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.
Abstract
Traditional single-objective strategies for coordinating electric vehicle (EV) charging and discharging are often unable to balance grid stability, user costs, and battery degradation simultaneously, limiting their applicability in intelligent energy management systems associated with modern electromagnetic power infrastructures. This challenge is particularly significant for industrial microgrids, such as those serving manufacturing facilities, where EV fleet integration must preserve the reliable power quality required by sensitive electrical equipment and electromagnetic energy systems. To address these issues, this study proposes an improved Multi-Objective Differential Evolution (MODE) algorithm that explicitly optimizes multiple conflicting objectives in parallel. The conventional differential evolution framework is enhanced through non-dominated sorting and crowding distance mechanisms to improve solution diversity and convergence. A coordinated EV scheduling model incorporating four optimization objectives, including user satisfaction, together with constraints on bus voltage, charging/discharging power, feeder thermal limits, and battery state of charge, is established. A two-dimensional matrix encoding strategy and an external Pareto archive are adopted to enhance optimization stability. 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 of 15% and a user satisfaction level of 0.92 for commuting scenarios. These findings verify the effectiveness of MODE for multi-objective coordinated scheduling and provide a practical optimization framework for sustainable EV energy management and industrial microgrids requiring stable electromagnetic power delivery.
With the rapid development of the electric vehicle (EV) industry, large-scale integration of EVs into the power grid has led to increasingly prominent problems such as low charging efficiency, intensified load fluctuations, and reduced economic benefits for users. To address these issues, an optimization model is constructed with charging time, load fluctuation, and user charging cost as the objectives, comprehensively considering uncertainties including renewable energy output, user charging behavior, and electricity price fluctuations. An uncertainty-aware multi-objective scheduling strategy based on an improved chaotic Lévy flight multi-objective particle swarm optimization (CLM-MOPSO) algorithm is proposed. Specifically, Weibull and Beta distributions are adopted to generate scenarios for wind and photovoltaic power output, while Poisson and normal distributions are used to characterize the uncertainty of user charging behavior. In addition, a stochastic electricity price process and load uncertainty sets are introduced to establish a robust optimization framework based on multi-scenario stochastic programming. On this basis, an improved CLM-MOPSO algorithm is designed, in which Tent chaotic mapping is utilized for high-quality population initialization, Lévy flight mutation is introduced to enhance the global search capability, and adaptive parameter adjustment together with an external archive mechanism is incorporated to improve the search efficiency while maintaining good convergence and diversity of the Pareto solution set. Finally, simulation studies based on real road network and power grid operation data are conducted, and the results verify the effectiveness of the proposed method. The results demonstrate that the proposed method significantly reduces charging time, mitigates load fluctuations, and lowers user charging costs, while also exhibiting strong robustness and potential for practical engineering applications.
Li-Kui Yi, Jia-Xuan Li, Yu-Qi Sun et al.· Energies· 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 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
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
Public transport corporations in Tamil Nadu face increasing operational and financial pressure due to rising diesel fuel prices, maintenance costs, and operational inefficiencies associated with the conventional bus systems. In many districts, diesel-powered public transport services also suffer from irregular vehicle dispatch and poor timetable adherence, leading to unreliable passenger service. Meanwhile, the rapid penetration of electric two-wheelers and four-wheelers indicates a broader transition towards electrified mobility. Extending electrification to public transport requires prudently designed operational planning, as electric buses operate under battery capacity constraints and charging coordination constraints. In such systems, strict adherence to the scheduling of trips and efficient energy management becomes critical for maintaining service reliability. To address these challenges, this study proposes a Tri-Level Hybrid Electric Bus Scheduling (TLH-EBS) framework integrating Particle Swarm Optimization for global search, Rule-Based Scoring Large Neighborhood Search for adaptive schedule improvement, and Mixed Integer Linear Programming for exact repair optimization. The framework simultaneously optimizes fleet size, depot charging infrastructure allocation, and daily bus assignment under timetable constraints. The proposed model has been applied in three interconnected corridors in Madurai District, which are Thirumangalam, Arapalayam, and Mattuthavani, covering 810 scheduled daily timetabled trips between 05:00 AM and 12:30 AM. Computational results show that the hybrid framework has achieved a 2.8% reduction in annual scheduling cost compared to the best conventional optimization method. Furthermore, compared to equivalent diesel-based operations, the optimized electric system has demonstrated approximately 32.3% annual cost savings, confirming the economic viability of integrated fleet–charger scheduling for district-level electric bus deployment.
Praveen Kumar Muthiah, Charles Raja Sathiasamuel, Arun Mozhi Subbukalai et al.· Sustainability· 0 citations
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