Jul 2026· 2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET)· pp. 1-5· 0 citations· 16 references
Abstract
The gradual depletion of conventional fossil-fuel resources, growing environmental concerns, and the increasing complexity associated with modern smart-grid deployment have accelerated the integration of renewable energy distributed generators (REDGs) and electric vehicles (EVs) into electrical power systems. Over recent decades, the global EV industry has experienced considerable growth in both vehicle production and market penetration. This study investigates the ancillary services offered by EVs and evaluates the implications of their large-scale integration into power networks. An optimization-based framework is developed to estimate EV hosting capacity, formulate an enhanced objective function, determine the optimal locations and capacities of charging stations, and evaluate the associated system operating costs. Simulation findings demonstrate that the proposed EV capacity estimation approach effectively represents vehicle charging and discharging behavior. Furthermore, a hybrid genetic algorithm-improved particle swarm optimization (HGAIPSO) technique is introduced to optimize the allocation of EV charging stations while incorporating coordinated charging strategies and renewable distributed generation. The proposed framework mitigates voltage-limit violations, minimizes energy losses and system costs, and improves the overall power-quality performance of the network. The effectiveness of the developed approach is validated through MATLAB-based simulations conducted on the IEEE 118 bus test system.
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
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
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
With the growing adoption of electric vehicle charging systems (EVCS), energy shortages and power performance challenges have become significant concerns. This study developed an intelligent energy management system (EMS) integrated with a photovoltaic (PV) system and utility grid to enhance the operational efficiency and sustainability of EVCS. The proposed system utilizes a novel optimization technique, the Markov decision‐based snake optimization with maximum power point tracking (MDbSO‐MPPT), to effectively manage power and energy performance. The EMS optimizes energy distribution, minimizes grid dependency, and ensures cost‐effective and reliable operation of the charging station. Simulation results demonstrate that the developed system achieves a remarkable 99.8% efficiency, outperforming conventional models with 98% and 99.5% efficiencies. The grid power consumption was also reduced to 63 kW compared to 100 and 182 kW for the benchmarks. The system also optimizes the PV power utilization and battery state of charge, reducing unnecessary grid interactions and improving sustainability. By leveraging renewable Energy and advanced optimization strategies, this intelligent EMS provides a scalable and efficient solution for modern EV charging needs.
K. Basheer, V. Jagathesan· Transactions on Emerging Tel...· 0 citations
The rapid growth of electric vehicles (EVs) has created significant challenges for the modernization of power systems, particularly in energy management, grid stability,
and infrastructure readiness.
This article presents a sophisticated energy management system (EMS) intended for a
photovoltaic (PV)-battery-electric vehicle (EV) charging station, employing a hybrid Particle Swarm
Optimization-Salp Swarm Algorithm (PSO-SSA). It depicts a complex setup that combines PV electricity production, battery storage, EV charging demand, and building consumption under actual
operating conditions. The suggested method focuses on improving battery operational behavior
(stress, peak power, and smoothness) while maintaining acceptable system reliability. The proposed
algorithm aims to enhance the balance between exploration and exploitation, thus allowing for more
effective power load coordination. The proposed EMS is evaluated under deterministic operating
conditions, and the simulation results are obtained via MATLAB software.
The simulation results show that the hybrid PSO-SSA improves battery operation considerably, while full PV utilization (100%) and reliability are kept at the same level. The proposed PSOSSA method reduces battery throughput by 3% and peak discharge power by 29% compared to the
baseline strategy.
These findings demonstrate that the proposed hybrid PSO-SSA is a robust and effective
optimization technique for PV-powered EV charging systems, particularly by mitigating battery
degradation and improving long-term operational efficiency.
Hasan Wahhab Salih Rabee, A. Abdulsada, Sadeq Abdullah Shaban· Recent Advances in Electrica...· 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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
