Aug 2026· Engineering, Technology & Applied Science Research· 0 citations· 21 references
TL;DR
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.
Abstract
In this paper, an integrated real-time framework for coordinated Electric Vehicle (EV) charging is proposed based on load forecasting and multi-objective optimization techniques. The framework integrates load forecasting using Long Short-Term Memory (LSTM) and multi-objective optimization to minimize peak load, charging cost, and grid stress while maintaining high user satisfaction under dynamic smart-grid conditions. The proposed framework combines multi-step LSTM forecasting with a convex optimization scheduler operating on a 15-min rolling horizon that incorporates feeder constraints, electricity tariffs, charger limits, and departure state-of-charge requirements. Unlike conventional charging strategies, the proposed method enables adaptive and grid-aware charging decision-making in real time. The framework is evaluated under various EV penetration scenarios ranging from 30 to 100 EVs and is compared with uncontrolled charging, off-peak charging, and load-balancing strategies. The results demonstrate an average 25% reduction in peak load, a 15–20% reduction in charging costs, smoother feeder operation, and a user satisfaction rate exceeding 95% in meeting charging requirements. Furthermore, the proposed framework improves operational stability, reduces computational burden, and enhances charging coordination compared with conventional forecasting and heuristic scheduling approaches. These findings demonstrate the feasibility and scalability of integrating Machine Learning (ML)-based forecasting with real-time optimization for future smart-grid and EV energy management systems.
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 fast penetration of renewable energy resources, electric vehicles (EVs), and distributed energy systems has made demand-side management (DSM) in modern smart grids very complex. Traditional DSM methods usually consider load forecasting and scheduling as separate tasks, which results in suboptimal energy consumption, higher operational cost, greater carbon emissions, and lower grid stability under dynamic operating conditions. To address the above limitations, an Explainable Hybrid Particle Swarm Optimization-Long Short-Term Memory (PSO-LSTM) framework for intelligent multi-objective demand-side management in renewable energy-integrated smart grids is proposed. The proposed framework integrates the temporal learning capability of LSTM to accurately forecast the short-term electricity demand and the global optimization capability of PSO to optimally schedule appliances, coordinate EV charging, allocate renewable energy, and implement dynamic demand response. Hence, the optimization problem is formulated as a constrained multi-objective mathematical model to minimize the electricity cost, peak-to-average ratio (PAR), carbon emissions, and scheduling delay and to maximize the renewable energy utilization, consumer comfort, and grid reliability. The framework is validated on multivariate smart grid datasets, including historical electricity consumption, weather, photovoltaic generation, electricity prices, battery state-of-charge, EV charging profiles, and grid load data. Experimental results show that the proposed framework PSO–LSTM obtains an accuracy of 98.24% for forecasting and decreases RMSE and MAE to 0.081 and 0.061, respectively. Moreover, the proposed model achieves 24.76% peak-load reduction, 21.35% electricity cost savings, 91.42% renewable energy utilization, and significant reductions in carbon emissions compared with ANN, SVM, standalone LSTM, and Deep Reinforcement Learning (DRL) models. The statistical analysis also validates the robustness and superiority of the proposed framework in multiple performance metrics. The proposed explainable hybrid framework provides a scalable, computationally efficient, and real-time intelligent energy management solution for next-generation sustainable smart grids and smart city applications.
Rajendra B. Sadaphale, Prakash Burade· International journal of com...· 0 citations
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
The growing penetration of renewable energy resources, electric vehicles (EVs) and distributed energy systems, demand-side management (DSM) in modern smart grids has become increasingly complex. Conventional DSM methods usually show lower predictive capability and ineffective load scheduling in a tight operating environment, which leads to increased operational costs, hindered grid stability. This paper presents a hybrid PSO–RNN framework for intelligent freedom of management for power systems. The proposed framework combines an RNN for precise short-term load forecasting with PSO for effective optimal load scheduling, EV charging coordination, and energy distribution by utilizing data on historical electricity consumption, outdoor weather conditions, renewable energy generation variables, electricity pricing data of local suppliers in Singapore,, average grid load from the previous day (24 hours) as well as #datetimeindex. The experimental evaluation shows that the proposed PSO–RNN model can reach 98.24% forecasting accuracy, reduce RMSE to 0.081 and MAE to 0.061 value, while achieving over 24.76 % of peak-load reduction; saving about 21.35 % of electricity cost; using approximately by up to 91.42 % renewable energy as well, compared with ANN, SVM, DRL and RNN standalone models. These features increase reliability, lower operation costs and assist in sustainable energy recovery. The designed framework can effectively serve as a scalable, adaptive and computationally efficient next-generation intelligent smart grid and real-time electricity demand side management solution.
Rajendra B. Sadaphale, P. Burade· International journal of com...· 0 citations
The growing adoption of electric vehicles (EVs) necessitates intelligent charging strategies to alleviate grid congestion and control rising operational costs. This study introduces an IoT-enabled centralized energy management framework for a PV-BESS-EV integrated smart parking system, leveraging real-time data on carbon emissions, grid pricing, and solar irradiance. A key innovation is its bi-objective optimization model, which simultaneously minimizes both cost and carbon footprint, setting it apart from traditional single-objective approaches. The study evaluates teaching-learning-based optimization (TLBO) and particle swarm optimization (PSO) for addressing the system’s nonlinear challenges. Results indicate that TLBO offers faster convergence and greater robustness, leading to improved load flattening, enhanced PV utilization, and stable battery energy storage system (BESS) state of charge (SoC). Overall, the framework provides a scalable solution that effectively balances economic and environmental objectives for modern grid-integrated EV charging systems.
O. K. Rajesh, N. Shanmugasundaram, V. Rajendran· International Journal of Pow...· 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
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