2026· ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)· 0 citations
Abstract
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.
In recent times, as the number of electrical vehicle (EV) became increased, many researches has been done on finding the optimal placement of Electrical Vehicle Charging Stations (EVCS). EV research has become one of the most fascinating, interesting, intriguing and exciting field areas in transportation system due to its low CO2 emission, lower energy consumption and high efficiency. It also has lower maintenance costs compared to normal internal combustion engine vehicles. Inappropriate placement of EVCS lead to under-utilization, grid strain, traffic and safety concerns, limited accessibility, environmental and zoning issues. In this study, Genetic Algorithm (GA) based approach model for electrical distribution network are developed to find best placement of Electrical Vehicle Fast Charging Station (EVFCS). Model has been developed to optimize the location of EVFCS in the power distribution network. The distance from each proximity area to EVFCS is considered as objective function of problem formulation and constructed for the acquired model. In this research, the power loss cost of distribution network, fixed installation cost and the line cost of EVFCS are analyzed. The duration time for EVCS is determined and the optimal time is identified. The GA use crossover, mutation, and selection to distribute EVFCS in a way that would best place them in various proximity areas. According to the results the proposed optimal controller outperforms best performance and efficient energy used.
Endris Muhammed Hassen, F. Kaya, Onur Akar et al.· Balkan Journal of Electrical...· 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 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
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
With the growing demand for electricity, the penetration of Renewable Distributed Generators (RDGs), alongside the transition from Internal Combustion Engine Vehicles (ICEVs) to Electric Vehicles (EVs), has become a pressing challenge for the stable and efficient operation of distribution networks. This research focuses on a critical task of determining the optimal integration of RDGs, including solar photovoltaic systems, wind turbines, biomass units, and EV charging stations, into an Unbalanced Radial Distribution System (URDS). This work proposes an optimization approach aiming to minimise the total costs (TCs), active power losses (APLs), voltage unbalance factor (VUF), and voltage deviation (VD) of the network under consideration simultaneously. The integration of RDGs is carried out using a metaheuristic technique, which accounts for the intermittent nature of renewable energy sources, the stochastic behaviour of EVs, and the variability of load demands over 24 h a day. Fuzzy decision-making is applied to select an optimal trade-off solution from the Pareto front. The effectiveness of the developed approach is assessed comprehensively on a Pakistani 60-bus URDS as a primary study, while the IEEE-123 bus system is employed as a validation case to demonstrate the applicability and scalability of the proposed methodology. Among the five analysed case studies, the simulation results indicate that coordinated integration of RDGs and EVCSs into the system yields significant benefits, including a decreased reliance on conventional centralised generation, with a reduction of 56.29% in costs, 46.61% in losses, 7.17% in voltage unbalance, and 27.13% in voltage deviation as compared to the base case.
Maaz Ahmad, M. I. Mohmand, Aamir Nawaz et al.· World Electric Vehicle Journ...· 0 citations
Green hydrogen (H2) has gained considerable attention as a cornerstone of a zero-emission future. The global transportation sector is shifting from gasoline-powered vehicles toward electric vehicles (EVs) and hydrogen vehicles (HVs) to reduce emissions. This paper investigates a microgrid (MG) model comprising photovoltaic panels, wind turbines, batteries, and hydrogen energy storage systems integrated with vehicle-to-grid (V2G) systems for EVs and HVs. The stochastic behavior of both EVs and HVs is characterized using probability density functions to capture vehicle uncertainty. A double-layer optimization model is proposed to enhance the reliability, cost-effectiveness, and sustainability of MGs. The upper layer employs the non-dominated sorting genetic algorithm II to optimize V2G scheduling, thereby minimizing MG load variance and total operational costs for EV and HV owners. The lower-layer approach is a multi-objective optimization problem that minimizes system costs over the entire life cycle while maximizing MG reliability, solved using the Gurobi optimizer. The framework is validated for an islanded MG in El-Kharga Oasis, Egypt, as an off-grid community with high renewable energy potential. Various scenarios are examined for EV and HV behavior, with the optimal cost achieved when G2V and V2G modes are applied to both HVs and EVs, resulting in a 12% cost reduction relative to the scenario without V2G. Further analysis is conducted to examine the impact of the loss-of-power-supply probability (LPSP) value on system economics. Results indicate that implementing an LPSP constraint of 0.05 reduces the annualized total cost by around 21.2% compared to the baseline case without the LPSP constraint.
M. Sayed, M. H. Alham, D. K. Ibrahim· Scientific Reports· 0 citations
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