Multi-converter-based architectures are common in hybrid energy storage systems used in electric vehicles, increase a high cost, high power losses, and high weight. The current work presents a conceptual break to such traditional designs with a direct-coupled HESS that incorporates a proton exchange membrane fuel cell, a lithium-ion battery, and a supercapacitor into one DC bus so that the interconnection power converters are not required. This suggested configuration is based on the new demand-driven model, where the distribution of power is determined depending on the actual load requirements and without referring to predictive algorithms. The proposed system was modeled to evaluate the feasibility of this approach and was heavily simulated in an environment based on MATLAB/Simulink, with three standardized drive cycles FTP75, EPA Highway and WLTC Class. The performance is shown with EMS distribution efficiency of 94.8% and response time of 3.2 milliseconds. It also shows a more stress-free architecture of components, 55.7% decrease in peak current of the battery and better capacity retention. Through successful decoupling of power distribution and predictive models, the results demonstrate that a simplified, converter-free topology is a high-performance alternative providing a possible future, more solid, efficient, and commercially viable route to more robust and efficient powertrains in electric vehicles.
To address the problems of insufficient consideration of source-load temporal characteristics, weak coordination between site and equipment configuration, and low efficiency of multi-objective optimization in the planning of urban smart local energy systems, this paper constructs a system planning method that integrates temporal characteristic analysis and two-layer multi-objective optimization. By constructing a source-load temporal coupling model, the dynamic characteristics of combined heat and power loads and renewable energy outputs such as wind, solar, hydrogen, and storage are accurately characterized. The temporal model provides dynamic data input for the lower-layer equipment configuration optimization. A two-layer optimization framework based on Voronoi diagrams and genetic algorithms is designed. The upper layer uses Voronoi diagrams to partition the space and select station sites. The lower layer employs an improved NSGA-II algorithm to optimize equipment selection and capacity configuration. The two layers interact iteratively: the upper layer provides zoning and load information to the lower layer, and the lower layer feeds back configuration costs to update the upper-layer planning. The framework achieves collaborative site selection and zoned energy supply for multiple energy stations. A fuzzy membership decision mechanism is introduced to enhance the engineering applicability of the Pareto solution set. Experimental results show that, for economics, the total annual cost of the system is reduced by approximately 12% compared to the traditional step-by-step optimization method. In terms of environmental protection, carbon emissions are reduced by approximately 15%. In terms of robustness, even considering demand-side response and load fluctuations of up to 20%, the system can still maintain an energy supply reliability of over 88%. The core innovation lies in the deep integration of high-resolution temporal dynamics with spatial-device collaborative optimization through a closed-loop iterative mechanism, which significantly improves planning accuracy, efficiency, and system adaptability. This method provides a theoretical and technical support for the low-carbon and efficient layout of urban smart local energy systems.
Off-grid power generation has become increasingly efficient for remote and developing regions with the advancement of renewable energy technologies. Areas with limited access to the conventional grid can now depend on self-sustaining hybrid systems that offer higher reliability and lower carbon emissions compared to traditional power sources. The main objective of this study is to propose an economically viable and optimally designed system model for electric vehicle (EV) charging infrastructure. The system aims to provide a practical and sustainable solution for EV charging, considering electric vehicles as a primary mode of transportation. The motivation behind this work is to promote the adoption of renewable energy in the transportation sector, thereby contributing to a cleaner and pollution-free environment. The selected location is the capital city of the central region of Oman. The electric load of a community was synthesized for 25 households, with 60% of them owning an electric vehicle. Comprehensive system modelling, optimization, and performance evaluation were carried out under varying operating conditions. The total Net Present Cost (NPC) and Cost of Electricity (COE) of the proposed system were estimated to be $7,11,333 and $28,394 respectively. In addition, a sensitivity analysis was conducted to study the impact of variations in average solar irradiance and diesel fuel prices in Oman, as these parameters significantly influence the overall production cost of the system.
To address the challenges of absorption difficulty and insufficient economy caused by the grid connection of high-proportion renewable energy, this paper constructs a non-cooperative asymmetric game model between a wind-PV-thermal integrated energy system and external independent power generation units participating in the spot and frequency regulation coordinated market under the framework of Liaoning Province’s frequency regulation market rules. Taking the profit maximization of each participant as the core objective, a joint clearing model that incorporates the costs, compensations, and constraints of both the spot and regulation service markets are established, and the Nash equilibrium is solved through the application of the strategy iteration algorithm. Case studies verify that through internal resource coordination, the integrated energy collaborative system significantly reduces marginal costs and frequency regulation quotes, effectively improves its own market revenue while promoting renewable energy absorption, providing an economical and reliable operation support for the new power system.
The long-term stable adequacy of power generation capacity and flexible adjustable resources is one of the keys to the stable operation of the power market. The uncertainty of new energy and the periodicity of hydropower output pose greater challenges to the adequacy of power market capacity and adjustable resources. To address this challenge, probabilistic modeling of error probabilities in different periods is conducted through the kernel density estimation method with separate parameters. Dynamic reserve capacity demand is calculated under confidence probability, and capacity adequacy performance payment is introduced. Finally, the incentive effects of the new method are compared with those of the traditional capacity payment system and market auction in the market, leading to the conclusion that the new method helps flexible adjustable units better recover costs.
In recent years, as the need for energy rises, the application of dispersed generation and shunt capacitors has become a more common solution to meet the growing demand of energy. The article presents a novel approach to optimise radial distribution networks, called the Crested Porcupine optimisation algorithm (CPOA), inspired by the behaviour of the Crested Porcupine. The proposed method is tested on the IEEE system, which has 33, 69 and 85 buses. The purpose of this work is to lower the cost and power loss while improving the voltage profile and voltage stability index (VSI) using the Capacitor Banks (CB) and Distributed Generator (DG) units at the correct location and of appropriate size. Inclusion of load models is also taken into consideration.
As the “dual-carbon” targets are set and the energy sector accelerates its shift toward green and low-carbon development, renewable sources such as wind power are being integrated at an unprecedented scale. This trend has imposed considerable strain on the stability and reliability of the evolving power system. To tackle this challenge, this study introduces a hybrid dynamic wind power forecasting approach that integrates machine learning with optimization algorithms. Furthermore, by incorporating a self-correcting parameter estimation process, a hybrid model is constructed. By continuously tuning the grid’s transmission capacity in real time, the proposed framework remains responsive to variations in wind power output. Based on observed fluctuation patterns, the framework continuously updates its system parameters, guaranteeing that the power grid operates optimally even under intricate and shifting environmental conditions. Using real-world data for validation, the proposed approach demonstrates clear strengths in both forecast precision and the operational efficiency of the power grid. By strengthening the grid’s resilience to wind power variability, this approach contributes to maintaining reliable and stable system operation. This technique offers tangible engineering backing for the seamless and efficient incorporation of wind energy into power grids.
Solar photovoltaic (PV) system operation and maintenance are important for achieving global sustainability objectives. Also, the reliability of PV is one thing that has always prevented it from reaching the full product guarantee due to hotspot, diode degradation, shading, and cracks issues. Traditional inspection methods are time-consuming, expensive, and can contain errors, which justifies the development of automation systems. This paper proposes a deep learning-based framework for anomaly detection using high-resolution RGB images, which overcomes the drawbacks of low-resolution grayscale datasets. To improve robustness, a dataset consisting of 20,000 PV module images with eleven fault categories and normal modules was systematically preprocessed by resizing, augmentation, and quality assurance. Six models, namely, CNN model, AlexNet, VGG16, ResNet18, DenseNet, and EfficientNetV2B0, were compared with each other through the evaluation metrics of accuracy, precision, recall, and F1 score. The experimental results show that the RGB transform can greatly benefit feature learning and model generalization. Overall, ResNet18 had the highest accuracy (91.1%) while EfficientNetV2B0 had balanced performance overall metrics. The results highlight Deep-Learning applications with fine quality datasets (i.e., achieving high performances of Anomaly Detection, Predictive Maintenance, and Sustainable Energy Generation).
This paper suggests a new Hybrid Butterfly-Firefly Machine Learning Optimization (HBFL-OM) framework to enhance the operational performance of the IEEE distribution systems by optimizing the position and setting parameters of a Hybrid Static Compensator-Smart Voltage Stabilizer (HSVC). The HBFL-OM algorithm is a hybridization of Butterfly Optimization Algorithm (BOA) global exploration method and the Firefly Algorithm (FA) local refinement, where the Support Vector Regression (SVR) is added to learn the predictions and converge faster. They are optimized at the same time with multi-objective functions, such as minimization of power loss, power quality (THD), improvement of reliability (SAIFI/SAIDI), and balancing of loads. The AHP and TOPSIS are used to determine the best bus to place HSVC. The performances of the proposed HBFL-OM on IEEE 33-bus and 69-bus test systems prove that the given algorithm is better than GA and PSO algorithms with the subsequent results: 22.1% reduced power losses, 26% improved THD, and 20% increased indices of reliability with the preservation of balanced load distribution. The framework offers a strong and data driven solution that can be optimally utilized to optimize power systems in real-time.
To address issues in traditional relay protection system state evaluation, such as insufficient training samples, lagging results, and manual setting management, this study proposes a real-time state evaluation model integrating digital twin and deep transfer learning. A high-fidelity digital twin system is constructed to establish bidirectional mapping and dynamic updates between the physical system and virtual twin. A sparse stacked autoencoder extracts discriminative features, and an online adaptation strategy based on deep transfer learning enables continuous self-optimization with streaming data. Experimental results show an overall accuracy of 98.2%, weighted F1-score of 0.978, and average evaluation delay reduced to 1.5 minutes. The intelligent setting management platform improves verification and download efficiency by 60% and 40% respectively, with error rate decreasing from 10.2% to 0.22%. The framework enables minute-level real-time evaluation, predictive maintenance, and closed-loop operation, providing a reliable approach for building resilient and smart grids.
Interior finishing materials are often regarded as architectural surfaces rather than active thermal components, and their role in building energy efficiency remains underexplored. As global cooling demand continues to rise and buildings account for nearly one-third of global energy use, developing low-carbon thermal modulation strategies has become an urgent priority. In this study, seven representative green interior materials (natural wood, bamboo composite, gypsum board, diatom coating, recycled cellulose fiberboard, cork sheet, and aerogel-enhanced composite) were experimentally evaluated under controlled cooling conditions to quantify their effects on indoor heat-transfer behavior and HVAC energy consumption. Surface temperature evolution, transient heat flux, and comfort stability were continuously monitored, and thermal response curves were fitted using a first-order decay model to extract the thermal time constant τ. The results show that aerogel and cellulose finishes substantially delayed heat penetration, exhibiting τ≈ 1.47 h and τ≈ 1.32 h, respectively, representing up to 42% longer response time compared to wood. Cooling energy consumption decreased by 10–18% with low-conductivity finishes, accompanied by smoother temperature fluctuations and enhanced comfort stability. A strong correlation emerged between thermal conductivity and normalized energy demand (R2≈0.89), allowing the development of a predictive selection model for material-driven HVAC performance. These findings demonstrate that finishing layers can serve as functional thermal regulators rather than passive decorative elements, offering a scalable and lightweight strategy for reducing operational building energy and enabling low-carbon retrofit pathways.
The increasing penetration of distributed photovoltaic (PV) generation and the rapid growth in electric vehicle (EV) adoption have substantially increased operational, safety, and economic pressures on distribution networks. Intermittent renewable output and stochastic charging demand have pushed existing security margins and the network’s new-energy acceptance capacity to unprecedented limits. To better exploit the consumption potential of coordinated PV-storage-charging resources and to raise the distribution network’s capacity to accommodate new energy and diverse loads, this study examined the multi-objective acceptance-capacity optimization problem for a coordinated PV-storage-charging system. We proposed an innovative bi-level optimization framework. The upper level introduced a multi-objective optimization algorithm to balance conflicting goals – maximizing PV acceptance capacity, minimizing voltage deviations at key nodes, and minimizing total investment and operating costs – and to identify optimal interconnection locations and capacity allocations for PV-storage-charging systems. On the basis of the upper-level decisions, the lower level utilized a second-order cone programming (SOCP) relaxation technology to transform the distribution network’s nonlinear power flow constraints into a convex optimization model that can be efficiently solved. This yielded a solvable convex model for detailed, cost-effective operational scheduling of the PV-storage-charging system within the planning horizon. The proposed optimization framework was validated on the IEEE 33-node standard distribution network model through numerical simulations. The experimental results showed that the proposed optimization framework significantly enhanced the overall operational performance of the distribution network. Compared to a conventional single-objective capacity configuration scheme, the proposed framework increased PV acceptance capacity by 32.7% on a representative operating day, reduced maximum voltage deviations at key nodes by 8.2%, and raised the voltage qualification rate to 99.3%. Coordination among PV modules, energy units, and charging piles also lowered investment and operating cost per unit capacity by approximately 26.3 yuan/kW. The results demonstrated that the proposed optimization framework effectively promoted on-site consumption and efficient utilization of renewable energy, mitigated voltage violations, and contributed to peak load shaving and valley load filling. The research results provide both theoretical insight and practical solutions for addressing technical challenges associated with high-penetration renewable energy sources and diverse loads in distribution networks.
The application of Grid-Forming (GFM) and Grid-Following (GFL) controllers has effectively enhanced the strength of increasingly weakened power system. However, the inherent high-order and nonlinear characteristics of wind farm models pose numerous challenges to the simulation and analysis of the dynamic stability of modern power systems. To address these challenges, this paper proposes an innovative aggregated modeling approach for wind farms, which enables large-scale simulation and serves as a powerful tool for modern power system stability analysis. Based on the distinct Thevenin equivalent circuits of GFL and GFM units, this study introduces their respective rotor current and stator voltage weighting coefficients for the aggregation of wind turbines operating under different control modes. The constructed model can accurately represent wind farm dynamic characteristics across varying grid strengths and fault conditions. To verify the proposed model’s effectiveness, this paper compares the accuracy of the modal aggregation method against other multi-machine representation methods under Fault Ride-Through (FRT) conditions. Results demonstrate a significant reduction in errors between the proposed aggregation model and detailed model in the pre-fault, fault-on and post-fault stages. In addition, the aggregated model is utilized to investigate the port characteristics of wind farms with different GFM-GFL unit proportions. It is shown that a reasonable increase in the proportion of GFM and GFL units can significantly enhance the operational stability of wind turbines under low Short-Circuit Ratio (SCR) conditions, and effectively expand their stable operating range in weak grid environments.
Natural gas, the cleanest fossil fuel, is increasingly important due to its abundance and lower carbon emissions. However, accurately forecasting gas demand remains challenging. To forecast gas usage, this study uses sophisticated machine learning (ML) techniques, including CatBoost, XGBoost, and MLP. Six prediction models and hyperparameter optimization are created and assessed. Hybrid XGBoost models, particularly XGBoost-SSA and XGBoost-SMA, demonstrate superior convergence and accuracy. Visual aids like correlation matrices and scatter plots provide insights into model performance. The research contributes to enhancing the efficiency of gas distribution operations, ensuring energy security, economic stability, and environmental sustainability. By integrating renewable energy and leveraging real-time analytics, the study addresses the evolving dynamics of gas consumption forecasting, offering valuable implications for energy policies and investment strategies.
Energy security is one of key components of economic growth and sustainable development. During the last decades, the European Union (EU) has made significant efforts to transition its energy sector towards a sustainable, renewables-based, and climate-neutral model. However, Russia’s invasion of Ukraine in February 2022 and the subsequent energy crisis throughout Europe revealed a significant dependence of Europe on Russian energy resources. The EU’s current strategy on complete replacement of Russian energy supplies and accelerating implementation of renewable sources of energy is challenging now but crucial for Europe’s long-term energy security. National energy security is just one of many challenges facing Ukraine. National integrity and determination along with strong international support are key elements for both Ukraine’s survival and post-war sustainable recovery, including energy sustainability. Following the EU’s strategy, Ukraine urgently needs to transform its energy sector in a sustainable manner. Among other approaches, the country should significantly rely on renewables, including biogas and biomethane production, along with radical improvements in national energy efficiency. In this article, we analyze the challenges of energy security for the European Union and Ukraine due to the Russian invasion of Ukraine, and effective strategies, which may be applied.
The current mixed energy storage (ES) system in the distribution network (DN) has become the main power system for new energy construction, but how to achieve joint optimization control of the mixed energy system in the DN is still the focus of current research. To achieve joint coordinated control of hybrid ES systems in new DNs, this study introduces a coordinated control model for ES systems based on multi-objective optimization (MOO) algorithms. The new model uses MOO algorithms to coordinate and optimize the ES system in the DN, thereby achieving accurate coordinated control of the ES system. The results show that using MOO algorithms, the network loss of the hybrid ES system is reduced by 1.258 MW, and the load disturbance was reduced by 0.24. At the same time, after using the new method, the operating cost of the hybrid ES system is reduced by about 40,000 yuan/year, and the grid losses of nodes are reduced by about 8.65%. The joint coordinated control method of the new ES system can improve the ES optimization effect of the system and reduce ES losses in the power grid. This study has good guiding significance for improving the ES efficiency of new DNs.
To address the significant heat loss issues in high-temperature steam pipes of thermal power plants, this study aims to optimize their energy-saving thermal insulation performance based on nanoporous aerogel super-insulation technology. Confronting the drawbacks of traditional materials like calcium silicate and rock wool, which include high thermal conductivity, bulky volume, and insufficient long-term reliability at temperatures above 600∘C, this paper innovatively proposes and designs a multi-layer composite insulation structure for application in 600∘C main steam pipelines. Through functional gradient design, this structure synergistically utilizes the Knudsen effect and nanoconfinement effect of nanoporous SiO2 aerogel felt to achieve an ultra-low equivalent thermal conductivity (as low as 0.0243 W/m⋅K at 650∘C), combined with the structural support of microporous calcium boards and the radiative reflection function of the outer cladding, thereby achieving multiple suppressions of gas-phase, solid-phase, and radiative heat transfer. The research comprehensively employs theoretical modeling, numerical simulation, and full-scale experimental validation. Results indicate that compared to traditional 100 mm calcium silicate insulation, the designed 80.5 mm composite structure reduces the average external surface temperature of the pipeline by 21.8% to 48.7∘C and decreases the surface heat flux density by 37.4% to 89.2 W/m2, equivalent to an annual saving of 2,528 tons of standard coal per single pipeline. Through coupled thermal-stress-fluid multiphysics field simulations and safety analysis, the structure is verified to have sufficient safety margins under thermal cycling, wind load, and manufacturing tolerances. Full-scale platform testing and long-term operational data further confirm the excellent stability of the system, with an annual thermal conductivity attenuation rate of only 4.2%, and the adoption of modular prefabricated construction shortens the project timeline by 31.2%. Although the initial investment increases by 50.8%, life cycle cost analysis shows a 28.8% reduction in total cost over 15 years, with a static payback period of approximately 1.2 years. This study provides an innovative solution for the energy-saving insulation of high-temperature steam pipes in thermal power plants, offering high performance, high reliability, and good economic benefits.
This article studies the decarbonization potential and scaling pathways of abandoned coal mine compressed air energy storage (CAES) systems. By constructing a comprehensive quantitative model covering direct emission reduction, indirect emission reduction, and sequestration-based emission reduction, it systematically analyzes the triple synergistic decarbonization mechanisms of zero-carbon operation, grid peak-shaving, and geological sequestration. The study shows that using zero-carbon compression and waste heat recovery technologies can eliminate the gas supplementary combustion in traditional systems to achieve direct emission reduction, enhance the grid’s ability to absorb renewable energy for significant indirect emission reduction benefits, and utilize the renovated mine spaces for CO2 geological sequestration to achieve negative emissions. Typical case analysis indicates that a 200 MW system has an annual decarbonization potential of about 130,000 tons of CO2, with 86.2% contributed by indirect emission reduction, highlighting its core value in replacing high-carbon power sources. The study further proposes a scaling development path centered on technology standardization, regional clustering, and policy coordination, pointing out that by reducing costs and improving efficiency of key equipment, establishing mine area storage clusters and coordinated grid dispatch, and innovating carbon markets and green finance mechanisms, abandoned coal mine CAES systems can become a key technology to support the new power system and achieve carbon neutrality goals.
Under the background of the “dual-carbon” strategy and the ongoing energy structure transition, the rapid penetration of distributed generation (DG) and electric vehicles (EVs) has introduced bidirectional uncertainties on both the supply and demand sides of distribution networks. To address the limitations of traditional transformer planning methods that fail to simultaneously capture the stochastic characteristics of DG and EVs, this paper proposes a multi-objective optimization model for transformer layout planning considering source-load uncertainties. The model characterizes the stochastic outputs of photovoltaic and wind power using Beta and Weibull distributions, respectively, and adopts a Monte Carlo simulation framework to represent the spatiotemporal distribution patterns of EV charging loads, thereby achieving coordinated modeling of source-load randomness. On this basis, a probabilistic optimization framework is established to minimize the total life-cycle cost-including transformer investment, network losses, and outage losses-subject to voltage, current, and capacity constraints. Simulation results on the enhanced IEEE 33-bus network verify that the proposed method can effectively improve voltage regulation, cut line losses and operating costs, and sustain system stability under substantial DG and EV penetration. The research provides a systematic modeling approach and optimization reference for transformer planning in distribution networks with high renewable energy and EV integration.
The heterogeneous characteristics of proton exchange membrane fuel cells (PEMFCs), alkaline electrolyzers (AEs), and batteries introduce significant challenges to the coordinated control of wind–photovoltaic (PV)–hydrogen–storage coupled systems. Furthermore, conventional state-based energy management strategies are incapable of dynamically and in real time optimizing the reference power of individual devices, thereby limiting both operational stability and utilization efficiency. To overcome these limitations, this study develops a model predictive control (MPC)-based coordinated control strategy for wind–PV–hydrogen–storage coupled systems, aiming to enhance the effective consumption of renewable energy. Within the proposed framework, reference power is dynamically and optimally allocated under system constraints, while power limits, weighting factors, and state–space equations can be flexibly formulated through a user-defined power management module implemented in MATLAB/Simulink. Simulation studies confirm the feasibility of the proposed strategy: the coordinated control not only accommodates the operating characteristics of AEs and PEMFCs, but also reduces redundant power conversion stages and switching devices. Moreover, the decoupling mechanism enables accurate determination of both the direction and magnitude of power flow, achieving superior tracking performance through the regulation of two free variables.
