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The paper introduces a robust methodology for evaluating complex electricity system efficiency, making several contributions. It systematically decomposes the electric power system into various processes, including power generation, transmission, distribution, and power consumption, with parallel sub-processes for production and domestic power consumption. Employing data envelopment analysis (DEA), the paper develops a network directional slacks-based (DSB) model to evaluate the whole system and the internal processes. The model is theoretically proven to be feasible, providing efficiency scores and identifying inefficiencies in the system and its processes. A noteworthy addition is the introduction of the DSB green Luenberger productivity index and its decomposition, offering an analysis of productivity changes in electricity systems. Empirical analysis covering 30 Chinese provinces from 2013 to 2019 uncovers the performance variations among provinces and processes, pointing out inefficiencies and changes in technology and efficiency over time. The empirical findings aim to support efforts in enhancing the overall efficiency of China's interprovincial power system. • We decompose the complex electric power system into four processes with dependence. • We build a network directional slacks-based (DSB) model for the complex power system. • We give a DSB green Luenberger productivity index (DPI) for our network model. • The efficiency score and DPI of the system are decomposed into those of processes. • We apply the model and indexes to electric power systems in Chinese provinces. [ABSTRACT FROM AUTHOR]

• Constructed an operation architecture of the coupling of energy supply and demand. • Established the comprehensive demand response mechanism based on time-sharing energy prices. • Established the minimum carbon emission cost economic scheduling model as an objective function. • The scheduling method could achieve the goal of low-carbon economic dispatch. Aiming at the problems of low energy utilization and environmental pollution in China's livestock and poultry breeding industry under the carbon peaking and carbon neutralization goals, this paper proposes an optimal scheduling model for multi-energy coupling integrated energy system with ladder-type carbon trading mechanism and demand response. First, based on the construction of a reward and punishment model with ladder-type carbon trading mechanism, this paper explores the potential interaction between the coupling ability of multiple heterogeneous energy sources and carbon emissions in consideration of the impact of carbon capture equipment on carbon emissions. Second, in view of the diversity and complexity of demands for agricultural energy supply, the energy supply side equipment model and the comprehensive demand response model of electricity, heat, and gas are established, and a low-carbon economic scheduling model of integrated agricultural energy system is proposed, aiming at minimizing the total operation cost of the system. Finally, the model is solved by CPLEX, and the coupling system of a 6-node power distribution network, a 6-node gas network, and a 4-node heat supply network is used to verify the effectiveness of the proposed model and method and optimize the operation of the integrated agricultural energy system. [ABSTRACT FROM AUTHOR]

• An upscaling regional photovoltaic power forecasting scheme based on ANN is proposed. • A novel method of reference PV plants selection is proposed. • A power correction method is proposed to guarantee power forecasting accuracy. • The rolling forecasting accuracy is improved by integrating multiple ANN models. • The forecasting error is 4.04%, 3.45% and 2.86% in resolution of 1, 5 and 10mins. Along with the increasing penetration of photovoltaic (PV) power generation, regional power forecasting becomes more and more critical for stable and economical operation of power system. The key challenge of regional PV power forecasting technology is the lack of complete and accurate historical power data since not all PV plants are equipped with the precise real-time output power monitoring system. Besides, the computation burden will be heavy when the number of PV plants in the target region is large. This paper therefore proposes an upscaling minute-level regional PV power forecasting scheme using the data of the selected reference PV plants. In this paper, a novel method of reference PV plants selection is proposed by comprehensively considering the prediction accuracy of artificial neural network (ANN) as well as Pearson correlation coefficient. The reference PV plant selection coefficient μ is introduced as the comprehensive indicator for reference PV plant selection, which incorporates Pearson correlation coefficient and MAPE. In addition, a PV output power correction method is assumed to guarantee the proper operation of regional power forecasting. Besides, this paper proposes a flexible approach to effectively decrease the accumulated error of rolling forecasting by integrating the forecasting results under different temporal resolutions. In specific, the power forecasting results in temporal resolutions of 1 min, 5 min and 15 min are simultaneously derived and the performance between the traditional rolling forecasting and the proposed method is compared. The validity of the proposed method is finally verified using the collected historical power data of PV plants installed in a city of Eastern China. For time resolution of 1 min, 5 mins and 10 mins, the corresponding RMSE are 6.56, 5.73 and 4.85 and corresponding MAPE are 4.04%, 3.45% and 2.86%. [ABSTRACT FROM AUTHOR]

• Integration of blockchain characteristics into the collaborative carbon emission reduction game model of both the supply and demand sides of electricity is proposed to encompass source control and terminal suppression. • Blockchain is utilized to trace electricity types within the electricity market and to quantify the carbon emissions generated by the demand side of electricity. • By incorporating blockchain, users can actively engage in individual-level carbon trading to control and reduce carbon emissions. • Blockchain facilitates enhanced carbon emission reduction efforts by the power supply side. The task of carbon emission reduction is severe in the power industry in China under the national goal of "carbon peaking and carbon neutrality". The current carbon reduction is mainly based on supply side, but the effect is limited. To solve this problem, this paper proposed a coordinated supply–demand carbon emission reduction strategy based on blockchain technology. Based on the carbon emission reduction of complementary thermal power and renewable energy under the carbon trading mechanism of power supply side, users are made to participate in the individual level carbon trading mechanism with the help of blockchain technology, and the carbon emission reduction in power demand side is guided through the market mechanism, thus forming a supply–demand collaborative carbon emission reduction strategy of source side control and terminal inhibition. By analyzing the decision changes of both the supply and demand sides of electricity before and after the introduction of blockchain, quantifying the influence of blockchain on electricity quantity, electricity price and users' utility in turn, and establishing the personal carbon trading mechanism supported by blockchain, a game model of two-side interaction between supply and demand was constructed. The simulation results show that the collaborative carbon emission reduction strategy based on blockchain gives full play to the potential of carbon reduction in power demand side, and the personal carbon trading mechanism can better inhibit terminal carbon emissions, which is conducive to the deep carbon emission reduction of the power industry. [ABSTRACT FROM AUTHOR]

Highlights • The small signal dynamic model for a hybrid 3-terminal HVDC system is developed. • This paper identifies the low-damped oscillatory modes of the hybrid system. • This paper investigates the impact of power-type controllers on the system. • The impact of AC system strength on system stability is investigated. • The findings are useful for systematic design of the HVDC system. Abstract The Hybrid Multi-Terminal High Voltage Direct Current (H-MTDC) transmission system can take advantages of Line-Commutated Converter (LCC) technology to reduce capital cost and Modular Multilevel Converter (MMC) technology to eliminate commutation failure (CF) issues, and recently attracts much attention in academic research and practical engineering. Based on the scheme of Wudongde H-MTDC project, which is under construction in China, this paper develops a small-signal dynamic model of the hybrid 3-terminal HVDC system, adopting an LCC station at rectifier and two MMC stations at inverter. Then, the small-signal model is validated by comparing its time-domain responses with the detailed electromagnetic transient (EMT) simulation results obtained from PSCAD/EMTDC. Based on eigen-analysis and participation factor methods, this paper identifies the oscillatory modes of the overall hybrid system. From the revealed oscillatory modes, it indicates that DC-side active power relevant state variables and MMC internal harmonics have major contributions to low-damped modes which may result in instability of the system. Then, the effects of power-type controllers, i.e., constant DC current controller of LCC station, DC-side voltage controller of MMC station and active power controller of MMC station, and circulating current suppressing controllers (CCSC) of MMC stations on the stability of the hybrid system are investigated. Finally, the effects of AC system strength on the stability of the hybrid system are analyzed. The results show that smaller proportional gains of constant DC current controller of LCC and DC-side voltage controller of MMC can enhance the system small-signal stability. And the active power controller gain of MMC station has slight influences on the system stability. Moreover, the CCSC gains are required to be properly selected based on the system setup to prevent from the possible internal harmonic instability of MMC stations. [ABSTRACT FROM AUTHOR]

• The Monte Carlo method is used to construct the charging load of EV in the planning area divided into four different vehicles type. • The Prim algorithm is adopted to search for the optimal network layout of energy transmission pipelines. • Proposed collaborative planning modeling promotes the time-of-use electricity price scheme to change the temporal and spatial characteristics of EV charging load. • The growth of EV charging load in the planning area has not increased the investment and operation-maintenance costs of DES equipment, and reduced carbon transaction costs as well as improved economic of system operation. • Simulations are conducted to verify the effeteness of proposed method. Under the dual-carbon goal, the number of electric vehicle (EV) has surged, and the uncertainty of the temporal and spatial characteristics of the load has become prominent, which brings the challenge for higher requirements to the equipment composition of distributed energy station (DES) and performance of station-network. This paper proposes a collaborative planning method for low-carbon DES with networks, which is focusing on the characteristics of EV charging behavior. First of all, based on the quantitative analysis of the temporal and spatial distribution of charging load of four typical types of EV, including private vehicle, public vehicle, taxi and bus, the Monte Carlo method is used to construct the charging load modeling of EV in the region area. In addition, the low-carbon DES is modeled equipped with photovoltaic (PV) and power to gas (P2G) devices, and the Prim algorithm is adopted to search for the optimal network layout of energy transmission pipelines in the region area. Under carbon trading mechanism, aiming at the lowest annualized total cost of the regional station-network, a station-network collaborative planning modeling strategy for the capacity allocation of low-carbon DES with networks is established. Finally, the case study for a project in Beijing China is implemented on MATLAB, to prove the effectiveness of the method proposed in the paper. The time-of-use electricity price scheme changes the temporal and spatial characteristics of EV load. For example, more than 2 MW the EV load can be brought into the valley price range, which reduces the carbon transaction cost and energy purchase cost of DES by 50 thousand yuan almost when peak-valley electricity price difference is close to 1.2 yuan. At the same time, the growth of EV charging load(the penetration rate of EV can reach up to 20%) in the planning area has not increased the investment and operation-maintenance costs of DES equipment. [ABSTRACT FROM AUTHOR]

• Forecasting-test framework for long-term power system planning. • Coal-fired generators can be completely withdrawn from the power system by 2060. • China's power capacity mix will transition to wind and solar generators dominated. • Biomass and gas-fired generators will take on the role of reserved generators. • The timing of achieving China's zero-coal power system may be advanced or delayed. Facing the challenges of climate deterioration, environmental pollution, and energy shortages, China has put forward the goals of "carbon dioxide peaking and carbon neutrality", and the electricity sector will be the key area and the main direction of carbon emissions reduction. To prospect China's power system and explore the potential for establishing China's zero-coal power system in the future, this paper proposes a forecasting-test framework for long-term power system planning. We predict, evaluate, and analyze China's power system from 2025 to 2060 using the proposed forecasting-test framework. During this process, the power capacity mix and the electricity generation mix of China's power system will gradually transition from coal-fired generators dominated to wind and solar generators dominated. Moreover, the roles of different generators and energy storages are illustrated through the seasonal comparison and sensitivity analysis of the installed capacity of generators. The results indicate that coal-fired generators can be completely withdrawn from the power system, confirming the feasibility and possibility of achieving China's zero-coal power system by 2060. Although the timing may be advanced or delayed in different scenarios, achieving China's zero-coal power system will require exercising nationwide planning and making joint efforts of various sectors from now on. [ABSTRACT FROM AUTHOR]

• The mechanism of GFL and GFM ES on the voltage security of the receiving-end system is analyzed. And the calculation method of MISCR considering ES is proposed. • The linearization of MISCR is realized by means of "accompanying network + model reconstruction". • An ES planning method for improving the security of receiving-end system considering the selection of PCSs is proposed, which including ES planning layer, system operation layer and accompanying network layer. China's power demand in high load density areas is satisfied by a centralized feed of several large-capacity HVDC links, forming a typical receiving-end system, which is facing problems such as shortage of dynamic reactive power and increased risk of commutation failure. Electrochemical energy storage (ES) has characteristics such as strong dynamic active and reactive power regulation ability, rapid response and flexible control mode, making it a high-quality resource in improving the security of the receiving-end system. Therefore, it is necessary to carry out research on the methods of planning of ES in receiving-end systems. This paper mainly analyzes the role of ES in providing dynamic reactive power support. According to the different control modes of power conversion system (PCS), the ES is divided into grid-following (GFL) and grid-forming (GFM), and multi-infeed short circuit ratio (MISCR) considering both is constructed. The MISCR is taken as an objective function of the ES planning model. At the same time, the method of "accompanying network + model reconstruction" is proposed to realize the linearization calculation of MISCR. In order to compensate for the economic cost of ES investment, its energy storage function should also be used to participate in peak shaving during normal operation. Therefore, an ES planning model considering PCS selection is established, which including ES planning layer, system operation layer and accompanying network layer. Finally, the effectiveness of the proposed planning method is verified by a modified IEEE-39 system. [ABSTRACT FROM AUTHOR]

• The framework combines a cloud-edge-end structure to break the information barriers of multi-source measured data. • The method executes different strategies according to voltage levels based on interaction of edge computing is proposed. • Harmonic source identification and evaluation are achieved through the multi-level interaction of edge computing. • TVE, CMHSE and HHSE method for cloud-edge-end collaborative computing strategy are developed into the system. With the increase of power electronic devices such as distributed renewable energies, energy storage, and flexible loads, the disturbance sources are rapidly raised and distributed in multiple voltage levels in the distributed network. The harmonics they produce are random and uncertain, and the massive amounts of data propose high demands on server computing capability, which restrict harmonic sources evaluation and significantly influence the judgments of the power quality in the distribution network. In order to improve the power quality and the harmonic source evaluation function of the distribution network, in this paper, a framework for multi-harmonic source identification and evaluation based on cloud-edge-end collaboration is proposed. Firstly, based on the analysis of the functional requirements harmonic sources evaluation in multiple voltage levels, a novel framework of cloud-edge-end collaboration is proposed. Secondly, the cloud-edge-end collaborative harmonic sources evaluation method which executes different service strategies according to voltage levels and grid operation based on the multi-level interaction of edge computing is proposed. Further, the working principle of the proposed novel framework with multiple evaluation methods including the dominant harmonic source identification, concentrated multiple harmonic sources evaluation (CMHSE) and hierarchical harmonic sources evaluation (HHSE) is described in detail in order to show the completed work processes of the proposed framework. Finally, the performance of the proposed method is verified based on the real data measured from different realistic urban power grids in China. The proposed method can effectively evaluate harmonic contributions in distribution systems and in turn provide a basis for harmonic mitigation measures. [ABSTRACT FROM AUTHOR]

• Proposes a scheme of premium power supply scheme based on leasing mitigation equipment against voltage sag (PSLM), including the stakeholders, the leasing modes and the leased property. • Presents an algorithm of SE trip probability when SE is equipped with ME, to measure the mitigation effect of ME. The three technical parameters of ME are included in the method. • Proposes the optimal configuration model of ME, including the two objective functions and three constraints. • Proposes a calculation model of rental of PSLM. Small and medium-sized sensitive users are eager to have premium power supply. The common solution is purchasing mitigation equipment (ME). However, some of they have the financial pressure that cannot afford the mitigation. In order to solve this problem, this paper proposes a premium power supply scheme based on leasing mitigation equipment against voltage sag (PSLM). First, the scheme of PSLM is proposed, which consists of stakeholders, leasing modes and leased property. Second, this paper presents an algorithm of sensitive equipment (SE) trip probability considering effect of ME, to calculate trip probability of SE when it is equipped with ME, which reflects the mitigation effect that is related to profits of scheme. Then technical parameters of ME are optimized by an optimal configuration model, considering tolerance of SE under voltage sag. The cost of the scheme is determined by the optimization of technical parameters of ME. Furthermore, this paper proposes a calculation model of rental of PSLM, to maximize the revenues of both stakeholders. Non-dominated sorting genetic algorithm-II is used to solve the proposed two models. Finally, the effectiveness and feasibility of the proposed PSLM is verified by a case study of a grain oil manufacturer in the south China. [ABSTRACT FROM AUTHOR]

• Mechanism of high frequency harmonic resonance of Luxi VSC-HVDC project is analyzed. • Time delay and system impedance will influence the high frequency harmonic resonance. • A method to suppress the high frequency harmonic resonance is proposed. This paper introduces the basic situation of high frequency harmonic resonance in Guangxi on April 10, 2017, which is based on the Luxi back-to-back voltage source converter based high voltage direct current transmission (VSC-HVDC) project in China. Firstly, the harmonic amplification mechanism and its key links in the event are analyzed in detail and verified by simulation. Secondly, the relationship among high frequency harmonic resonance, voltage feedforward control loop and system harmonic impedance is obtained, and the formulas among voltage feedforward control parameters, system harmonic impedance and high frequency harmonic resonance frequency amplitude are deduced. Then, a method to suppress high frequency harmonic resonance by adding a reasonably filter to the voltage feedforward control loop is proposed. Finally, the effectiveness of the proposed method in this paper is verified by simulation and field operation. [ABSTRACT FROM AUTHOR]

• The electro-thermo-mechanical coupling power flow model is formulated. • The calculation framework of the probabilistic power flow considering electro-thermo-mechanical dynamics (PPF-ETMD) is presented. • The uncertainties of the meteorological conditions are considered in the calculation of PPF-ETMD. • Two fast calculation methods are presented to improve the computation efficiency of PPF-ETMD. • The distributions of the conductor temperature, stress and sag can be obtained by performing PPF-ETMD. Conductor temperature and tension are important physical states that determine the security of the overhead line. The states are sensitive to the line power flow and the meteorological conditions around the conductor that have strong uncertainty. In this paper, the electro-thermo-mechanical coupling dynamics of the power system are modeled. Then, focusing on the calculation of the distributions of the electro-thermo-mechanical dynamics (ETMDs) of critical spans under the uncertain operational environment of the power system, the implementation framework and the fast calculation methods for the probabilistic power flow considering electro-thermo-mechanical dynamics (PPF-ETMD) are proposed. The work presented in this paper provides an effective method for operators to capture distributions of the ETMDs of critical spans under the uncertain environment and detect the underlying system security risk based on the thermal and mechanical essence of the security of the overhead line. Numerical simulations based on a regional power system in China and the IEEE 300-node system are carried out to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• We develop an optimal data-driven tower damage prediction model to predict the damage spatial arrangement of 10 kV towers under typhoon disaster. The model has a cell accuracy of 1 km × 1 km. • Metrological, power grid, and geographical information are comprehensively considered when predicting the damage spatial arrangement of 10 kV towers. Meanwhile, sample balancing techniques and correlation analysis are used for data processing and variable selection. • The optimal algorithm Gradient Boosting Regression (GBR) is selected by comprehensively considering the subjective and objective factors. It is able to find a right balance between the prediction accuracy and the timeliness. This paper presents a data-driven tower damage prediction model to predict the damage spatial arrangement of 10 kV towers under typhoon disaster. The 10 kV tower belongs to distribution network in China. Compared with high voltage level in transmission network, the 10 kV power towers are more vulnerable to typhoon disasters due to their lower design criterion and large amount. The data-driven model proposed in this paper can effectively predict the tower damage situation. It takes meteorological, power grid and geographic information into account and can be divided into two steps. The first step is to predict the damage probability of each tower by using data-driven method such as AdaBoost, Gradient Boosting Regression, K Nearest Neighbor Regressor, Random Forest and Support Vector Regression algorithms. In this step, the data space is constructed by data processing and variable correlation analysis. Then, we use the processed meteorological, power grid and geographic information as input and the damage probability as output for model comparison. The second step is to select the optimal model based on comprehensive index weighting. Through a comprehensive comparison of the efficiency and accuracy of the five models in various actual scenarios, the optimal model is Gradient Boosting Regression, which outperforms the other adverse algorithms and produces the prediction damage consistent with actual data. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

Certain edge failures in cyber–physical power systems (CPPSs) can cause cascading failures in the power system. Protecting these critical edges and essential equipment in the supporting communication network can avoid these failures. Here, based on complex network controllability regarding kinetic equations and considering the actual operating conditions of CPPSs, we filter out a class of transmission lines and a class of nodes in the power system communication network of the power system that show a significant impact on network controllability. We further validate the results of the filtering by examining the resulting network robustness. Through simulations, we observe that the power edges and communication nodes screened based on network controllability have the most significant impacts on system controllability. It is therefore crucial to protect these edges and nodes to achieve more stable power grid operation and prevent cascading failures. Finally, we validate our results using a case study on a city in Henan Province, China. Our simulations demonstrate that the addition of a communication system can enhance the controllability of the power system. • Different key components in both power grid and communication network are considered separately. • The impact of different coupling methods on the controllability of the system are evaluated. • The conclusions of this paper are validated in a real power grid. [ABSTRACT FROM AUTHOR]

• Tellegen's theorem is introduced into the LCC-HVDC receiving-end AC power grid, and the instantaneous quasi-power characteristics are derived. • The quasi-power difference differential is defined to characterize differences between the internal and external faults and solve the problem caused by the distributed capacitance and reactive currents crossing through the transmission line. • The proposed pilot protection is immune to the control strategy of the inverter, commutation failure, receiving system strength and other factors. • The proposed protection can detect the fault within 5 ms. Affected by the control strategy and nonlinear characteristics of the inverter station, the traditional pilot protection relays employed in AC transmission lines do not perform well or even fail at the receiving end of the line commutated converter-based high-voltage direct-current system, thereby posing concealed threats to the security of the system. Leveraging the universality of Tellegen's theorem, this paper introduces the theorem to the realm of the AC-DC hybrid system. The characteristics of quasi-power difference under various conditions are analyzed, and a pilot protection criterion is proposed based on the quasi-power difference differential value. The AC grid model of the receiving end of the Tian-Zhong HVDC project in China is established in PSCAD/EMTDC to carry out simulation and performance analysis for the proposed protection under different fault cases. Furthermore, the proposed protection criterion undergoes validation using on-site fault recording data. The test results show that the proposed method is immune to the inverter control strategies, commutation failure, receiving system strength, and other factors. The new method is more adaptive to the AC system connected to the inverter station. [ABSTRACT FROM AUTHOR]

• A user persona oriented toward the enterprise is constructed to characterize power consumption in the context of COVID-19. • Abstract labels are extracted from enterprise power profiles based on industrial, regional, periodic power profiles. • Enterprises with similar electricity consumption characteristics or the same label are grouped to allow recommendations for customized electricity products, policy, and services. The COVID-19 epidemic has led to devastating consequences worldwide due to strict social distancing measures, travel bans, city lockdowns, and other activity restrictions. Numerous lessons have been accumulated by various industries or sectors in the process of coping with COVID-19. In our paper, the actual electricity consumption of 1.145 million enterprises in a province of China is investigated in the normal period, the breakout period, and the recovery period. An artificial intelligence based enterprise profiling model is established to characterize power consumption profile geographically, temporally, and industrially. The recovery rate of enterprise power consumption is proposed as a key indicator representing the status of production resumption. Under lockdown measures, enterprise in different regions or sectors exhibits diversified responses in power consumption, the unsupervised learning model and the correlated coefficient extract the abstract characteristics and labels from various enterprise power profile. Ultimately, enterprises with similar electricity consumption characteristics are grouped and labeled to provide customized power services. Accurate enterprise profiling can effectively aid in facing with the challenges of insufficient orders, tight capital chains, and rising costs caused by the epidemic. [ABSTRACT FROM AUTHOR]

Reliability is a prerequisite for community integrated energy systems to provide consumers with uninterrupted high-quality energy supply. However, it is still challenging to consider reliability in the planning of community integrated energy systems due to the complexity of energy conversion relationships. Conventional reliability-constrained planning methods usually form a bi-level optimization problem in which the planning scheme optimization and reliability index calculation are separated into two subproblems. In this paper, a novel reliability-constrained planning method for community integrated energy systems is proposed. First, the fault incidence matrices are employed to construct the analytic reliability evaluation model of community integrated energy system. Then, the reliability evaluation model is linearized and explicitly integrated into the planning model, forming a mixed-integer linear programming model. This model can be efficiently solved by mathematical optimization algorithms while effectively balancing the operation cost and reliability at the planning stage. Case studies are conducted based on a practical community integrated energy system in North China to verify the effectiveness of the proposed method. • The reliability calculation is transformed into inequality constraints for planning. • The reliability-constrained planning is a mixed-integer linear programming model. • Challenge posed by complex energy conversion relationships in planning is addressed. • Optimal configuration is efficiently obtained considering economy and reliability. [ABSTRACT FROM AUTHOR]

• The utility death spiral of China power sector due to grid-abandoners is deeply researched. • The Bass diffusion model is applied to evaluate the installation of rooftop PV systems and hybrid PV-BESS. • The utility death spiral is unlikely to occur under low-solar radiation in China's power sector. • Several significant inner drivers of rooftop PV systems and hybrid PV-BESS expansion are investigated. • The high-solar radiation, falling costs of PV and BESS, and increasing retail price can provoke grid abandonment through rooftop PV systems and hybrid PV-BESS expansions. Nowadays, owing to the price and technological advantages, photovoltaic (PV) and battery energy storage systems (BESS) have rapidly developed in China. The self-production and consumption of PV and BESS are causing consumers to abandon the power grid. However, this potential of grid abandonment in China's power sector remains unclear. Therefore, this paper deeply investigates the interaction between grid abandonment and the distribution price of utility, namely the utility death spiral to explore the potential in China. A method based on the innovation diffusion theory is proposed to reveal the causal loop of the death spiral. The Bass diffusion model, an approach of innovation diffusion theory, is applied to estimate the installations of rooftop PV systems and hybrid PV-BESS. After that, the distribution price, grid utilization level, and utility recovery cost based on China's price structure are evaluated. Sensitivity analyses are conducted to investigate several significant inner drivers of the rooftop PV systems and hybrid PV-BESS expansions. Results indicate that the utility death spiral is unlikely to occur in China with low-solar radiation, while it will occur in a high-solar radiation scenario with 57% penetration of rooftop PV systems. Compared with the return rates of the utility requirement, the cost reduction of PV and BESS, and even a 1% growth in the retail price cause the death spiral. When the death spiral occurs, the permeations of PV and BESS in the cost reduction scenario are 51% and 56%, while the permeations are 52% and 51% in the scenario of 1% growth of the retail price. Additionally, several possible solutions to avoid the death spiral are discussed, such as discriminatory prices, business models, price mechanisms, and support mechanisms. [ABSTRACT FROM AUTHOR]

• A transfer and contrastive learning architecture identifying multi-label loads. • Contrastive learning architecture to extract deep features. • Transfer learning design solves the sparsity of multi-label appliances. • Gramian angular field encoding enhances the feature extraction efficiency. • Verifications on both public datasets and real-world measurements from China. To achieve the goal of peaking carbon emissions globally and carbon neutrality, smart energy management is a promising way to boost energy conservation and estimate the residential potential for regional demand response, among which the non-intrusive load monitoring technique is highlighted due to its effectiveness on the residential side. However, the identification of multi-label appliance switching operations is still a challenge in this field, which may critically affect the total identification results due to the few-shot learning problem and the complicated overlap of features belonging to different appliances. Therefore, this paper proposed a transfer and contrastive learning architecture to identify multi-label appliances effectively. In the first stage, Gramian angular field encoding is implemented to visualize power sequences to highlight the correlation between timestamps and enhance the feature extraction efficiency. Secondly, a contrastive learning architecture is implemented to learn the general distinguishing features between samples of different labels, and density-based spatial clustering of applications with noise clustering is utilized to detect multi-label samples. Thirdly, transfer learning is utilized to enhance the multi-label identification capacity of contrastive learning structures based on the existing trained model. Finally, the effectiveness of the proposed algorithm is verified through two low-frequency non-intrusive load monitoring public datasets and real-world measurements from a pilot project in China. The results show that the proposed architecture can achieve the efficacy of deep features extraction and few-shot learning in identifying multi-label appliance switching operations. [ABSTRACT FROM AUTHOR]

• Fast calculation methods for two types of overvoltage are developed. • The rising overvoltage during DC blocking period is revealed and studied. • An equivalent static load model of HVDC is proposed using singular perturbation method. • The relations between network structure and overvoltage is analyzed. • Voltage subsystem is proved to be a monotonic system holding order-preserving properties. In a multi-rectifier AC-DC system the blocking of a HVDC tie can cause severe transient overvoltage problems, which may trip off the renewable energy sources, endangering the safe operation of the system. This paper shows that the blocking of a HVDC tie can introduce two types of transient overvoltage. For Type I overvoltage, which is caused by the surplus reactive power injection immediately after the blocking, a simple estimation formula is proposed. Suppression strategies that can decrease the key matrix are examined using a matrix-theoretic approach. The formula is applicable for system integrated with renewable energy sources and it has reasonable accuracy for overvoltage level of interest. To study Type II overvoltage, a reduced-order mathematical model is first derived with singular perturbation method. Then it is demonstrated that this type of overvoltage is due to the increase of transient electromotive force of synchronous machines, and an analytical calculation procedure is suggested. In addition, it is found that the voltage subsystem of a multi-rectifier system is an input–output monotonic system with nice order-preserving properties. Increasing the proportional gain constant of excitation system or choosing generators with large open-circuit transient time constant can monotonically decrease this type of overvoltage. The simulations of an actual large-scale multi-rectifier system in China are described to validate the introduced results. [ABSTRACT FROM AUTHOR]

• A novel generator start-up sequence optimization strategy is proposed in this work, taking the integration time of wind-solar hybrid systems into account. • The proposed model requires fewer variables compared with other models, therefore the computational efficiency can be greatly improved. • The confidence gap decision theory based generator start-up sequence strategy is proposed to address the generation uncertainty of wind-solar hybrid systems. • Better generator start-up sequence solutions with higher computational efficiency and less conservatism can be attained by employing the proposed strategy. A reasonable start-up sequence of generators considering renewable energy generation integration can effectively improve the restoration efficiency of the power system concerned. The restoration strategy of generators is time-sensitive, however, the computation procedure of existing methods is not fast enough to meet the requirements of practical applications. In this paper, the generator start-up sequence optimization (GSSO) problem is addressed with the integration of wind-solar hybrid systems (WSSs) considered in bulk power systems. Specifically, a deterministic model for GSSO is formulated to maximize the total generation capability during the restoration procedure, in which fewer auxiliary variables are required to promote the computational efficiency. On this basis, in order to deal with the inherent and significant uncertainties of WSSs, a confidence gap decision theory (CGDT) based approach is proposed combining the advantages of stochastic optimization and the information gap decision theory based method while considering the worst-case realization of unknown probability distributions, so that a chance-constrained robust generator start-up solution is thereby attained. Moreover, the formulated CGDT based model is transformed into a mixed integer linear programming problem. Finally, the IEEE 39-bus power system and a modified version of an actual power system in China are employed to demonstrate the feasibility and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

• A load restoration scheme is proposed to coordinate the transmission, distribution, and gas systems. • The coordinated load restoration is achieved in a distributed manner to respect the information privacies. • The line-packs caused by slow gas slow dynamics is exploited to assist in load restoration. • An efficient algorithm is proposed to solve the distributed load restoration scheme with excellent calculation performance. Recent electricity systems have witnessed many power outage events, and the interrupted loads must be restored as soon as possible to reduce the enormous outage losses. However, the ever-increasing interdependencies of the transmission, distribution, and gas systems cause the restoration behaviors of a system to inevitably influence the others, which brings about new impacts on the load restoration decisions. Moreover, the different velocities of power and gas flows cause additional complexities in making load restoration decisions for the whole system. To address these problems, this paper proposes a new load restoration scheme coordinating transmission, distribution, and gas systems. Firstly, to respect the information privacy of individual energy subsystems, a distributed load restoration coordination structure is proposed with the interactions of different energy systems and corresponding load restoration models considering pipeline gas storage capacity caused by slow gas flow dynamics. Based on the distributed restoration coordination structure and considering that discrete variables in load restoration models hinder the efficient calculation, a projection function-based adaptive alternating direction method of multipliers is then proposed with iterative relaxation and projection of discrete variables. Finally, simulation results in the T118-D33-N56 test system and a real-world system in China demonstrate that coordination of transmission, distribution, and gas systems and considering pipeline gas storage capacity can shorten the load restoration time. The proposed solution method also shows good computational performance. [ABSTRACT FROM AUTHOR]

High-frequency oscillation is one of the critical issues threatening the stability of modular multilevel converter (MMC) based high-voltage direct current (HVDC) system. A new 2 kHz high-frequency oscillation has occurred in the wind farm-side MMC station of Rudong wind farm integrated MMC-HVDC project of China during no-load charging process. The mechanism of this high-frequency oscillation is different to previous ones. Apart from the time delay of MMC, the stray capacitance of converter transformer makes a significant contribution to the high-frequency oscillation. This paper investigates the high-frequency oscillation mechanism of wind farm-side MMC station during no-load charging process. First, the harmonic state space (HSS) model of a wind farm-side MMC station is established. In addition to the leakage inductance, the stray capacitance is considered in the converter transformer model. To reveal the significance of the converter transformer stray capacitance in this new high-frequency oscillation, the root loci of the system are calculated with varying stray capacitance. Then, the state variables with high degree of participation in the high-frequency oscillation modes are determined by the participation factor analysis. A reduced-order model suitable for the high-frequency oscillation analysis is further established based on the participation factor analysis results. Next, the high-frequency oscillation mechanism is revealed by impedance analysis. Finally, the effects of system parameters are analyzed by the combination of eigenvalue analysis and impedance analysis. The analysis results are validated by electromagnetic transient simulations. • High-frequency oscillation mechanism of MMC charging with transformer is revealed. • The transformer stray capacitance must be considered in the oscillation analysis. • Participation factor analysis is carried out to determine main influencing factors. • A reduced-order model suitable for high-frequency oscillation analysis is proposed. • AC voltage control of MMC has significant effects on the high-frequency oscillation. [ABSTRACT FROM AUTHOR]

• Inductance is an indispensable element for SFCLs in a multiterminal HVDC system. • Inductance can curb the rising rate of fault current at the early stage of a fault. • Resistance can effectively reduce the amplitude of lasting fault current. • Converter in a multiterminal HVDC system cannot be locked until a fault is cleared. • SFCL with proper resistor and inductor is suitable for multiterminal HVDC systems. Multiterminal Voltage Source Converter (VSC) High Voltage Direct Current (HVDC) networks are developing rapidly worldwide, especially in China with urgent needs of long-distance power transmission. Currently, the interrupting capacity of available DC circuit breakers (DCCBs) is a key obstacle to the development of multiterminal VSC HVDC networks. Superconducting fault current limiters (SFCLs) can compensate for the limitation of DCCBs. Conventionally, a resistive SFCL is regarded as essential and sufficient, while an inductive one is less related and optional. However, pure resistance cannot effectively suppress the rising rate of fault current at the very early stage of a fault, resulting in unnecessary blocking of the converter. Adequate inductance is required for such SFCLs to curb rapidly increasing fault currents within the first few milliseconds, which is critical to DCCBs to isolate fault without interrupting other DC transmission lines. In this paper, we compared and analyzed the performances of SFCLs with different resistances and inductances in a four-terminal VSC HVDC network using PSCAD/EMTDC software. The results indicate that even resistance and inductance show different current limiting effects, but they are both necessary to realize effective SFCL function for multiterminal VSC HVDC networks. The outcomes of this paper are of significance in FCL design for multiterminal VSC HVDC networks. [ABSTRACT FROM AUTHOR]

• This study concerns the risk of power system caused by magnetic storms. • Responses of power system to magnetic storms are studied. • Excitation current is proposed to be a suitable quantity for assessing the response. Geomagnetically induced currents (GICs) flowing through transformers result in a DC bias, which poses a threat to safe and stable power grid operation. To evaluate the influence of geomagnetic storms on power systems, it is necessary to study actual power system responses. In this paper, the power system response is represented separately using current and reactive power measurements acquired using phasor measurement units (PMUs). On the basis of the data obtained from a wide-area measurement system, the power system responses to geomagnetic disturbances at Yangquan Substation, Shanxi, China were obtained. By employing the plane wave method, the data acquired during two mild magnetic storms at the Shisanling Geomagnetic Observatory were used to calculate the electric field and GIC at Yangquan Substation. The response variables and calculated GIC results agreed well, and the response variables were thus applied to assess the GIC level in the power system. The results show that the fluctuations in the PMU measurements were caused by GICs in the power system, laying the foundation for a significantly more detailed geomagnetic storm risk monitor. [ABSTRACT FROM AUTHOR]

• A novel algorithm is proposed to detect ramp events in databases with different resolutions. • The proposed algorithm is able to deal with bad data points which previous methods cannot handle. • The detecting efficiency and accuracy is greatly improved by the proposed algorithm. • The proposed algorithm can be employed to provide accurate information about system flexibility for ancillary services. The flexibility of the power system gets more attention of power system operators due to the high penetration of renewable energy sources. This leads to the need of the ramp detection. Based on the detection results of ramp events (REs) in historical and forecast databases, the assessment of flexibility requirements in the power system can be significantly improved. Thus the system operator is able to procure minimum ancillary services and design flexible ramping products. Furthermore, the accuracy of the ramp forecasting can be increased. In the mean time, the rapid development of measuring facilities in the power system, such as the WAMS and the SCADA system, requires a robust detecting method to detect ramp events in the databases with different data properties and resolutions of the power system. This paper proposes a Parameter and Resolution Adaptive Algorithm (PRAA) to identify REs fast and accurately. A fast search method that can process raw databases with tens of millions of data points in seconds is proposed to merge adjacent REs with the same ramp direction. The bad data processing method is directly integrated into the proposed algorithm. A post-processing method is developed to increase the accuracy and robustness of the proposed algorithm. The raw wind power data sets from Bonneville Power Authority (BPA) and a real-world power system in China are utilized to validate the performance of the proposed PRAA. [ABSTRACT FROM AUTHOR]

• A model to evaluate the impact of EV charging loads on cable is presented. • Impacts on cable ampacity, derating factor and temperature are analyzed. • The impacts are analyzed in typical charging scenarios and EV penetration. To meet the rapidly growing electric vehicle (EV) charging demand, it is important to determine maximum ampacity and avoid overloading of Medium Voltage (MV) cables in power distribution systems. Previous works on modelling and analysis of relationship between the load demand of MV cables and EV penetration level did not consider the contribution of harmonic currents. This paper presents a methodology for modelling and analysis of the impact of EV charging loads on cable ampacity and temperature in presence of different levels harmonic currents under two typical scenarios of EV charging modes, i.e., uncontrolled domestic charging scenario and uncontrolled off-peak domestic charging. The proposed method determines the ampacity and temperature of three-core cable taking account for cable geometry, harmonic levels, stochastic nature of the charging power, start time of individual charging, seasonal temperature under different EV penetrations. A generic urban distribution network with residential load ratio of 0.7, which is representative of urban residential distribution network in China, is adopted as an example. Results show that the ampacity of a 400 mm2 10 kV cross-linked polyethylene (XLPE) cable decreased by 10.79 A and 2.03% when the harmonic level is at the maximum allowed limit as given in IEC 61000-3-2. Meanwhile, the ampacity decreased by 23.34 A and 4.09% at the maximum limit given in IEC 61000-3-4. In presence of 36.15% and 41.66% total harmonic distortion current which are the most serious harmonic distortion in the example network with 30% EV penetration, cable ampacity decreased by 42.86 A, 8.06% and 61.35 A, 11.35% respectively. [ABSTRACT FROM AUTHOR]

• A parallel horizon splitting (PHS) method for long time horizon TCUC is proposed. • An optimality gap can be provided by using the PHS method. • Numerical results for one year horizon TCUC problems show the method is efficient. Long-term transmission constrained unit commitment (LT-TCUC) is an important tool in generation expansion planning and has got much attention in recent years. When traditional Lagrangian relaxation (LR) and mixed integer programming (MIP) based methods are used to solve LT-TCUC problems, serious challenges related with the solution efficiency are encountered. This paper proposes a fast method for solving TCUC with long time horizon based on horizon splitting. The basic idea is to partition the whole horizon into several short time intervals, and then relax the coupling constraints between consecutive intervals (ramp rate constraints and minimum up/down time constraints). Sub-TCUC problem in each time interval is then solved independently (in parallel) with CPLEX and the solutions are further adjusted based on MIP to satisfy the relaxed coupling constraints. In this way, the feasibility of the solution is guaranteed and the solutions to the sub-TCUC problems are connected. Salient features of the method include that the solution efficiency is guaranteed and the solution quality can be estimated based on the optimality gap. The proposed method is tested on the modified IEEE 24-bus system, 118-bus systems, and a regional grid in China with one-month or one-year time horizon. Numerical results show that the solution method is efficient. [ABSTRACT FROM AUTHOR]

Timely, effective, and robust artificial intelligence (AI) technology is urgently needed to improve decision-making efficiency in the presence of renewable energy with high penetration by elevating the level of power grid intelligence. However, at this stage, AI technology lacks reliability and transparency, making it unable to play a full role in application areas with high-security requirements such as power systems. This paper presents a multi-hierarchical interpretable method for power system dispatch and operation based on the graph deep Q-network (GDQN) model to achieve active power corrective control. The multi-hierarchical interpretable method combined with an improved sample-balanced deep shapley additive explanation (SE-DSHAP) method and a subgraph explainer can promote a more intuitive and comprehensive explanation of decision-making for power systems with complex topology. Operators will obtain more noteworthy power grid areas through the proposed interpretable method as the basis of auxiliary decision-making to realize efficient and accurate control. Then, two cases studied in a modified 36-bus system in the IEEE 118-bus system and the 300-bus network of the China power grid validate the effectiveness of the proposed method. • A graph deep Q-network model achieve active power corrective in power systems. • The SE-DSHAP method improve the computational speed and accuracy. • A subgraph explainer bring a comprehensive understanding of the model predictions. • Two cases validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• Combined with the error reconstruction rate, an adaptive VMD technology is developed and used for data preprocessing. • Two mainstream deep learning methods (LSTM, DBN) are used as sub-series predictors, and the classic PSO algorithm is utilized to determine the number of neurons in the DBN network. • A novel hybrid prediction model based on the PSO-DBN nonlinear combination mechanism is proposed. Wind power forecasting plays a vital role in enhancing the efficiency of power grid operation and increasing the competitiveness of power market. In this paper, a novel hybrid forecasting model is developed by using the decomposition strategy, nonlinear weighted combination, and two deep learning models to overcome the drawbacks of the linear weighted combination and further enhance wind power forecasting accuracy and stability. Firstly, the variational mode decomposition (VMD) technique is employed to decompose the original wind power series to extract local features. Then, the long short-term memory neural networks (LSTM) and deep belief networks based on particle swarm optimization (PSO-DBN) are utilized to construct sub-series prediction models. Finally, the multiple sub-series forecasting models are integrated by a nonlinear weighted combination method based on PSO-DBN to construct a hybrid model for short-term wind power forecasting. To verify the performance of the developed forecasting model, wind speed data of 10 min from a wind farm in Shaanxi Dingbian, China are selected as case studies. The results show that the proposed method in this paper is more effective than other existing methods. [ABSTRACT FROM AUTHOR]

• The MLM and DAM is coupled and connected by electricity decomposition. • The W-T alliance is conducive to peak load regulation of the system. • The joint participation of W-T alliance in the market can reduce wind curtailment. • The joint participation of W-T alliance in the market increases economic benefits. In 2015, "Several Opinions on Further Deepening the Reform of the Power System" was issued. The new round of power system reforms proposes to give full play to the vitality of the power generation-side bidding market, which will help to establish a fair, reasonable, and active power market. Due to factors such as a lack of peak shaving capacity and imperfect market mechanisms, China's renewable energy participation in the power market faces many problems. To solve these, this paper proposes a two-level optimization model for a wind power plant and thermal power unit to participate in the medium and long-term electricity market, and day-ahead market transactions. First, the paper proposes the electricity quantity and electricity price determination method of annual bilateral negotiated transactions, monthly centralized bidding transactions and listings, and delisting transactions, and briefly describes how to decompose contract electricity into the day-ahead market in the medium and long-term market. Then, two ways for the wind power plant and thermal power unit to participate in the power market are proposed. With maximization of the market benefits as the objective function, and through comprehensive consideration of the system reserve, wind curtailment penalty, green certificate trading, and other issues, the two models of independent participation and joint participation in market transactions were established to study the problem of maximum profit on the power generation side. Finally, the analysis results of the calculation example show that the joint participation of the wind power plant and thermal power unit in the power market has additional benefits compared to independent participation. The wind power plant can complete the assessment index of the renewable energy quota for the thermal power unit, which does not need to purchase green certificates to complete the assessment indicators. The thermal power unit can provide reserve services for the wind power plant, avoiding output whenever necessary, reducing the cost of the curtailment penalty, and overcoming the threat of wind power plant output fluctuations to the system. The two alliances have greater profit margins. [ABSTRACT FROM AUTHOR]

• The prediction method for voltage sag events is guided by the classical fuzzy time-series prediction method. • This paper proposes a data preprocess method to eliminate redundant monitoring data and obtain better prediction performance. • This method introduces Fuzzy C-Means Algorithm to transform monitoring data into interval symbols. • This method builds a fuzzy relation based on hidden Markov to describe the relationship between disturbances and interval symbols. • This method can provide the user with the occurrence time and residual voltage information of the next voltage sag in the future. To inform the power utility and users, and help them reduce the huge financial losses due to voltage sag, it is important to obtain information on voltage sag events in advance. This paper proposes a method for predicting voltage sag characteristics based on fuzzy time series. First, we propose a homologous aggregation method to eliminate redundant data representing the same disturbance event and obtain the time series of voltage sag (TSOVS), which can describe the trend of the voltage sag data. Second, this paper introduces a fuzzification method for the time series of voltage sag based on the fuzzy c-means algorithm (FCMA), which transforms the time series of voltage sag into a fuzzy time series composed of interval symbols, to characterize the mapping relationship between the disturbance and voltage sag event. Furthermore, a hidden Markov model (HMM) of voltage sag is constructed to reveal the transformation relationship among elements in the fuzzy time series, considering the causal relationship between the disturbance and voltage sag event. Finally, the occurrence time and residual voltage of the voltage sag in the future were predicted based on this transformation relation. The measured voltage sags in a province in central China were used to verify the accuracy of the proposed method, prediction results with an accuracy of up to 90%. [ABSTRACT FROM AUTHOR]

• Under-excitation state is considered in the study of stator interturn short-circuit (SISC) fault of synchronous condensers. • The change trend of reactive power after SISC in under-excitation state is opposite to that in over-excitation state. • The transformation of the 300Hz harmonic of stator vibration with exciting current in under-excitation running relies on parameters and SISC fault degree of synchronous condensers. • Results are verified in a TTS-300-2 synchronous condenser working in a converter station, China. A stator interturn short-circuit (SISC) fault in synchronous condensers can bring about serious damage if the fault is not detected in time. At present, researches on SISC fault characteristics and diagnosis methods are still scarce because only in recent years are synchronous condensers adopted in power grids. Different from previous studies, this paper considers the practical operation condition of synchronous condensers that need to change exciting current frequently and work in either under-excitation or over-excitation according to the actual demand of power grids. Detailed reactive power and unbalanced magnetic pull (UMP) expressions under SISC are deduced firstly based on the analysis of the magnetic flux density variation. It can be seen that the reactive power increases under under-excitation running and decreases under over-excitation running after SISC. The second, fourth and sixth harmonics of stator UMP/vibration are enlarged under the SISC. Specifically, the sixth harmonic augments with the increase of exciting current in the over-excitation running, while the variation of the sixth harmonic with exciting current depends on parameters and fault degree of synchronous condensers under the under-excitation operation. Moreover, the two-dimensional finite-element analysis (FEA) and experimental tests are performed on a customized synchronous machine. In addition, the FEA of a TTS-300-2 synchronous condenser is accomplished in order to validate the proposed theoretical analysis further. The research conclusions of fault characteristics in this paper provide potential solutions for the monitoring and diagnosis of SISC in synchronous condensers. [ABSTRACT FROM AUTHOR]

[Display omitted] • Proposing the multiple hierarchical and multiple time scales CPS framework of DSRs. • Depicting the energy flow, information flow and capital flow relationships of DSRs. • Proposing a feasible specific modeling approach based on the CPS model of DSRs. • Planning a multiple time scales DSM digital system which run parallelly. The electricity market reform in China stimulates the capacity of demand-side resources regulation and provides a new approach to promote large-scale renewable energy access to power grid. However, there is no effective modeling method to describe the participation of demand-side resources in power grid regulation, which seriously affects the research on the response strategy of demand-side resources under the electricity market environment. Aiming at the problem above, this paper presents a multiple hierarchical and multiple time scales modeling method of demand-side resources based on cyber-physical system theory. First, a multiple hierarchical and multiple time scales modeling framework of demand-side resources based on cyber-physical system theory is proposed. Second, on the basis of the modeling framework, the multi-dimensional variable coupling relationships of the energy flow, the information flow and the capital flow of demand-side resources are analyzed. Finally, an example of feasible modeling method under this framework is presented, and the correctness of the presented modeling method is verified. This paper gives full consideration to the coupling relationship of multiple hierarchies, multiple time scales and multi-dimensional variables of demand-side resources under the power market environment, and proposes a demand-side response resources model based on cyber-physical system theory, which lays a solid foundation for future research on demand-side resources' participation in power grid regulation. [ABSTRACT FROM AUTHOR]

Highlights: • Cover wind speeds and multi-state units in power system reliability assessment. • Infuse correlation structure into cross entropy based importance sampling method. • Positive correlation of wind speeds has adverse impacts on power system reliability. • Positive correlation of wind speed and load improves the power system reliability. Abstract With the rapid integration of wind energy, the increasing uncertainty and high reliable property of power systems have resulted in great difficulties in reliability assessment. To solve the problem, traditional cross entropy based importance sampling (CE-IS) methods are improved in this paper. The improved method is capable of efficiently assessing the reliability of composite power systems with wind energy integrated. First, we introduce the differences between the improved CE-IS (ICE-IS) and traditional CE-IS. Particularly, ICE-IS takes the correlation of random variables (RVs) into account and models multi-state RVs with multinomial distribution. Therefore, ICE-IS can obtain much better suboptimal distributions for the RVs than CE-IS, which accelerates the reliability assessment. Then the procedures of ICE-IS are detailed by two parts, which are a pre-simulation stage and a main simulation stage, respectively. Finally, we modify IEEE-RTS 79 and IEEE-RTS 96 test systems based on wind speed data observed in northwest China. Several case studies are designed and carried out on the modified systems to validate the proposed method. [ABSTRACT FROM AUTHOR]

Protection of dc line faults is a major challenge for the operation of multi-terminal high-voltage dc (MT-HVDC) grids. To avoid blocking converter and system shutdown, ultra-fast protection is required to detect and isolate line fault with the application of hybrid dc circuit breaker. This paper proposes a pilot protection scheme based on improved Hausdorff distance to detect the overhead line fault for MT-HVDC grid. Compared with literature, this protection method owns higher withstanding ability of noise interference, start-up delay and data anomaly. Moreover, the fault segment can be correctly identified as independent of fault location, and protection sensitivity is maintained even under internal ground fault with high impedance of up to 1000 Ω. The issue of dead zone fault recognition is considered and solved in this paper with reasonable sacrifice in the sampling frequency requirement. Based on Zhangbei HVDC grid in China, a four-terminal dc grid model is developed in PSCAD/EMTDC environment for fault simulation, and the data processing is performed in MATLAB platform. The feasibility and effectiveness of this protection scheme are verified by a simulation study that considers different scenarios. [ABSTRACT FROM AUTHOR]

• Typical heating operation mode of hybrid energy storage system is proposed. • Mathematical model of HESS optimal operation is established. • Operation mode of HESS with wind power and electrode operation is proposed. During the heating season in the "Three North" area of China, the wind curtailment has become a serious problem due to the lack of space for grid-connected wind power. Firstly, from the perspective of improving the acceptance capacity of wind power curtailment, this paper establishes a model of hybrid energy storage. Then considering the influence of the frequently changing electrodes of the regenerative electric boiler on its working life, this paper introduces the optimization coefficient of electrodes, and the optimal operation strategy of hybrid energy storage system is put forward. Finally, based on the actual data of 200 MW wind farm, this paper compares and analyzes the advantages and disadvantages of different control methods. The results show that the proposed method can further improve the power system's ability to accept wind power, under the requirements of meeting the heating demand and reducing the number of electrode adjustment of electric boilers. [ABSTRACT FROM AUTHOR]

• The intrinsic capacity of the thermal units in the system peak load regulation is studied on the generation side. • An improved linear UC model considering startup and shutdown trajectories of thermal power units is embedded with the peak load regulation compensation rules. • A real prefecture-level urban power system, and its modified test systems are used to verify the proposed methodology. To balance the peak–valley difference of the system load in electrical power systems, the peak load regulation problem has become a major barrier, resulting in challenges to unit commitment (UC). In addition to the new construction of specific system peak load regulation resources on the generation side and the demand response programs on the demand side, this paper focuses on the capacity of thermal power units in system peak load regulation. Considering the temporal distribution of system load off-peak hours, the potentiality of the deeper peak load regulation mode and the short-time startup and shutdown regulation mode of thermal power units are analysed. This paper presents an optimal scheduling model for power system peak load regulation considering the short-time startup and shutdown operations of a thermal power unit. First, an improved linear UC model considering the thermal units' startup and shutdown trajectories is established. Next, for different peak load regulation modes of thermal units, the corresponding peak load compensation rules are processed and converted into linear formulations. An integrated optimal scheduling model for power system peak load regulation with a suitable rolling optimization strategy is proposed. A real prefecture-level urban power system in southwest China and its modified test systems are used to test and verify the validity and effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Hydropower generation relies heavily on neighboring upstream plants due to the natural cascading structure in hydropower systems. In competitive markets, discharging decisions become more self-interested and opaque, making it difficult for downstream plants to predict inflows and make decisions. To address this scheduling information asymmetry (SIA) problem, this paper proposes a combination approach that consists of three parts: (1) constructing a bilevel model, in which the upper-level searches for optimal parameters and the lower-level model simulates the upstream plant's operating behavior with parameters provided by the upper-level model, (2) adopting the proposed parallel hypercubic searching algorithm (PHSA) to solve the non-convex bilevel model, and (3) accelerating the solving process using a searching domain reduction (SDR) method derived from engineering experience in the three-dimensional case. Case studies on the Lancang River show that the combination approach is efficient, accurate, and robust in estimating operational parameters and historical operating states of the rival's upstream plant. Kruskal-Wallis and Tukey-type tests indicate that the proposed combination approach outperforms alternative methods. • An inverse bilevel model is used to tackle scheduling information asymmetry problem. • The parallel hypercubic searching algorithm is proposed to solve the bilevel model. • A searching domain reduction method is proposed to accelerate the solving process. • Case studies in China indicate the solution is efficient, accurate, and robust. • Kruskal-Wallis and Tukey-type tests show the proposal superior over the alternatives. [ABSTRACT FROM AUTHOR]

• Proposing many-objective optimal power flow for hierarchical partition-managed power system. • Formulating distributed power flow calculation optimization solved by a pattern search algorithm. • Proposing I-NSGA-III with distributed power flow calculation for many-objective OPF problems. It is more and more challenging to obtain the global optimal power flow (OPF) information of a whole system with the developing trend of power systems toward hierarchical partition-managed ones. Traditional centralized OPF problems are not adaptive to modern partition-managed power systems because of the emergence of distributed sources and multi-stakeholders and the protection of their private data. This paper extends the traditional OPF to a many-objective optimization problem which is helpful to the independent power flow calculation (PFC) of the various subregions in partition-managed networks. To address the coordination of various regions, the individual distributed PFC problem is transformed into a whole optimization problem through node tearing and line disconnection methods, and then the pattern search algorithm is employed to address its solution. Then, the distributed PFC is integrated into the improved NSGA-III (I-NSGA-III) approach to solve the entire many-objective OPF (Ma-OPF) for partition-managed power systems. The effectiveness and feasibility of the proposed distributed approach are demonstrated in multiple benchmarks of IEEE 30/39/118-bus systems and a practical partitioned power system in Guizhou Province of China. The results show that the average voltage difference between distributed PFC and centralized PFC is within 1.12% while the phase angle difference is within 0.44%. In the application of OPF problems, the proposed distributed approach shows the high ability to yield approximate solutions of the original I-NSGA-III with centralized PFC. It is also found from the simulation that the distributed approach shows competitive performance compared to other considered centralized PFC-based evolutionary algorithms and weighted interior point method while it does not require complete information about various regions and is beneficial to the privacy protection of multi-stakeholders. [ABSTRACT FROM AUTHOR]

• The transmission line model and its simplified model of pantograph-catenary arc conduction path on the highly insulated ballastless railway are established for the first time. • The theory of finite difference time domain algorithm for solving transmission line model is extended from pure resistance load to more general load type. • The conducted harmonic current and the electromagnetic emission of catenary after the occurrence of pantograph-catenary arc are calculated and verified by actual test, which provides a theoretical basis for electromagnetic environment assessment near the airport. Rail transit entering the airport facilitates people's travel, but it also brings potential electromagnetic interference. In this paper, based on the transmission line model of the arc harmonic conduction current generated by the pantograph and the catenary with the highly insulated ballastless railway, the electromagnetic emission model of the traction network is proposed in the working frequency band of the nondirectional beacon when the train passes through the neutral section of the electric phase separation. And the simplification of the transmission line model is realized by deforming the transmission line equation. Then, the finite difference time domain algorithm is used to solve the distributed current on the transmission line. However, the terminal of the conventional finite difference time domain algorithm can only be a pure resistive load. Therefore, the backward difference and the Implicit Wendroff difference format are used to extend the finite difference time domain algorithm to a more general case. Combined with a real ballastless railway in China, the effectiveness and accuracy of the proposed method are verified. From the perspective of magnetic field attenuation, ballastless railway is easier to pass the electromagnetic environment assessment of the airport than ballasted railway. [ABSTRACT FROM AUTHOR]

• The intrinsic properties of the HVDC transmission line are analyzed. • The fundamental relationship between protection performance and transmission line model is revealed. • An ultra-fast current differential protection for HVDC transmission line is proposed. • The performance of the proposed protection method is verified. Protection of transmission lines is the most important defense against faults in multi-terminal HVDC system. Up until now, no single protection can balance the operation speed and sensitivity needed for all fault types. To solve this problem, a novel current differential protection for HVDC transmission line is proposed in this paper. The intrinsic properties of a HVDC transmission line are analyzed first. Then, essence of ideal current differential protection is revealed and fundamental relations between line models and performance degradation of existing differential protections are discussed. Based on this analysis, a novel current differential protection scheme, including fault detection and faulty pole selection, is developed, and Bergeron model is used to describe the electrical wave propagation process along line, resulting in only simple computations are needed in each calculation cycle. Finally, a simulation model of Wudongde ± 800 kV HVDC project in China is established in PSCAD/EMTDC for verification. By discussing diversified faults and transient processes, simulation results prove that regardless of fault type, distance and transition impedance, the proposed differential protection can respond correctly. Main contribution of this paper is reducing the operation time of differential protection from hundreds of milliseconds to several milliseconds. [ABSTRACT FROM AUTHOR]

• A two-stage AHIC assessment model is proposed, in which the first-stage problem seeks the maximum admissible capacity and the optimal location of the infeed HVDC links, while the second-stage problem checks the adaptability of the produced first-stage decisions on basis of ensuring system security under normal and serious contingency cases, i.e., bi-pole blocking and communication failure of HVDC links. Leveraging this model, a reliable AHIC can be obtained. • A modified multi-infeed operation effective short-circuit ratio (MMOESCR) index based on the voltage sensitivity is formulated to take into account the function of Var reserves at different AC buses in resisting commutation failure (CF) of HVDC links. This metric is coupled into the proposed model to quantify the CF immunity of the receiving-end power grid. • A solution algorithm based on generalized Benders decomposition (GBD) is developed to solve the proposed model. This approach is applied to assess the AHIC of Henan Grid (a large-scale provincial grid in China), which can provide more efficiently future installation plan of multi-feed HVDC links in the targeted AC network. To fulfil the cross-regional bulk power delivery of renewable energy and alleviate the power shortage pressure of load centers, a number of line-commutated converter-based high voltage direct current (LCC-HVDC) transmission projects have been deployed in worldwide. Due to the system-wide operating risk sharply escalated with the intensively infeed of multiple large-capacity HVDC links, a vital knowledge gap exists in understanding the maximal admissible HVDC infeed capacity (AHIC) of receiving-end power grids. To tackle this issue, this paper proposes a novel AHIC evaluation method, which determines the maximal allowed total number and capacity of HVDC links that the receiving-end grid can accommodate while taking into account multi-preconceived fault security requirements. An improved dynamic system strength criterion is developed to assessing the immunity of post-contingency HVDC commutation failures (CF), which creatively considers the support effect from all AC buses based on the voltage sensitivity. The original two-stage model is decomposed into a construction master problem and an operation optimization subproblem, and then being iteratively solved using the generalized Benders decomposition algorithm. The effectiveness of the proposed approach is demonstrated on a modified IEEE RTS-79 system and a practical large-scale system. [ABSTRACT FROM AUTHOR]

• The arc is yellow during the flashover of snow-accreted insulator in the artificial climate chamber. While in the natural environment, the discharge arc appears blue and purple, and the phenomenon of arc levitation and snow layer shedding can be observed. • The flashover path of FXBW-35/70 is affected by the snowing degree significantly. The discharge path gradually transitions from the creepage distance under slight snowing to the dry arc distance in the case of severe snowing. • There are many discrepancies between different types of the insulators, such as the creepage distance, dry arc distance and other structural parameters, and the number of air gap after snowing, which affect arc dynamic development properties. Hence, the electrical performance of composite insulator is better than that of glass insulator. The safe and reliable operation of power system in China is significantly affected by snow-covered insulator. At present, many studies on snow-accreted insulator have been undertaken by domestic and foreign scholars in the laboratory. However, whether the laboratory test is equivalent to the field test and whether it can be directly used to guide the external insulation design of transmission line still need to be evaluated. In this regard, AC flashover test of snow-coated insulator was carried out in the laboratory and field in this paper, and then the characteristics of AC discharge path and the variation of leakage current of snow-accreted insulator with time were obtained, and its flashover performance was compared and analysed. The research results show that the amount of snow on the insulator gradually decreases from the stagnation point at the edge of the shed on the windward side to both sides, which is attributed to the uneven distribution of the local collision coefficient of snow crystal particles on the insulator surface. The arc is yellow during the flashover of snow-accreted insulator in the artificial climate chamber. While in the natural environment, the discharge arc appears blue and purple, and the phenomenon of arc levitation and snow layer shedding can be observed. For composite insulator FXBW-35/70 covered with different degrees of snowing in the natural environment, the flashover path develops along the creepage distance in case of the slight snowing, while the path propagates over the dry arc distance under severe snowing condition. Consequently, the flashover voltage decreases by 48.7%. There are many discrepancies between different types of the insulators, such as the creepage distance, dry arc distance and other structural parameters, and the number of air gap after snowing, which affect arc dynamic development properties. Hence, the electrical performance of composite insulator is better than that of glass insulator, and the snowing flashover voltage of the former is 5.56–9.47% higher than that of the latter. [ABSTRACT FROM AUTHOR]

• A two-layer optimization frame to estimate and improve the net profit of BESSs in the whole life cycle. • The semi-empirical degradation model of lithium-ion batteries and economic models of BESSs are embedded into the optimization frame. • The optimization frame applies to different lithium-ion batteries. • The optimal configuration and operation varied from the types of lithium-ion batteries. Battery energy storage systems (BESSs) have been widely employed on the user-side such as buildings, residential communities, and industrial sites due to their scalability, quick response, and design flexibility. However, cell degradation is caused by the charging and discharging of batteries, which reduces the economy of BESSs. For the optimal configuration and operation of BESSs, the battery degradation process should be integrated into the optimal problem by considering the equivalent full cycles over the optimization time horizon. This paper proposes a two-layer optimization frame to estimate and improve the net profit of BESSs in the whole life cycle, the outer layer optimizes the rated capacity and power of BESSs, and the inner layer optimizes the daily operation strategy of BESSs. To this end, the semi-empirical degradation model of lithium-ion batteries and economic models of BESSs are embedded into the optimization frame. Particle swarm optimization (PSO) algorithm and fmincon toolbox of MATLAB are adopted to solve the two-layer frame to maximize the net profit of BESSs. Simulation results of the BESS for a typical industrial user in China demonstrate that the proposed frame can effectively improve the net profit of BESSs. [ABSTRACT FROM AUTHOR]

• Gauss-Hermite integral based multi-point estimation method is proposed to calculate the probabilistic transient stability criterion derived through the PDSR. • To guarantee the calculation accuracy and speed at the same time, the optimal number for the sampling points is determined based on PDSR hyperplanes and improved particle swarm optimization algorithm (IPSO). • Extreme learning machine (ELM) is introduced to describe the uncertainty of practical wind power output. • Calculation accuracy of the proposed method is higher than other commonly-used point-estimation methods without much loss of computational efficiency under different wind power penetration and correlation levels. With the increasing penetration of wind power, probabilistic transient stability assessment becomes critical to deal with the uncertainties and correlations of wind power, thus improving the reliability of power system. This paper proposes an improved multi-point estimation based probabilistic transient stability assessment method. Extreme learning machine (ELM) is introduced to describe the uncertainty of practical wind power output. Gauss-Hermite integral based multi-point estimation method is proposed to calculate transient stability criterion derived through practical dynamic security region (PDSR). Optimal number of the sampling points is determined based on PDSR hyperplanes and an improved particle swarm optimization algorithm (IPSO). Test results on the New England 10-generators 39-buses system and a practical provincial power system in China show that the calculation accuracy of the proposed method is higher than that of other commonly-used point-estimation methods without much loss of computational efficiency under different wind power penetration and correlation levels. [ABSTRACT FROM AUTHOR]

The residential air conditioners (RACs) are increasing rapidly in urban power systems and have been widely considered as good regulation resources for improving the system flexibility and resiliency. However, in practical power systems, it is difficult to comprehensively acquire millions of RACs' operating data and buildings' thermal data, which makes the available regulation capacity of RACs tricky to evaluate. To address this issue, this paper proposes a Gaussian Mixture Model (GMM)-based evaluation method by utilizing partial easily observable data. First, a control framework of large-scale RACs is developed to provide regulation services for the power system. Based on the thermal–electrical models of RACs and buildings, a quantification method of the available regulation capacities is proposed under the premise of guaranteeing all the users' comfortable indoor temperatures. Considering the practical condition of insufficient data acquisition of large-scale heterogeneous RACs, a GMM-based evaluation method is designed to calculate the probability of semi-info RACs' expected regulation capacities by sampling a small portion of full-info RACs' characteristics. Moreover, the Expectation Maximization Algorithm and the Bayesian Information Criterion are employed to optimize the multi-dimensional parameters and the component number of the GMM, which significantly improve the evaluation accuracy with lower complexity. The proposed models and methods are verified in a demonstration project on demand response in China. • Controlling large-scale air conditioners for providing regulation services. • A Gaussian Mixture Model based regulation capacity evaluation method is proposed. • Expectation Maximization Algorithm and Bayesian Information Criterion are employed. • The regulation capacity can be evaluated with over 98% accuracy. [ABSTRACT FROM AUTHOR]

The rapid development of solar and wind power, with their inherent uncertainties and intermittency, pose huge challenges to system stability. In this paper, a grid-connected hybrid power system that fully utilizes the complementarity characteristics in hydro, solar and wind power sources is proposed, which is capable of realizing an economic, managerial, social and environmental equilibrium in daily generation scheduling. A multi-objective optimization model is then proposed to determine the best possible solutions under nine typical scheduling scenarios, with ɛ -constraint method and fuzzy satisfying approach applied. A real case from the large-scale demonstration project in Qinghai, China clearly proved that the grid-connected hydro–solar–wind power system can provide significant comprehensive benefits. Specifically, on sunny days of normal season, the highest economic benefits can reach 1205820 × 1 0 4 CNY , consumer surplus is up to 6760 × 1 0 4 CNY , and the carbon emissions are as low as 159 × 1 0 3 tonnes. Meanwhile, hydropower, preferred as a flexible renewable power source, can effectively conduct the peak regulation, making the residual loads deviation lowest of 740.447 MWh. Future studies can focus on the long-term perspectives, further analyze the storage technology, demand response and carbon taxes. • A grid-connected hydro–wind–power generation system fully considers the generation complementarity between the different power sources. • The multi-objective scheduling optimization is developed implementing the economic-managerial-social-environmental trade-offs. • Nine typical scheduling scenarios with trapezoidal fuzzy numbers are established to handle renewables uncertainties. • Reliability analyses for each power source under different scenarios are conducted. [ABSTRACT FROM AUTHOR]

• A DRL based approach is applied to adaptive control of STATCOM-ADC parameters. • An ANN-based estimator is proposed for system identification. • The proposed novel control agent is adaptive and robust. • An online application strategy is proposed to avoid frequent parameter adjustment. Due to the fluctuation characteristics of the wind turbine (WT) production, the design of the control system needs to ensure the stability of the power system at different wind speeds. In this context, a WT-oriented adaptive robust control agent for a static synchronous compensator having additional damper controller (STATCOM-ADC) is proposed in this paper. An artificial neural network based estimator for system identification which can identify the equivalent transfer function of the whole system in real time is used in this paper. Then a Deep Deterministic Policy Gradient (DDPG) algorithm is adopted to train the agent on learning the adaptive robust control strategy for STATCOM-ADC. Compared with other control strategies, the proposed method has modelled self-learning abilities, and the parameter settings provided by the agent for one operation state of the system are still applicable to other states. Simulation results on the actual wind farm located in China Ningxia province show that the proposed method can enhance the stability of the system under different fault conditions and wind speeds. [ABSTRACT FROM AUTHOR]

• Transfer function matrix from mechanical disturbances to mechanical powers is found to be, inherently, diagonally dominant. • Power spectral density of responses of non-source generators is predictable with generator terminal voltage. • Large mismatch between predicted and measured spectral density indicates the presence of oscillation source. • The method is able to locate oscillation sources to control devices. The location of the control devices that produce forced oscillation sources is crucial to the elimination of forced oscillations. This paper proposes two frequency domain approaches to reliably locate oscillation sources from mechanical parts and excitation systems of generators to their control devices. Firstly, the transfer function matrix from mechanical disturbances to mechanical powers is demonstrated to be inherently diagonally dominant. The property is used to monitor and preliminarily locate oscillation sources from mechanical parts. Moreover, this paper proposes a systematic method of power spectral density prediction to further locate oscillation source to control device by using the generator terminal voltage (or generator current) to predict the power spectral density of generator responses. It is shown that for non-source generators, the responses are determined by the generator terminal voltage. The generator with distinct mismatch between predicted and measured power spectral density is identified as the oscillation source. The mismatches between predicted and measured power spectral density of different responses are different, further enabling the method to identify the control device producing the oscillation source. The method of power spectral density prediction is used as a general method to provide further and detailed location results. Demonstrative examples of a 39-bus system and the 547-machine 8647-bus North China Power Grid show the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]