinated multi-storage energy management strategy that integrates BESS, HESS, and constrained EV participation within a unified optimization framework. The results show that the BESS-only configuration achieves the lowest cost (NPC ≈ $20.33 billion), while the PV/WT/BESS/HESS configuration results in the highest cost (NPC ≈ $25.48 billion). The proposed PV/WT/BESS/HESS/EV configuration provides a balanced solution with an NPC of approximately $22.79 billion while maintaining near-zero LPSP. EV integration enhances system flexibility, reducing the required BESS capacity by 44.8% relative to the HESS-only configuration, while also lowering reliance on hydrogen-based long-term storage, thereby mitigating the high capital costs associated with extended-duration storage components. These findings demonstrate that coordinated multi-storage management significantly improves the techno-economic viability and operational resilience of large-scale off-grid microgrids in remote regions.
Noha Nabil Abd-Elhady, Mohammed Fathy Ahmed, Salama Abu-Zaid et al.· Scientific Reports· 0 citations
One of the most important steps in creating a sustainable society is electrifying the transportation industry. There will be a number of benefits, including lower oil use, lower emissions, and grid integration of renewable energy sources. To promote widespread EV adoption, electric vehicle charging station (EVCS) deployment is crucial because it will mitigate “range anxiety,” or the worry about how far an EV can go before its battery runs out. In order to reduce costs and emissions, this work will build an electric vehicle charging station that incorporates renewable distributed generation (DG). Standalone microgrid EVCS is examined in various scenarios where energy sources including solar power, wind, and diesel generators are taken into account to meet the EVCS’s needs. The model was developed using HOMER software with accurate input data representing its operational, economic, and physical characteristics. This study aims to design an optimal hybrid EVCS integrating renewable energy sources. The system is implemented using HOMER software and evaluated using techno-economic parameters such as net present cost (NPC) and cost of energy (COE). The paper is organized as follows: Section 2 presents the site description, Section 3 explains the system configuration, Section 4 discusses results, and Section 5 concludes the study.
Santoshkumar Hampannavar, B. Deepa, G.E Amrutha et al.· E3S Web of Conferences· 0 citations
The prompt adoption of Electric Vehicles (EVs) offers substantial challenges to modern power distribution systems, incorporating enlarged power demand, voltage variability, and elevated energy losses. To solve such problems, this paper proposes an integrated optimization scheme for the simultaneous allocation of EV Charging Stations (EVCSs) and Distributed Generators (DGs) within a microgrid. Using the IEEE 33-bus radial distribution network as a test case, an objective problem is expressed for reducing active power loss and voltage variation while increasing the Voltage Stability Index (VSI). The proposed framework is solved using three metaheuristic algorithms: the novel Walrus Optimization Algorithm (WaOA), alongside the well-established Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA). Simulations across various scenarios reveal that uncoordinated EVCS integration severely degrades system performance, whereas the optimal co-placement of EVCSs and DGs dramatically enhances operational efficiency. The WaOA consistently demonstrated superior performance, notably in a scenario with three optimally placed DGs, achieving a 53.79% reduction in active power losses (from 202.53 kW to 93.59 kW) and a 50.44% reduction in reactive power losses. In addition, it significantly enhanced the voltage profile, boosted the minimum VSI from 0.6956 to 0.92721, and reduced the lowest voltage deviation to 0.000128 p.u. Comparative study confirms that WaOA outperforms PSO and WOA in both convergence speed and solution quality. This study underscores the critical importance of coordinated planning for EVCS and DG integration, providing a robust strategy to enhance grid reliability and support the sustainable transition to electric mobility.
Ahmed I. Omar, Mahmoud M. Elbaz, Mahmoud N. Ali et al.· Scientific Reports· 0 citations
The increasing penetration of electric vehicles (EVs) and distributed energy resources (DERs), including photovoltaic systems, wind turbines, battery energy storage systems, and hydrogen fuel cells, is reforming modern distribution networks. However, their stochastic behavior presents additional operational challenges, particularly in unbalanced radial feeders. This paper proposes an uncertainty-aware multi-objective planning framework for the optimal placement and coordinated operation of EV charging stations together with DERs, aiming to balance technical performance and economic cost. Uncertainties in load demand, renewable generation, and EV charging demand are represented using probabilistic models. Monte Carlo simulation generates hourly scenarios over a 24-h horizon, which are reduced using K-means clustering to retain scenario diversity while reducing computational burden. Energy loss, the voltage deviation index (VDI), and operating cost are optimized instantaneously, while the proposed approach is examined on the IEEE 37-bus unbalanced distribution feeder through MATLAB–OpenDSS co-simulation. Multi-objective particle swarm optimization (MOPSO), multi-objective genetic algorithm, and second-order cone programming (SOCP) are employed to obtain Pareto-optimal solutions. Results show that MOPSO delivers the best-compromise solution with 3100.42 kWh energy loss, VDI of 190.16 p.u., and 1463.27 USD/day operating cost, while SOCP provides competitive benchmark performance. A 20-year life cycle cost analysis further supports the economic feasibility of the coordinated planning solution. Overall, the coordinated EV charging stations (EVCS)–DER planning strategy strengthens feeder performance under uncertainty by improving operating conditions and maintaining more stable network behavior.
Punam Das, Sadhan Gope, D. Das et al.· Journal of Renewable and Sus...· 0 citations
Electric vehicles (EVs) are increasingly considered a significant challenge to the stability of smart grids as they are integrated into urban distribution systems. Stochastic load variations are introduced by uncoordinated EV charging, leading to voltage distortion, transformer overloading, and increased power losses. Existing optimization methods, such as Particle Swarm Optimization (PSO) and Model Predictive Control (MPC), are characterized by limited real-time adaptability, high computational cost, and insufficient scalability under uncertain operating conditions. To address these limitations, the EvoGrid-Optimizer is proposed as a novel evolutionary algorithm for the dynamic multi-objective optimization of EV charging schedules in smart grids. The proposed framework simultaneously optimizes operational cost, voltage deviation, and peak load demand using a weighted fitness formulation, while grid operational constraints are satisfied, and is evaluated on a simulated IEEE 33-bus distribution network with stochastic EVs load and renewable energy sources. The simulation results demonstrate that superior performance is achieved by the EvoGrid-Optimizer across all key performance indicators when compared to baseline uncoordinated charging, PSO, and MPC. Specifically, a reduction in operational cost of 22.4%, a voltage deviation of 0.037 p.u., a peak load of 1480 kW, and a load balancing efficiency of 93.8% are achieved.
Safwan Nadweh, Mohamad Abed, Nabil Mohammed et al.· 2026 6th International Confe...· 0 citations
The increased charging demand of electric vehicles (EVs) and fuel cell EVs has created an additional burden on the fossil fuel‐based electric grid. The article proposes a hybrid energy system (HES)‐based integrated EV charging and hydrogen refueling stations with mobility‐assisted storage flexibility, that is, a mobile charging unit (MCU). As the system has multiple sources and services, a priority‐based energy management framework is designed to maximize use of renewable energy sources (RES). To achieve techno‐economic benefits, a lexicographic optimization with slack‐based demand modeling is introduced to coordinate renewable generation, hydrogen production, grid interaction, and MCU scheduling under realistic operational constraints. Results demonstrate that the proposed system significantly improves RES utilization up to 95% and reduces grid dependency up to 86.53%. Multiobjective optimization enhances economic performance by 19.33% and customer satisfaction by 95% in all seasons. The proposed system provides a significant annual profitability and sustainability for multienergy charging infrastructure.
Kiran Nathgosavi, V. Kalkhambkar, Pratyasa Bhui· Energy Storage· 0 citations
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