Aug 2026· Scientific Reports· Vol 16· 0 citations· 72 references
Medicine
Abstract
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.
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 increasing adoption of electric vehicles (EVs) is expected to significantly transform the transportation sector due to their environmental benefits and reduced carbon emissions. However, the rapid growth in EV charging demand introduces several challenges in Radial distribution Systems (RDS), including increased power losses, voltage instability, and imbalance between generation and consumption. This study addresses these challenges by optimizing the integration of EV charging stations (EVCSs) and shunt capacitors (SCs) within the RDS. The objective is to maximize Net Present Value (NPV) by simultaneously reducing power losses, improving voltage profiles, and reducing Expected Interruption Cost (EIC), while incorporating compensation coefficients for reliability assessment. To achieve this, a Hybrid Gray Wolf–Cuckoo Optimization (HGCO) algorithm is proposed, combining the exploitation capability of Gray Wolf Optimization (GWO) with the exploration strength of the Cuckoo Search (CU) algorithm. The proposed method is validated on IEEE 33‐bus and 118‐bus systems. Simulation results demonstrate a reduction in active power loss (APL) of 34.45% and 31.08% for the respective systems. Annual energy loss cost savings are 35 268 and 209 153.10 $/year, while EIC is reduced by 14 029.80 and 48 699.30 $/year. The proposed approach also achieves a net annual profit of 27 049 and 221 281 $/year, confirming its economic effectiveness. In addition, the study incorporates Vehicle‐to‐Grid (V2G) operation using an aggregated modeling approach, enabling bidirectional power flow and further enhancing system performance. The obtained results confirm that the proposed HGCO‐based approach effectively enhances both the technical performance and economic viability of modern RDS.
B. Kumar, Aneesa Farhan· Energy Storage· 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
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.
B. Sherkhane, S. Chavan, Aishwrya A. Apte· Future Energy· 0 citations
Electric vehicles (EVs) have been developed to reduce the emissions of carbon dioxide (CO2) produced by vehicles with internal combustion engines. As the adoption of EVs increases, the development of a robust and efficient electric vehicle charging station (EVCS) is essential for the widespread adoption of EVs, providing the necessary infrastructure to recharge vehicle batteries. The strategic placement of an EVCS is critical, because it directly affects the efficiency and reliability of the distribution system. Properly located charging stations can enhance grid stability, optimize energy distribution, and reduce peak load pressures. Conversely, a poorly planned EVCS placement can lead to grid congestion, increased operational costs, and potential reliability issues. Strategy for maximizing EV utilization through EVCSs in the Radial Distribution Network System (RDNS) by considering factors such as load voltage deviation and line losses. In this study, the RDNS is segmented into zones, followed by the application of various optimization algorithms, including Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). This approach identifies the optimal locations for the EVCSs in the network for different load growth rates. This was followed by Forward Backward Sweep (FBS) power flow analysis to determine the active and reactive power losses, voltage deviations, and voltage profiles. Extensive simulations using IEEE 33-node test feeders validated the proposed techniques using the MATLAB tool.
M. S. Arjun, N. Mohan, K. Sathish et al.· ITEGAM- Journal of Engineeri...· 0 citations
This paper presents a comprehensive techno-economic framework for the optimal integration of distributed energy resources (DERs)—specifically Photovoltaic (PV) solar generation—and Electric Vehicle Charging Stations (EVCS) into modern electrical distribution grids. The methodology is applied to a realistic 25-bus, 11 kV radial distribution network modeled after the El-Shohada district in Menofia Governorate, Egypt. To balance inherently conflicting technical and financial objectives, a multi-objective penalty function is formulated. It simultaneously minimizes active power losses, mitigates voltage deviations, and maximizes investor profitability based on contemporary Egyptian energy tariff structures. The recently developed metaheuristic Growth Optimization (GO) algorithm is utilized to determine the optimal bus locations and capacities of these units over a 24-hour horizon, factoring in stochastic load profiles, solar irradiance, and EV charging behavior. Statistical validation based on 30 independent optimization runs demonstrates the high computational efficiency of the GO algorithm, yielding an optimal equilibrium that enhances grid reliability while providing a solid economic foundation.
Adel A. Abou El-Ela, Eman Salah, Mohamed M. Hegab· Scientific Reports· 0 citations
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