The increasing penetration of electric vehicles (EVs) and hydrogen fueling stations (HFSs) brings up additional power demand with high flexibility, which shows great potential to enhance the performance of the distribution network. This paper proposes a reliability-oriented optimal operation model considering the coordinated flexibility of EVs and HFSs to reduce interruption loss during outages for the distribution network. In the proposed model, to incorporate the flexibility of HFSs, the multi-state Markov model considering multi-mode operation and inherent reliability is formulated for HFSs. To estimate the comprehensive demand of different types of vehicles, a novel behavior model is established to consider the respective characteristics of EVs and hydrogen-based fuel cell electric vehicles, and support vector regression (SVR) is deployed for traffic volume forecasting. The Monte Carlo simulation (MCS) technique is utilized for system state analysis during contingencies. Based on the simulation results, the system reliability, operation economy, and renewable energy accommodation capability are comprehensively evaluated. The proposed method is tested on the modified IEEE 34-bus system considering multi-time periods. The results indicate that, compared with existing methods that do not account for coordination with HFSs, the proposed coordinated method reduces the expected energy not supplied (EENS) and loss of load probability (LOLP) by 33.8% and 18.9%, respectively, and lowers the interruption costs by 25.9%.
The unit commitment (UC) problem is a typical mixed integer linear programming (MILP) problem in power systems. It often encounters significant computational challenges in power industries. With the development of power systems, the structure of the UC model and the scenarios that need to be considered become more complex and unpredictable, which increases the hardness of the UC solution. However, a systematic investigation of the key factors determining UC solution difficulty remains a hanging problem, limiting the development of targeted efficient algorithms. This paper presents a comprehensive analysis of three critical factors determining UC computational complexity: constraint stress, root relaxation quality, and convergence criteria. Through the indicator definition, this paper quantitatively evaluates proposed key factors and reveals the influence mechanisms of the proposed key factors on the hardness of UC solution through extensive case statistical analysis. The analysis of this paper provides effective references for efficient and customized UC solution algorithms.
Rapid renewable energy deployment driven by global decarbonization goals is accelerating worldwide. However, seasonal variability and evolving climate patterns pose great challenges to the long-term adequacy of power supply. This study focuses on the long term climate risks associated with inadequate renewable supply. First, the climate risk is defined and quantified as the seasonal inadequacy of renewable supply. Then, the risk formulation is incorporated into the optimal planning model as a three-stage frame work. Finally, a 2030 China-provincial case study is performed. The case results show that the climate-risk-aware planning scheme raises total system costs by approximately 0.5% but cuts expected load curtailment by nearly 40%. Additionally, residual climate risk is concentrated in a few coastal and southwestern provincial-level regions, primarily in winter, and in southern coastal regions in summer. The introduction of climate risk reshapes capacity portfolios, raises costs, and exposes seasonal reliability gaps in coastal areas.
Dielectric elastomers are a class of smart materials capable of converting electrical energy into mechanical motion, offering great potential for lightweight and efficient actuation systems. In this work, a dielectric elastomer-transmission-wing (DETW) structure was developed to transform the in-plane vibration of a dielectric elastomer actuator into flapping motion. The static performance of the dielectric elastomer actuator was first analyzed, followed by the fabrication of a flapping-wing prototype. An energy-based model was established for DETW using the Lagrangian approach, which incorporates air load and key dissipative factors such as material viscosity. The model enables a clear understanding of how mechanical loads influence the flapping behavior and dynamic response of the structure. Experiments further confirmed the feasibility of the DETW concept and the prediction accuracy of the model.
Despite growing interest in decentralized multilevel energy markets, challenges persist in security, privacy, and coordination among distributed energy resource operators (DEROs), distribution system operators (DSOs), and transmission system operators (TSOs). Current approaches often rely on centralized structures and lack a unified optimization and communication framework. Hence, this paper proposes a decentralized transactive energy architecture for interconnected transmission-distribution networks, structured as a multilevel electricity market (MLEM). DEROs engage in peer-to-peer (P2P) transactions at the distribution level, while DSOs and TSOs coordinate grid-level operations through a hierarchical optimization structure. A nested ADMM-based algorithm is introduced, where an inner loop computes P2P exchanges in closed form and an outer loop enables decentralized coordination across the hierarchy. A blockchain security layer with a PoS-inspired validator-selection mechanism ensures data integrity, and the communication process is evaluated using an NS-3 wireless network model. The framework is tested on a combined 9-bus transmission and two 15-bus distribution networks. The method reaches consensus in 37 outer and 238 inner iterations; the 1-hour horizon solves in 66.98 s, with only an 8.35% increase for 24 h. TSO-DSO schedules converge within 1% accuracy, and the blockchain maintains a stable 1-second block. The results confirm reliable convergence and consistent communication performance.
The rapid development of ultra-high voltage direct current (UHVDC) transmission has sharply increased the demand for high-performance insulating oils. However, the limited supply of premium naphthenic oils such as KI50X necessitates viable alternatives. This study compares the surface discharge behavior of three transformer oils with distinct hydrocarbon compositions, namely, a low-aromatic naphthenic oil (N-LA), a high-aromatic naphthenic oil (N-HA), and a paraffin-based oil (P-LA), in oil-pressboard insulation systems. A synchronized platform was built to monitor partial discharge (PD) activity and white mark formation under AC stress. A digital workflow was developed to extract the geometric and intensity features of white marks. Density functional theory (DFT) was used to analyze the ionization energy and Fukui index distributions of representative hydrocarbons. The proposed mechanisms were validated using a finite element method-cellular automata (FEM-CA) simulation. Two discharge modes were observed: N-LA exhibited stable, low-voltage surface discharges (similar to 19 kV) with sustained white mark growth, and N-HA and P-LA exhibited streamer discharges in bulk oil above 30 kV. Aromatics suppress discharge inception by homogenizing local fields via space charge and scavenging radicals through reactive hydrogen sites. In contrast, paraffins in P-LA enhanced dielectric strength but promoted gas formation once PD began.
Modular dual-active-bridge (DAB) converter is widely used for fast electric vehicle (EV) charging due to galvanic isolation and high power processing capability. However, the increasing high power demand for EV chargers introduces new challenges in ensuring the high reliability of power conversion systems. Various modulation techniques for DAB, such as single-phase-shift (SPS), extended-phase-shift (EPS), and dual-phase-shift (DPS), have been proposed, while the combined impact of these techniques and power sharing strategies on reliability remains underexplored. Therefore, this paper presents a comparative reliability evaluation of SPS, EPS, and DPS under the optimization schemes of minimum backflow power and peak current, along with the power sharing strategies of even sharing and module shedding with and without rotation. The results show that appropriate combinations where DPS with minimum peak current under module shedding with a rotation strategy achieve the best balance between efficiency and reliability. Furthermore, the investigation of the trade-off between rotation frequency and system-level reliability shows that high-frequency rotation leads to excessive thermal cycling and reduces the lifetime, which provides a new guideline for optimizing module shedding for increased reliability performance.
The sub/super-synchronous oscillation caused by the interaction between renewable energy generation and the power grid seriously affects the stability of the power system. A data-driven rapidly predictive identification method for the dominant oscillation frequency of sub/super-synchronous oscillation is proposed in this paper. The proposed method collects short-term operational data within the oscillation initiation process to predictively identify the dominant frequency when the oscillation presents a relatively stable periodic trajectory, which provides an information basis for the rapid suppression of oscillation. To solve the problem that sub/super-synchronous oscillation data is difficult to obtain, a transfer-learning based approach is developed to transfer knowledge learned in the forced oscillation domain to the sub/super-synchronous oscillation domain. Additionally, residual connection modules, non-local modules, and channel attention modules are introduced into the proposed model architecture, enhancing the model's predictive identification accuracy. Moreover, a weight propagation process is implemented to obtain the weight distribution of electrical channels, enhancing the interpretability of the model. Finally, the proposed method is verified via simulations and measured oscillation data. The results show that the method has high accuracy, strong interpretability, and generality.
Arc faults within the transformers can generate sudden pressure surges, constituting significant hazards that may precipitate oil tank explosions and severely compromise power system stability. Conventional power-frequency arc discharge experiments encounter limitations in isolating pressure wave characteristics due to persistent gas generation and arc reignition. To circumvent these challenges, an oil-immersed impulse voltage discharge platform was conceived and engineered to investigate pressure wave propagation dynamics. A pressure numerical simulation model and theoretical model of oil-solid interface reflection and refraction were subsequently established to elucidate the pressure propagation mechanism. The experimental and simulation results show that the pressure wave generated by pulsed arc discharge in oil propagates radially in the form of spherical waves. Due to the viscous loss and wave front expansion of transformer oil, the peak pressure decays exponentially with distance, with a decay coefficient beta=1.15. When pressure waves encounter metal obstacles inside transformer oil, there are two propagation paths: direct transmission through and multiple reflections through, and a mode transformation of pressure waves occurs at the oil-solid interface, mainly propagating through obstacles in the form of transverse waves. This work quantitatively delineates the energy pressure wave coupling, propagation dynamics, and attenuation mechanisms, providing critical insights for assessing and mitigating arc fault-induced transformer explosion risks.
The real-time and accurate calculation of electricity indirect carbon emissions is not only the critical component for quantifying the carbon emission levels of the power system but also an effective mean to guide electricity users in carbon reduction and promote power industry low-carbon transformation. Fundamentally, calculating indirect carbon emissions involves allocating direct carbon emission data from the power source side, indicating that accurate indirect emission results rely on the precise measurement of power source emissions. However, existing research on indirect carbon emissions in large-scale power systems rarely accounts for variations in carbon emission characteristics under different operating conditions of power sources, such as rated/non-rated operating conditions and ramping up/down conditions, making it difficult to reflect source-side and load-side carbon emission information variation during providing ancillary services. Quadratic and exponential functions are proposed to characterize the energy consumption profiles of coal-fired and gas-fired power generation, respectively, to construct a refined carbon emission model for power sources. By leveraging the theory of power system carbon flow, we analyze how variable operating conditions of power sources impact indirect carbon emissions. Case studies demonstrate that changes in power source emissions under variable conditions have a significant effect on the indirect carbon emissions of power grids.
To enhance the accuracy of short-term photovoltaic power output prediction and address issues such as insufficient spatial resolution of meteorological forecast data and weak generalization ability of models, this paper proposes a prediction method that integrates spatial downscaling meteorological data with a convolutional neural network (CNN)-iTransformer-long short-term memory (LSTM) model. First, the rime-optimized random forest regression algorithm (RIME-RF) is employed to perform spatial downscaling on numerical weather prediction (NWP) data, thereby improving its local applicability. Second, a CNN-iTransformer-LSTM hybrid prediction model is constructed. This model utilizes a CNN as a spatial feature extractor to capture local patterns in meteorological data, employs an iTransformer to model the global dependencies among multiple variables, and leverages an LSTM to enhance the learning of short-term temporal dynamic features, thereby achieving efficient collaborative mining of multi-scale features. Finally, experiments are conducted using actual data from a photovoltaic power station in Hebei, China, during various seasons and weather conditions. The results show that the proposed model outperforms the comparison models in terms of the root mean square error (RMSE), mean absolute error (MAE), and R2, maintaining high prediction accuracy and stability even under complex weather conditions such as overcast and rainy days. The downscaling process further enhances the prediction performance, verifying the effectiveness and practicality of this method.
The energy transition inspired by carbon neutrality targets and the increasing threat of extreme events raise multi-objective development requirements for power systems. This paper proposes a multi-objective resource allocation model to determine the type, number and location of flexible resources to increase the values of resilience, carbon reduction and renewable energy consumption. To evaluate the values of resilience, a restoration model for transmission systems is established that considers the coordination of fossil-fuel generators, energy storage systems (ESSs) and renewable energy generators in building restoration paths. The collaborative power-carbon-tradable green certificate (TGC) market model is then applied to evaluate the resource values in terms of carbon reduction and renewable energy consumption. Finally, the model is formulated as a mixed-integer linear programming (MILP) with a nonconvex feasible domain, and the normalized normal constraint (NNC) method is applied to obtain approximate Pareto frontiers for decision makers. Case studies validate the effectiveness of the proposed model in improving multi-factor values and analyze the impact of resource regulation capacity on values of restoration and carbon reduction.
Although wind energy is volatile, the output of a wind-storage plant is partially dispatchable, making it a promising paradigm on the generation side. A grid-friendly wind-storage plant ought to be able to continuously output the desired power over a certain period of time. This paper proposes a dependable dynamic capacity provision scheme of a wind-storage plant over a daily horizon. It stipulates a minimum number of periods during which the committed capacity must be fulfilled and a maximum mismatch during the remaining periods when the desired power output is not achievable. In the general case, the day-ahead piecewise constant capacity provision results in a two-stage stochastic program formulated as a mixed-integer linear program. Specifically, for constant capacity provision, a decomposition algorithm is developed to determine the global optimal solution, and the complexity grows linearly with the number of scenarios. Given the committed capacity trajectory, the real-time operation problem is modeled as a four-state stochastic dynamic program. The discrete state-action values are derived recursively via the principle of optimality. Real-time dispatch actions are generated by using the action-value tabular leveraging inexact ultra-short-term forecasts. Numerical tests over one year demonstrate that the proposed method successfully fulfills reliable operation on 355 days and achieve an optimality gap of 9.47% compared with the ex-post optimum, which is comparable to model predictive control using exact 2–3-hour-ahead wind power forecasts.
The hybrid series-parallel microgrid attracts more attention by combining the advantages of both the series-stacked voltage and parallel-expanded capacity. Low-voltage distributed generations (DGs) are connected in series to form the intra-string, and then multiple strings are interconnected in parallel. For the existing control strategies, both intra-string and inter-string depend on the centralized or distributed control with high communication reliance. It has limited scalability and redundancy under abnormal conditions. Alternatively, in this study, an intra-string distributed and inter-string decentralized control framework is proposed. Within the string, a few DGs close to the AC bus are the leaders to get the string power information and the rest DGs are the followers to acquire the synchronization information through the droop-based distributed consistency. Specifically, the output of the entire string has the active power-angular frequency (omega-P) droop characteristic, and the decentralized control among strings can be autonomously guaranteed. Moreover, the secondary control is designed to realize multi-mode objectives, including on/off-grid mode switching, grid-connected power interactive management, and off-grid voltage quality regulation. As a result, the proposed method has the ability of plug-and-play capabilities, single-point failure redundancy, and seamless mode-switching. Experimental results are provided to verify the effectiveness of the proposed practical solution.
This paper presents a programmable frequency scan algorithm based on harmonic balance. The core idea involves treating systems under perturbation as nonlinear time-periodic (NTP) systems. Steady-state harmonics are first solved via Newton. Raphson iteration through a set of nonlinear equations, and then input-output variables are selected to estimate the linear transfer function of the original NTP system without perturbations. The applications and insights of the proposed algorithm are discussed, particularly in guiding existing frequency scan algorithms, which are restricted by time-domain signal generation or measurement. This improvement is achieved through linear stability analysis of NTP systems with perturbations.
Wind energy is an environmentally friendly and sustainable energy source that is abundantly available in nature. Wind energy collection devices based on triboelectric nanogenerators (TENGs) can efficiently collect distributed wind energy in the environment. In this work, we propose a TENG to collect wind energy based on the vortex-induced vibration effect. It consists of three parts: an octahedral base to encapsulate the TENG, a central axis and a cylindrical fan blade. Such a design improves the environmental tolerance of TENGs and allows them to collect wind energy from all directions. At a wind speed of 3.5 m/s, the average output power of the device was 49.6 mu W under a 70 M Omega load resistance, while in a high-humidity environment, this value reached 45.5 mu W. The device is capable of continuously powering small commercial electronic devices at a wind speed of 3.5 m/s, indicating its potential for urban distributed wind energy harvesting.
With increasing awareness of environmental protection and rising carbon emission costs, participation in electricity and carbon markets for energy-intensive industrial users will become an effective way to reduce operating costs and carbon emissions. In this regard, a novel Stackelberg game framework is developed in this study for coordinated participation in coupled electricity-carbon markets. Specifically, generalized carbon emission models and electricity consumption models for different energy-intensive industrial users are established, and a Stackelberg game-based interactive operation strategy is proposed for load aggregators (LAs) and energy-intensive industrial users in joint electricity-carbon markets, where the LA works as a leader who chooses proper interactive prices to maximize the comprehensive benefit, whereas energy-intensive industrial users serve as followers who minimize the total energy costs in response to the interactive prices set by the LA. Then, the existence and uniqueness of the Stackelberg equilibrium (SE) are analyzed, and a decentralized solution algorithm is suggested to reach the SE. Finally, the simulation results demonstrate that the proposed interactive operation strategy can not only increase the profit of the LA but also reduce the cost of energy-intensive industrial users, which achieves a win-win result.
The proliferation of distributed and renewable energy resources introduces additional operational challenges to power distribution systems. Transactive energy management, which allows networked neighborhood communities and houses to trade energy, is expected to be developed as an effective method for accommodating additional uncertainties and security mandates pertaining to distributed energy resources. This paper proposes and analyzes a two-layer transactive energy market in which houses in networked neighborhood community microgrids will trade energy in respective market layers. This paper studies the blockchain applications to satisfy socioeconomic and technological concerns of secure transactive energy management in a two-level power distribution system. The numerical results for practical networked microgrids located at IliinoisTech-Bronzevilie in Chicago illustrate the validity of the proposed blockchain-based transactive energy management for devising a distributed, scalable, efficient, and cybersecured power grid operation. The conclusion of the paper summarizes the prospects for blockchain applications to transactive energy management in power distribution systems.
Radiator cooling configurations need to account for both efficient heat dissipation and energy conservation requirements. Rapid and rational determination of cooling system configurations constitutes a critical aspect of transformer design, enhancing electrical power energy utilization efficiency. Computational fluid dynamics (CFD) is widely recognized as a well-established technique for simulating and optimizing heat dissipation systems. However, this approach is time-consuming because of pre-processing procedures, such as meshing. This paper proposes a fast iterative optimization model for calculating the outlet oil temperature and airflow distribution. Based on the analytical model results, this paper identifies the optimal energy-saving range for radiator cooling configurations, incorporating the cooperative effects of cooling efficiency, air pressure drop during heat transfer, and inlet-outlet temperature difference. The analytical model demonstrated errors in energy dissipation and temperature difference calculations within an acceptable range. The calculation time was reduced by more than 99%. Radiator configurations within the optimal range effectively minimize energy waste while meeting the target temperature difference and enhancing cooling efficiency. Finally, the PC2600-22/520 radiator was utilized to validate the accuracy of the analytical model and the rationality of the co-optimal intervals.
Grid-forming (GFM) control is a key technology for ensuring the safe and stable operation of renewable power systems dominated by converter-interfaced generation (CIG), including wind power, photovoltaic, and battery energy storage. In this paper, we challenge the traditional approach of emulating a synchronous generator by proposing a frequency-fixed GFM control strategy. The CIG endeavors to regulate itself as a constant voltage source without control dynamics due to its capability limitation, denoted as the frequency-fixed zone. With the proposed strategy, the system frequency is almost always fixed at its rated value, achieving system active power balance independent of frequency, and intentional power flow adjustments are implemented through direct phase angle control. This approach significantly reduces the frequency dynamics and safety issues associated with frequency variations. Furthermore, synchronization dynamics are significantly diminished, and synchronization stability is enhanced. The proposed strategy has the potential to realize a renewable power system with a fixed frequency and robust stability.
