Search Results (66)

The growing integration of plug-in electric vehicles (PEVs) into microgrids presents both challenges and opportunities, particularly through vehicle-to-grid (V2G) services. This paper proposes a dynamic horizon optimization (DHO) framework with adaptive pricing for real-time scheduling of PEVs in a renewable-powered microgrid. The system integrates solar and wind energy, V2G capabilities, and time-of-use (ToU) tariffs. The DHO strategy dynamically adjusts control horizons based on forecasted load, generation, and electricity prices, while considering battery health. A PEV-specific pricing scheme couples ToU tariffs with system marginal prices. Case studies on a microgrid with four heterogeneous EV charging stations show that the proposed method reduces peak load by 23.5%, lowers charging cost by 12.6%, and increases average final SoC by 12.5%. Additionally, it achieves a 6.2% reduction in carbon emissions and enables V2G revenue while considering battery longevity. Full article

The rapid adoption of electric vehicles (EVs) requires efficient charging infrastructure planning. This study proposes a multi-objective optimization model for siting and capacity planning of EV charging stations, distinguishing between fast and slow chargers. The model integrates investment, dynamic electricity costs, and user experience, factoring in congestion-adjusted travel distances, time-of-use pricing, and queuing delays using an enhanced M/M/c approach. A comparison of algorithm reveals that the simulated annealing (SA) algorithm outperforms the genetic algorithm (GA) and ant colony optimization (ACO) in minimizing total costs. A case study in Changchun’s urban core demonstrates the model’s applicability, resulting in an optimal plan of 15 stations with 110 fast and 40 slow chargers, providing 11,544 kVA capacity at an annual cost of 38.2651 million yuan. Compared to traditional models that ignore charger types and simplify delays, the proposed model reduces total system costs by 4.31%, investment costs by 5.31%, and user costs by 3%, while easing delays in high-demand areas. This framework provides practical insights for urban planners and policymakers, helping balance investment and user satisfaction, and promoting sustainable EV mobility. Full article

Given the environmental threats, the adoption of green and clean mobility is crucial for decarbonizing the mobility sector. Green mobility will bring a mass integration of electric vehicle charging stations (EVCSs) to ensure sufficiency for electric vehicle (EVs) users. To achieve this, intelligently distributing the charging load of EVs is essential to prevent stress on local electrical grids. The uneven distribution of EV charging at specific EVCSs leads to load imbalances compared to underutilized stations, necessitating dynamic load-balancing (in real time) mechanisms to optimize grid demands and prevent overloading. To address this challenge, the authors propose an algorithm for balancing EV loads at EVCSs using dynamic charging prices. This algorithm is intended to be integrated into the OCPP. Simulation results indicate that lower pricing at Station A (0.22 $/kWh) attracts more users, reducing congestion at higher-priced Stations B (0.31 $/kWh) and E (0.29 $/kWh). The proposed model encourages users to utilize less crowded stations, achieving a fairer distribution of EV charging demand while providing cost benefits to users selecting those stations. Full article

The rapid proliferation of residential electric vehicles (EVs), rooftop photovoltaics (PVs), and behind-the-meter batteries is transforming energy communities while introducing new operational stresses to local distribution grids. Short-duration transformer overloads, often overlooked in conventional hourly or optimization-based planning models, can accelerate asset aging before voltage limits are reached. This study introduces a second-by-second, multi-agent-based simulation (MABS) framework that couples empirically calibrated Distributed Energy Resource (DER) adoption trajectories with real-time-price (RTP)–driven household charging decisions. Using a real 160-household feeder in Denmark (2024–2025), five progressively integrated DER scenarios are evaluated, ranging from EV-only adoption to fully synchronized EV–PV–battery coupling. Results reveal that uncoordinated EV charging under RTP shifts demand to early-morning hours, causing the first transformer overload within four months. PV deployment alone offers limited relief, while adding batteries delays overload onset by 55 days. Only fully coordinated EV–PV–battery adoption postponed the first overload by three months and reduced total overload hours in 2025 by 39%. The core novelty of this work lies in combining empirically grounded adoption behavior, second-level temporal fidelity, and agent-based grid dynamics to expose transient overload mechanisms invisible to coarser models. The framework provides a diagnostic and planning tool for distribution system operators to evaluate tariff designs, bundled incentives, and coordinated DER deployment strategies that enhance transformer longevity and grid resilience in future energy communities. Full article

This study presents a novel cyber-resilient, data-driven optimisation framework for real-time energy management in electric vehicle (EV)-integrated smart grids. The proposed framework integrates a hybrid optimisation engine—combining genetic algorithms and reinforcement learning—with a real-time analytics module to enable adaptive scheduling under uncertainty. It accounts for dynamic electricity pricing, EV mobility patterns, and grid load fluctuations, dynamically reallocating charging demand in response to evolving grid conditions. Unlike existing GA/RL schedulers, this framework uniquely integrates adaptive optimisation with resilient forecasting under incomplete data and lightweight blockchain-inspired cyber-defence, thereby addressing efficiency, accuracy, and security simultaneously. To ensure secure and trustworthy EV–grid communication, a lightweight blockchain-inspired protocol is incorporated, supported by an intrusion detection system (IDS) for cyber-attack mitigation. Empirical evaluation using European smart grid datasets demonstrates a daily peak demand reduction of 9.6% (from 33 kWh to 29.8 kWh), with a 27% decrease in energy delivered at the original peak hour and a redistribution of demand that increases delivery at 19:00 h by nearly 25%. Station utilisation became more balanced, with weekly peak normalised utilisation falling from 1.0 to 0.7. The forecasting module achieved a mean absolute error (MAE) of 0.25 kWh and a mean absolute percentage error (MAPE) below 20% even with up to 25% missing data. Among tested models, CatBoost outperformed LightGBM and XGBoost with an RMSE of 0.853 kWh and $R^2$ of 0.416. The IDS achieved 94.1% accuracy, an AUC of 0.97, and detected attacks within 50–300 ms, maintaining over 74% detection accuracy under 50% novel attack scenarios. The optimisation runtime remained below 0.4 s even at five times the nominal dataset scale. Additionally, the study outlines a conceptual extension to support location-based planning of charging infrastructure. This proposes the alignment of infrastructure roll-out with forecasted demand to enhance spatial deployment efficiency. While not implemented in the current framework, this forward-looking integration highlights opportunities for synchronising infrastructure development with dynamic usage patterns. Collectively, the findings confirm that the proposed approach is technically robust, operationally feasible, and adaptable to the evolving demands of intelligent EV–smart grid systems. Full article

Aiming at the incentive effect of real-time electricity prices on load demand response in the context of the electricity market, this paper proposed a dual layer optimization operation method for regional energy storage considering dynamic electricity prices and battery capacity degradation. The innovation of the proposed method lies in introducing user satisfaction and establishing real-time electricity price models based on fuzzy theory and consumer satisfaction, making dynamic electricity prices more realistic. At the same time, the proposed dual layer optimization operation model for regional energy storage has modeled the capacity degradation performance of energy storage batteries, which more accurately reflects the practicality of energy storage batteries. Finally, the particle swarm optimization (PSO) algorithm is utilized to efficiently optimize charging/discharging strategies, balancing economic benefits with battery longevity. The correctness of the proposed method is verified through simulation examples using MATLAB. Simulation results demonstrate that real-time electricity prices based on consumer satisfaction increase load demand response resources, resulting in stronger absorption of new energy sources, improving by 73.7%, albeit with reduced economic efficiency by 11.27%. While the real-time electricity prices based on fuzzy theory exhibit weaker absorption of new energy sources improving by only 36.4%, but achieve the best overall economic performance. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper proposes a real-time pricing optimization framework for large-scale EV charging networks incorporating renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The core objective is to dynamically determine spatiotemporal electricity prices that simultaneously reduce system peak load, improve renewable energy utilization, and minimize user charging costs. A rigorous mathematical formulation is developed integrating over 40 system-level constraints, including power balance, transmission capacity, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber resilience. Real-time electricity prices are treated as dynamic decision variables influenced by charging station utilization, elasticity response curves, and the marginal cost of renewable and grid-supplied electricity. The problem is solved over 96 time intervals using a hybrid solution approach, with benchmark comparisons against mixed-integer programming (MILP) and deep reinforcement learning (DRL)-based baselines. A comprehensive case study is conducted on a 500-station EV charging network serving 10,000 vehicles integrated with a modified IEEE 118-bus grid model and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar and wind profiles are used to simulate realistic operational conditions. Results demonstrate that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% improvement in renewable energy utilization, and user cost savings of up to 30% compared to baseline flat-rate pricing. Utilization imbalances across the network are reduced, with congestion mitigation observed at over 90% of high-traffic stations. The real-time pricing model successfully aligns low-price windows with high-renewable periods and off-peak hours, achieving time-synchronized load shifting and system-wide flexibility. Visual analytics including high-resolution 3D surface plots and disaggregated bar charts reveal structured patterns in demand–price interactions, confirming the model’s ability to generate smooth, non-disruptive pricing trajectories. The results underscore the viability of advanced optimization-based pricing strategies for scalable, clean, and responsive EV charging infrastructure management in renewable-rich grid environments. Full article

The introduction of Renewable Energy Sources (RESs) into the electricity grid is changing the price dynamics of the electricity market and creating room for flexibility on the consumption side. This paper investigates different aspects of the interaction between the RES share, electricity spot prices, and electric vehicle (EV) charging strategies. Based on empirical data from Germany, France, and the Czech Republic for the period 2015–2025, four research hypotheses are tested using correlation and regression analysis, cost simulations, and classification algorithms. The results confirm a negative correlation between the RES share and electricity prices, as well as the effectiveness of smart charging in reducing costs. At the same time, it is shown that the occurrence of negative prices is significantly affected by a high RES share. The correlation analysis further suggests that higher production from RESs increases the potential for price optimisation through smart charging. The findings have implications for policymaking aimed at flexible consumption and efficient RES integration. Full article

This paper analyzes the control strategy for urban battery-swapping stations by optimizing the charging policy based on real-time battery demand and the time required for a full charge. The energy stored in available batteries serves as an electricity buffer, allowing energy to be drawn from the grid when costs or equivalent ${\mathrm{CO}}_2$ emissions are low. An optimized charging policy is derived using dynamic programming (DP), assuming average battery demand and accounting for both the costs and emissions associated with electricity consumption. The proposed algorithm uses a prediction of the expected traffic in the area as well as the expected cost of electricity on the net. Battery tests were conducted to assess charging time variability, and traffic density measurements were collected in the city of Valencia across multiple days to provide a realistic scenario, while real-time data of the electricity cost is integrated into the control proposal. The results show that incorporating traffic and electricity price forecasts into the control algorithm can reduce electricity costs by up to 11% and decrease associated ${\mathrm{CO}}_2$ emissions by more than 26%. Full article

The large-scale integration of renewable energy and electric vehicles(EVs) into power distribution systems presents complex operational challenges, particularly in coordinating market mechanisms with grid stability requirements. This study proposes a new dispatching method based on dynamic electricity prices to coordinate the relationship between the market and the physical characteristics of the power grid. The proposed approach introduces a multi-agent transaction model incorporating voltage regulation metrics and network loss considerations into market bidding mechanisms. For EV integration, a differentiated scheduling strategy categorizes vehicles based on usage patterns and charging elasticity. The methodological innovations primarily include an enhanced scheduling algorithm for coordinated optimization of renewable energy and energy storage, and a dynamic coordinated optimization method for EV clusters. Implemented on a modified IEEE test system, the framework demonstrates improved voltage stability through price-guided energy storage dispatch, with coordinated strategies effectively balancing peak demand management and renewable energy utilization. Case studies verify the system’s capability to align economic incentives with technical objectives, where time-of-use pricing dynamically regulates storage operations to enhance reactive power support during critical periods. This research establishes a theoretical linkage between electricity market dynamics and grid security constraints, providing system operators with a holistic tool for managing high-renewable penetration networks. By bridging market participation with operational resilience, this work contributes actionable insights for developing interoperable electricity market architectures in energy transition scenarios. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper presents the first real-time quantum-enhanced electricity pricing framework for large-scale EV charging networks, marking a significant departure from existing approaches based on mixed-integer programming (MILP) and deep reinforcement learning (DRL). The proposed framework incorporates renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The objective is to dynamically determine spatiotemporal electricity prices that reduce system peak load, improve renewable utilization, and minimize user charging costs. A rigorous mathematical formulation is developed, integrating over 40 system-level constraints, including power balance, transmission limits, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber-resilience. Real-time electricity prices are treated as dynamic decision variables influenced by station utilization, elasticity response curves, and the marginal cost of renewable and grid electricity. The model is solved across 96 time intervals using a quantum-classical hybrid method, with benchmark comparisons against MILP and DRL baselines. A comprehensive case study is conducted on a 500-station EV network serving 10,000 vehicles, coupled with a modified IEEE 118-bus grid and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar/wind profiles are used to simulate realistic conditions. Results show that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% gain in renewable utilization, and up to 30% user cost savings compared to flat-rate pricing. Network congestion is mitigated at over 90% of high-traffic stations. Pricing trajectories align low-price windows with high-renewable periods and off-peak hours, enabling synchronized load shifting and enhanced flexibility. Visual analytics using 3D surface plots and disaggregated bar charts confirm structured demand-price interactions and smooth, stable price evolution. These findings validate the potential of quantum-enhanced optimization for scalable, clean, and adaptive EV charging coordination in renewable-rich grid environments. Full article

This research presents an innovative blockchain-based solution for the charging and energy trading of electric vehicles (EVs). By combining the strengths of two prominent consensus mechanisms, Proof of Work (PoW) and Proof of Stake (PoS), the proposed system balances security, decentralization, and energy efficiency. PoW secures the blockchain, while PoS enhances energy efficiency and scalability, key factors in meeting the growing demand for EV infrastructure. The system’s decentralized nature allows for EV owners, charging stations, and stakeholders to interact and transact transparently, without relying on centralized entities. The research conducts a comprehensive simulation to assess the performance of the proposed hybrid blockchain model, demonstrating significant improvements in cost-effectiveness, scalability, and energy management. Additionally, dynamic pricing mechanisms within the blockchain enable real-time energy trading, optimizing charging times and balancing grid demand efficiently. Through the use of smart contracts, automated pricing adjustments, and incentive-driven user behaviors, the proposed system paves the way for more sustainable, cost-effective, and efficient energy solutions in the future. Full article

The rapid growth of electric vehicle (EV) adoption presents significant challenges in planning efficient charging infrastructure, including suboptimal station placement, energy consumption, and rising infrastructural costs. The conventional methods, such as grey wolf optimization (GWO), fail to address real-time user demand and dynamic factors like fluctuating grid loads and environmental impact. These approaches rely on fixed models, often leading to inefficient energy use, higher operational costs, and increased traffic congestion. This paper proposes a novel framework that integrates deep Q networks (DQNs) for real-time charging optimization, coupled with multimodal bioinspired algorithms like ant lion optimization (ALO) and moth flame optimization (MFO). Unlike conventional geographic placement models that overlook evolving travel patterns, this system dynamically adapts to user behavior, optimizing both onboard and offboard charging systems. The DQN enables continuous learning from changing demand and grid conditions, while ALO and MFO identify optimal station locations, reducing energy consumption and emissions. The proposed framework incorporates dynamic pricing and demand response strategies. These adjustments help balance energy usage, reducing costs and preventing overloading of the grid during peak times, offering real-time adaptability, optimized station placement, and energy efficiency. To improve the performance of the system, the proposed framework ensures more sustainable, cost-effective EV infrastructural planning, minimized environmental impacts, and enhanced charging efficiency. From the results for the proposed system, we recorded various performance parameters such as the installation cost, which decreased to USD 1200 per unit, i.e., a 20% cost efficiency increase, optimal energy utilization increases to 85% and 92% during peak hours and off-peak hours respectively, a charging slot availability increase to 95%, a 30% carbon emission reduction, and 95% performance retention under the stress condition. Further, the power quality is improved by reducing the sag, swell, flicker, and notch by 2 V, 3 V, 0.05 V, and 0.03 V, respectively, with an increase in efficiency to 89.9%. This study addresses critical gaps in real-time flexibility, cost-effective station deployment, and grid resilience by offering a scalable and intelligent EV charging solution. Full article

Governments are focusing on citizen participation in the energy transition, e.g., with dynamic electricity tariffs, which pass part of the wholesale price volatility to end users. While often the cheapest alternative, these tariffs require micromanagement for optimization. In this research, an automated system capable of supplying electricity for home use at minimal cost called Smart Relays and Controller (SRC) is presented. SRC scrapes prices online, charges a battery system during the cheapest time slots and supplies electricity to the home energy system from the cheapest source, either the battery or the grid, while optimizing battery life. To validate the system, a comparison is made between SRC, a programmable scheduler and PVPC (Spain’s dynamic tariff) using twenty-eight months of hourly historical data. SRC is shown to be superior to both the scheduler and PVPC, with the scheduler performing worse than SRC but better than PVPC (T.T., p < 0.001). SRC achieves a 36.16% discount over PVPC, 13.89% when factoring in battery life. The savings are 44.24% higher with SRC than with a scheduler. Neither inflation nor incentives to reduce costs are considered. While we studied Spain’s tariff, SRC would work in any country offering dynamic electricity tariffs, with benefit margins dependent on their particularities. Full article

This study examines reinforcement learning (RL) and fuzzy logic control (FLC) for optimizing battery energy storage in residential systems with photovoltaic (PV) power, grid interconnection, and dynamic or fixed electricity pricing. Effective management strategies are crucial for reducing costs, extending battery lifespan, and ensuring reliability under fluctuating demand and tariffs. A 24 h simulation with minute-level resolution modeled diverse conditions, including random household demand and ten initial state of charge (SOC) levels from 0% to 100%. RL employed proximal policy optimization (PPO) for adaptive energy scheduling, while FLC used rule-based logic for charge–discharge cycles. Results showed that FLC rapidly restored SOC at low levels, ensuring immediate availability but causing cost fluctuations and increased cycling, particularly under stable pricing or low demand. RL dynamically adjusted charging and discharging, reducing costs and smoothing energy flows while limiting battery cycling. Feature importance analysis using multiple linear regression (MLR) and random forest regression (RFR) confirmed SOC and time as key performance determinants. The findings highlight a trade-off between FLC’s rapid response and RL’s sustained cost efficiency, providing insights for optimizing residential energy management to enhance economic and operational performance. Full article