Search Results (8,546)

Composite interphase spacers are essential components in ultra-high-voltage (UHV) transmission lines to suppress conductor galloping. This study investigates the first reported case of a core-rod fracture in a 500 kV composite spacer and elucidates its degradation mechanism through multi-scale characterization, electrical testing combined and electric field and mechanical simulation. Macroscopic inspection and industrial computed tomography (CT) show that degradation initiated at the unsheltered high-voltage sheath–core interface and propagated axially, accompanied by continuous interfacial cracks and void networks whose volume ratio gradually decreased along the spacer. Material characterizations indicate moisture-driven glass-fiber hydrolysis, epoxy oxidation, and progressive interfacial debonding. Leakage current test further indicates humidity-sensitive conductive paths in the degraded region, confirming the presence of moisture-activated interfacial channels. Electric-field simulations under two shed configurations demonstrated that local field intensification was concentrated within 20–30 cm of the HV terminal, where the sheath and core surface fields increased by approximately 9.3% and 5.5%. Mechanical modeling demonstrates a pronounced bending-induced stress concentration at the same end region. The combined effects of moisture ingress, electrical stress, mechanical loading, and chemical degradation lead to the decay-like fracture. Improving sheath hydrophobicity, enhancing interfacial bonding, and optimizing end-fitting geometry are recommended to mitigate such failures and ensure the long-term reliability of UHV composite interphase spacers. Full article

The increasing penetration of microgrids (MGs) in modern power distribution systems requires advanced operational strategies to ensure both economic efficiency and technical reliability. This study developed an optimal economic framework for battery energy storage in MG connected to distribution systems in order to minimize operational costs while considering renewable integration and battery charging and discharging cost and degradation cost as well, and their impact on grid technical constraint. An MG is interconnected to the IEEE-33 radial distribution feeder through an additional bus, where the BESS operates to minimize the total operating cost over a 24 h horizon. The formulation captures the charging and discharging dynamics of the BESS, BESS degradation, state-of-charge constraints, electricity price signals, and the network’s operational limits. The optimization problem is solved using Mixed Integer Linear Program (MILP) to obtain the optimal scheduling of BESS charging and discharging which minimizes the total operating cost and maintains grid constraint within the allowable limit by optimizing the power exchange between the MG and the distribution grid. Simulation results showed that the proposed approach reduces operational costs and optimize grid power exchange, while maintaining technical reliability of the distribution system by enhancing its voltage profiles, improving its feeder loading capability, and reducing the system losses. This study provides a practical tool for enhancing both economic and technical performance in MG-connected distribution systems. Full article

Traditional electrical resistivity tomography (ERT) technology confronts bottlenecks such as the volume effect in the detection of termite nests in levees, while the ERT based on deep learning has insufficient interpretation accuracy due to small sample data. This study proposes an intelligent ERT diagnosis framework that integrates generative adversarial networks (GANs) with semantic segmentation models. The GAN-enhanced networks (GFU-Net and GFL-Net) are developed, incorporating a Squeeze-and-Excitation (SE) attention mechanism to suppress false anomalies. Additionally, a comprehensive loss function combining binary cross-entropy (BCE) and the Focal loss function is used to address the issue of sample imbalance. Using forward modeling based on the finite difference method (FDM), a termite nest hidden danger ERT dataset, which includes seven types of high-resistance anomaly configurations, is generated. Numerical simulations demonstrate that GFL-Net achieves a mean intersection-over-union (mIoU) of 97.68% and a spatial positioning error of less than 0.04 m. In field validation on a red clay embankment in Jiangxi Province, this method significantly improves the positioning accuracy of hidden termite nests compared to traditional least squares (LS) inversion. Excavation verification results show that the maximum error in the horizontal center and top burial depth of the termite nest identified by GFL-Net is less than 7% and 16%, respectively. The research findings provide reliable technical support for the accurate identification of termite nest hidden dangers in embankments. Full article

In this study, boron was introduced into bamboo-derived porous carbon (BPC) through dry thermal treatment using boric acid. During heat treatment, boric acid was converted to B₂O₃, which subsequently interacted with the oxygen-containing surface groups of BPC, leading to the formation and evolution of B–O–B and B–C bonds. This boron-induced bonding network reconstruction enhanced π-electron delocalization and surface polarity, while maintaining the intrinsic microporous framework of BPC. Among the prepared samples, B-BPC-1 exhibited an optimized balance between the conductive domains and defect concentration, resulting in lower internal resistance and improved ion transport behavior. Correspondingly, B-BPC-1 delivered a better capacitive performance than both undoped BPC and commercial activated carbon. These results indicate that controlling boron incorporation under appropriate heat-treatment conditions effectively improves charge-transfer kinetics while maintaining a stable pore morphology. The proposed dry thermal doping method provides a practical and environmentally benign route for developing high-performance porous carbon electrodes for electric double-layer capacitor applications. Full article

Facing the challenge of balancing electric vehicle (EV) user experience with distribution network security, this paper develops a multi-objective optimization model for charging station selection that simultaneously considers user-side costs and grid-side stability indicators, including voltage deviation and system power loss. To solve this complex problem, an improved NSGA-II algorithm with enhanced constraint handling is introduced. Case studies on the IEEE 33-bus system demonstrate that the proposed approach effectively limits maximum voltage deviation to 2% with only a minimal 3% increase in user cost and reduces network losses by 12%. This achieves an optimal balance between user satisfaction and grid security, providing quantitative support for coordinated charging management and infrastructure planning. Full article

As a specialized recurrent neural network (RNN), the Zeroing Neural Network (ZNN) has demonstrated efficacy in solving diverse real-time, time-varying problems. This paper proposes a ZNN-based model framework to address the challenge of time-varying demand response within building energy management systems, specifically for thermo-electric integrated systems. The framework encompasses model construction, control design, performance analysis, and robustness testing. Results indicate that the system’s target power effectively adapts to real-time electricity pricing with minimal response latency. The ZNN controller achieves extremely low tracking error, with a maximum observed value of merely −1.38 kW. Furthermore, the system exhibits strong robustness against disturbances in the coefficient of performance (COP) and achieves lower tracking error with increased thermal storage tank heat capacity. The ZNN has significant performance advantages in time-varying demand response scenarios. Full article

In accordance with the European common rules for the internal electricity market, suppliers offer end users contracts with dynamic energy prices. To reduce energy costs, prosumers must manage their installations with energy storage devices (ESSs). The authors propose a novel control strategy with a relatively simple simulation-based algorithm that effectively reduces daily energy costs by managing the ESS charging and discharging schedule under different types of dynamic energy tariffs. The algorithm operates in a running window mode to ensure ongoing control updates in response to the changing conditions of the prosumer’s installation operation and dynamically changing energy prices. A feature of the control system is its ability to regulate the power exchanged with the supply network in response to an external signal from a superior control system or a network operator. This feature allows the control system to participate in regulatory services provided by the prosumer to the DSO. The effectiveness of the proposed control algorithm was verified in the PSCAD V4 Professional environment and with the MS Excel SOLVER for Office 365 optimisation tool. The results showed good accuracy with respect to the cost reduction algorithm and confirmed that the additional regulatory service can be effectively implemented within the same prosumer ESS control system. Full article

This paper presents a physics-informed reinforcement learning framework that embeds thermodynamic constraints directly into the policy network of a continuous control agent for HVAC optimization. We introduce a Thermodynamically-Constrained Deep Deterministic Policy Gradient (TC-DDPG) algorithm that operates on continuous actions and enforces physical feasibility through a differentiable constraint layer coupled with physics-regularized loss functions. In a simulation-based evaluation using a custom Python multi-zone resistance-capacitance (RC) thermal model, the proposed method achieves a 34.7% reduction in annual HVAC electricity consumption relative to a rule-based baseline (95% CI: 31.2–38.1%, n = 50 runs) and outperforms standard DDPG by 16.1 percentage points. Thermal comfort during occupied hours maintains PMV ∈ [−0.5, 0.5] for 98.3% of operational time, peak demand decreases by 35.8%, and simulated coefficient of performance (COP) improves from 2.87 ± 0.08 to 4.12 ± 0.10. Physics constraint violations are reduced by approximately 98.6% compared to unconstrained DDPG, demonstrating the effectiveness of architectural enforcement mechanisms within the simulation environment. We present a reference prototype and commit to a future public release of the code, configurations, and hyperparameters sufficient to reproduce the reported results. The paper explicitly addresses the limitations of simulation-based studies and presents a staged roadmap toward hardware-in-the-loop testing and pilot deployments in real buildings. Full article

As typical dynamic loads, electric vehicles (EVs) introduce significant uncertainty into distribution network operations due to the randomness of their travel behavior and charging demand. To achieve precise spatiotemporal forecasting of charging loads, this paper constructs a multi-dimensional transportation network model that accounts for dynamic road impedance factors and introduces a unit-distance energy consumption calculation method based on road impedance. By integrating the division of urban multifunctional zones and differentiated state-of-charge (SOC) threshold distributions across various EV types, a mapping model between travel chains and charging behaviors is established. Subsequently, large-scale travel and charging events are generated using an origin–destination (OD) probability matrix and Monte Carlo sampling to derive the spatiotemporal distribution of regional EV charging loads. Simulation results for a representative city in southwest China show that the predicted charging loads exhibit a dual-peak pattern, with significant differences across regions and vehicle types, and align well with observed load trends, validating the effectiveness and engineering applicability of the proposed method. Full article

Accurate electricity load forecasting underpins smart-grid operation and broader economic planning. Yet multivariate load series are driven by weather, economic activity, and seasonal effects whose intertwined, scale-dependent dynamics make forecasting challenging. While graph neural networks (GNNs) capture spatio-temporal dependencies, they often underrepresent multi-scale structure. MTSGNN (multi-scale trend–seasonal GNN) is introduced to bridge this gap. MTSGNN couples a multi-scale trend–seasonal GNN module with a Hawkes-enhanced temporal decoder. The former decomposes load signals into multiple temporal scales and models cross-variable interactions at each scale, while the latter embeds a Hawkes process to capture decaying and self-/mutually exciting temporal influences. To effectively combine long-term trends with periodic variations, a Trend–Seasonal Spatio-Temporal Fusion mechanism is proposed, which jointly learns and integrates trend and seasonal representations across both space and time. MTSGNN is designed for multi-step load forecasting with a historical window of 120 time steps and a prediction horizon of 120 future steps. Evaluations on five real-world datasets demonstrate that MTSGNN consistently surpasses existing approaches for multi-step power load prediction, establishing a new benchmark in forecasting accuracy. Full article

This paper proposes an integrated sensor for voltage and current distribution network insulators, based on electric field coupling and TMR magnetic sensing, to address the issues of traditional voltage and current separation measurement, insulator safety after primary and secondary fusion, uncertainty in voltage measurement gain, and interference resistance in TMR current measurements. Through simulation and optimization, the design of the embedded voltage-sensing unit in the insulator is achieved, ensuring uniform electric field distribution, determining the transfer function, and minimizing partial discharge, thereby ensuring insulator safety and improving voltage measurement accuracy. Additionally, a self-integrating circuit design is used to widen the low-frequency dynamic range and increase the voltage division ratio. Moreover, an open-type two-stage magnetic ring current sensor based on TMR is proposed, with optimized magnetic ring dimensions to detect currents from low to medium ranges, addressing eccentricity errors and improving sensitivity, immunity to interference, and magnetic field uniformity. The experimental results show that this integrated sensor can effectively ensure measurement accuracy, stability, and dynamic range. Full article

In this paper we present the development of an energy and power system modelling, simulation, and analysis (ePowerSim.jl) package in Julia programming language. ePowerSim.jl is designed to present a uniform data interface for static, quasi-static, dynamic analysis, as well as network operation optimisation. It provides a co-simulation framework for the further development and experimentation of various types of models of electric power systems components or abstract entities that have mathematical formalism or data representation. ePowerSim.jl makes extensive use of cutting edge packages such as DifferentialEquations.jl, Dataframes.jl, NamedTupleTools.jl, Helics.jl, ForwardDiff.jl, JuMP.jl, and BifurcationKit just to mention a few in the Julia ecosystem. Models of synchronous generator, synchronous condenser, excitation systems, and governors developed in the package were used to model IEEE 9 bus and IEEE 14 bus test networks and subsequently validated by a real-time digital simulator of electric power systems (RTDS). The results obtains for static and dynamic models simulation in ePowerSim.jl show a close match with a simulation of the same system in RTDS. A maximum error of $0.00001$ pu and $0.0001$ pu were obtained for steady states and transient state respectively. Similarly, a maximum deviation of $0.0001$ pu was obtained during validation for voltage magnitude during transient state at buses in the network. Full article

The deployment of electric trucks is recognized as a crucial tool for reducing dependence on traditional fossil fuels and mitigating pollution from transportation systems. However, insufficient and unbalanced distribution of charging stations may hinder the use of electric trucks. This study develops an explicit mixed-integer linear program to optimize the siting and sizing of charging stations for electric trucks in general transport networks. The model incorporates the queuing dynamics of electric trucks at charging stations through a formulated set of first-come-first-served constraints, enabling the direct computation of the charging waiting time for each truck. The objective function minimizes the total system cost, comprising the charging station construction cost, the electric truck procurement cost, the electricity consumption cost, and the operational cost, consisting of travel times, queuing times, and the delay penalties of the trucks. To address the computational challenges in solving large-scale network problems, we propose a hybrid solution strategy combining a rolling horizon framework with a genetic algorithm, which enhances computational efficiency through problem decomposition and iterative optimization. Finally, numerical experiments are conducted on three road networks, including the Sioux Falls network and the Chicago network, to validate the effectiveness of the proposed model and algorithm. Full article

This paper addresses the retail pricing problem for electricity retailers who also act as virtual power plant (VPP) operators, aggregating distributed energy resources (DERs). In future power markets where multiple such retailers compete for customers, a key challenge is to design pricing strategies that balance economic profitability with low-carbon objectives. Existing research often overlooks the impact of retailers’ heterogeneous resource portfolios, particularly the share of low-carbon resources like photovoltaics (PVs), on their competitive advantage and pricing decisions. To bridge this gap, we propose a novel retail pricing model that integrates a non-cooperative game framework with Markov Decision Processes (MDPs). The model enables each retailer to formulate optimal real-time pricing strategies by anticipating competitors’ actions and customer responses, ultimately reaching a Nash equilibrium. A distinctive feature of our approach is the incorporation of spatially differentiated carbon emission factors, which are adjusted based on each retailer’s share of PV generation. This creates a tangible low-carbon incentive, allowing retailers with greener resource mixes to leverage their environmental advantage. The proposed framework is validated on a modified IEEE 30-bus system with six competing retailers. Simulation results demonstrate that our method effectively incentivizes optimal load distribution, alleviates network congestion, and improves branch loading indices. Critically, retailers with a higher share of PV resources achieved significantly higher profits, directly translating their low-carbon advantage into economic value. Notably, the Branch Load Index (BLI) was reduced by 12% and node voltage deviations were improved by 1.32% at Bus 12, demonstrating the model’s effectiveness in integrating economic and low-carbon objectives. Full article

Against the backdrop of high-penetration distributed photovoltaic (DPV) integration into distribution networks, the limited measurability of small-scale DPV systems poses significant challenges to accurately estimating the baseline load of electric vehicle (EV) clusters. To address this issue, effective forecasting of DPV power output becomes essential. This paper proposes a domain-adversarial architecture for ultra-short-term DPV power prediction, designed to support baseline load estimation for EV clusters. The power output of DPV systems is influenced by scattered geographical distribution and abrupt weather changes, leading to complex spatiotemporal distribution shifts. These shifts result in a notable decline in the generalization capability of traditional models that rely on historical statistical patterns. To enhance the robustness of models in complex and dynamic environments, this paper proposes a domain-adversarial architecture for ultra-short-term DPV power forecasting, explicitly designed to address spatiotemporal distribution shifts by extracting spatiotemporal invariant features robust to distribution shifts. First, a Graph Attention Network (GAT) is utilized to capture spatial dependencies among PV stations, characterizing asynchronous power fluctuations caused by factors such as cloud movement. Next, the spatiotemporally fused features generated by the GAT are adaptively partitioned into multiple distribution domains using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), providing pseudo-supervised signals for subsequent adversarial learning. Finally, a Temporal Convolutional Network (TCN)-based domain-adversarial mechanism is introduced, where gradient reversal training forces the feature extractor to discard domain-specific characteristics, thereby effectively extracting spatiotemporal invariant features across domains. Experimental results on real-world distributed PV datasets validate the effectiveness of the proposed method in improving prediction accuracy and generalization capability under transitional weather conditions. Full article