Aug 2026· 2026 6th International Conference on Emerging Smart Technologies and Applications (eSmarTA)· pp. 1-8· 0 citations· 25 references
Abstract
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.
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
This study proposes a comprehensive multi-objective optimization framework for demand-side management of a hybrid microgrid comprising photovoltaic (PV) panels, wind turbines (WT), a battery energy storage system (BESS), a fuel cell (FC), and a grid connection. The framework simultaneously minimizes the Peak-to-Average Ratio (PAR) and total operating cost through dynamic load scheduling under real-time pricing (RTP). A renewable energy utilization strategy prioritizes clean energy dispatch, while an intelligent battery management scheme optimizes charging and discharging decisions according to renewable generation availability, load demand, and electricity price signals. To address the limitations of conventional weighted-sum optimization approaches, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is employed to generate a diverse and well-distributed Pareto front without requiring predefined objective weights. The proposed framework is evaluated under three energy system configurations: (i) grid-only operation, (ii) grid-integrated renewable energy and battery storage, and (iii) grid-integrated renewable energy, battery storage, and fuel-cell support. The results demonstrate that hybrid renewable energy configurations significantly improve both economic and operational performance compared with conventional grid-dependent operation. The proposed framework generated multiple Pareto-optimal operating strategies with different trade-offs between operating cost and PAR. The minimum-cost solution achieved an operating cost of 131.73 Cents, while a representative compromise solution achieved 155.98 Cents with improved demand-side management performance. Comparative evaluation against NSGA-II, MOPSO, SPEA2, and the Weighted Sum Method reveals that NSGA-III consistently achieves superior Pareto-front quality, convergence characteristics, solution diversity, and robustness across 30 independent trials. The findings demonstrate the effectiveness of NSGA-III for multi-objective energy management and provide a scalable optimization framework for enhancing the economic efficiency, operational flexibility, and sustainability of future smart microgrid systems.
Mohd Bilal, Arshad Mohammad, Imdadullah et al.· Scientific Reports· 1 citation
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
The proposed framework improves operational stability, reduces computational burden, and enhances charging coordination compared with conventional forecasting and heuristic scheduling approaches, and demonstrates the feasibility and scalability of integrating Machine Learning (ML) based forecasting with real-time optimization for future smart-grid and EV energy management systems.
D. Janyavula, V. G. Kumar, S. N. Saxena· Engineering, Technology &...· 0 citations
Experimental results demonstrate that the proposed MODE approach achieves a grid load standard deviation of 6.95 and a peak-to-valley ratio of 1.63 while maintaining an average battery depth of discharge and a user satisfaction level of 0.92 for commuting scenarios.
X. Mao· Advanced Electromagnetics· 0 citations
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