Search Results (716)

This paper presents an optimal scheduling framework for a multi-energy hub (EH) that integrates electricity, natural gas, wind energy, energy storage systems, and demand response (DR) programs. The EH incorporates key system components including transformers, converters, boilers, combined heat and power (CHP) units, and both thermal and electrical energy storage. A novel aspect of this work is the joint coordination of multi-carrier energy flows with DR flexibility, enabling consumers to actively shift or reduce loads in response to pricing signals while leveraging storage and renewable resources. The optimisation problem is formulated as a mixed-integer linear programming (MILP) model and solved using the CPLEX solver in GAMS. To evaluate system performance, five case studies are investigated under varying natural gas price conditions and hub configurations, including scenarios with and without DR and CHP. Results demonstrate that DR participation significantly reduces total operating costs (up to 6%), enhances renewable utilisation, and decreases peak demand (by around 6%), leading to a flatter demand curve and improved system reliability. The findings highlight the potential of integrated EHs with DR as a cost-effective and flexible solution for future low-carbon energy systems. Furthermore, the study provides insights into practical deployment challenges, including storage efficiency, communication infrastructure, and real-time scheduling requirements, paving the way for hardware-in-the-loop and pilot-scale validations. Full article

The temperature rise test for transformers is time-consuming, energy-intensive, and has low detection efficiency. To improve the efficiency of the temperature rise test and reduce energy consumption, this paper proposes a temperature rise prediction method for oil-immersed transformer windings. This method is based on identifying the parameters of a thermal circuit model. Firstly, a fifth-order thermal circuit model of oil-immersed transformers is put forward. Then, based on a two-hour temperature rise curve, the thermal capacity and resistance model is identified through genetic algorithms. The obtained parameters are used to compute the temperature rise curve, steady-state average temperature rise, and top oil temperature rise. The results show that the heat capacities of the low-voltage (LV) winding, high-voltage (HV) winding, oil tank, and oil of a 400 kVA transformer are approximately 50 kJ/K, 75 kJ/K, 320 kJ/K, and 90 kJ/K, respectively. Additionally, the thermal resistances from the LV winding to oil, HV winding to oil, oil tank, and air are about 8 mK/W, 5 mK/W, 1 mK/W, and 11 mK/W, respectively. When the transformer capacity increases, the heating power of the windings escalates, and the oil resistance of HV windings decreases from 8 mK/W for a 400 kVA capacity to 5 mK/W for an 800 kVA capacity. The absolute prediction error for transformers of 400 kVA, 630 kVA, and 800 kVA is 2.9 °C. These findings can facilitate the swift detection and assessment of the winding temperature rise in oil-immersed transformers. Full article

Economic dispatch using wind power plants plays a role in reducing the price of electricity production by dispatching power among different generating units for thermal and wind power plants, and supplying load demand while meeting the power system equality and inequality constraints. Adding wind power plants to the economic dispatch model can significantly reduce electricity production costs and reduce carbon dioxide emissions. In this paper, fuel cost and emission minimization are considered as the objective function of the economic dispatch problem, taking into account transmission loss using the B matrix. The quadratic model of the fuel cost and emission criterion functions is modeled without considering a valve-point loading effect. The real power generation limits for both wind and conventional generating units are considered. In addition, a closed-form expression based on the incomplete gamma function is provided to define the impact of wind power, which includes the cost of wind energy, including overestimation and underestimation of available wind power using a Weibull-based probability density function. In this research work, Lagrange’s algorithm is proposed to solve the Wind–Thermal Economic Emission Dispatch (WTEED) problem. The developed Lagrange classical optimization algorithm for the WTEED problem is validated using the IEEE test systems with 6-, 10-, and 40-generation unit systems. The proposed Lagrange optimization method for WTEED problem solutions demonstrates a notable improvement in both economic and environmental performance compared to other heuristic optimization methods reported in the literature. Specifically, the fuel cost was reduced by an average of 4.27% in the IEEE 6-unit system, indicating more economical power dispatch. Additionally, the emission cost was lowered by an average 22% in the IEEE 40-unit system, reflecting better environmental compliance and sustainability. These results highlight the effectiveness of the proposed approach in achieving a balanced trade-off between cost minimization and emission reduction, outperforming several existing heuristic techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) under similar test conditions. The research findings report that the proposed Lagrange classical method is efficient and accurate for the convex wind–thermal economic emission dispatch problem. Full article

In a large wind power base, it becomes unrealistic to rely only on synchronous generators to resist the uncertainty of wind power. A feasible way is to make wind turbines (WTs) and battery energy storage systems (BESSs) participate in frequency regulation. Taking into account the frequency regulation service of WTs and BESSs, the Coordinated Operation Strategy (COS) of the Wind–BESS–Thermal power model will become difficult to solve due to strong nonlinearity. To cope with this challenge, an improved Primary Frequency Regulation (PFR) model is first established considering the frequency regulation of WTs and BESSs. Based on the improved PFR model, the analytical expression of frequency stability constraints is deduced. Next, in view of the wind power uncertainty, the box-type ensemble robust optimization theory is introduced into the day-ahead optimal scheduling, and a robust COS model considering wind power uncertainty and frequency stability constraints is proposed. Then, a linear equivalent transformation method is designed, based on which the original COS model is transformed into a Mixed Integer Linear Programming (MILP) problem. Finally, a modified IEEE 39-bus system is adopted to test the effectiveness of the proposed method. Full article

With the continuous increase in renewable energy penetration, traditional frequency regulation strategies in power grids struggle to maintain frequency stability under high renewable-share conditions. To address the shortcomings of the current deadband settings in regional grid frequency regulation, this paper proposes an optimized deadband-configuration scheme for renewable energy power plants and evaluates its effectiveness in enhancing the frequency regulation potential of renewable units. By developing frequency response models for thermal power, wind power, photovoltaic generation, and energy storage, the impact of different deadband widths on dynamic frequency response and steady-state deviation is analyzed. Three representative output scenarios for renewable units are constructed, and under each scenario the coordinated control performance of the proposed and the existing deadband configurations is compared. Simulation studies are then conducted based on a typical high renewable penetration scenario. The results show that, compared with the existing regional-grid deadband settings, the proposed configuration more fully exploits the regulation potential of renewable units, improves overall frequency-response capability, significantly reduces frequency deviations, and shortens recovery time. This research provides both theoretical foundations and practical guidance for frequency-support provision by renewable energy power plants under high penetration conditions. Full article

Aiming at the stochasticity, uncertainty, and strong perturbation of the linear Fresnel solar thermal power collection subsystem, this study establishes a multivariate prediction model for the linear Fresnel collector subsystem based on complex environmental characteristics and designs a PID controller and MPC controller for the tracking and control of the outlet temperature. By analyzing the heat transfer process of the collector, constructing a model in Multiphysics for three-dimensional modeling of the collector, extracting data through simulation, fuzzy clustering the data and using different clustering centers for parameter identification in order to obtain the multi-model. By using the field data from the site of Dunhuang Dacheng Linear Fresnel Molten Salt Collector Field, considering the inlet temperature, normal direct irradiance and wind speed are used as the perturbation quantities, and the flow rate of molten salt is used as the control quantity. Considering three representative weather conditions, the switching criterion of minimizing the real-time point error is adopted for switching the outlet temperature of the collector. Simulation analysis results show that under the same conditions, the tracking error of the single model is relatively large, with the output temperature error fluctuating between −100 °C and 100 °C and containing many burrs. In contrast, the output temperature error of the multi-model switching control is controlled within 50 °C, which features a smaller tracking error and a faster tracking speed compared with the single-model control. When faced with large disturbances, the multi-model MPC switching control achieves better tracking performance than the multi-model PID switching control. It tracks temperatures closer to the set value, with a faster tracking speed and more excellent anti-interference performance. Full article

High-power-density electric machines play a key role in decarbonising transportation technologies. A critical component of the movement towards high-performance machines is the structure and manufacture of the windings, as this is the dominant source of machine loss. Manufacturing time is important to the effectiveness of the production line, with equivalent importance to the electromagnetic and thermal characteristics. Edgewise windings are increasingly considered to have high potential to be quickly and automatically manufactured. However, they are rarely studied considering all the aspects, these being electromagnetic, thermal, and manufacturing characteristics. This paper will experimentally assess the performance of edgewise machines compared to a stranded winding machine, covering all the aforementioned aspects. Two edgewise winding types are considered, parallel slot and parallel tooth. Firstly, a baseline 11 kW stranded winding machine will be introduced, then two edgewise type machines are proposed to be compared to the baseline machine. These comparisons will initially be made based on simulated torque and thermal performance, then the manufacturing time and quality are assessed for each of the coil structures, showing the achievable time reduction by using edgewise coil structures. Motorettes are used to validate thermal performance of the structures, which are used to calibrate simulation models and evaluate the performance of a full machine equivalent model. Under the thermal limit condition, it is shown that the edgewise parallel tooth windings can achieve a torque increase of 27.8% compared to stranded and 24% compared to edgewise parallel slot. Full article

This paper proposes an optimal scheduling strategy for coal-dedicated railway integrated energy systems, leveraging coordinated electric boiler and thermal storage operation to enhance economic efficiency, improve wind power integration, and reduce carbon emissions. By decoupling the traditional “heat-led-electricity” constraint of combined heat and power (CHP) units, the approach increases operational flexibility and wind power accommodation capacity. A demand response model further optimizes demand-side dispatchability, while a tiered carbon trading mechanism systematically addresses emission costs. The resulting day-ahead scheduling model minimizes total costs—including electricity procurement, wind curtailment penalties, carbon trading, and maintenance expenses—demonstrating superior performance in case studies: a 12.5% reduction in carbon emissions, 56.8% lower operating costs versus conventional methods, and full (100%) wind power utilization. Full article

In order to determine thermally safe driving parameters of ignition coils for hydrogen internal combustion engines (ICE), a reliable estimation of internal power losses is essential. These losses include resistive winding losses, magnetic core losses due to hysteresis and eddy currents, dielectric losses in the insulation, and electronic switching losses. Direct experimental assessment is difficult because the components are inaccessible, while conventional computer-aided engineering (CAE) approaches face challenges such as the need for accurate input data, the need for detailed 3D models, long computation times, and uncertainties in loss prediction for complex structures. To address these limitations, we propose an artificial intelligence (AI)-based framework for estimating internal losses from external temperature measurements. The method relies on an artificial neural network (ANN), trained to capture the relationship between external coil temperatures and internal power losses. The trained model is then employed within an optimization process to identify losses corresponding to experimental temperature values. Validation is performed by introducing the identified power losses into a CAE thermal model to compare predicted and experimental temperatures. The results show excellent agreement, with errors below 3% across the −30 °C to 125 °C range. This demonstrates that the proposed hybrid ANN–CAE approach achieves high accuracy while reducing experimental effort and computational demand. Furthermore, the methodology allows for a straightforward determination of the coil safe operating area (SOA). Starting from estimates derived from fitted linear trends, the SOA limits can be efficiently refined through iterative verification with the CAE model. Overall, the ANN–CAE framework provides a robust and practical tool to accelerate thermal analysis and support coil development for hydrogen ICE applications. Full article

Energy harvesting (EH) is a rapidly evolving domain that is primarily focused on capturing and converting ambient energy sources into more convenient and usable forms. These sources, which range from traditional renewable sources such as solar or wind power to thermal gradients and vibrations, present an alternative to typical power generation. The temptation to use energy harvesting systems is in their potential to power low-power devices, such as environment monitoring devices, without relying on conventional power grids or standard battery implementations. This improves the sustainability and self-sufficiency of IoT devices and reduces the environmental impact of conventional power systems. Applications of EH include wearable health monitors, wireless sensor networks, and remote structural sensors, where frequent battery replacement is impractical. However, these systems also face challenges such as intermittent energy availability, limited storage capacity, and low power density, which require innovative design approaches and efficient energy management. The paper provides a general overview of the subsystems present in the energy harvesting systems and a comprehensive overview of the energy transducer technologies used in energy harvesting systems. Full article

Integrating renewable energy, particularly wind power, into modern power systems introduces challenges concerning stability and reliability. These issues require enhanced regulation to balance power supply with load demand. Flexible loads and energy storage provide viable solutions to stabilize the grid without relying on new resources. This paper proposes building thermostatically controlled loads (BTLs), such as heating, ventilation, and air conditioning (HVAC) systems, as flexible demand-side management tools to address the challenges of intermittent energy sources. A new concept is introduced, portraying BTLs as a stochastic battery with losses, offering a compact representation of their dynamics. BTLs’ thermal characteristics, user-defined set points, and ambient temperature changes determine the power limits and energy capacity of this stochastic battery. The model is simulated using DIgSILENT Power Factory, which includes thermal power plants, gas turbines, wind power plants, and BTLs. A dynamic dispatch strategy optimizes power generation while utilizing BTLs to balance grid fluctuations caused by variable wind energy. Performance analysis shows that integrating BTLs with conventional thermal plants can reduce variability and improve grid stability. The study highlights the dual role of simulating overall flexibility and applying dynamic dispatch strategies to enhance power systems with high renewable energy integration. Full article

This study investigates the thermal performance of induction motors powered by multilevel H-bridge inverters using a novel pulse-width phase shift triangle modulation (PSTM-PWM) technique. Conventional PWM methods introduce significant harmonic distortion, increasing copper and iron losses and causing overheating and reduced motor lifespan. Through experimental testing and comparison with standard PWM techniques (LS-PWM and PS-PWM), the proposed PSTM-PWM reduces harmonic distortion by up to 64% compared to the worst one and internal motor losses by up to 5.5%. A first-order thermal model is used to predict motor temperature, validated with direct thermocouple measurements and infrared thermography. The results also indicate that the PSTM-PWM technique improves thermal performance, particularly at a triangular waveform peak value of 3.5 V, reducing temperature by around 6% and offering a practical and simple solution for industrial motor drive applications. The modulation order was set to M = 7 to reduce both the losses in the power inverter and to prevent the generation of very high voltage pulses (high dV/dt), which can deteriorate the insulation of the induction motor windings over time. Full article

This review explores the pressing need for decarbonization strategies in the off-grid Indigenous communities of Northern Quebec, particularly focusing on Nunavik, where reliance on diesel and fossil fuels for heating and electricity has led to disproportionately excessive greenhouse gas emissions. These emissions underscore the urgent need for sustainable energy alternatives. This study investigates the potential for improving building energy efficiency through advanced thermal insulation, airtight construction, and the elimination of thermal bridges. These measures have been tested in practice; for instance, a prototype house in Quaqtaq achieved over a 54% reduction in energy consumption compared to the standard model. Beyond efficiency improvements, this review assesses the feasibility of renewable energy sources such as wood pellets, solar photovoltaics, wind power, geothermal energy, and run-of-river hydropower in reducing fossil fuel dependence in these communities. For instance, the Innavik hydroelectric project in Inukjuak reduced diesel use by 80% and is expected to cut 700,000 t of CO₂ over 40 years. Solar energy, despite seasonal limitations, can complement other systems, particularly during sunnier months, while wind energy projects such as the Raglan Mine turbines save 4.4 million liters of diesel annually and prevent nearly 12,000 t of CO₂ emissions. Geothermal and run-of-river hydropower systems are identified as long-term and effective solutions. This review emphasizes the role of Indigenous knowledge in guiding the energy transition and ensuring that solutions are culturally appropriate for community needs. By identifying both technological and socio-economic barriers, this review offers a foundation for future research and policy development aimed at enabling a sustainable and equitable energy transition in off-grid Northern Quebec communities. Full article

With the rapid growth of renewable energy integration, power systems are facing increasing uncertainty and variability in operation. The intermittent and uncontrollable nature of wind and solar generation requires operational decisions to anticipate future fluctuations, creating strong temporal coupling across days. This leads to large-scale mixed-integer linear programming (MILP) with a large number of binary variables, which is computationally intensive—especially in year-long simulations. As a result, there is a growing need for efficient modeling approaches that can reduce complexity while preserving key temporal features. This paper proposes a temporal–spatial acceleration framework for long-term power system operation simulation. In the temporal dimension, a monthly K-means clustering algorithm is applied to reconstruct typical scenario days from 8760 h time series, preserving the characteristics of seasonal and intraday variability. In the spatial dimension, thermal units with similar characteristics are aggregated, and binary decision variables are relaxed into continuous variables, transforming the MILP into a tractable LP model, and thereby reducing computational burden. Case studies are performed based on the six-bus and the IEEE RTS-79 systems to validate the framework, being able to provide a practical solution for renewable-integrated power system planning and dispatch applications. Full article

Under the background of “dual-carbon” strategy and global energy transition, the metallurgical industry, which accounts for 15–20% of industrial energy consumption, urgently needs to reduce the energy consumption and emission of DC power supply of electric furnaces. Aiming at the existing 400–800 V/≥3000 A industrial-frequency transformer-rectifier system with low efficiency, large volume, heat dissipation difficulties and other bottlenecks, this thesis proposes and realizes a high-frequency integrated DC power supply scheme for high-power electric furnaces: high-frequency transformer core and rectifier circuit are deeply integrated, which breaks through and reduces the volume of the system by more than 40%, and significantly reduces the iron consumption; multiple cores and three windings in parallel are used for the system. The topology of multiple cores and three windings in parallel enables several independent secondary stages to share the large current of 3000 A level uniformly, eliminating the local overheating and current imbalance; the combination of high-frequency rectification and phase-shift control strategy enhances the input power factor to more than 0.95 and cuts down the grid-side harmonics remarkably. The authors have completed the design of 100 kW prototype, magneto-electric joint simulation, thermal structure coupling analysis, control algorithm development and field comparison test, and the results show that the program compared with the traditional industrial-frequency system efficiency increased by 12–15%, the system temperature rise reduced by 20 K, electrode voltage increased by 10–15%, the input power of furnace increased by 12%, and the harmonic index meets the requirements of the traditional industrial-frequency system. The results show that the efficiency of this scheme is 12–15% higher than the traditional IF system, the temperature rise in the system is 20 K lower, the voltage at the electrode end is 10–15% higher, the input power of the furnace is increased by 12%, and the harmonic indexes meet the requirements of GB/T 14549, which verifies the value of the scheme for realizing high efficiency, miniaturization, and reliable DC power supply in metallurgy. Full article