Search Results (2,247)

This study presents a forecast-driven Advanced Forecasting Model (AFM) and Virtual Power Plant (VPP) framework for a hybrid renewable energy system comprising utility-scale solar PV, wind generation, and a Battery Energy Storage System. Long Short-Term Memory neural networks provide real-time short-term forecasts to dynamically schedule power flows based on battery state-of-charge, grid import limits, and system constraints. Solar irradiance forecasting achieved MAE = 10.674 W/m², RMSE = 16.348 W/m², and MAPE = 14.18%, while wind speed forecasting achieved MAE = 0.880 m/s, RMSE = 1.115 m/s, and MAPE = 22.01%. Two dispatch scenarios were evaluated over a 72 h window: a reactive baseline and the proposed AFM/VPP strategy. The AFM reduced total grid imports by 57.48% (1466.34 MWh to 623.47 MWh), increased renewable utilization, and minimized curtailment. Financial analysis indicates an accelerated break-even (Year 6 vs. Year 9), a higher net present value, and cumulative 20-year profits exceeding R26.01 billion despite marginally higher capital expenditure. Emissions analysis shows annual CO₂ reductions from 123,680 t to 61,841 t, yielding 1.236 million tons of avoided emissions over 20 years. These results confirm that forecast-driven dispatch enhances operational efficiency, economic performance, and environmental sustainability, establishing a scalable approach for VPP operation in renewable-rich energy systems. Full article

Accurately predicting the capacity degradation of lithium-ion batteries is crucial for ensuring the reliability and safety of electric vehicles and energy storage systems. However, existing methods—including those based on physical principles, deep learning, and traditional machine learning—all face challenges in balancing accuracy, computational efficiency, and adaptability to non-linear aging dynamics. This study proposes a new framework that combines multi-scale data preprocessing and a divide-and-conquer strategy to address these limitations. Firstly, a hybrid Wavelet–SG filter is applied to suppress noise, and a set of specialized XGBoost micro models is trained, with each model predicting capacity for a specific cycle, enabling precise trajectory prediction at different aging stages. The evaluation on the Toyota-MIT-Stanford dataset (118 batteries under different operating protocols) shows that this method achieves an average MAPE of 1.16% and a maximum of no more than 2.5% on the unfamiliar protocol test set. In terms of accuracy, it achieves performance comparable to CNN, LSTM, and CNN-LSTM benchmarks. Importantly, its parallel architecture enables fast inference (400 milliseconds on CPU), making it suitable for edge deployment in battery management systems. The model also has interpretability consistent with physical laws and can autonomously capture stage-dependent degradation mechanisms. This work provides a reliable, efficient, and interpretable solution for real-world battery health monitoring. Full article

There are significant difficulties with power quality and stability as a result of active cooperation between renewable energy sources and load demand. To maintain power stability between renewable energy supplies and the microgrid/utility grid, novel solutions must be implemented. By using an artificial and computational intelligence controller to schedule power from multiple sources (photovoltaic, wind, grid, and battery) under a set of constraints, such as weather, load-shedding hours, and peak pricing hours, this paper introduces a novel approach for power management in grid-connected hybrid renewable systems with PV–wind and energy storage systems. The approach involves using an artificial neural network (ANN) to process all of the inputs and creating an ANN rule set from a modelled hybrid renewable system. A rule-based power scheduler is developed, and simulations are run for a full day. The suggested fuzzy control approach can detect ongoing variations in grid load-shedding patterns, PV–wind power generation, load demands, and battery state-of-charge to enable prompt and accurate decision-making. The proposed ANN rule-based scheduler can handle nonlinearity by integrating metaheuristics into computer-assisted decision-making and can function effectively with imprecise inputs, negating the need for an exact numerical model. The MATLAB/Simulink R2023a software was used for simulation, and the system operated as efficiently as possible. The simulation results suggested that an ANN offers a foundation for extension to handle numerous particular scenarios. Full article

This paper presents a comprehensive review of voltage stability challenges in South Africa’s constrained power grid, particularly in the context of rising hybrid renewable energy integration. With the growing deployment of inverter-based resources (IBRs) like solar PV, wind, and battery energy storage systems (BESS), especially under programmes through the Independent Power Procurement Office, voltage stability has emerged as a key concern, particularly in weak grid areas like the Northern Cape Province. We highlight how weak grids characterized by low short-circuit capacity, long transmission lines, and limited reactive power support are more susceptible to voltage instability, especially with high penetration of non-synchronous generation. Using a modified IEEE 14-bus system with hybrid generation, the study simulates a weak grid scenario. Findings point to significant reactive power losses and capacitive over-voltages in long and lightly loaded lines, mirroring some of the weak-grid-transmission challenges experiences in an area of the South African power grid. The study underscores the importance of dynamic load modelling (e.g., ZIP and exponential models) and inverter behaviour in stability analysis. It concludes that hybrid systems, when optimally designed and integrated with storage, can help support grid stability. However, proactive planning, advanced modelling, and compliance with evolving grid codes remain essential for securing reliable renewable integration. Full article

Addressing the challenge of power supply stability caused by the intermittent nature of photovoltaic power generation in off-grid microgrids, this study uses a commercial park in Wuhan as a case study and optimizes the capacity configuration of a photovoltaic–storage–hydrogen fuel cell hybrid microgrid system based on HOMER Pro software. First, a topology of the off-grid microgrid is constructed, comprising photovoltaic (PV), lithium-ion batteries, hydrogen fuel cells, and a diesel generator as backup. The power output characteristics, efficiency curves, and life-cycle cost models of each component are accurately established. On this basis, two typical operation strategies, namely Load Following (LF) and Cycle Charging (CC), are proposed and compared. The influence of different strategies on the optimal capacity configuration and operational economics is systematically analyzed, and the Cycle Charging strategy is identified as the optimal operation strategy for this scenario. Subsequently, a multi-scenario capacity optimization design is further conducted based on the optimal operation strategy. The minimization of net present cost (NPC) is taken as the primary objective, while multiple evaluation indicators such as renewable fraction (RF), levelized cost of electricity (LCOE), energy storage cycle life degradation, and system redundancy rate are comprehensively considered. The results show that, while ensuring 100% power supply reliability, the proposed model reduces the net present cost (NPC) by approximately 14.4% compared with the conventional PV-storage scheme. The renewable fraction (RF) reaches 95.8%, while the reliance on lithium-ion battery capacity is significantly reduced (battery capacity configuration decreased by 24.3%). This effectively extends the energy storage lifespan and enhances the overall economic and environmental benefits. The results provide a theoretical basis and technical reference for the planning and design of off-grid microgrids with high penetration of renewable energy. Full article

The sustainable development of all economic sectors, including transport, requires decarbonization approaches that reduce greenhouse-gas emissions while preserving operational viability. This article develops a segment-based preliminary multi-criteria framework for evaluating the rational applicability of decarbonization technologies to commercial fishing vessels and demonstrates it for existing medium-to-large trawlers. The central premise is that decarbonization technologies cannot be ranked universally for the whole fishing fleet because vessel type, fishing gear, operating cycle, autonomy, onboard energy demand, and port dependence strongly affect practical applicability. Ten alternatives are assessed: sustainable drop-in biofuels/biodiesel/HVO (Hydrotreated Vegetable Oil), LNG/BioLNG/LBG, methanol, hydrogen fuel cells, ammonia, hybrid systems, operational measures, hull-form or hydrodynamic modifications, waste heat recovery and wind-assisted propulsion. Seven benefit-type criteria are combined using trawler-specific Rank-Order Centroid weights, Simple Additive Weighting, and a dynamic rationality extension for 2026, 2030, 2040, and 2050. The 2026 baseline results place operational measures and sustainable drop-in biofuel/HVO pathways in the leading practical group, while hydrogen and ammonia remain weak because of storage, safety, infrastructure, cost, and integration constraints. By 2050, a mixed long-term group emerges where HVO, LNG/BioLNG/LBG, methanol, ammonia, and hydrogen are all relevant, with no single dominant alternative. The framework supports early-stage screening before vessel-specific LCA, LCCA, CFD, safety assessment, and retrofit or newbuild design. Although this methodological approach was demonstrated for existing medium-to-large trawlers, the authors believe that it can be adapted for retrofit cases, other fishing vessel segments, and other types of seagoing vessels. Full article

The intermittent nature of renewable energy systems and the mismatch between power generation and load demand necessitate the integration of efficient energy storage systems (ESSs). Among large-scale energy storage technologies, pumped hydro-energy storage systems (PHESs) are widely recognized as one of the most cost-effective and longest-lifetime storage solutions under favorable geographical conditions. This study proposes and optimizes a hybrid renewable energy system (HRES) for the Wadi Baish region in Saudi Arabia as a real case study, where the significant elevation difference between the nearby mountains and the existing lake provides favorable conditions for PHES implementation. A nested optimization framework is developed to determine the optimal sizing and operation of the HRES components. The external optimization loop employs the non-dominated sorting genetic algorithm II (NSGA-II) to optimize system sizing, while the internal optimization loop uses mixed-integer linear programming (MILP) to optimally dispatch the PHES, battery energy storage system (BESS), and hydrogen energy storage system (HESS). In addition, demand-side management (DSM) is coordinated with the MILP dispatch strategy to improve system performance and reliability. The results show that the optimized system can supply a 10 MW average load with a renewable energy penetration of 98.7%. The proposed configuration achieves a total lifecycle cost of USD 231.37 million and avoids approximately 898.58 kt of CO₂ emissions over the project lifetime. PHES operates as the primary bulk energy storage technology due to its high storage capacity and low degradation characteristics. Furthermore, the degradation-aware model predicts battery replacement every 12 years and HESS replacement every 5 years. Compared with rule-based control, the MILP-based dispatch strategy reduces grid dependency by 87%. The coordinated DSM and MILP operation also reduces the levelized cost of energy to USD 0.066/kWh while improving overall system reliability. These findings demonstrate the importance of coordinated energy management and accurate degradation modeling in the optimal design and operation of renewable-based HRES configurations. Full article

Effective thermal management is crucial for the performance, thermal safety, and lifespan of lithium-ion batteries in electric vehicles (EVs). Thermal management strategies are essential for preventing overheating, thermal imbalance, and the associated risk of thermal runaway. Nanofluids are emerging and attracting considerable attention as potential coolants for high-power energy storage and electronics systems. This review updates and summarizes the most recent advances in nanofluid-based cooling strategies for battery thermal management systems (BTMSs) over the past five years, emphasizing their implications for battery thermal safety. Three main nanofluid-based cooling strategies have been evaluated in depth, including nanofluid-based indirect liquid cooling, nanoparticle-enhanced PCM cooling, and nanofluid-based heat pipe cooling. Various nanofluid formulations, including mono, hybrid, and ternary nanofluids, have been considered and evaluated for their heat dissipation under high charge/discharge and abuse-relevant conditions. Thermal and hydraulic performance characteristics, including maximum temperature, maximum temperature difference, and pressure drop, have been comprehensively evaluated for different nanofluid-based cooling strategies. The findings demonstrated that nanofluids significantly improved heat transfer rates and enhanced temperature control efficiency. In particular, hybrid and ternary nanofluids exhibit superior thermal performance and effectively suppress the escalation of safety-critical temperatures. Beyond summarizing cooling performance, this review further discusses the role of nanofluid-based cooling strategies as functional thermal-control layers within intelligent BTMS architectures. Particular attention is given to their compatibility with sensing networks, BMS-/VCU-level supervisory control, predictive thermal models, actuator responsiveness, fault-warning algorithms, and long-term reliability under realistic driving and fast charging conditions. Therefore, this review provides architecture-oriented insights for developing safe, energy-efficient, and control-ready BTMSs for next-generation high-power and connected EVs. Full article

Thin-film solar cells (TFSCs) remain a cornerstone of the global transition toward renewable energy, characterized by consistent reductions in manufacturing costs and steady gains in power conversion efficiency. In addition to electricity generation, TFSCs play an important role in advanced solar thermal cooling, heating, and energy storage systems, where their tunable optical absorption, low thermal mass, and flexibility enable integration with photovoltaic–thermal (PV/T) collectors, thermally driven cooling cycles, and hybrid thermal–electrical storage architectures. This paper provides a comprehensive review of prominent TFSC technologies, including copper indium gallium selenide (CIGS), cadmium telluride (CdTe/CdS), amorphous silicon (a-Si), copper zinc tin sulfide (CZTS), organic photovoltaics (OPVs), and metal halide perovskite solar cells (PSCs), with a focus on their material structures, performance specifications, and current efficiency benchmarks. Compared to state-of-the-art reviews, this article distinguishes itself by addressing next-generation innovations, cross-domain solar thermal–photovoltaic applications, and economic analysis. Specifically, the integration of machine learning and simulation-based material dynamics is examined to accelerate material discovery, process optimization, and the characterization of novel TFPV components relevant to coupled thermal–electrical energy systems. Furthermore, the study explores how additive manufacturing is transforming the industry through the development of high-efficiency electrodes, electrohydrodynamic atomization for thin-film deposition, and the fabrication of flexible solar arrays suitable for thermally integrated and building-scale energy systems, including space applications. By integrating advancements in module efficiency, scalable manufacturing approaches, and techno-economic analysis, this paper positions TFSCs as sustainable, resource-abundant technologies essential for next-generation solar thermal cooling, heating, and energy storage infrastructures. Full article

High-altitude regions provide abundant wind and solar resources but impose severe environmental constraints on energy storage systems. To address these challenges, this study proposes a bi-level optimal sizing method for wind–solar–hydrogen storage systems considering altitude-induced impacts. A system model integrating electrochemical storage and hydrogen storage is established, and a hybrid electricity–hydrogen storage dispatch strategy is designed to exploit their complementary characteristics. The upper-level optimization minimizes lifecycle cost using the Golden Sine Algorithm-Subtraction Average Based Optimizer (GSABO), while the lower level conducts 8760 h simulations to optimize the loss of power supply probability (LPSP) and excess energy rate (EER). A case study in western Sichuan, China, at an altitude of approximately 3500 m, demonstrates the method achieves 0% EER and 0.8% LPSP, reducing total costs by 50.65% compared to single electrochemical storage. Full article

Polymer-engineered MXene composites have emerged as a versatile materials platform for electrochemical energy storage, offering a means to address key limitations associated with ion transport, structural instability, and interfacial reactivity. This review provides a unified perspective on how polymer integration modifies the structure–transport–stability relationships of MXene-based systems across Na-ion batteries, aqueous Zn-ion batteries, and supercapacitors. In Na-ion systems, polymer-mediated interlayer engineering and porosity control improve ion accessibility and mitigate diffusion limitations arising from the large ionic radius of Na⁺. In aqueous Zn-ion systems, polymer electrolytes and interfacial layers regulate Zn²⁺ solvation and deposition behavior, suppressing dendritic growth and parasitic reactions. In supercapacitors, polymer–MXene hybrids establish coupled ionic–electronic transport pathways and mechanically compliant architectures, enabling stable electrochemical performance under high-rate and deformable conditions. Particular emphasis is placed on the underlying mechanisms responsible for suppressing oxidation, preserving structural integrity, and extending cycle life, including interfacial passivation, desolvation regulation, and structural confinement. These coupled effects govern long-term electrochemical stability across different energy storage systems. Finally, recent advances in operando characterization, data-driven materials design, and scalable processing are discussed in the context of future development. By linking material design strategies to fundamental mechanisms, this review outlines a coherent framework for the rational development of polymer–MXene composites toward practical energy storage applications. Full article

As the power density of office computing clusters rises to 200–250 W per chip, the substantial heat generated during operation not only impairs chip performance and shortens lifespan but also compels heating, ventilation, and air conditioning (HVAC) systems to operate at high loads. This increases energy consumption by 30–40% and causes indoor temperature fluctuations that reduce office workers’ comfort. Targeting centralized thermal management for such clusters, this study proposes a hybrid cooling strategy integrating outdoor natural cold air (as a continuous heat sink) with phase change materials (PCMs, for transient heat peak absorption). Six adjustable heating plates (power range: 50–250 W per unit, simulating 7 nm office chips) mimicked heat dissipation in a six-chip cluster. Latent heat storage (LHS) units served as passive cooling, with fan coils as auxiliary for natural/forced convection. By using PCMs (melting point: 48 °C) to absorb transient peaks and coils to utilize outdoor cold air, the system maintained circulating water at approximately 60 °C (steady-state equilibrium temperature under full-load conditions) and kept chip temperatures below 80 °C (industrial safety threshold). The hybrid system reduced combined pump and fan power to 125 W, achieving 75% energy savings compared to the HVAC system (500 W) and 40% savings compared to using only natural cold air (210 W pump and fan power). Positive pressure in the outdoor unit (increasing coil air velocity by 1.2 m/s relative to natural convection) further improved heat dissipation efficiency by 15%. Finally, this study quantifies the influence of PCM thermal conductivity and filling mass on the system’s temperature control performance through numerical simulations, providing direct evidence for parameter design of LHS units. Full article

To address long-duration, high-impact extreme events, this study investigates resilience enhancement optimization dispatching for hydro–wind–solar power systems under continuous multi-day extreme scenarios. A mathematical model is constructed with the resilience objective of minimizing the average load deviation percentage and the economic objective of maximizing the total power generation of the system, while considering constraints such as water balance. The solution steps are provided in this paper. A case study of the Laxiwa hydropower station and nearby wind and photovoltaic power stations demonstrates the following: (1) The compensatory regulation capability of hydropower can be leveraged to enhance power system resilience under continuous multi-day extreme scenarios, and there is a trade-off between resilience and economic objectives. (2) The ability of hydropower to enhance power system resilience is limited by several factors, such as installed capacity, existing reservoir storage, minimum output constraints, and available storage capacity, making it insufficient to fully prevent issues like power shortage, the curtailment of renewable energy, and water spillage. (3) The impact of extreme wind and solar power outputs on the power system exhibits a cumulative effect under continuous multi-day extreme scenarios, and in concurrent scenarios, there is a certain offsetting effect between the impacts of under- and over-generation. This paper provides technical support and a reference for optimizing resilience-oriented scheduling and exploring mechanisms in hybrid hydro–wind–solar power systems under extreme conditions. Full article

The increasing penetration of variable renewable energy sources requires flexible solutions to ensure system stability and economic efficiency. In this context, this study presents a comprehensive assessment of hybrid plants combining wind farms (WF) and photovoltaic (PV) systems integrated with battery energy storage systems (BESS), using the Casetona project in Spain as a real-world study. Three configurations (PV + WF + BESS, PV + BESS, and WF + BESS) are evaluated based on 2024 operational data combined with simulation tools. Under the assumptions of this study (2024 data, Spanish market), the results indicate that WF generation outperforms PV, mainly due to higher capacity factors and better alignment with high-price periods, while PV output is affected by price cannibalization. Under current Spanish market conditions and at the assumed BESS cost (236 €/kWh), energy arbitrage is not economically viable, yielding negative net present value across all configurations. In contrast, participation in automatic frequency restoration reserve services provides higher revenues under current Spanish market conditions, with the WF + BESS configuration achieving the best performance. From the perspective adopted in this study, the sustainability analysis reveals that the hybrid system enables annual greenhouse gas emissions reductions between 13,695 and 49,195 ${\mathrm{t}}_{C{O}_{2,eq}}$, depending on the displaced generation source. Although BESS does not directly reduce emissions, it enhances renewable integration, reduces curtailment, and improves grid flexibility. The results also highlight the importance of regulatory frameworks and market design in determining the economic viability of storage systems. While the quantitative results are specific to the case study and sensitive to regulatory parameters, this study provides a comprehensive and transferable methodology for evaluating hybrid renewable systems with storage, supporting informed decision-making in the transition toward low-carbon and resilient energy systems. Full article

This study investigates the coordinated impact of a synchronous condenser (SC), battery energy storage system (BESS), and static synchronous compensator (STATCOM) on enhancing voltage and frequency stability in a modified IEEE 9-bus power system under severe disturbances. The aim is to quantify the individual and combined contributions of these technologies during both fault ride-through (FRT) and load-increment events. The methodology includes dynamic modelling of all three devices in DIgSILENT PowerFactory. The SC is represented as a synchronous machine with inertia and AVR-based voltage control; the BESS employs converter-based active power and frequency-droop control; and the STATCOM provides fast reactive power injection through a dual-loop voltage regulator. Key indicators include nadir (minimum frequency), Rate of Change of Frequency (RoCoF), steady-state deviation, voltage sag depth, and recovery characteristics. Results indicate distinct roles for each device. The SC increases inertia and improves damping, but it also introduces small, well-damped oscillations. The BESS significantly enhances frequency stability by mitigating nadir, reducing RoCoF, and accelerating recovery, with negligible effect on voltage regulation. The STATCOM substantially reduces voltage sag and speeds up voltage recovery, but it does not influence frequency behaviour. When combined, the hybrid SC–BESS–STATCOM system demonstrates strong complementarity: the SC supports inertia, the BESS stabilizes active-power imbalance, and the STATCOM ensures fast reactive-power compensation. Full article