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The rapid integration of renewable energy is increasing the need for fast and sustained load-side frequency regulation, and public electric vehicle (EV) charging networks are promising providers. Their participation, however, is constrained by the volatile charging demand and strict service requirements, which make it difficult to balance regulation revenue with charging quality. This paper proposes a three-layer coordinated framework for multi-site charging networks participating in secondary frequency regulation, comprising market bidding, rolling planning, and fast-response tracking. At the market layer, baseline charging schedules are co-optimized with symmetric regulation capacity bids. At the planning layer, completion margin and progress protection constraints are introduced as tractable service safeguards that preserve charging continuity and deadline compliance. At the execution layer, coordinator-assisted distributed station-level tracking and charger-level urgency-aware allocation track automatic generation control (AGC) commands while correcting the charging progress in real time. The station-level problem is decomposed into local box-constrained subproblems coordinated by a scalar dual signal, enabling real-time AGC tracking with limited inter-station information exchange. Case studies on a reproducible simulated network with 20 stations and 600 chargers show that the proposed method improves ancillary service benefits while maintaining strong tracking performance and markedly improving the charging continuity, deadline compliance, and spatial load balance. Full article

With the widespread adoption of electric vehicles (EVs), the deep integration of transportation and power grids has emerged as a significant trend. EV charging stations, acting as dynamic loads, present challenges to real-time power balance and economic dispatch in microgrids, while EVs serving as mobile energy storage units offer new opportunities for system flexibility. To address these issues, this paper proposes a hardware-in-the-loop (HIL) co-design method for vehicle–grid-integrated microgrid energy management systems, covering the entire workflow from simulation to embedded deployment. This method resolves the core challenges of multi-objective optimization algorithm deployment on embedded platforms (i.e., high computational complexity, strict real-time constraints, and heterogeneous communication protocol integration) via deployability analysis, hybrid code generation, real-time task restructuring, and consistency validation. A prototype microgrid system integrating photovoltaic panels, wind turbines, diesel generators, an energy storage system, and EV charging loads was built on the RK3588 embedded platform. An improved multi-objective particle swarm optimization (MOPSO) algorithm is employed to optimize operational costs. Experimental results verify the effectiveness of the proposed co-design method. Compared with traditional rule-based control strategies, the MOPSO algorithm reduces the total daily operating cost of the VGIM system by approximately 50%. After integrating vehicle-to-grid (V2G) scheduling, the operating cost is further reduced. In addition, this method ensures the consistency of algorithm functionality and performance during the migration from HIL simulation to embedded deployment, and the RK3588-based embedded system can complete a single optimization iteration within 60 s, which fully satisfies the real-time requirements of industrial applications. This work provides a feasible technical pathway for the reliable deployment of vehicle–grid-integrated microgrids in practical industrial scenarios. Full article

Optimized dispatch of microgrids is crucial for improving the economic performance and long-term sustainability of modern low-carbon power systems. In particular, accurate economic dispatch modeling for battery energy storage systems (BESSs) is essential for properly evaluating the operational benefits and lifetime costs of microgrids. However, when both battery cycle aging and calendar aging are considered, the resulting scheduling model becomes highly nonlinear, high-dimensional, non-convex, and multimodal, which poses substantial challenges to conventional optimization methods. To alleviate the above problem, a symmetry-guided multi-strategy differential hybrid slime mold algorithm (MDHSMA) is introduced for the day-ahead economic dispatch of microgrids under a refined battery degradation framework. First, a chaotic bimodal mirrored Latin hypercube sampling strategy is designed to exploit symmetry during population initialization, thereby enhancing diversity and improving structured coverage of the search space. Second, a history-driven adaptive differential evolution mechanism is integrated to balance global exploration and local exploitation more effectively during the iterative search process. Third, a state-aware stagnation handling framework is incorporated to maintain population vitality and further improve convergence accuracy and robustness. MDHSMA is evaluated against 12 state-of-the-art optimizers on the CEC2017 and CEC2022 benchmark suites and two representative engineering optimization problems to verify its overall performance. In addition, it is applied to a microgrid case study with refined BESS degradation modeling. The results show that MDHSMA achieves the lowest comprehensive operating cost by effectively coordinating electricity arbitrage and battery life consumption. Moreover, it guides the energy storage system toward shallow charge–-discharge patterns, thereby mitigating accelerated degradation caused by excessive cycling. These results confirm the effectiveness and practical value of the proposed method for sustainable microgrid dispatch in complex nonconvex optimization scenarios. Full article

External electricity supplementation for islanded microgrids at expressway service areas is often settled under tiered electricity pricing based on cumulative energy consumption, where marginal prices increase discontinuously once tier thresholds are exceeded. This mechanism reshapes battery dispatch behavior and may alter economically optimal storage sizing. This paper proposes a unified planning–-operation optimization framework for wind–PV–battery microgrids that jointly determines the storage capacity and hourly scheduling while enforcing power balance, battery state-of-charge dynamics, and tiered settlement costs. By introducing tier-wise energy allocation variables and tier cap constraints, the nonlinear settlement rule is reformulated into an equivalent piecewise-linear structure, leading to a mixed-integer linear programming (MILP) model that can be solved using standard optimization solvers. A season-weighted annualized case study using four typical seasonal days reveals critical cross-tier dispatch behaviors, where charging–discharging schedules shift near tier boundaries and external electricity purchases are actively suppressed from entering higher-priced tiers. The proposed framework quantifies the premium-avoidance value of storage and provides a practical decision support tool for premium risk-aware sizing and operation of islanded expressway service-area microgrids. Full article

To address the coupled challenges of renewable power volatility, high operating cost, and electrolyzer degradation in grid-connected wind–PV hydrogen production systems, this paper proposes a hierarchical day-ahead scheduling strategy under time-of-use (TOU) electricity prices. The upper layer performs price-responsive economic dispatch to coordinate renewable utilization, battery operation, grid transactions, and aggregate hydrogen-production power with the objective of minimizing lifecycle operating cost. The lower layer introduces a health-aware non-uniform rotation mechanism to allocate the aggregate power command among electrolyzer units, thereby reducing fluctuation exposure and balancing lifetime consumption across the array. Practical constraints, including multi-state electrolyzer operation, unit-commitment logic, battery state-of-charge dynamics, hydrogen storage limits, and system power balance, are explicitly considered. A case study of a wind–PV hydrogen production project in Northern China shows that the proposed strategy shifts electricity purchases to valley-price periods and promotes electricity export during peak-price periods. Compared with the benchmark strategy, hydrogen production during low wind–PV generation periods increases from 342,000 to 381,000 Nm³, the share of fluctuating operating time decreases from 62.5% to 12.5%, and the average daily start–stop frequency declines from 8.0 to 4.8. Consequently, the degradation penalty is reduced by about 40%, and lifecycle operating cost decreases by 27.3%. Full article

This study proposes an adaptive variable structure control strategy for charging stacks to address the issues of reduced conversion efficiency during wide-voltage-range operation and insufficient module allocation flexibility in multi-vehicle scenarios. By dynamically adjusting the number and series/parallel configurations of modules, the strategy ensures that modules consistently operate in high-efficiency regions, thereby achieving high energy conversion efficiency across a wide voltage range. First, the operational characteristics of the three-phase PWM rectifier and the dual active bridge (DAB) converters are analyzed, and their corresponding mathematical and loss models are established. Subsequently, the charging demands acquired by the charging stack are analyzed, and an adaptive variable structure control strategy is designed based on the module margin of the charging stack. When modules are surplus, the feasible range of series/parallel configurations for each port is constrained, and module combinations are optimized with the objective of minimizing system losses. When modules are insufficient, an adaptive module reservation scheduling strategy is employed to ensure temporal fairness in vehicle connection order while supplying power to multiple vehicles, effectively reducing the average charging time. Finally, the effectiveness of the proposed control strategy is validated through simulations conducted on the Matlab/Simulink platform. Results demonstrate that compared to traditional fixed-structure systems, the proposed strategy improves peak efficiency by up to 2.53% at 400 V and 1.12% at 800 V, while reducing the average charging time by 3.07% in the disconnection scenario and 12.1% in the asynchronous access scenario. Full article

The rapid integration of electric vehicles (EVs) into transportation systems and power grids has significantly increased the complexity of optimization challenges related to routing, charging coordination, scheduling, and energy management. Despite significant research growth, the field remains conceptually fragmented, lacking a unified framework to systematically organize Electric Vehicle Optimization Problems (EVOPs). To address this gap, this study presents a systematic review of 144 peer-reviewed articles published between 2011 and January 2025 and proposes a structured EVOP taxonomy based on problem characteristics and dominant decision variables. The analysis examines mathematical formulations, solution methodologies, and emerging research trends. The results indicate the predominance of metaheuristic methods, while exact techniques are mainly limited to small-scale problems. Additionally, there is a growing trend toward multi-objective and stochastic models that incorporate uncertainty and dynamic decision-making environments. However, challenges remain regarding large-scale validation, standardized benchmarking, and integrated multi-domain modeling. The proposed taxonomy provides a coherent framework that facilitates comparison across optimization domains and supports the development of scalable and intelligent EV management systems. Full article

As the electrification of transportation systems accelerates, incentivizing electric vehicle (EV) participation in vehicle-to-grid (V2G) operations is becoming increasingly crucial. This paper introduces a dynamic EV charging and discharging strategy that incorporates integrated energy station (IES) congestion and electricity purchase and sale scenarios. The proposed strategy seeks to facilitate orderly EV charging and discharging within a real-time simulation framework that integrates the transportation network (TN), IES, and the external grid (EG). First, we develop a real-time collaborative simulation framework that combines microscopic traffic flow (MTL) and IES–grid energy interaction models to account for mutual feedback among these components. Second, we propose an EV IES selection strategy aimed at maximizing discharge revenue, which takes into account various factors, including driving distance, time costs, battery degradation, discharge benefits, and government subsidies. Finally, we design a dynamic discharge pricing model based on real-time vehicle arrival patterns at the IES and the status of electricity purchases and sales. Simulation results show that the EV IES selection strategy, optimized for discharge revenue, reduces average user waiting time by 5.36%, decreases network time loss by 3.86%, and increases EV discharge revenue by 6.79%. Furthermore, the introduction of dynamic pricing leads to additional reductions in waiting time and network time loss by 3.46% and 4.80%, respectively. The proposed mechanism and pricing strategy effectively mitigate traffic congestion, enhance user discharge revenue, and provide flexible scheduling options for IES operations. Full article

This paper presents a distributed V2G-enabled multiport DC charging system with a hierarchical charging management strategy. Unlike conventional architectures based on centralized power converter cabinets, the proposed system distributes bidirectional power converters within individual multiport dispensers, each equipped with a local charging power management device. This architecture improves system scalability, fault tolerance, and operational flexibility while enabling vehicle-level charging and V2G services. A hierarchical control framework is introduced, consisting of high-level optimal charging scheduling, mid-level power coordination among distributed dispensers, and low-level converter control. Key elements include modular power units that can be dynamically configured and expanded, providing a cost-effective and adaptable solution for growing EV markets. Experimental results obtained from a 45 kW modular DC charging prototype demonstrate an efficiency improvement of up to 2% at rated power compared to a non-modular charger. In contrast, the optimized charging strategy achieves an overall charging cost reduction of approximately 11% and a peak load demand reduction of up to 31%. Furthermore, stable bidirectional power flow, effective power sharing, and total harmonic distortion within regulatory limits are experimentally validated during both charging and V2G operation. The prototype is implemented to validate the proposed charging system in the laboratory environment. Full article

Fast charging of electric vehicles can introduce phase-dependent harmonic distortion and voltage unbalance in low-voltage feeders, which may reduce admissible charging capacity even when voltage magnitudes remain within conventional limits. This paper proposes a phase-aware predictive scheduling framework for harmonic hosting management in feeders with a high penetration of electric vehicle charging. The proposed method formulates feeder operation as a predictive decision problem that jointly determines charging power levels, phase allocation, and the selective activation of multifunctional compensation resources under harmonic distortion, voltage unbalance, and neutral-current constraints. Unlike previous studies centered on harmonic characterization, static hosting assessment, or local converter-level mitigation, the proposed approach treats harmonic hosting as an active feeder-level network management problem. The framework is evaluated through time-series harmonic power-flow simulations using charger harmonic emission profiles and realistic feeder parameters. The numerical results indicate that coordinated phase-aware scheduling can increase admissible charging capacity, improve compliance margins for power-quality indices, and reduce mitigation efforts with respect to uncontrolled charging and non-coordinated compensation strategies. Overall, the results support the use of phase-aware scheduling as a feeder-level strategy to improve electric vehicle charging integration under harmonic and unbalanced constraints. Full article

Amid the global decarbonization of urban transportation, the large-scale deployment of electric buses faces major challenges, including concentrated charging demand, increased peak electricity demand, and inefficient energy utilization at transit depots. Existing studies usually optimize depot energy system configuration and bus scheduling separately, which often leads to biased system-level decisions. To address this limitation, this study proposes a collaborative optimization framework that integrates cross-line scheduling with the configuration of photovoltaic–storage–charging systems at depots to improve overall resource utilization. Specifically, this study formulates a mixed-integer linear programming (MILP) model to minimize the total daily system cost. The proposed model comprehensively captures multiple factors, including the costs of bus investment, charging infrastructure, photovoltaic deployment, energy storage deployment, and carbon emissions. In this study, Benders decomposition is used as a solution framework to handle the coupling structure of the model. Case studies show that, compared with conventional operation modes, the combination of cross-line scheduling and fast charging technology produces a significant synergistic effect. This combination reduces the required fleet size from 17 to 14 buses and substantially lowers investment in depot infrastructure, thereby minimizing the total system cost. Sensitivity analysis further shows that the deployment scale of photovoltaic systems has a clear threshold effect on electricity costs, whereas the core economic value of energy storage systems depends on peak shaving and arbitrage under time-of-use electricity pricing. Overall, this study demonstrates the critical role of integrated planning in improving the economic efficiency and operational feasibility of electric bus systems. It provides important theoretical support and practical guidance for depot design and resource scheduling in low-carbon public transportation networks. Full article

To enhance the sustainability of temporary office buildings, energy-saving and emissions-reduction technologies, as well as the optimization of photovoltaic (PV) energy storage systems in such structures, are of great importance. In this study, a distributed energy storage system was developed for a temporary office building in Jincheng, China. Measurements showed climatic factors had the greatest effect on building energy consumption due to the building envelope’s low thermal performance and airtightness. The air conditioning system accounted for the highest proportion (87%) of building energy consumption. The PV system’s peak output occurred in the morning due to illumination conditions and module orientation. On this basis, a time-of-use (TOU)- and state-of-charge (SOC)-aware scheduling strategy was developed for the PV-ESS of the temporary office building to improve renewable-energy utilization and reduce user-end electricity cost. Unlike purely theoretical optimization studies, this work focuses on the practical application and validation of the scheduling framework in a real temporary office building using monitored data. The electricity cost decreased by 0.3 RMB/kWh, and the revenue from electricity sales during the scheduling period increased by 0.03 RMB/kWh after model optimization. The optimized scheduling strategy resulted in significantly fewer charge–discharge cycles of the storage battery, substantially decreasing the battery’s storage capacity and the system’s investment costs. Full article

In electric vehicle logistics, limited range and charging station capacity pose critical challenges to route planning, with direct implications for the sustainability of transportation systems. Conventional electric vehicle routing problem (EVRP) models that account for charger capacity typically rely on discrete-time approximations or fixed charging rules, failing to capture continuous-time waiting behavior or flexible charging decisions. These limitations may lead to additional vehicle dispatch, resulting in energy waste and increased carbon emissions. This study develops a novel EVRP model that simultaneously incorporates constraints on both station and battery capacity, and proposes a tailored genetic-algorithm-based heuristic to address computational challenges. The model innovatively employs a set of linear constraints to precisely represent limited chargers in continuous time, clearly distinguishing vehicle charging from waiting. Moreover, it enables vehicles to autonomously determine optimal charging amounts based on route and battery state, rather than following preset rules. Numerical results on an eight-customer instance show that the proposed model reduces total task completion time from 98.9 units to 60.4 units, a 38.9% improvement, compared to the conventional vehicle-count-based capacity constraint. On a 20-customer instance, the proposed heuristic obtains an objective value of 101.99 within 15 s, whereas Gurobi requires 205 s to achieve a marginally better value of 99.00. For a 60-customer network, the proposed GA converges within 30 s, and sensitivity analysis on charger availability further validates the model’s effectiveness. These results validate the model’s capability under limited charging resources and the algorithm’s scalability for time-sensitive logistics scheduling. Full article

To reduce charging time and improve operational efficiency at integrated energy stations (IESs) for electric vehicles (EVs), this paper develops a sustainability-oriented collaborative optimization model by coupling vehicle routing behavior with charging decision-making. Firstly, a dynamic road network model is established to simulate vehicle arrivals at IESs from different network nodes. Then, considering grid peak–valley electricity prices, station electricity procurement costs and EV charging demand, a dynamic pricing strategy for IESs is proposed to guide EVs to charge at off-peak hours so as to realize peak shaving and valley filling for the power grid. Meanwhile, the NSGA-III algorithm is improved through the introduction of Good Point Set initialization and an adaptive crossover mechanism, and the Good Point Set initialization and Adaptive Crossover NSGA-III (GPS-AC-NSGA-III) algorithm is proposed to solve the scheduling optimization problem. Finally, the CRITIC-based TOPSIS method is employed to identify the optimal compromise solution from the Pareto-optimal set. Case studies further prove the effectiveness of the proposed multi-objective collaborative optimization model for EVs and IESs. Compared with scenarios without dynamic Dijkstra-based navigation and dynamic pricing, the IES daily revenue increased by 39.83%, pollutant emissions decreased by 0.4%, and the peak-to-valley load difference ratio was reduced by 4.94%. The results indicate that dynamic Dijkstra-based vehicle routing improves travel efficiency, while the proposed dynamic pricing strategy enhances station profitability and smooths grid load fluctuations. Overall, the proposed framework contributes to sustainable transportation and energy systems by reducing pollutant emissions, improving energy efficiency, and enhancing the operational stability of integrated energy infrastructure, thereby supporting the transition toward low-carbon and sustainable urban energy systems. Full article

Wind–solar coupled hydrogen production DC microgrids have significant potential for improving renewable energy utilization and reducing the cost of hydrogen production. However, the randomness of wind–solar power causes frequent electrolyzer start–stop operations, accelerating lifetime degradation, while a single energy storage system cannot simultaneously suppress power fluctuations and regulate energy. Therefore, this study proposes a two-stage day-ahead energy scheduling optimization framework. A DBSCAN–K-means hybrid clustering method generates representative wind–solar power scenarios. A supercapacitor-based strategy mitigates high-frequency power fluctuations using empirical mode decomposition. Furthermore, a dual-scenario-driven electrolyzer scheduling strategy adapted to different wind–solar output conditions is developed, where power allocation is determined by battery state-of-charge and electrolyzer operating states, enabling stepwise power compensation and dynamic operating-state optimization. Case studies comparing wind–solar-only supply, a conventional strategy, and the proposed strategy demonstrate that the proposed strategy balances hydrogen production and economic objectives, and reduces annual electrolyzer start–stop cycles by 73%, thereby prolonging electrolyzer lifetime. Furthermore, the proposed framework enhances renewable energy utilization, reduces curtailment, and lowers lifecycle costs, thereby contributing to the development of sustainable hydrogen production systems. Full article