Search Results (2,295)

Accurate short-term wind speed forecasting is essential for the sustainable operation and planning of coastal wind farms. This study develops an improved BP-PSO hybrid model that integrates particle-swarm optimization, time-ordered walk-forward validation, and uncertainty quantification through block-bootstrap confidence intervals and Monte-Carlo dropout prediction intervals. Using multi-year and seasonal datasets from four coastal stations in China—from Bohai Bay (LHT, XCS, ZFD) to Zhejiang Province (SSN)—the proposed model achieves high predictive accuracy, with RMSE values between 1.09 and 1.54 m/s, MAE between 0.79 and 1.10 m/s, and R² exceeding 0.70 at most sites. The multi-year configuration provides the most stable and robust results, while autumn at ZFD yields the highest errors due to intensified turbulence. XCS and SSN exhibit the most consistent performance, confirming the model’s spatial adaptability across distinct climatic regions. Compared with the ARIMA and persistence baselines, BP-PSO reduces RMSE by over 50%, demonstrating improved efficiency and generalization. These results highlight the potential of intelligent data-driven forecasting frameworks to enhance renewable energy reliability and sustainability by enabling more accurate wind-power scheduling, grid stability, and coastal energy system resilience. Full article

The rapid advancement of technologies such as artificial intelligence, big data, and cloud computing has driven continuous expansion of global data centers, resulting in increasingly severe energy consumption and carbon emission challenges. According to projections by the International Energy Agency (IEA), the global electricity demand of data centers is expected to double by 2030. The construction of green data centers has emerged as a critical pathway for achieving carbon neutrality goals and facilitating energy structure transition. This paper presents a systematic review of the role of waste heat recovery technologies in data centers for achieving low-carbon development. Categorized by aspects of waste heat recovery technologies, power production and district heating, it focuses on assessing the applicability of heat collection technologies, such as heat pumps, thermal energy storage and absorption cooling, in different scenarios. This study examines multiple electricity generation pathways, specifically the Organic Rankine Cycle (ORC), Kalina Cycle (KC), and thermoelectric generators (TEG), with comprehensive analysis of their technical performance and economic viability. The study also assesses the feasibility and environmental advantages of using data center waste heat for district heating. This application, supported by heat pumps and thermal energy storage, could serve both residential and industrial areas. The study shows that waste heat recovery technologies can not only significantly reduce the Power Usage Effectiveness (PUE) of data centers, but also deliver substantial economic returns and emission reduction potential. In the future, the integration of green computing power with renewable energy will emerge as the cornerstone of sustainable data center development. Through intelligent energy management systems, cascaded energy utilization and regional energy synergy, data centers are poised to transition from traditional “energy-intensive facilities” to proactive “clean energy collaborators” within the smart grid ecosystem. Full article

This paper presents a novel, robust, and reliable control strategy for renewable energy production systems, leveraging artificial neural networks (ANNs) to optimize performance and efficiency. Unlike conventional ANN approaches that rely on perturbation-based methods, we develop a fundamentally different ANN model incorporating equilibrium points (EPs) that achieve superior regulation of photovoltaic (PV) systems. The efficacy of the proposed approach is evaluated through comparative analysis against the conventional control strategy based on perturb and observe (MPPT/PO), demonstrating a 3.3% improvement in system efficiency (98.3% vs. 95%), a five times faster response time (6 s vs. 30 s), and six-fold reduction in voltage ripple (1% vs. 5.95%). A critical aspect of ANN-based controller design is the learning phase, which is addressed through the integration of deep reinforcement learning (DRL) for primary PV system control. Specifically, a hybrid control architecture combining the Artificial Neural Network based on Equilibrium Points (ANN/EP) model with DRL (ANN/PE-RL) is introduced, utilizing a synergistic integration of two reinforcement learning agents: Twin Delayed Deep Deterministic Policy Gradient (TD3) and Deep Deterministic Policy Gradient (DDPG). The TD3-based hybrid approach achieves an average reward value of 434.78 compared to 422.767 for DDPG, representing a 2.84% performance improvement in tracking maximum power points under imbalanced conditions. This hybrid approach demonstrates significant potential for improving the overall performance of grid-connected PV systems, reducing energy losses from 1.95% to below 1%, offering a promising solution for advanced renewable energy management. Full article

Remote island regions often depend on isolated power grids dominated by small-scale thermal power plants. Decarbonizing these systems is challenging due to limited interconnection capacity and variable renewable output, highlighting the need for flexible resource balance. This study develops an optimization model that minimizes system costs and CO₂ emissions by integrating variable renewable energy and Vehicle-to-Grid (V2G) while considering the minimum-output constraints of thermal power generation. The model is applied to Tanegashima Island, Japan. The results demonstrate that all optimized scenarios reduced the cost and emissions compared with the baseline. In the cost-minimizing scenario, the total annual cost decreased from 2.81 to 2.46 billion yen, while CO₂ emissions decreased from 56.5 to 44.4 kt. In the CO₂-minimizing scenario, V2G further reduced emissions to 43.8 kt at a lower cost (2.54 billion yen) than the system without V2G. However, renewable curtailment remained high due to the minimum-output constraint of thermal generators. These findings confirm that while V2G is a cost-effective, distributed flexibility resource, it cannot fully eliminate renewable curtailment under current operational limits. Enhanced coordination, behavioral engagement, and complementary measures—such as relaxing thermal constraints and expanding storage—are required to unlock its full potential in isolated power systems. Full article

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

The integration of electric railways with renewable energy sources is crucial for advancing sustainable transportation and building clean, low-carbon, and efficient energy systems in alignment with global sustainable development goals. However, the application of photovoltaic (PV) integration into railway traction power supply systems may exacerbate resonance phenomena between electric locomotives and the traction network. It is therefore necessary to study the impedance frequency characteristics (IFCs) of traction networks to minimize harmonic resonance overvoltage. In this paper, a harmonic impedance model of the sustainable traction power supply system (STPSS) is established, and an impedance analysis method is adopted to reveal the influence law of grid-connected PV inverters on the IFCs of STPSSs. Additionally, to improve the stability of STPSSs, a multi-parameter co-tuning method based on an improved particle swarm optimization algorithm is proposed. This method constructs a multi-objective function that includes resonance frequency, impedance magnitude, and filtering cost, thereby realizing the automatic optimization of the control parameters and filtering parameters of PV inverters. The results demonstrate a 56% reduction in the maximum impedance magnitude within the 0–5 kHz frequency range and a 10.8% cost reduction in the LCL filter implementation, confirming the effectiveness of the proposed optimization model. Results show that the maximum impedance magnitude of the optimized system in the frequency range of 0–5 kHz can be reduced by 56%. Moreover, the cost of LCL filters can be reduced by 10.8% through component value optimization. These findings validate the effectiveness of the proposed method. Full article

The integration of a high proportion of renewable energy has significantly reduced the grid inertia level and markedly increased the risk of transient frequency instability in power systems. Meanwhile, the large-scale integration of diverse heterogeneous resources—such as wind power, photovoltaics, energy storage, and high voltage direct current (HVDC) transmission systems—has considerably enriched the portfolio of frequency regulation assets in modern power grids. However, the marked disparities in the dynamic response characteristics and actuation speeds among these resources introduce significant nonlinearity and high-dimensional complexity into the system’s transient frequency behavior. As a result, conventional methods face considerable challenges in achieving accurate and timely prediction of such responses. However, the substantial differences in the frequency regulation characteristics and response speeds of these resources have led to a highly nonlinear and high-dimensional complex transient frequency response process, which is difficult to accurately and rapidly predict using traditional methods. To address this challenge, this paper proposes an online prediction method for transient frequency response that deeply integrates physical principles with data-driven approaches. First, a frequency dynamic response analysis model incorporating the frequency regulation characteristics of multiple resource types is constructed based on the Single-Machine Equivalent (SME) method, which is used to extract key features of the post-fault transient frequency response. Subsequently, information entropy theory is introduced to quantify the informational contribution of each physical feature, enabling the adaptive weighted fusion of physical frequency response features and Wide-Area Measurement System (WAMS) data. Finally, a physics-guided machine learning framework is proposed, in which the weighted physical features and the complete frequency curve predicted by the physical model are jointly embedded into the prediction process. An MLP-GRU-Attention model is designed as the data-driven predictor for frequency response. A physical consistency constraint is incorporated into the loss function to ensure that predictions strictly adhere to physical laws, thereby enhancing the accuracy and reliability of the transient frequency prediction model. Case studies based on the modified IEEE 39-bus system demonstrate that the proposed method significantly outperforms traditional data-driven approaches in terms of prediction accuracy, generalization capability under small-sample conditions, and noise immunity. This provides a new avenue for online frequency security awareness in renewable-integrated power systems with multiple heterogeneous frequency regulation resources. Full article

This study explores the relationship between electricity consumption and financial development in 20 emerging market economies (EMEs) from 2000 to 2020. Employing the panel ARDL–PMG estimator and a two-step system GMM to address endogeneity, we identify a significant positive long-run cointegrating relationship, where electricity consumption fosters financial development. The estimated error correction term suggests a stable equilibrium, with deviations corrected at a 29% annual rate, in the short-run adjustment. These results underscore the significance of targeted energy investments in driving financial market growth. Policies promoting grid action, renewable integration, and innovative financing tools, such as green bonds, can align electricity expansion with financial stability objectives. By incorporating recent global disruptions and applying advanced econometric methods, this study provides updated empirical evidence and actionable policy insights on the electricity–finance nexus in EMEs. Full article

To accommodate the high penetration of intermittent renewable energy sources like wind and solar power into the grid, coal-fired units are required to operate with enhanced deep peak-shaving and variable load capabilities. This study develops a dynamic model of the boiler–turbine coordinated control system (BTCCS) for ultra-supercritical once-through boiler (OTB) coal-fired units operating under wet conditions. A mechanistic model framework is established based on mass and energy conservation. In case of missing steady-state data, this work proposes a mechanism-integrated parameter identification method that determines model parameters using only dynamic running data while incorporating physical constraints. Model validation demonstrates that the proposed approach accurately reproduces the variable-load operation of the BTCCS within the range of 50–350 MW. Mean relative errors of output variables are all less than 7.5%, and root mean square errors of output variables are less than 0.3 MPa, 1.4 kg/s, 0.25 m, and 20.7 MW, respectively. Open-loop simulations further confirm that the model captures the essential dynamic characteristics of the system, making it suitable for simulation studies and control system design aimed at improving operational flexibility and safety of OTB coal-fired units under wet conditions. Full article

The maritime industry is under increasing pressure to reduce greenhouse gas emissions, especially in countries such as Saudi Arabia that are actively working to transition to cleaner energy. In this paper, a new hybrid shipboard power system, which incorporates wind turbines, solar photovoltaic (PV) panels, proton-exchange membrane fuel cells (PEMFCs), and a battery energy storage system (BESS) together for propulsion and hotel load services, is proposed. A multi-loop Energy Management System (EMS) based on proportional–integral control (PI) is developed to coordinate the interconnections of the power sources in real time. In contrast to the widely reported model predictive or artificial intelligence optimization schemes, the PI-derived EMS achieves similar power stability and hydrogen utilization efficiency with significantly reduced computational overhead and full marine suitability. By taking advantage of the high solar irradiance and coastal wind resources in Saudi Arabia, the proposed configuration provides continuous near-zero-emission operation. Simulation results show that the PEMFC accounts for about 90% of the total energy demand, the BESS (±0.4 MW, 2 MWh) accounts for about 3%, and the stationary renewables account for about 7%, which reduces the demand for hydro-gas to about 160 kg. The DC-bus voltage is kept within ±5% of its nominal value of 750 V, and the battery state of charge (SOC) is kept within 20% to 80%. Sensitivity analyses show that by varying renewable input by ±20%, diesel consumption is ±5%. These results demonstrate the system’s ability to meet International Maritime Organization (IMO) emission targets by delivering stable near-zero-emission operation, while achieving high hydrogen efficiency and grid stability with minimal computational cost. Consequently, the proposed system presents a realistic, certifiable, and regionally optimized roadmap for next-generation hybrid PEMFC–battery–renewable marine power systems in Saudi Arabian coastal operations. Full article

Under the drive of the “dual carbon” goals, the insufficient frequency regulation capability of nuclear power as a baseload source and the dynamic demand of integrating a high proportion of renewable energy into the grid have increasingly highlighted conflicts. The inherent minute-level regulation inertia of nuclear power units struggles to cope with second-level frequency fluctuations in the grid, leading to an increased risk of system instability. There is an urgent need for energy storage technologies to fill the millisecond-level power support gap for nuclear power frequency regulation. This paper, focusing on nuclear power frequency regulation scenarios, constructs a “Technology–Economy–Policy” multidimensional energy storage evaluation system for the first time. Through a systematic analysis of 11 key indicators, such as response time and safety, the paper selects energy storage technologies suitable for nuclear power frequency regulation scenarios and proposes a hybrid energy storage optimization strategy. The research provides a systematic evaluation framework and empirical support for the selection of energy storage for nuclear power frequency regulation, with significant practical value in improving grid dynamic stability and promoting the construction of new power systems under the “dual carbon” goals. Full article

Against the backdrop of the rapid development of the energy internet, the role of energy storage systems in grid stability, energy balance, and renewable energy integration has become increasingly important. Among these systems, estimating the state of health (SOH) of battery storage systems, particularly lithium batteries, is crucial for ensuring system reliability and safety. While data-driven methods have poor interpretability and physics-based models are computationally expensive, physics-informed neural networks (PINNs) offer a compromise but struggle with high-dimensional inputs and dynamic variable coupling. This paper proposed a novel Kolmogorov–Arnold networks with physics-informed neural network (KAN-PINN) framework for lithium-ion battery SOH estimation. By leveraging KANs’ superior high-dimensional approximation capabilities and embedding the Verhulst model as a physical constraint, the framework enhances nonlinear representation while ensuring predictions adhere to degradation physics. Experimental results on a public dataset demonstrate the model’s superiority, achieving an RMSPE of 0.300 and MAE of 1.342%, along with strong interpretability and robustness across battery chemistries and operating conditions. Full article

Algeria’s transition toward sustainable energy requires the exploitation of its abundant solar and wind resources for green hydrogen production. This study assesses the techno-economic feasibility of an off-grid PV/wind hybrid system integrated with a hydrogen subsystem (electrolyzer, fuel cell, and hydrogen storage) to supply both electricity and hydrogen to decentralized sites in Algeria. Using HOMER Pro, five representative Algerian regions were analyzed, accounting for variations in solar irradiation, wind speed, and groundwater availability. A deferrable water-extraction and treatment load was incorporated to model the water requirements of the electrolyzer. In addition, a comprehensive sensitivity analysis was conducted on solar irradiation, wind speed, and the capital costs of PV panels and wind turbines to capture the effects of renewable resource and investment cost fluctuations. The results indicate significant regional variation, with the levelized cost of energy (LCOE) ranging from 0.514 to 0.868 $/kWh, the levelized cost of hydrogen (LCOH) between 8.31 and 12.4 $/kg, and the net present cost (NPC) between 10.28 M$ and 17.7 M$, demonstrating that all cost metrics are highly sensitive to these variations. Full article

A sustainable non-lithium battery is proposed, integrated with renewables to cater for the intermittency and differences between daily supply and demand. A room temperature sodium–sulfur (RT Na-S) battery presented in this study offers a promising energy density of 177 Wh/kg of the pouch cell. A framework is introduced for the design of an RT Na-S battery system, alone and combined with a supercapacitor, and its operating schedule for two case studies: (a) a photovoltaic (PV) system for a household and (b) a wind turbine for an industrial site. Daily power supply and demand profiles are included in both cases. In the first design step, the required mass and volume of the battery cells are determined. In the second step, the system architecture is designed, and simulations of the renewable-energy storage system–demand are carried out for four consecutive days. An RT Na-S battery–supercapacitor system is recommended in association with the wind turbine that involves high frequency and high power pulses, where the supercapacitor caters for power exceeding 0.1 C. A standalone RT Na-S battery is recommended for the PV system. The simulations predicted that each storage system covered all the net power and energy demands without any contributions from the grid. Full article

Renewable wind energy systems widely employ doubly fed induction generators (DFIGs), where efficient converter control ensures grid-integrated power system stability and reliability. Conventional proportional–integral (PI) controller tuning methods often encounter challenges with nonlinear dynamics and parameter variations, resulting in reduced adaptability and efficiency. To address this, we present an owl search optimization (OSO)-based tuning strategy for PI controllers in DFIG back-to-back converters. Inspired by the hunting behavior of owls, OSO provides robust global search capabilities and resilience against premature convergence. The proposed method is evaluated in MATLAB/Simulink and benchmarked against particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA) under step wind variations, turbulence, and grid disturbances. Simulation results demonstrate that OSO achieves superior performance, with 96.4% efficiency, reduced power losses (~40 kW), faster convergence (<400 ms), shorter settling time (<345 ms), and minimal oscillations (0.002). These findings establish OSO as a robust and efficient optimization approach for DFIG-based wind energy systems, delivering enhanced dynamic response and improved grid stability. Full article