Search Results (754)

Active distribution systems (ADS) are increasingly strained by rising energy demand and the widespread deployment of distributed energy resources (DERs) and electric vehicle charging stations (EVCS), which intensify voltage deviations, power losses, and peak demand fluctuations. This study develops a coordinated optimization framework for Mobile Battery Energy Storage Systems (MBESS) and Dynamic Feeder Reconfiguration (DFR) to enhance network performance across technical, economic, and environmental dimensions. A Non-dominated Sorting Genetic Algorithm III (NSGA-III) is employed to minimize six objectives the active and reactive power losses, voltage deviation index (VDI), voltage stability index (FVSI), operating cost, and CO₂ emissions while explicitly modeling the MBESS transportation constraints such as energy consumption and single-trip mobility within coupled IEEE 33-bus and 33-node transport networks, which provide realistic mobility modeling of energy storage operations. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to select compromise solutions from Pareto fronts. Simulation results across six scenarios show that the coordinated MBESS–DFR operation reduces power losses by 27.8–30.1%, improves the VDI by 40.5–43.2%, and enhances the FVSI by 2.3–2.4%, maintaining all bus voltages within 0.95–1.05 p.u. with minimal cost (0.26–0.27%) and emission variations (0.31–0.71%). The MBESS alone provided limited benefits (5–12%), confirming that coordination is essential for improving efficiency, voltage regulation, and overall system sustainability in renewable-rich distribution networks. Full article

The rapid growth of robotic warehouses and smart logistics has increased the demand for efficient scheduling of multi-tier shuttle systems (MTSSs). MTSS scheduling is a complex robotic task allocation problem, where throughput, energy efficiency, and service quality must be jointly optimized under operational constraints. To address this challenge, this study proposes a hybrid optimization framework that integrates the Minimum-Cost Maximum-Flow (MCMF) algorithm with the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The MTSS is modeled as a cyber–physical robotic system that explicitly incorporates task flow, energy flow, and information flow. The lower-layer MCMF ensures efficient and feasible task–robot assignments under state-of-charge (SOC) and deadline constraints, while the upper-layer NSGA-II adaptively tunes cost-function weights to explore Pareto-optimal trade-offs among makespan, energy consumption, and waiting time. Simulation results show that the hybrid framework outperforms baseline heuristics and static optimization methods and reduces makespan by up to 5%, the energy consumption by 2.8%, and the SOC violations by over 90% while generating diverse Pareto fronts that enable flexible throughput-oriented, service-oriented, or energy-conservative scheduling strategies. The proposed framework thus provides a practical and scalable solution for energy-aware robotic scheduling in automated warehouses, thus bridging the gap between exact assignment methods and adaptive multi-objective optimization approaches. Full article

The integration of renewable energy systems and electrified transportation requires advanced energy storage solutions capable of providing both high energy density and fast dynamic response. Hybrid energy storage systems offer a promising approach by combining complementary battery chemistries, exploiting their respective strengths while mitigating individual limitations. This study presents the design, modeling, and optimization of a hybrid energy storage system composed of two high-energy lithium nickel manganese cobalt batteries and one high-power lithium titanate oxide battery, interconnected through a triple dual-active multi-port converter. A nonlinear model predictive control strategy was employed to optimally distribute battery currents while respecting constraints such as state of charge limits, current bounds, and converter efficiency. Equivalent circuit models were used for real-time state of charge estimation, and converter losses were explicitly included in the optimization. The main contributions of this work are threefold: (i) verification of the model predictive control strategy in diverse applications, including residential renewable energy systems with photovoltaic generation and electric vehicles following the World Harmonized Light-duty Vehicle Test Procedure driving cycle; (ii) explicit inclusion of the power converter model in the system dynamics, enabling realistic coordination between batteries and power electronics; and (iii) incorporation of converter efficiency into the cost function, allowing for simultaneous optimization of energy losses, battery stress, and operational constraints. Simulation results demonstrate that the proposed model predictive control strategy effectively balances power demand, extends system lifetime by prioritizing lithium titanate oxide battery during transient peaks, and preserves lithium nickel manganese cobalt cell health through smoother operation. Overall, the results confirm that the proposed hybrid energy storage system architecture and control strategy enables flexible, reliable, and efficient operation across diverse real-world scenarios, providing a pathway toward more sustainable and durable energy storage solutions. Full article

In response to the challenges of electric mobility in off-grid contexts, this study introduces a novel and pragmatic solution: an intelligent, embedded EV charging system capable of anticipating energy availability using external weather forecasts. An embedded Model Predictive Control (MPC) scheme was implemented on an ESP32 microcontroller, incorporating real-time solar and wind forecasts transmitted via LoRa. Unlike conventional approaches that are often centralized or resource-intensive, the proposed architecture enables localized, forecast-aware decision making, while respecting physical constraints (SOC, power limits, system stability) within the limits of embedded hardware. The proposed system was fully validated through functional simulations (data acquisition, processing, display, and physical actuation). Results confirm the feasibility of real-time, stable, and proactive energy management, laying the foundation for smart, resilient, and autonomous renewable-based EV charging stations tailored to remote areas and decentralized microgrids. Full article

The increasing adoption of electric vehicles has driven the development of charging technologies that meet growing demands for power, efficiency, and grid compatibility. This review presents a comprehensive analysis of the EV charging ecosystem, covering Level 3 DC charging stations, power converter topologies, and the role of energy storage systems in supporting grid integration. Commercial solutions and academic prototypes are compared across key parameters such as voltage, current, power, efficiency, and communication protocols. The study highlights trends in charger architectures—including buck, boost, buck–boost, LLC resonant, and full-bridge configurations—while also addressing the integration of stationary storage as a buffer for fast charging stations. Special attention is given to wide-bandgap semiconductors like SiC and GaN, which enhance efficiency and thermal performance. A significant gap persists between the technical transparency of commercial systems and the ambiguity often observed in prototypes, highlighting the urgent need for standardized research reporting. Although converter efficiency is no longer a primary constraint, substantial challenges remain regarding infrastructure availability and the integration of storage with charging stations. This paper seeks to offer a comprehensive perspective on the design and deployment of smart, scalable, and energy-efficient charging systems, with particular emphasis on cascaded and bidirectional topologies, as well as hybrid storage solutions, which represent promising pathways for the advancement of future EV charging infrastructure. Full article

With the increasing penetration of renewable energy such as solar and wind power into the grid as well as the addition of modern types of versatile loads such as electric vehicles, the grid system is more prone to system failure and instability. One of the possible solutions to mitigate these conditions and increase the system efficiency is the integration of virtual power plants into the system. Virtual power plants can aggregate distributed energy resources such as renewable energy systems, electric vehicles, flexible loads, and energy storage, thus allowing for better coordination and optimization of these resources. This paper proposes a genetic algorithm-based optimization to coordinate the different elements of the energy management system of a virtual power plant, such as the energy storage system and charging/discharging of electric vehicles. It also deals with the random behavior of the genetic algorithm and its failure to meet certain constraints in the final solution. A novel method is proposed to mitigate these problems that combines a genetic algorithm in the first stage, followed by a gradient-based method in the second stage, consequently reducing the overall electricity bill by 50.2% and the simulation time by almost 95%. The performance is evaluated considering the reference set-points of operation from the obtained solution of the energy storage and electric vehicles by performing tests using a detailed model where power electronics converters and their local controllers are also taken into account. Full article

This paper presents an optimization-based scheduling strategy for battery energy storage systems (BESS) in alternating current microgrids, considering both grid-connected and islanded operation. The study addresses two independent objectives: minimizing energy losses in the distribution network and reducing carbon dioxide emissions from dispatchable power sources. The problem is formulated using a full AC power flow model that simultaneously manages active and reactive power flows in BESS located in the microgrid, while enforcing detailed operational constraints for network components, generation units, and storage systems. To solve it, a parallel implementation of the Particle Swarm Optimization (PPSO) algorithm is applied. The PPSO is integrated into the objective functions and evaluated through a 24-h scheduling horizon, incorporating a strict penalty scheme to guarantee compliance with technical and operational limits. The proposed method generates coordinated charging and discharging plans for multiple BESS units, ensuring voltage stability, current limits, and optimal reactive power support in both operating modes. Tests are conducted on a 33-node benchmark microgrid that represents the power demand and generation from Medellín, Colombia. This is compared with two methodologies reported in the literature: Parallel Crow Search and Parallel JAYA optimizer. The results demonstrate that the strategy produces robust schedules across objectives, identifies the most critical network elements for monitoring, and maintains safe operation without compromising performance. This framework offers a practical and adaptable tool for microgrid energy management, capable of aligning technical reliability with environmental goals in diverse operational scenarios. Full article

With the rise in traffic congestion, time has become an increasingly critical factor for electric vehicle (EV) users, leading to a surge in demand for fast and convenient charging services at locations of their choosing. Mobile Charging Stations (MCSs) have emerged as a new and practical solution to meet this growing need. However, the limited energy capacity of MCSs combined with the increasing volume of charging requests underscores the necessity for intelligent and efficient management. This study introduces a comprehensive mathematical framework aimed at optimizing both the deployment of MCSs and the scheduling of their battery recharging using battery swapping technology, while considering grid constraints, using the Deep Q-Network (DQN) algorithm. The proposed model is applied to real-world data from Chattanooga to evaluate its performance under practical conditions. The key goals of the proposed approach are to maximize the profit from fulfilling private EV charging requests, optimize the utilization of MCS battery packages, manage MCS scheduling without causing stress on the power grid, and manage recharging operations efficiently by incorporating photovoltaic (PV) sources at battery charging stations. Full article

Nanoparticle superlattices—periodic assemblies of uniformly spaced nanocrystals—bridge the nanoscale precision of individual particles with emergent collective properties akin to those of bulk materials. Recent advances demonstrate that multivalent ions and charged polymers can guide the co-assembly of nanoparticles, imparting electrostatic gating and enabling semiconductor-like behavior. However, the specific roles of anion geometry, valency, and charge density in mediating ion transport remain unclear. Here, we employ coarse-grained molecular dynamics simulations to investigate how applied electric fields (0–0.40 V/nm) modulate ionic conductivity and spatial distribution in trimethylammonium-functionalized gold nanoparticle superlattices assembled with four phosphate anions of distinct geometries and charges. Our results reveal that linear anions outperform ring-shaped analogues in conductivity due to higher charge densities and weaker interfacial binding. Notably, charge density exerts a greater influence on ion mobility than size alone. Under strong fields, anions accumulate at nanoparticle interfaces, where interfacial adsorption and steric constraints suppress transport. In contrast, local migration is governed by geometrical confinement and field strength. Analyses of transition probability and residence time further indicate that the rigidity and delocalized charge of cyclic anions act as mobility barriers. These findings provide mechanistic insights into the structure–function relationship governing ion transport in superlattices, offering guidance for designing next-generation ion conductors, electrochemical sensors, and energy storage materials through anion engineering. Full article

To overcome the challenges of conventional low-carbon retrofits for existing buildings—such as high construction volume, cost, and implementation difficulty—this study proposes a minimally invasive design and optimization method for Photovoltaic–Energy Storage–Direct Current–Flexible (PEDF) systems. The goal is to maximize energy savings and economic benefits while minimizing physical intervention. First, the minimally invasive retrofit challenge is decomposed into two coupled problems: (1) collaborative PV-ESS layout optimization and (2) flexible energy management optimization. A co-optimization framework is then developed to address them. For the layout problem, a model with multiple constraints is established to minimize retrofitting workload and maximize initial system performance. A co-evolutionary algorithm is employed to handle the synergistic effects of electrical pathways on equipment placement, efficiently obtaining an optimal solution set that satisfies the minimally invasive requirements. For the operation problem, an energy management model is developed to maximize operational economy and optimize grid interactivity. A deep reinforcement learning (DRL) agent is trained to adaptively make optimal charging/discharging decisions. Case simulations of a typical office building show that the proposed method performs robustly across various scenarios (e.g., office, commercial, and public buildings). It achieves an energy saving rate exceeding 20% and reduces operational costs by 10–15%. Moreover, it significantly improves building–grid interaction: peak demand is reduced by 33%, power fluctuations are cut by 75%, and voltage deviation remains below 5%. The DRL-based policy outperforms both rule-based strategies and the DDPG algorithm in smoothing grid power fluctuations and increasing the PV self-consumption rate. Full article

The rapid growth of 5G base stations (BSs) and electric vehicles (EVs) introduces significant challenges for distribution network operation due to high energy consumption and variable loads. This paper proposes a coordinated day-ahead scheduling framework that integrates 5G BS task migration, storage utilization, and EV charging or discharging with mobility constraints. A mixed-integer second-order cone programming (MISOCP) model is formulated to optimize network efficiency while ensuring reliable power supply and maintaining service quality. The proposed approach enables dynamic load adjustment via 5G computing task migration and coordinated operation between 5G BSs and EVs. Case studies demonstrate that the proposed method can effectively generate an optimal day-ahead scheduling strategy for the distribution network. By employing the task migration strategy, the computational workloads of heavily loaded 5G BSs are dynamically redistributed to neighboring stations, thereby alleviating computational stress and reducing their associated power consumption. These results highlight the potential of leveraging the joint flexibility of 5G infrastructures and EVs to support more efficient and reliable distribution network operation. Full article

Peer-to-peer (P2P) energy trading has emerged as a novel approach to enhancing the coordination and utilization of distributed energy resources (DERs) within modern power distribution networks. This study presents a techno-economic analysis of different DER characteristics, focusing on the integration of photovoltaic (PV) systems and energy storage systems (ESS) within a community-based P2P energy trading framework in Aswan, Egypt, under a time-of-use (ToU) electricity tariff. Eight distinct cases are evaluated to assess the impact of different DER characteristics on P2P energy trading performance and an unbalanced low-voltage (LV) distribution network by varying the PV capacity, ESS capacity, and ESS charging power. To the best of the authors’ knowledge, this is the first study to comprehensively examine the effects of different DER characteristics on P2P energy trading and the associated impacts on an unbalanced distribution network. The findings demonstrate that integrating PV and ESS can substantially reduce operational costs—by 37.19% to 68.22% across the analyzed cases—while enabling more effective energy exchanges among peers and with the distribution system operator (DSO). Moreover, DER integration reduced grid energy imports by 30.09% to 63.21% and improved self-sufficiency, with 30.10% to 63.21% of energy demand covered by community DERs. However, the analysis also reveals that specific DER characteristics—particularly those with low PV capacity (1.5 kWp) and high ESS charging rates (e.g., ESS 13.5 kWh with 2.5 kW inverter)—can significantly increase transformer and line loading, reaching up to 19.90% and 58.91%, respectively, in Case 2. These setups also lead to voltage quality issues, such as increased voltage unbalance factors (VUFs), peaking at 1.261%, and notable phase voltage deviations, with the minimum Vb dropping to 0.972 pu and maximum Vb reaching 1.083 pu. These findings highlight the importance of optimal DER sizing and characteristics to balance economic benefits with technical constraints in P2P energy trading frameworks. Full article

Grid-based distribution networks represent an advanced form of smart grids that enable modular, region-specific optimization of power resource allocation. This paper presents a novel planning framework aimed at the coordinated deployment of distributed generation, electrical loads, and energy storage systems, including both dispatchable and non-dispatchable electric vehicles. A three-dimensional objective system is constructed, incorporating investment cost, reliability metrics, and network loss indicators, forming a comprehensive multi-objective optimization model. To solve this complex planning problem, an improved version of the NSGA-II is employed, integrating hybrid encoding, feasibility constraints, and fuzzy decision-making for enhanced solution quality. The proposed method is applied to the IEEE 33-bus distribution system to validate its practicality. Simulation results demonstrate that the framework effectively addresses key challenges in modern distribution networks, including renewable intermittency, dynamic load variation, resource coordination, and computational tractability. It significantly enhances system operational efficiency and electric vehicles charging flexibility under varying conditions. In the IEEE 33-bus test, the coordinated optimization (Scheme 4) reduced the expected load loss from 100 × 10⁻⁴ yuan to 51 × 10⁻⁴ yuan. Network losses also dropped from 2.7 × 10⁻⁴ yuan to 2.5 × 10⁻⁴ yuan. The findings highlight the model’s capability to balance economic investment and reliability, offering a robust solution for future intelligent distribution network planning and integrated energy resource management. Full article

To address the challenge of coordinating economic cost control and low-carbon objectives in microgrid scheduling, while overcoming the performance limitations of the traditional Zebra Optimization Algorithm (ZOA) in complex problems, this paper proposes a Synergistic Zebra Optimization Algorithm (SZOA) and integrates it with innovative management concepts to enhance the microgrid scheduling process. The SZOA incorporates three core strategies: a multi-population cooperative search mechanism to strengthen global exploration, a vertical crossover–mutation strategy to meet high-dimensional scheduling requirements, and a leader-guided boundary control strategy to ensure variable feasibility. These strategies not only improve algorithmic performance but also provide technical support for innovative management in microgrid scheduling. Extensive experiments on the CEC2017 (d = 30) and CEC2022 (d = 10, 20) benchmark sets demonstrate that the SZOA achieves higher optimization accuracy and stability compared with those of nine state-of-the-art algorithms, including IAGWO and EWOA. Friedman tests further confirm its superiority, with the best average rankings of 1.20 for CEC2017 and 1.08/1.25 for CEC2022 (d = 10, 20). To validate practical applicability, the SZOA is applied to grid-connected microgrid scheduling, where the system model integrates renewable energy sources such as photovoltaic (PV) generation and wind turbines (WT); controllable sources including fuel cells (FC), microturbines (MT), and gas engines (GS); a battery (BT) storage unit; and the main grid. The optimization problem is formulated as a bi-objective model minimizing both economic costs—including fuel, operation, pollutant treatment, main-grid interactions, and imbalance penalties—and carbon emissions, subject to constraints on generation limits and storage state-of-charge safety ranges. Simulation results based on typical daily data from Guangdong, China, show that the optimized microgrid achieves a minimum operating cost of USD 5165.96, an average cost of USD 6853.07, and a standard deviation of only USD 448.53, consistently outperforming all comparison algorithms across economic indicators. Meanwhile, the SZOA dynamically coordinates power outputs: during the daytime, it maximizes PV utilization (with peak output near 35 kW) and WT contribution (30–40 kW), while reducing reliance on fossil-based units such as FC and MT; at night, BT discharges (−20 to −30 kW) to cover load deficits, thereby lowering fossil fuel consumption and pollutant emissions. Overall, the SZOA effectively realizes the synergy of “economic efficiency and low-carbon operation”, offering a reliable and practical technical solution for innovative management and sustainable operation of microgrid scheduling. Full article

To address the complex trade-offs among charging efficiency, battery lifespan, energy efficiency, and safety in high-power electric vehicle (EV) fast charging, this paper presents an intelligent control framework based on contrastive learning and manifold-constrained multi-objective optimization. A multi-physics coupled electro-thermal-chemical model is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem, incorporating both continuous and discrete decision variables—such as charging power and cooling modes—into a unified optimization framework. An environment-adaptive optimization strategy is also developed. To enhance learning efficiency and policy safety, a contrastive learning–enhanced policy gradient (CLPG) algorithm is proposed to distinguish between high-quality and unsafe charging trajectories. A manifold-aware action generation network (MAN) is further introduced to enforce dynamic safety constraints under varying environmental and battery conditions. Simulation results demonstrate that the proposed framework reduces charging time to 18.3 min—47.7% faster than the conventional CC–CV method—while achieving 96.2% energy efficiency, 99.7% capacity retention, and zero safety violations. The framework also exhibits strong adaptability across wide temperature (−20 °C to 45 °C) and aging (SOH down to 70%) conditions, with real-time inference speed (6.76 ms) satisfying deployment requirements. This study provides a safe, efficient, and adaptive solution for intelligent high-power EV fast-charging. Full article