Search Results (521)

This research presents the development of a new Hybrid Operational Strategy model for energy management optimization designed to evaluate the feasibility of implementing hybrid renewable energy modules in ports, aiming to improve their efficiency, sustainability, and innovation. The proposed system integrates photovoltaic, wind, and hydrokinetic energy sources, incorporating electronic components and assessing two energy storage technologies—Pump-as-Turbine (PAT) and battery systems—to determine the most viable solution for practical deployment. The optimization algorithm allows a concurrent refinement process for the power generation data of each renewable source. Four scenarios were analyzed within this optimization framework: two assessing the performance of single modules employing each storage technology individually, and two exploring configurations with multiple modules operating in parallel, either with independent storage units or a single centralized system. Battery storage was identified as the most feasible option based on the optimization outcomes. Considering the demand characteristics and generation capacity of the hybrid module, the configuration yielding the best overall performance consisted of a single module incorporating battery storage, achieving 90% demand coverage and demonstrating economic viability with a Net Present Value (NPV) of 9182.79 € and an Internal Rate of Return (IRR) of 10.88%. Full article

Advanced aerospace vehicle concepts demand concurrent advances in energy storage technologies that improve both specific energy and safety. Commercial lithium-ion batteries commonly employ polyolefin microporous separators and carbonate-based liquid electrolytes, which can deliver room-temperature ionic conductivities on the order of 10⁻³–10⁻² S/cm but rely on inherently flammable solvents. Room-temperature ionic liquids (RTILs) offer a nonvolatile, nonflammable alternative with a stable electrochemical window; however, many RTILs exhibit poor compatibility and wetting with polyolefin separators. Here, we evaluate highly porous, cross-linked polyimide (PI) gel separators based on 4,4′-oxydianiline (ODA) and biphenyl-3,3′,4,4′-tetracarboxylic dianhydride (BPDA), cross-linked with Desmodur N3300A, formulated with repeating unit lengths (n) of 30 and 60. These PI gel separators exhibit an open, fibrillar network with high porosity (typically >85%), high thermal stability (onset decomposition > 561 °C), and high char yield. Six imidazolium-based RTILs containing 10 wt% LiTFSI were screened, yielding nonflammable separator/electrolyte systems with room-temperature conductivities in the 10⁻³ S/cm range. Among the RTILs studied, 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (EMIM-TFSI) provided the best overall performance. Ionic conductivity and its retention after four months of storage at 75 °C were evaluated in the EMIM-TFSI/LiTFSI system, and the corresponding gel separator exhibited a tensile modulus of 26.66 MPa. Collectively, these results demonstrate that PI gel separators can enable carbonate-free, nonflammable RTIL electrolytes while maintaining the ionic conductivity suitable for lithium-based cells. Full article

The high penetration of renewable energy brings significant power fluctuations and operational uncertainties to distribution networks. Traditional power allocation methods for hybrid energy storage systems (HESSs) exhibit strong parameter dependency, limited frequency-domain recognition accuracy, and poor dynamic coordination capability. To overcome these limitations, this study proposes an adaptive power allocation strategy for HESSs under renewable energy integration scenarios. The proposed method employs the Grey Wolf Optimizer (GWO) to jointly optimize the mode number and penalty factor of the Variational Mode Decomposition (VMD), thereby enhancing the accuracy and stability of power signal decomposition. In conjunction with the Hilbert transform, the instantaneous frequency of each mode is extracted to achieve a natural allocation of low-frequency components to the battery and high-frequency components to the supercapacitor. Furthermore, a multi-objective power flow optimization model is formulated, using the power commands of the two storage units as optimization variables and aiming to minimize voltage deviation and network loss cost. The model is solved through the Particle Swarm Optimization (PSO) algorithm to realize coordinated optimization between storage control and system operation. Case studies on the IEEE 33-bus distribution system under both steady-state and dynamic conditions verify that the proposed strategy significantly improves power decomposition accuracy, enhances coordination between storage units, reduces voltage deviation and network loss cost, and provides excellent adaptability and robustness. Full article

Battery energy storage systems (BESSs) are increasingly required to provide grid-support services under weak-grid conditions, where the stability of virtual synchronous generator (VSG) control largely depends on the health status and dynamic characteristics of the battery unit. However, existing VSG strategies typically assume fixed parameters and neglect the intrinsic coupling between battery aging, DC-link energy variations, and converter dynamic performance, resulting in reduced damping, degraded transient regulation, and accelerated lifetime degradation. This paper proposes a health-responsive VSG control strategy enabled by real-time energy-dynamics sensing. By reconstructing the DC-link energy state from voltage and current measurements, an intrinsic indicator of battery health and instantaneous power capability is established. This energy-dynamics indicator is then embedded into the VSG inertia and damping loops, allowing the control parameters to adapt to battery health evolution and operating conditions. The proposed method achieves coordinated enhancement of transient stability, weak-grid robustness, and lifetime management. Simulation studies on a multi-unit BESS demonstrate that the proposed strategy effectively suppresses low-frequency oscillations, accelerates transient convergence, and maintains stability across different aging stages. Full article

The optimal allocation of Photovoltaic (PV) and wind-based renewable energy sources and Battery Energy Storage System (BESS) capacity is an important issue for efficient operation of a microgrid network (MGN). The impact of the unpredictability of PV and wind generation needs to be smoothed out by coherent allocation of BESS unit to meet out the load demand. To address these issues, this article proposes an efficient Energy Management System (EMS) and Demand Side Management (DSM) approaches for the optimal allocation of PV- and wind-based renewable energy sources and BESS capacity in the MGN. The DSM model helps to modify the peak load demand based on PV and wind generation, available BESS storage, and the utility grid. Based on the Real-Time Market Energy Price (RTMEP) of utility power, the charging/discharging pattern of the BESS and power exchange with the utility grid are scheduled adaptively. On this basis, a Jellyfish Search Algorithm (JSA)-based bi-level optimization model is developed that considers the optimal capacity allocation and power scheduling of PV and wind sources and BESS capacity to satisfy the load demand. The top-level planning model solves the optimal allocation of PV and wind sources intending to reduce the total power loss of the MGN. The proposed JSA-based optimization achieved 24.04% of power loss reduction (from 202.69 kW to 153.95 kW) at peak load conditions through optimal PV- and wind-based DG placement and sizing. The bottom level model explicitly focuses to achieve the optimal operational configuration of MGN through optimal power scheduling of PV, wind, BESS, and the utility grid with DSM-based load proportions with an aim to minimize the operating cost. Simulation results on the IEEE 33-node MGN demonstrate that the 20% DSM strategy attains the maximum operational cost savings of €ct 3196.18 (reduction of 2.80%) over 24 h operation, with a 46.75% peak-hour grid dependency reduction. The statistical analysis over 50 independent runs confirms the sturdiness of the JSA over Particle Swarm Optimization (PSO) and Osprey Optimization Algorithm (OOA) with a standard deviation of only 0.00017 in the fitness function, demonstrating its superior convergence characteristics to solve the proposed optimization problem. Finally, based on the simulation outcome of the considered bi-level optimization problem, it can be concluded that implementation of the proposed JSA-based optimization approach efficiently optimizes the PV- and wind-based resource allocation along with BESS capacity and helps to operate the MGN efficiently with reduced power loss and operating costs. Full article

Renewable energy sources account for approximately one-quarter of the total electric power generating capacity in the United States. These sources increase system complexity, with potential negative impacts caused by their inherent variability. A microgrid, a decentralized local grid, offers an excellent solution for integrating these sources into the system’s generation mix in a cost-effective and efficient manner. This paper presents a comprehensive fault tree analysis for the reliability assessment of microgrids, ensuring their safe operation. In this work, fault tree analysis of a microgrid in grid-tied mode with solar, wind, and battery energy storage systems is performed, and the results are reported. The analyses and calculations are performed using the Relyence software suite. The fault tree analysis was performed using various calculation methods, including exact (conventional fault tree analysis), simulation (Monte Carlo simulation), cut-set summation, Esary–Proschan, and cross-product. Once these analyses were completed, the results were compared with the ‘exact’ method as the base case. Critical risk measures, such as unavailability, conditional failure intensity, failure frequency, mean unavailability, number of failures, and minimal cut-sets, were documented and compared. Importance measures, such as marginal or Birnbaum, criticality, diagnostic, risk achievement, and risk reduction worth, were also computed and tabulated. Details of all cut-sets and the probability of failure are presented. The calculated importance measures would help microgrid operators focus on events that yield the greatest system improvements and maintain an acceptable range of risk levels to ensure safe operation and improved system reliability. Full article

Electric vehicles (EVs), electric buses (EBs), and battery energy storage system (BESS), as both controllable power sources and load, play a great role in providing flexibility for the power grid, especially with the increased renewable energy penetration. However, there is still a lack of studies on EVs’ pricing strategy as well as the EBs-charging piles matching problem. To address these issues, a multi-objective optimal operation model is presented to achieve the lowest load fluctuation level, minimum electricity cost, and maximum discharging benefit. An improved load boundary prediction method (ICDLBPM) and a novel pricing strategy are proposed. In addition, reduction in the number of EBs charging piles would not only impact normal operation of EBs, but also even lead to load flexibility decline. Thus a handling method of the EBs-charging piles matching problem is presented. Several case studies were conducted on a regional distribution network comprising 100 EVs, 30 EBs, and 20 BESS units. The developed model and methodology demonstrate superior performance, improving load smoothness by 45.78% and reducing electricity costs by 19.73%. Furthermore, its effectiveness is also validated in a large-scale system, where it achieves additional reductions of 39.31% in load fluctuation and 62.45% in total electricity cost. Full article

The continuous degradation of mangrove ecosystems, considered among the most vulnerable worldwide, reveals multiple threats driven by human activities and climate change. In the Peruvian context, particularly in the Tumbes Mangrove ecosystem, these pressures are intensified by the absence of integrated spatial and educational infrastructures capable of supporting conservation efforts while engaging local communities. In response, this research proposes a Sustainable Interpretation Center for Conservation and Environmental Education in Ecologically Sensitive Areas of the Tumbes Mangrove, Peru. The methodology includes climate data analysis, identification of local flora and fauna, and site topography characterization, supported by digital tools such as Google Earth, AutoCAD 2025, Revit 2025, and 3D Sun Path. The results are reflected in an architectural proposal that incorporates sustainable materials compatible with sensitive ecosystems, including eco-friendly structural solutions based on algarrobo timber, together with resilient strategies addressing climatic variability, such as lightweight structures, elevated platforms, and passive environmental solutions that minimize impact on the mangrove. Furthermore, the proposal integrates a photovoltaic energy system consisting of 12 solar panels with a unit capacity of 450 W, providing a total installed capacity of 5.4 kWp, complemented by a 48 V LiFePO₄ battery storage system designed to ensure energy autonomy during periods of low solar availability. In conclusion, the proposal adheres to principles of sustainability and energy efficiency and aligns with the Sustainable Development Goals (SDGs) 7, 8, 12, 14, and 15, reinforcing the use of clean energy, responsible tourism, sustainable resource management, and the conservation of marine and terrestrial ecosystems. Full article

The rapid advancement of energy collection and storage systems (ECSSs) is fundamentally reshaping global energy markets and accelerating the transition toward low-carbon energy systems. This study provides a comprehensive assessment of the economic benefits and systemic effects of advanced ECSS technologies, including photovoltaic-thermal (PV/T) hybrid systems, advanced batteries, hydrogen-based storage, and thermal energy storage (TES). Through a mixed-methods approach combining techno-economic analysis, macroeconomic modeling, and policy review, we evaluate the cost trajectories, performance indicators, and deployment impacts of these technologies across major economies. The paper also introduces a novel economic-mathematical model to quantify the long-term macroeconomic benefits of large-scale ECSS deployment, including GDP growth, job creation, and import substitution effects. Our results indicate significant cost reductions for ECSS by 2050, with battery storage costs projected to fall below USD 50 per kilowatt-hour (kWh) and green hydrogen production reaching as low as USD 1.2 per kilogram. Large-scale ECSS deployment was found to reduce electricity costs by up to 12%, lower fossil fuel imports by up to 25%, and generate substantial GDP growth and job creation, particularly in regions with supportive policy frameworks. Comparative cross-country analysis highlighted regional differences in economic effects, with the European Union, China, and the United States demonstrating the highest economic gains from ECSS adoption. The study also identified key challenges, including high capital costs, material supply risks, and regulatory barriers, emphasizing the need for integrated policies to accelerate ECSS deployment. These findings provide valuable insights for policymakers, industry stakeholders, and researchers aiming to design effective strategies for enhancing energy security, economic resilience, and environmental sustainability through advanced energy storage technologies. Full article

The dynamic growth of the electrified vehicle (xEV) market, including both electric and hybrid vehicles, has increased the demand for advanced Battery Management Systems (BMS). From an energy-systems perspective, xEV batteries act as distributed energy storage units that strongly interact with power grids, renewable generation, and charging infrastructure, making their efficient control a key element of low-carbon energy systems. Traditional BMS methods face challenges in accurately estimating key battery states and parameters, especially under dynamic operating conditions. This review systematically analyzes the progress in applying artificial intelligence, machine learning, and other advanced computational and data-driven algorithms to improve the performance of xEV battery management with a particular focus on energy efficiency, safe utilization of stored electrochemical energy, and the interaction between vehicles and the power system. The literature analysis covers key research trends from 2020 to 2025. This review covers a wide range of applications, including State of Charge (SOC) estimation, State of Health (SOH) prediction, and thermal management. We examine the use of various methods, such as deep learning, neural networks, genetic algorithms, regression, and also filtering algorithms, to solve these complex problems. This review also classifies the research by geographical distribution and document types, providing insight into the global landscape of this rapidly evolving field. By explicitly linking BMS functions with energy-system indicators such as charging load profiles, peak-load reduction, self-consumption of photovoltaic generation, and lifetime-aware energy use, this synthesis of contemporary research serves as a valuable resource for scientists and engineers who wish to understand the latest achievements and future directions in data-driven battery management and its role in modern energy systems. Full article

In the background of electricity market reform, combined heat and power (CHP) units must balance electricity market revenues with reliable heat supply. However, the flexibility of CHP units to confront various features of renewable outputs remains to be explored more thoroughly. In this study, day-ahead electricity price curves are classified into four typical categories adopting k-means clustering, featured by diverse temporal trends associated with the output of renewables. An integrated model—capturing the CHP, the battery energy storage system (BESS), and heating network dynamics—supports day-ahead operational optimization. The results suggest that distinct operational strategies are to be implemented under different price profiles. Moreover, incorporating a BESS and exploiting thermal inertia of the network expands arbitrage opportunities and profit from the electricity market. Lastly, an alternation in the operational goal of CHP units is proposed, namely, from thermal-economy-guided to comprehensive-economy-oriented. Comparative results underscore the benefits of the revised strategies. Full article

With the integrated development of new energy and oil and gas production, introducing wind–solar–storage microgrids in coalbed methane well screw pump discharge systems enhances the renewable energy proportion while promoting green development. However, the cyclical, volatile, and random characteristics of wind and photovoltaic generation create scheduling challenges, with insufficient green power consumption reducing renewable energy utilization efficiency and increasing grid dependence. This study establishes an operation scheduling optimization model for coalbed methane well screw pump discharge systems under wind–solar–storage microgrids, minimizing daily operation costs with screw pump rotational speed as decision variables. The model incorporates power constraints of generation units and production constraints of screw pumps, solved using particle swarm optimization. Results demonstrate that energy storage batteries effectively smooth wind and photovoltaic fluctuations, enhance regulation capabilities, and improve green power utilization while reducing grid purchases and system operation costs. At different coalbed methane extraction stages, the model optimally adjusts screw pump rotational speed according to renewable generation, ensuring high pump efficiency while minimizing operation costs, enhancing green power consumption capacity, and meeting daily drainage requirements. Full article

To further enhance economic efficiency and optimize energy conservation and emission reduction performance, an optimized energy management strategy (EMS) tailored for the hybrid power system of rubber-tyred gantry cranes is proposed. Wavelet packet decomposition (WPD) was employed as the signal processing approach, and this method was further integrated with EMS for hybrid power systems. Through a three-layer progressive architecture comprising WPD frequency–domain decoupling, fuzzy logic real-time adjustment, and PSO offline global optimization, a cooperative optimization mechanism has been established in this study between the frequency-domain characteristics of signals, the physical properties of energy storage components, and the real-time and long-term states of the system. Firstly, the modeling and simulation of the power system were conducted. Subsequently, an EMS based on WPD and limit protection was developed: the load power curve was decomposed into different frequency bands, and power allocation was implemented via the WPD algorithm. Meanwhile, the operating states of lithium batteries and supercapacitors were adjusted in combination with state of charge limits. Simulation results show that this strategy can achieved reasonable allocation of load power, effectively suppressed power fluctuations of the auxiliary power unit system, and enhanced the stability and economy of the hybrid power system. Afterward, a fuzzy controller was designed to re-allocate the power of the hybrid energy storage system (HESS), with energy efficiency and battery durability set as optimization indicators. Furthermore, particle swarm optimization algorithms were adopted to optimize the EMS. The simulation results indicate that the optimized EMS enabled more reasonable power allocation of the HESS, accompanied by better economic performance and control effects. The proposed EMS demonstrated unique system-level advantages in enhancing energy efficiency, extending battery lifespan, and reducing the whole-life cycle cost. Full article

State-of-charge (SoC) balancing across multiple battery energy storage systems (BESS) is a central challenge in renewable-rich mini-grids. Heterogeneous battery capacities, differing states of health, stochastic renewable generation, and variable loads create a high-dimensional uncertain control problem. Conventional droop-based SoC balancing strategies are decentralized and computationally light but fundamentally reactive and limited, whereas model predictive control (MPC) is insightful but computationally intensive and prone to modeling errors. This paper proposes a Hierarchical Predictive–Adaptive Control (HPAC) framework for SoC balancing in mini-grids using deep reinforcement learning. The framework consists of two synergistic layers operating on different time scales. A long-horizon Predictive Engine, implemented as a federated Transformer network, provides multi-horizon probabilistic forecasts of net load, enabling multiple mini-grids to collaboratively train a high-capacity model without sharing raw data. A fast-timescale Adaptive Controller, implemented as a Soft Actor-Critic (SAC) agent, uses these forecasts to make real-time charge/discharge decisions for each BESS unit. The forecasts are used both to augment the agent’s state representation and to dynamically shape a multi-objective reward function that balances SoC, economic performance, degradation-aware operation, and voltage stability. The paper formulates SoC balancing as a Markov decision process, details the SAC-based control architecture, and presents a comprehensive evaluation using a MATLAB-(R2025a)-based digital-twin simulation environment. A rigorous benchmarking study compares HPAC against fourteen representative controllers spanning rule-based, MPC, and various DRL paradigms. Sensitivity analysis on reward weight selection and ablation studies isolating the contributions of forecasting and dynamic reward shaping are conducted. Stress-test scenarios, including high-volatility net-load conditions and communication impairments, demonstrate the robustness of the approach. Results show that HPAC achieves near-minimal operating cost with essentially zero SoC variance and the lowest voltage variance among all compared controllers, while maintaining moderate energy throughput that implicitly preserves battery lifetime. Finally, the paper discusses a pathway from simulation to hardware-in-the-loop testing and a cloud-edge deployment architecture for practical, real-time deployment in real-world mini-grids. Full article

The power allocation in Vanadium Redox Flow Battery (VRB) energy storage systems faces a conflict between long-term lifespan and real-time power coupling. Using a single-layer optimization method to directly address multiple objectives simultaneously may lead to conflicts among these objectives. Therefore, this paper presents a multi-objective two-layer optimization allocation strategy. Its core is hierarchical scheduling for long/short-term goals to optimize multi-attribute objectives precisely. A two-layer model comprising an initial allocation layer and an operational optimization layer is constructed to ensure the prioritization of long-term lifespan objectives based on a predefined hierarchical structure. The initial allocation layer focuses on the long-term objective of energy storage capacity lifespan, by prioritizing minimal capacity degradation. A differential evolution algorithm is then applied to perform preliminary allocation of the total power demand. The operational optimization layer aims to achieve optimal State of Charge (SOC) balance across all units and minimize power losses. An Adaptive Multi-Objective Harris Hawks Optimizer (ASAMOHHO) based on adaptive simulated annealing is established to find the Pareto optimal solution set, and ultimately determining the real-time power allocation plan for each unit. Comparative simulations with conventional methods were conducted, and the results demonstrate that the proposed strategy provides an efficient and practical solution for efficient VRB scheduling. Full article