Search

This document is a guest editorial from the journal IET Generation, Transmission & Distribution. It emphasizes the need to improve the reliability and resilience of modern power systems in the face of various threats. The editorial highlights the importance of modeling, assessing, and enhancing the reliability and resilience of power grids that are increasingly incorporating grid-edge resources. The document also provides a summary of the papers included in a special issue of the journal, which cover topics such as planning reliable energy systems, analyzing power grid operations, and enhancing service restoration and grid-support load response. Additionally, the text discusses the issue of equity in power distribution system restoration processes and proposes a model called the equity-aware power distribution system restoration model. The model aims to balance equity and resilience in restoring power after outages by focusing on fair distribution of energy resources while maintaining operational efficiency. The text also includes brief biographies of several authors who have contributed to the field of power electronics and power system reliability. [Extracted from the article]

Multi‐terminal high‐voltage direct current technology based on voltage‐source converter stations (VSC‐MTDC) is expected to be one of the most important contributors to the future of electric power systems. In fact, among other features, it has already been shown how this technology can contribute to improve transient stability in power systems by the use of supplementary controllers. Along this line, this paper will investigate in detail how these supplementary controllers affect electromechanical oscillations, by means of small‐signal stability analysis. The paper analyses two control strategies based on the modulation of active‐power injections (P‐WAF) and reactive‐power injections (Q‐WAF) in the VSC stations which were presented in previous work. Both control strategies use global signals of the frequencies of the VSC‐MTDC system and they presented significant improvements on transient stability. The paper will provide guidelines for the design of these type of controllers to improve both large‐ and small‐disturbance angle stability. Small‐signal stability analysis (in Matlab) has been compared with non‐linear time domain simulation (in PSS/E) to confirm the results using CIGRE Nordic32A benchmark test system with a VSC‐MTDC system. The paper analyses the impact of the controller gains and communication latency on electromechanical‐oscillation damping. The main conclusion of the paper is that transient‐stability‐tailored supplementary controllers in VSC‐MTDC systems can be tuned to damp inter‐area oscillations too, maintaining their effectiveness. [ABSTRACT FROM AUTHOR]

Electric power information has garnered particular interest in conventional electrical power systems because it plays a crucial role in the electric power industry. Additionally, electric power communication networks ensure the overall power system's reliability and safety. The concepts and techniques used in digital twin (DT)‐based online tools for electric power communication networks are introduced in this paper. Moreover, online DT tools are proposed as daily learning and training resources for both new staff and communication commissioners, which can be used for the internet and the company intranets. Users can control DT and physical equipment simultaneously, and device status can be seen in real time. The design, modelling, control principles, data collection, and visual display of the DT tools are all covered in this study. To validate the applicability of the proposed framework, a case study for optical fibre damage and the recovery process in a typical communication interruption malfunction is investigated. The research presented in this paper has the potential to provide useful tools and new paradigms for designing online DT training and education systems. [ABSTRACT FROM AUTHOR]

Reactive power management (RPM) in electric power systems is usually based on a rule‐based control derived from the transmission system operator's (TSO) experience. This approach faces challenges as the number of decisions and the complexity of the system operation is increasing. With the increasing generation from renewables and the evolution of electricity markets, the available resources must be optimally utilized. This paper compares the optimization‐based approach (OBA) and the experience‐based expert approach (EBA) for RPM. The OBA is based on security‐constrained optimization with minimum redispatch cost as the objective function for a reference contingency. In contrast, the EBA's actions are based on the system operator's experience. Comparison is made in terms of the generator redispatch cost, active and reactive power redispatch volume, active power losses, nodal voltages, and the number of actions to ensure secure operation. The analysis using a reduced model of an actual transmission system shows that OBA is more beneficial than EBA, with up to 22% and 42% reduction in redispatch cost and volume, respectively. Moreover, the control decisions from both approaches are seen to be similar. This study aims to show the usefulness of the OBA and motivate TSOs to move towards optimization‐based reactive power management. [ABSTRACT FROM AUTHOR]

The locational marginal price (LMP) theory is the foundation of nowadays electricity markets. With the rapid growth of intermittent renewable generation, new features are emerging in power systems, such as the uncertainty of power supply, the scarcity of flexibility, and the increase of distributed generation. In this situation, the electricity pricing scheme needs to be augmented with new features to meet requirements of incentive compatibility, cost causation‐awareness, revenue adequacy, and transparency in a new market environment. Though a broad spectrum of literature has investigated extensions of the LMP addressing different challenges, a panorama of these extension directions is absent. To this end, this paper provides a systematic review of existing LMP extension research from four aspects, including pricing with non‐convexity, pricing with multiple intervals, pricing with uncertainty, and pricing in distribution systems. These extension directions are derived from the impacts of the power system evolution on dispatch and pricing models. For each extension direction, the motivation and basic problem setup are introduced, followed by a critical survey of related research progress. Future directions and research gaps of the LMP extension are also indicated in this paper. [ABSTRACT FROM AUTHOR]

Earlier works have reported that rectifier AC faults also can cause commutation failure during fault‐off recovery process. Whereas the occurrence of CF is merely ascribed to inverter AC faults in conventional wisdom. Note that the existing CF mitigation approaches are designed mostly by the evolution of inverter electrical quantities, which will fail to take action to CF caused by rectifier faults, posing threat to the operation of power system. Thus, the paper firstly presents the essential reasons for CF caused by rectifier ac faults, combined with the fault information transmission process and control response. It is found that DC current surge and improper control interaction are the main reasons. Accordingly, a novel current cooperative optimization (CCO) control strategy is proposed, which aimed at reducing the risk of CF caused by rectifier ac faults. Simulation results verify the correctness of the mechanism and the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper proposes an intelligent Deep Learning (DL) based approach for Data‐Driven Security‐Constrained Unit Commitment (DD‐SCUC) decision‐making. The proposed approach includes data pre‐processing and a two‐stage decision‐making process. Firstly, historical data is accumulated and pre‐processed. Then, the DD‐SCUC model is created based on the Gated Recurrent Unit‐Neural Network (GRU‐NN). The mapping model between system daily load and decision results is created by training the DL model with historical data and then is utilized to make SCUC decisions. The two‐stage decision‐making process outputs the decision results based on various applications and scenarios. This approach has self‐learning capabilities because the accumulation of historical data sets can revise the mapping model and therefore improve its accuracy. Simulation results from the IEEE 118‐bus test system and a real power system from China showed that compared with deterministic Physical‐Model‐Driven (PMD)‐SCUC methods, the approach has higher accuracy, better efficiency in the practical use case, and better adaptability to different types of SCUC problems. [ABSTRACT FROM AUTHOR]

The rampant growth in penetration of gas‐fired distributed units (GFDUs) is recasting the role of the integrated energy service provider (IESP) as a price‐maker player. Nevertheless, it is threatening the well‐being and reliability of the natural gas networks (NGNs). In this regard, this paper investigates the strategic participation of integrated 20‐node NGN and an 33‐bus active distribution system (ADS) in the day‐ahead wholesale market (WM), which consists of a six‐bus transmission network (TN), through a bi‐level optimization framework. The upper‐level objective is to minimize the operational costs of the IESP as well as the cost of energy that is purchased/sold in day‐ahead WM. The lower‐level problem models the optimal response of the wholesale market operator (WMO) that has the objective of maximizing social well‐fare by clearing the day‐ahead wholesale market. To alleviate the NGN's pipeline congestion, the linepack capability of the pipelines is deployed, while demand response program (DRP) and interruptible loads (IL) are integrated into the ADS side. As a computationally efficient method, robust optimization (RO) is utilized to handle the uncertainties. The results emphasize the necessity of integrating NGN into this problem. Eventually, different case studies illustrate the impact of line pack and DRP in reducing market‐clearing price. [ABSTRACT FROM AUTHOR]

Due to the growing proportion of uncertain and fluctuating renewable energy sources in the power system, reasonable generation expansion considering the uncertainties is desperately needed. This paper proposes a novel three‐stage wind‐thermal generation expansion planning model based on representative day unit commitment that incorporates the uncertainty of wind power and load and the probabilistic reliability criteria. The probabilistic reliability criteria are considered by simulating random outage events of thermal units using a robust approach. The consequent robust reliability model is reformulated as mixed‐integer linear programming models and integrated into the multi‐stage generation expansion planning model. A decomposed strategy based on the Benders decomposition is adopted to tackle with the large‐scale original model. Case studies are performed on both the revised IEEE 14‐bus and IEEE 118‐bus test systems to validate the effectiveness of proposed model and method. Numerical results show that the proposed generation planning method balances the economy and reliability of the system. [ABSTRACT FROM AUTHOR]

High‐voltage direct current breaker is among the essential components of high‐voltage direct current grids. Such a breaker generally needs a direct current reactor to reduce the fault currents rate. However, direct current reactors have destructive effects on the multi‐terminal high‐voltage direct current grid dynamic stability, and in such a system, despite the variety of controllers, the system dynamics are highly sensitive to the operating point. Therefore, additional damping control will be needed. This paper proposes a modification to be applied to the traditional droop controller of high‐voltage direct current grids to cope with the influence of these large reactors, improving the direct voltage stability and decreasing power variations in the transient events by introducing a direct current power system stabilizer. The proposed method for direct voltage control has been investigated through the analytical model of the system. Stability improvement has been studied following the application of the proposed method by investigating zeros, poles, and frequency response analysis. Moreover, a method is proposed for optimal design and optimal placement of direct current power system stabilizer. The system analysis and time‐domain simulations demonstrate a decent damping improvement attained by the proposed method. All simulations and analytical studies are conducted on Cigré DCS3 test high‐voltage direct current grid in MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

In the power system, the harmonic is one of the important concerns of power quality, which has a significant impact on the safe operation of power grids and the energy efficiency for users. When there are not enough special harmonic monitoring devices installed on public transport, it is important to identify the harmonic source and assess the harmonic contribution based on the load data of the user's electricity meter. This paper proposes a data selection and matching idea based on change‐point monitoring (CPM), canonical correlation analysis (CCA) and dynamic adjusted shape context (DASC) for determining the harmonic contributions of individual customers and utilities. In the proposed approach, the CPM is used to select the valuable data by detecting the violent fluctuation area of harmonic voltage and the DASC to determine the harmonic contribution of individual customers by matching the user load sequence and harmonic voltage sequence based on CCA, filtering the harmonic contribution from the utility side. Simulation data is used to evaluate the performance of the method and a real‐world case study is also conducted. It is found that the proposed technique can be used to reveal the harmonic impact of the loads if data is selected resonably. [ABSTRACT FROM AUTHOR]

The concept of flexibility is defined as the power systems' ability to effectively respond to changes in power generation and demand profiles to maintain the supply–demand balance. However, the inherent flexibility margins required for successful operation have been recently challenged by the unprecedented arrival of uncertainties, driven by constantly changing demand, failure of conventional units, and the intermittent outputs of renewable energy sources (RES). Tackling these challenges, energy storage systems (ESS) as one important player of the new power grids can enhance the system flexibility. It, therefore, calls for an efficient planning procedure to ensure flexibility margins by considering ESS's role in modern power systems. This paper proposes a novel mixed integer linear programming (MILP) model for transmission expansion planning (TEP) framework taking into account the role of compressed air energy storage (CAES) integration on improvements in system flexibility. The proposed framework is housed with a quantitative metric of grid‐scale system flexibility, while a new offline repetitive mechanism is suggested to account for the N − 1 reliability criterion. The model is applied to different test systems, where the numerical results demonstrate the impacts of CAES units on system flexibility, investment plans, and the total costs. [ABSTRACT FROM AUTHOR]

Power flow calculations are an essential stage in many planning and control applications for distribution systems.To use these in control applications, however, the calculation time needs to be improved, and this can be done by the use of a trained ANN. This paper presents the considerations for constructing ANNs for DSs, and describes a method for training the system in order to support switching events representing a change in topology. The solutions for three DSs, balanced as well as unbalanced, are presented and the various considerations affecting the most appropriate ANN construction are discussed. The results are compared to the solution from the classical complex Newton‐Raphson and the fixed‐point iterative methods. The solutions have very high precision and good results are found for switched laterals. The computational performance is also compared and an improvement of two orders of magnitude is observed. [ABSTRACT FROM AUTHOR]

In a transmission line, sag is intentionally provided to relax the tension on the wire when placed between two terminals. However, thermal stress and extreme weather conditions can cause increases in transmission line sag, which can eventually damage transmission line infrastructure. In this regard, this paper examines a number of sag measurement techniques based on physical or optical properties and integrated with signal processing and communication technologies. The performance of different sag measurement methods is compared in terms of accuracy, precision and key feature. Several challenges and limitations of the existing sag measurement mechanisms are reported. A number of recommendations to address the identified challenges are made. Moreover, an efficient communication framework for sag related information exchange is proposed. This comprehensive review of the sag measurement techniques will facilitate the evolution of new and efficient real‐time sag measurement and monitoring methods. [ABSTRACT FROM AUTHOR]

Renewable‐dominant mixtures with hydrogeneration and variable renewable energies (VREs) have been the focus for decarbonization of power systems. Hydrogeneration with large reservoir becomes the main regulating source. The long‐term generation scheduling (LTGS) on monthly reservoir regulation for the approaching year becomes more significant because reserving adequate water storage is related to intramonth balance, considering that hydrogeneration has to supplement deficit energy of VRE generation. However, conventional LTGS method that is based on monthly averages overestimates the hydrogeneration's intramonth energy supporting capability, whereas rolling short‐term generation scheduling method cannot account the long‐term effect of reservoir regulation decisions. This paper proposes a new LTGS method accounting intramonth imbalance risk that the energy deficit of VRE generation exceeds the maximum supporting energy of hydrogeneration. Since intramonth imbalance risk is coupled with LTGS in an uncertain and implicit way, intramonth extreme scenarios are introduced based on distribution quantiles of natural inflow of reservoirs and VRE generation, and the probabilistic intramonth imbalance risk constraints can be transformed as the hydrogeneration must supplement energy deficit of VRE generation during the extreme scenarios. A case study is conducted on a Wind‐PV‐Hydro energy base of southwestern China. The proposed method is found able to improve the feasibility and reliability of LTGS compared to other LTGS methods. Moreover, the high calculation efficiency of conventional LTGS method is inherited. [ABSTRACT FROM AUTHOR]

With the advent of small‐scale heat and electricity producers in distribution energy systems, the interdependencies between energy carriers have been increased. Moreover, the rapid deployment of micro CHP, electric heat pumps, electricity‐to‐heat appliances etc., calls for new local market frameworks to be employed in distribution energy systems. In response, this paper presents a new energy market framework based on the concept of peer‐to‐peer negotiations to facilitate energy transactions between agents at the distribution level while addressing the interdependencies between different energy carriers. Moreover, linear optimization problems are proposed to investigate the optimal strategies of market participants with various configurations. These optimization problems not only obtain the optimum strategy of agents to participate in the day‐ahead peer‐to‐peer electricity market, the day‐ahead heat market, and gas distribution systems but also obtain their optimal operation scheduling for the entire next day. The numerical results successfully demonstrate the applicability and merits of the proposed market framework. [ABSTRACT FROM AUTHOR]

Due to the increasing interactions between the natural gas system (NGS) and electric power system (EPS), the failures of power‐lines or gas pipelines in one system may influence another system and consequently trigger widespread disruptions. Hence, identifying these critical components and protecting them from malfunctions are essential to prevent the collapse of integrated gas and power systems (IGPSs). Considering the effects of both network topologies and operational features on the resiliency evaluation, a novel assessment framework is proposed in this paper to screen out critical power‐lines and gas pipelines. First, the framework of IGPSs is introduced and an optimal energy flow (OEF) model is constructed to quickly obtain the operation states under various disturbances. Furthermore, the resiliency metrics (RMs) are developed from structural and operational perspectives respectively. Specifically, structural RMs consider the coupling topologies to capture the structure‐related resiliency, while operational RMs analyse the resiliency related to gas and power flow to capture physical operation characteristics. Finally, an assessment algorithm synthesizing all the metrics is proposed to quantify the comprehensive resiliency, based on which critical power‐lines and pipelines (CPPs) can be detected. The effectiveness of the proposed approach is validated by an IGPS test system. [ABSTRACT FROM AUTHOR]

Cyberattacks have emerged as novel threats to modern power systems. By exploiting the vulnerabilities of insecure devices, an attacker can inject viruses to lurk and collect system conditions through sniffing and then launch well‐designed attacks. Collaboratively applying bilateral cyber‐physical information can help to detect anomalous system states caused by sniffing, which can isolate virus impacts on the cyber side and ensure the stable operation of power systems. Here, a dynamic weight ensemble isolation forest algorithm is proposed to mine anomaly system states utilizing bilateral information based on the hypothesis that the fused cyber‐physical system state under assault has the shortest average path length in a constructed random forest. In addition, the proposed algorithm is able to realize improved performance in power system anomaly status mining and adapt to constantly changing power systems. The method is verified by simulations on a co‐simulation platform. The results show that the method outperforms other anomaly detection methods. [ABSTRACT FROM AUTHOR]

The high penetration of uncertain resources challenges the security of power system operation. By taking the impact of rescheduling under contingencies into consideration, reliability‐constrained unit commitment (RCUC) is developed to address this challenge. Although several efforts have been made in modelling reliability constraints, the existing methods can only manage oversimplified low‐order temporal‐independent contingencies without considering wide‐range contingencies or their state‐transition‐process issue. To quantify the impact of rescheduling on the normal‐state scheduling process denoted by UC problem, this paper builds up a Bayesian inference method for encoding reliability constraints in wide‐range temporal‐dependent contingencies. Three predictors, for example, expected‐generator‐rescheduling‐power, expected‐energy‐not‐serviced and lost‐of‐load‐probability, are selected to describe the possible corrective behaviours in rescheduling process and quantified by using Bayesian inference method. Then, these predictors are reformatted as a set of linearized constraints to be incorporated into UC. The proposed RCUC comprehensively considers the effect of rescheduling in wide‐range temporal‐dependent contingencies. Therefore, it can reveal the influence of generator rescheduling in wide‐range contingencies and keep better reliability performance than those methods reported in previous RCUC studies. The modified IEEE 30‐bus test system and IEEE 118‐bus test system are used to show the proposed model's effectiveness. [ABSTRACT FROM AUTHOR]

Short‐term load forecasting is essential to power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate the intrinsic time‐related dimensions of electric load data and the decomposition methods into machine learning models so that their prediction accuracy and robustness still have much room for improvement. To solve this problem, this paper proposes a decomposition‐based multi‐time dimension long short‐term memory (DB‐MTD‐LSTM) model for short‐term electric load forecasting (STELF). In DB‐MTD‐LSTM, empirical mode decomposition with adaptive noise (CEEMDAN) is first introduced to smooth non‐linear non‐stationary electric load data and constrain the modal aliasing or noise of decomposed electric load data in the traditional decomposed method. A joint relevant time dimensions method (JRTDM) is then developed using autocorrelation analysis to rationally extract the temporal characteristics of decomposed data in multiple time dimensions. An improved LSTM called MTD‐LSTM is developed by combining JRTDM with LSTM, which can effectively apply multi‐dimensional time characteristics of the decomposed load to improve the predictive accuracy and robustness. Several datasets from Australia and China are performed to check the predictive performance of DB‐MTD‐LSTM. Experimental results verify that DB‐MTD‐LSTM has better predictive accuracy and satisfactory robustness compared with state‐of‐the‐art and conventional predictive models. [ABSTRACT FROM AUTHOR]

This paper proposes a vehicle‐to‐grid (V2G) operation methodology of electric vehicles (EVs) for frequency regulation, EV charging, and state‐of‐charge (SOC) management. This V2G operation methodology exhibits multifunctionality in regard to instantaneous power adjustment of EVs for primary frequency control during charging. The proposed method can stabilize grid frequency under the high penetration of renewable energy by simultaneously managing the desired state‐of‐charge (SOC) of the batteries. The results indicate that both charging and charged EVs can assist in frequency regulation so that V2G services can help embracing renewable energy sources and stabilize a power system. The feasibility of this method is verified by a practical power grid model to demonstrate the efficacy of EV charging control for SOC management using DIgSILENT/PowerFactory. [ABSTRACT FROM AUTHOR]

Zero‐sequence currents in high‐voltage power systems during normal operation can have a significant influence on nearby infrastructure. It is therefore necessary to gain insight into typical levels of zero‐sequence currents in a variety of operational situations. Measurements can provide this insight. When performing measurements, one needs to know the uncertainty associated with the measurement to draw accurate conclusions. The measurement uncertainty associated with three different practical strategies to measure zero‐sequence currents during normal operation are studied: a direct and indirect measurement strategy using current clamps and oscilloscopes and a measurement strategy using phasor measurement units (PMU). Measurement uncertainty is studied using two methods: by analytical expressions and by Monte Carlo simulations. Both methods give consistent outcomes for the measurement uncertainty and show the same tendencies of the zero‐sequence current measurement uncertainty as a function of the positive‐sequence current. This paper studies measurement strategies with error sources based on realistic measurement devices that could be used in practice. The following outcomes were found: in the direct strategy, the primary current transformers are the largest uncertainty sources; in the indirect strategy, the current clamps are the largest uncertainty sources; in the PMU strategy, the PMU total vector error is the largest uncertainty source. [ABSTRACT FROM AUTHOR]

This paper proposes a new assessment method for lightning trip‐out of transmission lines that includes an improved analytic hierarchy process (IAHP) algorithm based on a lift in association rules. First, the k‐means clustering algorithm is used to discretize the geomorphic information along transmission lines, and ten feature factors are obtained. Next, by analysing the correlation between each feature factor and a lightning trip‐out event using the lift algorithm, the strong correlation factors are extracted. On this basis, an IAHP algorithm optimized by lift is proposed to establish an objective judgment matrix. Then, multiple indexes are combined as a criterion layer of the IAHP, and a lightning risk assessment model of transmission lines is established. Finally, the proposed method is validated by simulations using 28 transmission lines with 220 kV voltage grade of a provincial power grid of China. An assessment model is established using the lightning trip‐out data in the period 2010–2014, and then this model is employed to estimate the lightning trip‐out risk in the period 2015–2018. Based on the number of phase faults, the agreement between the estimation results and the actual values is 76%. The proposed model provides a new method for the lightning risk assessment of transmission lines. [ABSTRACT FROM AUTHOR]

The electric power system is one of the most important critical infrastructures of a country. Recently, the number of natural and man‐made disasters is increased, which can impose extensive damages and costs to the power system. A resilient power system can withstand against, adapt to and recover from these disasters. Power system resilience is quantified by mathematical tools which are called "resilience metrics". Currently, a lot of resilience metrics are proposed in the power system literature. In this paper, based on the extensive research in the critical infrastructure resilience literature which specifically concentrates on the "area‐based" resilience metrics, a new area‐based resilience metric is proposed which can measure the power system resilience considering the government policymaker criteria, which are rarely noticed before. The proposed and conventional area‐based resilience metrics are evaluated based on the real data from the 2012 Superstorm Sandy in the USA, which led to significant damage to the power distribution system. The simulation results show that the proposed area‐based resilience metric is very simple, can successfully address actual power system performance curves and is more meaningful and tangible than the conventional area‐based metrics for the government policymaker. The proposed area‐based resilience metric has also a general form and can be used for other critical infrastructures. [ABSTRACT FROM AUTHOR]

Distributed generators have grown in significance for the technical and financial operation of electric power systems in recent years. The integration of these generators into the electrical distribution network (EDN) has experienced rapid growth, primarily driven by advancements in renewable energy sources (RESs), particularly photovoltaic distributed generators (PVDGs). With the increasing implementation of solar energy as a RES, designing the optimal integration of PVDGs into medium voltage direct current (MVDC) networks is crucial to comprehensively analyzing technical and economic factors. To address this issue, a new multiple objective function (MOF) is proposed, which combines various techno‐economic parameters such as total active power loss (APL), voltage deviation (VD), and the investment cost of PVDGs (ICPVDG) installed in the test system. The objective is to minimize the MOF simultaneously to achieve the optimal incorporation of PVDGs. This study aims to solve the allocation problems related to the location and size of PVDG units in the modified MVDC test systems IEEE 27 and 33‐bus. Simulation results demonstrate the superior accuracy and effectiveness of the equilibrium optimization algorithm (EOA) in achieving the minimum MOF. The utilization of multiple PVDG units reduces overall active power losses and improves voltage profiles across all buses. [ABSTRACT FROM AUTHOR]

The objective of this work intends to propose a novel current interruption technology in vacuum for LVDC power systems, based on the instability of vacuum arc under composite transverse magnetic fields (TMFs). The interruption characteristics were experimentally determined in an overdamping C‐L‐R circuit. The experimental results indicated that the DC current of 800 V/500 A could be successfully interrupted within 5 ms by the proposed technology. Moreover, the interruption process could be divided into two stages, the Stable and Unstable stages. In the Stable stage, the arc voltage linearly increased from 20 to 35 V, where the arc current was limited slightly. In the Unstable stage, the arc voltage would rapidly increase and exceed the supply voltage, where the current would be forced to zero. Finally, the double‐break LVDC vacuum switches, no matter in series or parallel, were difficult to improve the interruption capability, because of the asynchronous occurrence of the Unstable stage in series vacuum interrupters and the fast current‐commutation between parallel vacuum interrupters. This technology takes advantages of high interruption capability, short arcing time and low complexity, which is a promising solution to expand the application of vacuum switch to the LVDC power systems. [ABSTRACT FROM AUTHOR]

Increasing penetration of renewable energy sources (RESs) into power systems has brought new challenges to guarantee transient voltage stability (TVS) of the system, due to complex and different characteristics of the RES compared with the synchronous generator. The related theories to the TVS for power systems with RES (PSRESs) are incomplete, and it is difficult to construct accurate physical model of the PSRES by using traditional TVS analysis method. Here a novel data mining‐based approach for extracting key factors that affect the TVS of a PSRES is put forward. The original Relief algorithm is modified to deal with the imbalance of sample size between stable and unstable samples of the practical data set and improve the calculation accuracy. Then, a data mining scheme based on the modified Relief algorithm is presented to acquire key factors affecting the TVS. With the proposed scheme, the influence degrees of different factors on the TVS can be evaluated quantitatively by their weighting values, and then the key factors as well as the influence patterns can be determined. Test results which are conducted on the modified IEEE‐39 test system with RESs are presented to demonstrate the accuracy and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Maintaining frequency stability is a crucial but challenging task for the stable operation of a power system. The distributed energy storage (DES) can charge or discharge for both upward and downward frequency regulation, exploring and effectively using the regulation capabilities will provide a strong backup for frequency regulation. Here, a dynamic DES control strategy for providing primary frequency regulation is proposed. The different behaviours of storage owners are considered when they respond to the regulation requests from the aggregator. This kind of uncertainty would lead to a mismatch between the final aggregation result and the expected target. Hence, the multi‐armed bandit approach is applied to learn users' response behaviour and select the optimal set of users to mitigate the mismatch. Case studies on the IEEE RTS 24‐bus system demonstrate that the proposed method's mismatch between the actual aggregation result and the regulation target is less than half as much as the conventional method, and it can restore the frequency 20 events earlier than the traditional method. [ABSTRACT FROM AUTHOR]

An islanded microgrid is cyber‐physical system, and the control relies on the communication system significantly. Improper parameters of the cyber system can result in instability of a microgrid system. To evaluate the impact of a networked control system on control performance, a cyber model is developed to represent data acquisition periods and communication delays. Simplification of the networked control system model is proposed to enhance the computational performance, making the analytical method applicable for large‐scale systems. Based on the analysis, a two‐dimensional stability region of a microgrid in the space of cyber parameters can be obtained. To validate the proposed method, a microgrid control scheme is proposed for power dispatch and regulation based on the droop and proportional‐integral (PI) feedback control. The analytical method is compared to the time‐domain simulation, and it is shown that the stability regions are nearly identical. The critical values of cyber parameters are determined based on the analytical results. The proposed control strategy with the given cyber parameters is validated for transient stability following dynamic events. Simulation results indicate that the design of a microgrid as a cyber‐physical system needs to be guided by critical values for cyber parameters to prevent system instability. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) have emerged as a key platform for integrating distributed energy resources into distribution systems. However, a high penetration of renewable energy resources in an MG causes an imbalance frequently between energy generation and load. One solution to this problem is internal energy trading among MGs, where MGs trade (i.e. buy or sell) energy with other MGs in the network. In internal trading, multiple buyer MGs may compete to obtain energy from seller MGs with surplus energy. By contrast, seller MGs also compete to sell their surplus to those confronted with a deficit. Considering this scenario, we propose a novel rank‐based energy scheduling strategy for networked MGs in a distribution system to solve the competition between buyer and seller MGs. The technique for order of preference by similarity to the ideal solution method, which is a multi‐criteria decision‐making approach, is applied to prioritise the MGs. The proposed strategy determines the amount of internal energy to trade for each seller/buyer MG. Different from existing mechanisms, the proposed rank‐based approach evaluates each MG with respect to four criteria: ratio of energy, forecasting accuracy, contribution of supply, and historical performance. The proposed strategy is compared with existing no‐rank‐based scheduling and absolute contribution‐based method. Moreover, the effect of each criterion on the cost of each MG is illustrated by considering and disregarding the criterion in the proposed strategy that demonstrates the effectiveness of the proposed strategy in reducing the cost of MGs according to their rank in the network. [ABSTRACT FROM AUTHOR]