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• Analysis of traction power supply system is conducted considering cross-bonding cable. • Model simplification method based on multiport network hybrid parameters is designed. • The calculation difference of loss before and after consideration is more than 10 %. • Cross-bonding cables should be set near the position with large train power. The urban rail transit traction power supply system adopts DC power supply. The upstream and downstream rails are generally connected by cross-bonding cables at intervals, so as to decrease the rail equivalent resistance and reduce the rail potential and loss. The existing modelling methods for power flow calculation do not consider the influence of cross-bonding cables due to the complexity of modelling, which causes some deviations in the calculation results. Hence, a modelling method for power flow calculation of urban rail transit traction power supply system considering cross-bonding cables is proposed in this paper. Firstly, the influence of cross-bonding cables on power flow model is analyzed. Subsequently, a model simplification method based on multiport network hybrid parameters is designed. Finally, power flow calculations considering cross-bonding cables are carried out with a metro line. The calculation results show that the calculation difference of traction network loss before and after consideration is more than 10 %. Moreover, this paper analyzes the influence law of the setting of cross-bonding cables on the power flow state, and gives some suggestions on the setting in actual engineering application. [ABSTRACT FROM AUTHOR]

• A new control method for DER is suggested. • The proposed control method balances the load between feeders. • DER connects to feeder through SST. • DER-controlled circuit shared the load between two feeders like ring connection. • Voltage profiles are improved. The multi-purpose use of distributed energy resources (DER) can bring a lot of income to its owners. For example, the load imbalance of the radial feeders in the distribution network is an old problem that occurs due to the simple structure of the radial topology and the unidirectional power flow in traditional networks. With emerging DERs and the development of power electronic technology, previous concepts are changing continuously. This paper aims to balance the load between two feeders whilst, it yields better voltage profiles and more appropriate utilization of distribution feeders. This paper proposes a new control method for DERs (including PV panels plus battery storage) that makes the radial system pretends a ring topology properties. It is done with the help of a solid-state transformer (SST). The relationships of the DER current controller are formulated based on current passes through the ring connection. Case studies are carried out on improved IEEE 33-node and 13-node systems with DERs and SSTs. The results show that the suggested control method can simulate the real ring topology and operating objective namely, load balancing is reached. [ABSTRACT FROM AUTHOR]

Detection of cyber attacks in smart power distribution grids with unbalanced configurations poses challenges due to the inherent nonlinear nature of these uncertain and stochastic systems. It originates from the intermittent characteristics of the distributed energy resources (DERs) generation and load variations. Moreover, the unknown behavior of cyber attacks, especially false data injection attacks (FDIAs) in the distribution grids with complex temporal correlations and the limited amount of labeled data increases the vulnerability of the grids and imposes a high risk in the secure and reliable operation of the grids. To address these challenges, this paper proposes an unsupervised adversarial autoencoder (AAE) model to detect FDIAs in unbalanced power distribution grids integrated with DERs, i.e., PV systems and wind generation. The proposed method utilizes long short-term memory (LSTM) in the structure of the autoencoder to capture the temporal dependencies in the time-series measurements and leverages the power of generative adversarial networks (GANs) for better reconstruction of the input data. The advantage of the proposed data-driven model is that it can detect anomalous points for the system operation without reliance on abstract models or mathematical representations and data labels. To evaluate the efficacy of the approach, it is tested on IEEE 13-bus and 123-bus systems with historical meteorological data (wind speed, ambient temperature, and solar irradiance) as well as historical real-world load data under three types of data falsification functions. The comparison of the detection results of the proposed model with other data-driven methods verifies its superior performance in detecting cyber attacks in unbalanced power distribution grids. • An unsupervised neural network model combines the GANs and autoencoder for FDI attacks. • The proposed model utilizes the outputs of autoencoder and critic score for attack detection. • The proposed model is tested on unbalanced power distribution grids integrated with DERs. • Simulation results verify the model's superior performance compared to other data-driven models. [ABSTRACT FROM AUTHOR]

Stochastic fluctuations in power injections from distributed energy resources (DERs) combined with load variability can cause constraint violations (e.g., exceeded voltage limits) in electric distribution systems. To monitor grid operations, sensors are placed to measure important quantities such as the voltage magnitudes. In this paper, we consider a sensor placement problem which seeks to identify locations for installing sensors that can capture all possible violations of voltage magnitude limits. We formulate a bilevel optimization problem that minimizes the number of sensors and avoids false sensor alarms in the upper level while ensuring detection of any voltage violations in the lower level. This problem is challenging due to the nonlinearity of the power flow equations and the presence of binary variables. Accordingly, we employ recently developed conservative linear approximations of the power flow equations that overestimate or underestimate the voltage magnitudes. By replacing the nonlinear power flow equations with conservative linear approximations, we can ensure that the resulting sensor locations and thresholds are sufficient to identify any constraint violations. Additionally, we apply various problem reformulations to significantly improve computational tractability while simultaneously ensuring an appropriate placement of sensors. Lastly, we improve the quality of the results via an approximate gradient descent method that adjusts the sensor thresholds. We demonstrate the effectiveness of our proposed method for several test cases, including a system with multiple switching configurations. • Bilevel optimization minimizes number of sensors capturing all voltage violations. • Conservative linear approximations tackle power flow nonlinearities. • MILP reformulation proves tractable across various test cases. • Post-processing minimizes false alarms, validated through testing. [ABSTRACT FROM AUTHOR]

• The proposed data-driven control method eliminates the need for an accurate offline system model. • The presented control method incorporates a regularization term and introduces slack variables to effectively handle uncertainties and disturbances. • The FADMM method is utilized to enhance privacy and scalability by decomposing the centralized optimization problem into several sub-problems. The networked microgrid system (NMG) has become an effective alternative for integrating various types of distributed energy resources (DERs). However, because of the increasing share of DERs and high R/X ratio in the system, the uncertainty and variability of DERs' power generation may cause significant voltage violations in the system. To address these issues, this paper introduces a novel distributed data-driven voltage control strategy based on data-enabled predictive control (DeePC) to address voltage violations problems in the NMG. The DeePC algorithm uses only historical system trajectories, eliminating the need for parameter identification procedures of power system. Based on the system data model, the proposed controller coordinates MGs in the NMG in a distributed manner by employing a consensus version of the fast alternating direction method of the multiplier algorithm (FADMM), which achieves a better privacy and scalability. Simulation studies in a modified two-MG NMG demonstrate the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• The output power volatility of wind-photovoltaic and the operating characteristics of the electrolyzer is considered. • A capacity optimization method for off-grid RES-H2 production systems considering the load-follow-source mechanism is proposed. • A multi-objective optimization model with minimum cost, minimum energy storage capacity and optimal power supply efficiency was constructed. • An improved optimization algorithm based on two common algorithms is designed. Based on the uncertainty of the output of renewable energy such as wind and photovoltaic (PV), a capacity optimization allocation method considering the load-follow-source mechanism is proposed to solve the shortages of traditional methods, which is low in utilization rate of energy and low in economy. Firstly, considering the fluctuations of renewable energy sources (RES) and the operating characteristics of the electrolyzer, this paper proposes a capacity optimization method for off-grid hydrogen(H2) production system of renewable energy sources considering the load-follow-source operation mechanism. Then, combined with this mechanism, a capacity optimization allocation model is established, the objectives contain minimizing the levelized cost of hydrogen (LCOH), minimizing the capacity of energy storage, and optimizing the efficiency of power supply. Finally, an adaptive non-dominated sorting differential evolution algorithm (ANSDE) with external archiving is proposed to solve the established model. The calculation results show that the load-follow-source mechanism is effective for improving the economy of the system, and the proposed algorithm is high in efficiency and good in convergence comparing with traditional algorithms. [ABSTRACT FROM AUTHOR]

Utilizing Distributed Generators (DGs) and Energy Storage Systems (ESSs) enhances power system reliability, drawing significant research attention. However, these systems pose challenges, compelling scientists to explore optimization methods. Our paper presents an innovative solution, Parallel Multi-Objective Multi-Verse Optimization (PMOMVO), aimed at optimizing DGs and Battery Energy Storage Systems (BESSs) allocation. This optimization addresses voltage violations and operation costs, crucial concerns for system operators and consumers. By leveraging a parallel approach, PMOMVO significantly accelerates the optimization process. We compared its results with a base case scenario, demonstrating the superior efficiency of our parallel method. It not only enhances the optimization performance but also proves its efficacy by generating optimal solutions from the Pareto front set. This research showcases the benefits of PMOMVO, offering a faster, more efficient, and reliable way to optimize power systems, benefiting both operators and consumers. • Introduces a novel bi-level optimization approach for DG and BESS allocation and operation. • Proposing a multi-objective formulation to identify Pareto optimal solutions, offering a balance between voltage stability and cost-effectiveness. • Optimizes asset operation strategies, considering both peak and off-peak load times, enhancing resource utilization. • Proposes a parallel processing method based on modified MOMVO, significantly accelerating computations for large distribution systems problems. • Achieves a substantial improvement in the speed-up index, ensuring swift and effective DG and BESS allocation and operation in complex systems. [ABSTRACT FROM AUTHOR]

• Novel Methodology : Introduces a new double-ended terminal method for locating vegetation high impedance faults (VHIFs) in power distribution systems using the third harmonic component. • Efficiency and Cost-Effectiveness : This technique requires only two measurement terminals, significantly reducing the need and expense of extensive sensor deployment. • Overcomes Traditional Limitations : Unlike traditional methods, it eliminates the need for complex mathematical formulation models, improving accuracy in fault localization. • Demonstrated Accuracy : Simulation tests validate the method's effectiveness, achieving error rates below 1.6 %, highlighting its potential for real-world applications. • Practical Implications : Offers a proactive approach to power system protection, contributing to improved operation and maintenance of power distribution networks. Vegetation-related high-impedance faults (VHIFs) on overhead distribution systems can cause damage, maintenance issues, and even wildfires, impacting both utilities and ecosystems. Existing fault location techniques struggle to accurately locate VHIFs. In this paper, we propose a novel double-ended terminal location method based on the third harmonic component of the fault system. The proposed technique determines the distance of the VHIF by approximating the third harmonic voltage at the fault local, utilizing measurements from the remote terminal located at the far-end of the feeder. This method efficiently locates VHIFs with just two measurement terminals, cutting the need and cost of extensive sensor deployment. Moreover, the proposed method overcomes the limitations of traditional fault location techniques by using a third harmonic model of the faulted system, eliminating the need for complex mathematical models to approximate the fault behavior and voltage at the fault local. Simulation tests were conducted to validate the proposed method, demonstrating its effectiveness in accurately locating VHIFs. The technique achieved error rates below 1.6 %, making it a promising solution for real-world applications in power distribution systems. The ability to precisely locate VHIFs meets a proactive protection philosophy and contributes to improved power system protection and ensures uninterrupted power supply. [ABSTRACT FROM AUTHOR]

• Locational nodal pricing for distribution system based on DER value. • DER locational value can be determined to mitigate voltage and capacity violations. • Novel linear approach to determine DER value in distribution systems is introduced. • DER value in terms of dollars per kilowatt can provide locational value. • Scores for critical nodes emphasize those with the greatest value. Under most NEM policies, DER customers sell excess electricity at full retail price. Although this structure helped the widespread growth of DER, it is argued to subsidize DER at the expense of non-DER customers. In that regard, value-based compensation emerged, and as of 2022, at least 12 states have conducted studies to investigate alternative NEM compensation methods to inform new policy decisions. DER can provide various benefits to the distribution system, including energy, reliability, resilience, environmental, societal, and investment deferral. Each benefit has an individual value, and calculating these values is challenging. This paper presents a practical methodology to calculate the DER location value. The proposed approach utilizes established utility planning and reliability criteria. It analyzes the distribution system's current status, capturing any planning or reliability criteria violations, and determines the utility upgrade investment costs to remediate them. The proposed approach then determines the size, locational, and temporal profile of the DER that can be added to the distribution network and defer any of the utility upgrade investment costs, thus obtaining the value to the system and assigning a monetary value. The results provide a nodal distribution value of DER to the distribution system. [ABSTRACT FROM AUTHOR]

• A three-stage RCM framework is proposed for power distribution systems. • Critical component identification is carried out using BWM and TOPSIS. • The proposed ranking is benchmarked with a state-of-the-art SECA approach. • The optimal maintenance strategy is determined to minimize maintenance costs. • Cold-line, hot-line, and run-to-failure are considered for strategy determination. In this paper, a three-stage reliability-centered maintenance (RCM) framework for critical feeder identification, failure modes prioritization, and optimal maintenance strategy assignment is presented for the power distribution system. This three-stage methodology addresses a notable gap in the literature by incorporating these three stages together. The first stage ranks critical feeders using the BWM and TOPSIS methods. In the second stage, the failure modes are prioritized using feeder rank, severity, occurrence, and detection factors and are assigned a score representing their priority. Finally, in the third stage, a maintenance strategy assignment, formulated as a mixed-integer linear programming (MILP) optimization problem is proposed. The optimization problem considers three strategies, cold-line, hot-line, and run-to-failure (RTF), assigned to each failure mode. Besides, the possibility of implementing cold-line repairs overlapping with the upstream grid's scheduled outages and the availability of several maintenance crews are taken into account. The objective function is to minimize the total maintenance costs, which include operation, equipment, energy not supplied (ENS), expected energy not supplied (EENS), and future corrective maintenance costs. The proposed approach is implemented on a real distribution system. The results indicate that the proposed RCM framework leads to a 26.32 % reduction in the EENS compared to selecting the RTF strategy for all components. Besides, the proposed optimization-based method leads to a 29 % lower objective function than the business-as-usual maintenance planning, and the proposed three-stage RCM framework outperforms a previously published state-of-the-art method by decreasing the objective function and the EENS. [ABSTRACT FROM AUTHOR]

• The AC interference model of high-speed railway to buried pipeline is established. • Simulates the mechanism of dynamic AC interference caused by high-speed railway. • Calculates the influence of various factors on the AC interference of pipelines. The large-scale construction of high-speed railways and buried pipelines has led to the parallel or intersection of these lines over long distances in some areas, resulting in dynamic AC interference to buried pipelines. In this paper, the modeling method of the AC interference from high-speed railway employing auto-transformer (AT) power supply mode to the buried pipeline was investigated, and the accuracy of the modeling method was verified by the simulation calculation of the actual field case and the comparison with the field data. Through modeling and calculation, the influence of train running position, rail to ground transition resistance and crossing angle on AC interference of high-speed railway is discussed. Based on the above research, it is analyzed that the dynamic AC interference is caused by the change of current distribution and the train load current. The main mechanism of AC interference caused by high-speed railway operation on nearby pipelines is inductive coupling. Especially when the rail to ground transition resistance is less than 50 Ω km, due to the unbalanced distribution of power supply circuit current, the peak AC interference voltage appears on pipeline where the train is located. [ABSTRACT FROM AUTHOR]

• Proposal of SOP-based SSM, IPM and MCM considering the requirements of load static voltage characteristics. • Formulation of the different SOP-based DSR models as mixed integer nonlinear programming problems. • Convexification of the original optimization model with a static voltage load model. • SOP-based MCM outperforms the others in continuously supplying critical loads. Soft open points (SOPs) can perform load shifting and voltage improvement, and are critical to improving distribution service restoration (DSR) during power outages. Although multiple DSR strategies utilizing local resources have been reported, their effectiveness has not been sufficiently studied. To solve this problem and utilize the flexibility of SOP, a comparative study of different strategies for SOP-based DSR is presented in this paper. First, various SOP-based DSR models, including single-source, single-island model (SSM), islanding partition model (IPM) and multi-source collaborative model (MCM), are proposed and formulated as mixed-integer nonlinear programming (MINLP) problems, incorporating static voltage characteristics of loads. Second, to enhance the solution efficiency, mixed-integer second order cone programming is introduced to convert the MINLP problems into easily solvable convex optimization problems. A restorative index is defined to evaluate the effectiveness of various SOP-based DSR strategies. Finally, a comparative simulation analysis of the proposed models on the IEEE 33-bus system illustrates that SOP-based MCM achieves an 94 % power output for distributed energy resources, while exhibiting the lowest RI for the total loads, standing at just 9.5 %. Furthermore, the RI for critical loads consistently remains at 0, demonstrating the superiority of SOP-based MCM in comparison to other strategies. [Display omitted] [ABSTRACT FROM AUTHOR]

• A chance-constrained power balance model is introduced for the IES fault recovery. • The IES fault recovery strategy is proposed considering natural gas delay. • The developed model is converted into a solvable second-order cone problem. After the integrated energy system (IES) encounters extreme disasters, the uncertainty of renewable energy may lead to the imbalance of power supply and demand. This makes the IES fault recovery strategy very complicated. Considering the influence of the electric-gas coupling characteristic of natural gas delay, this paper proposes a fault recovery strategy, including the load recovery and economic efficiency of the operation of the IES. In the recovery stage, a chance-constrained power balance model is employed to solve the power supply and demand imbalance caused by uncertainty. Next, the chance-constrained power balance model is processed via distributionally robust optimization, and it is reformulated into a second-order cone programming (SCOP) problem by dual transformation and conditional value-at-risk approximation. To deal with nonlinear terms, the piecewise linearization method is used to model the load recovery result, renewable energy output, and total operation cost of each period during the fault. Finally, the effectiveness of the proposed strategy is verified by an IES composed of an IEEE 39-node electricity subsystem, a 20-node natural gas subsystem, and a 6-node thermal subsystem. [ABSTRACT FROM AUTHOR]

• The quantitative expression of the current phase difference between both sides of the PV transmission line for faults with transition resistance is analyzed. • A Dice similarity coefficient-based pilot protection method for 500 kV transmission lines is proposed, considering the angle difference and magnitude difference between currents on both sides of the line. • The proposed method has a higher sensitivity than existing protection methods and performs better to transition resistance. • The effectiveness and accuracy of the proposed method under different fault locations, fault types, transition resistances, fault inception angles, line lengths, PV power supply capacities, have been verified by simulation and field data tests. Due to the shortage of fossil fuel resources and energy transformation efforts, the installed capacity of photovoltaic (PV) power supplies has been steadily increasing, resulting in the problem of low sensitivity for PV transmission line segregated-phase current ratio differential protection. Furthermore, there are increasing cases of large-scale gigawatt-level PV power supply integrated into the 500kV grid, bringing new challenges to the security and stability of the grid. In this paper, according to the fault characteristics of photovoltaic power supply, the current phase characteristics on both sides of the line are quantitatively analyzed during internal faults with transition resistance, and then the phase and amplitude differences of the currents on both sides of the line are used to propose a Dice similarity coefficient-based pilot protection method for photovoltaic transmission lines. PSCAD/EMTDC simulation and field data test results show the proposed method can accurately identify internal faults under the conditions of different fault locations, fault types, transition resistances, fault inception angles, line lengths, PV power supply capacity, and levels of noise. The method also performs well with CT saturation, simultaneous faults, evolving faults, and channel asymmetry. Additionally, the proposed method has a fast operation speed and a great performance to transition resistance. [ABSTRACT FROM AUTHOR]

Residential communities are increasingly adopting renewable energy sources (RES) to minimize energy consumption costs. However, these RES are weather-dependent and uncertain, posing challenges to ensuring reliable operations. Addressing the uncertainties in power supply management becomes a critical research question. Energy storage systems play a crucial role in providing battery-powered supply for residential loads under uncertain conditions. The operation of microgrids is directly influenced by uncertainties. This paper proposes data-driven-based net load uncertainty quantification fusion mechanisms for cloud-based energy storage management with renewable energy integration. Firstly, a fusion model is developed using SVR, LSTM, and CNN-GRU algorithms to estimate day-ahead load and PV power forecasting errors. After that, two mechanisms are proposed to determine the day-ahead net load error. In the first mechanism, the net load error is directly forecasted, while in the second mechanism, it is derived from the forecast errors of load and PV power. The net error analysis is conducted with a statistical mean and standard deviation, resulting in different uncertainty-bound confidence intervals around the forecasted value. Subsequently, the cloud energy storage system operation cost is calculated with the best uncertainty quantification mechanism for two different case studies. This approach allows for better management of uncertainties in energy storage systems and enables more informed decision-making under varying conditions. • Learning-based fusion algorithm for net load forecasting. • Develop uncertainty mechanism for uncertainty quantification. • Economic assessment of cloud energy storage. [ABSTRACT FROM AUTHOR]