International Journal of Electrical Power & Energy Systems最新文献

This paper proposes a new solution for the classification of short-circuits in medium-voltage distribution grids. The solution requires the measurements of the phase voltages at the time of the short-circuit and the voltage amplitudes of the short-circuit wave resulting from the fault. The values of the voltage before the short-circuit are used to calculate the expected values of the short-circuit wave amplitudes for the different types of short circuits. These calculations are based on an analysis of the charge flow between the capacitances of the power line due to the disturbance. The short-time matrix pencil method was used to detect the incoming short-circuit waves. Tests of the algorithm were carried out on the IEEE 34-bus Feeder model, taking into account the influence of voltage sensors on the measured signal values. The classification algorithm uses only non-zero components due to their relatively low attenuation. Despite that, it achieves high performance in the classification of single- and two-phase short-circuits.

In this study, the potential of cascading failure as a result of transmission line outages through large power systems is evaluated. Transmission lines are more dispose of the outage resulting from different fault occurrences and extend the uncontrolled failures compared to other power system devices. In this case, power system dynamic security must evaluate the possibility of cascading failures using a power system control center (PSCC) in order to limit the spread of outages caused by line failures. For this issue, using the PSCC online information consisting of power system operating variables, it is possible to present a variables-based scheme to estimate power system cascading failures concerning fault events. In order to cover the issue, based on developing a set of dominant operating variables (DOVs), this paper presents an online scheme of identifying cascading failures caused by line outages in the presence of line operational diversities and communication restrictions. In this scenario, during each time interval, by evaluating the proposed DOVs, the line information consisting of dynamic securities and sensitivities to cascading failure are investigated which the lines with the potential of cascading failures are identified online. Proper DOVs are determined for this issue by studying a variety of mathematical formulations according to theory of mutual information (MI) and measuring the entropy among the variables. In a real-time environment, by using the proposed DOVs and substituting them through online boundary equations based on the Least Square Error (LSE) method, the potentials of power system cascading failures without acting any line outage are provided. The effectiveness of the suggested technique is evaluated using a typical IEEE-39 bus and IEEE-118 bus, and proper results with high accuracy are achieved by evaluating the system potential concerning large cascading failures.

This paper presents an analytical study for short-term voltage stability based on a novel indicator. First, it is shown that the reduced Jacobian matrix of network equations can be transformed to obtain an approximately symmetric matrix which is usually positive definite when the system under study is stable. The minimum eigenvalue of the matrix is suggested as a short-term Voltage Solvability Indicator (VSI). Then, properties of the reduced conductive matrix and susceptance matrix which have significant impacts on VSI are examined. The focus of the study is power systems with multiple constant power injections, while VSI can also be applied to analyze the impact of generic power system models. It is shown that as the penetration level of constant power injections increases, VSI decreases and the system becomes more vulnerable to voltage collapse. Finally, the relationship between the eigenvalues of the reduced Jacobian matrix and the structure-preserving Jacobian matrix is established. As a result, the suggested VSI can be efficiently computed by exploring the sparsity of structure-preserving Jacobian matrix of network equations. Case studies of several test systems including a simplified 575-machine 8117-bus East China power system are reported, demonstrating the effectiveness and practicability of the proposed method.

Hybrid HVDC transmission technology advancement has led to significant progress. A reliable and effective DC line protection scheme is crucial for DC transmission systems. The traditional protection schemes suffer from insufficient sensitivity and reliability during high-impedance faults on DC lines. To address the issue, this study introduces a hybrid HVDC line single-ended protection scheme based on the coordination of converter control objectives. In the fault-ride-through(FRT) stage, different control strategies and objectives are applied to the converter based on the fault pole and fault direction identification results. Internal and external faults are discerned by calculating measured voltage with the coordination of the converter control objective. This approach maintains heightened sensitivity to high-impedance DC line faults. Moreover, it operates independently of double-ended communication and demands a low sampling rate. Extensive simulation results robustly substantiate the efficacy of the proposed protection scheme.

For DC grid based on the hybrid modular multilevel converter (MMC), the traditional DC fault current clearance scheme takes a long time and greatly affects the active power transmission. A novel DC fault current clearance coordinated control strategy is proposed, which can quickly interrupt the fault current and reduce the impact of DC fault on the DC grid and AC system. For fault-line connected MMC (FLMMC), a negative DC voltage control is employed, which can improve the current attenuation speed and ensure reliable fault isolation. For non-fault-line connected MMC (NFLMMC), the active current-limiting control (ACLC) based on the virtual reactor is adopted, which can reduce the current flow to the fault location and further shorten fault isolation time. To reduce the impact on the AC system during the DC fault, a short-time active power support control is designed. Finally, a four-terminal DC grid simulation model is built based on the RT-LAB OP5600 real-time digital simulation platform. The simulation results show that the proposed coordinated control strategy for the DC grid can quickly clear DC fault current, shorten DC fault isolation time, and strengthen active power support capability.

The non-convexity of the Optimal Power Flow (OPF) feasible region complicates the solution process and affects the applicability of various optimization techniques, which is crucial for understanding the OPF problem. This paper systematically investigates the non-convexity properties of the AC OPF feasible (power) injection region (FIR) and identifies key factors influencing its non-convexity from both analytical and numerical perspectives. Specifically, a necessary condition for FIR convexity and a sufficient condition for FIR non-convexity are derived. Based on these findings, it is concluded that the feasible region of ACOPF is inherently non-convex, with network losses playing a significant role. To avoid misjudgment of non-convexity, a non-convexity degree index for the FIR is introduced, and a numerical method to compute it is proposed. Numerical results on 9-bus and 57-bus systems indicate that the non-convexity degree of a lossless FIR is 0, whereas for a lossy FIR, it ranges from 70% to 100%. Furthermore, factors contributing to non-convexity and their impact on the location of the optimal solution and the effectiveness of convex relaxation methods (CRMs) are discussed. The numerical results demonstrate that for the same system, the optimality gap of CRMs can be as low as 0.02% in lossless networks but increases to 0.28% or more in lossy networks. These findings elucidate the relationship between network losses and the optimality gap of CRMs, providing deeper insights into the characteristics of the ACOPF problem.

Electricity price forecasting (EPF) is a complex task due to market volatility and nonlinearity, which cause rapid and unpredictable fluctuations and introduce heteroscedasticity in forecasting. These factors result in varying prediction errors over time, making it difficult for models to capture stable patterns and leading to poor performance. This study introduces the Heteroscedastic Temporal Convolutional Network (HeTCN), a novel Encoder-Decoder framework designed for day-ahead EPF. HeTCN utilizes a Temporal Convolutional Network (TCN) to capture long-term dependencies and cyclical patterns in electricity prices. A key innovation is the heteroscedastic output layer, which directly represents variable uncertainty, enhancing performance under fluctuating market conditions. Additionally, a multi-view feature selection algorithm identifies crucial factors for specific periods, improving forecast precision. The framework employs an improved loss function based on maximum likelihood estimation (MLE), which adjusts for the heteroscedastic nature of electricity prices by predicting both the mean and variance of the price distribution. This approach mitigates the impact of extreme price spikes and reduces overfitting, resulting in robust and reliable predictions. Comprehensive evaluations demonstrate HeTCN’s superiority over existing solutions such as DeepAR and the Temporal Fusion Transformer (TFT), with average improvements of 25.3%, 24.9%, and 17.4% in the mean absolute error (MAE), symmetric mean absolute percentage error (sMAPE), and the root of mean squared error (RMSE) compared to DeepAR, and 17.6%, 14.4%, and 13.6% relative to TFT across five distinct electricity markets. These results underscore HeTCN’s effectiveness in managing volatility and heteroscedasticity, marking a significant advancement in electricity price forecasting.

In future integrated electricity-natural gas distribution systems (IEGDS), hydrogen and methane converted from excessive renewable generation will be mixed with natural gas to form hydrogen enriched compressed natural gas (HCNG) for fewer carbon emissions. Consequently, optimal planning of the HCNG-integrated IEGDS plays an essential role in increasing the economy and technical benefits. However, integrating HCNG operation into the planning of IEGDS still has many challenges, such as unrealistic modeling, nonlinear constraints, and the potential risk of voltage violations. The purpose of this paper is to develop a tractable optimization model for planning and operation of the HCNG-integrated IEGDS, aiming to minimize the investment and operating cost, reduce the voltage violation, and improve the renewable power penetration rate. To achieve this, a bilevel optimization model is developed for optimal planning of wind turbines and soft open points (SOPs) in the IEGDS considering comprehensive HCNG operation, in which both hydrogen and methane injection are considered and optimized for different system operation scenarios. Furthermore, the proposed nonlinear model is reformulated to a tractable bilevel mixed-integer second-order cone model using several reformulation techniques and solved by the reformulation and decomposition algorithm. Case studies, performed using the publicly available datasets, are conducted to demonstrate the economic and technical improvement by implementing the proposed planning model. The annual planning results show that incorporating the coordination between SOPs and methane injection results in a 6.77% reduction in investment cost, a 22.64% reduction in operating cost, a 62.69% decrease in voltage violation, and a 23.58% increase in the wind power penetration rate. Moreover, SOPs are more applicable to the safe operation of the IEGDS under high energy load conditions.

As the frequency of extreme weather events continues to rise, there is an urgent need to strengthen the safe and stable operation of active distribution networks (ADNs), and it is of great value to establish highly resilient ADNs to withstand multi-faults caused by extreme weather events. This paper proposes a multi-period restoration approach for the resilience increase of ADNs by considering fault rapid recovery and component repair under typhoon disasters. Firstly, based on the structural reliability theory, the failure rate model of the main components is established, and in light of the system information entropy, the typical fault scenario selection strategy is designed to determine the branches with high fault probability. Then, according to the fault islanding division and network reconfiguration, a fault rapid recovery method is suggested for the ADNs, where the impact of typhoon disasters on the output features of distributed generators (DGs) are taken into account, and meanwhile, the network structure and the output power of the DGs are jointly optimized to minimize the operating cost of the ADNs. Further, a fault component repair model is formulated by adopting the adaptive ant colony algorithm, and a multi-period restoration approach is proposed for the ADNs to fulfill a rolling optimization of the network reconfiguration and fault component repair. The improved IEEE 33-node and IEEE 118-node systems are used for the approach verification, and the results show that the proposed approach can effectively improve the overall load restoration level and increase the component repair efficiency. Following a multi-criteria resilience evaluation system, the proposed approach enables the ADNs to more effectively withstand typhoon disasters, offering a resilience increase of 6.93 % and 32.24 % regarding the 33-node and 118-node systems.

This paper introduces a comprehensive and resilient multi-energy system (MES) designed for independent planning and real-time implementation. A robust daily coordinated planning model is proposed, incorporating adjustable optimization with fundamental operational and uncertainty constraints. The model integrates various energy sources and systems, including photovoltaics, wind turbines, combined heat and power (CHP) units, energy storage system (ESS), electric vehicle (EV), electric boilers, and power-to-gas (P2G) facilities, to manage electricity, natural gas, and heat demands. The objective is to minimize MES operational costs while meeting electricity and heat requirements, considering renewable energy uncertainties. It includes the development of a two-stage flexible robust optimization model that accounts for energy equilibrium, capacity constraints, and demand response mechanisms. The model incorporates price-based demand response with both switchable and interruptible loads, enhancing system controllability and flexibility. Additionally, a scenario generation and reduction technique based on the Kantorovich distance is employed to effectively manage forecast errors and uncertainties. A novel modified Slime Mold Algorithm (SMA) is utilized to solve the optimization problem, demonstrating superior convergence and computational efficiency compared to traditional meta-heuristics. The slime mold algorithm is further enhanced with chaos theory, using a sine map to introduce dynamic exploration capabilities. The findings indicate that the proposed multi-energy system model effectively balances electricity, natural gas, and heat loads while accommodating renewable energy fluctuations. The enhanced slime mold algorithm provides optimal solutions swiftly, ensuring reliable and cost-effective multi-energy system operation.