Search

Urban power grids are facing many operational and expansion challenges to meet further demand growth and increased reliability requirements. Advanced transmission technologies have been considered by the electric utilities to effectively increase the utilization of existing infrastructure and operational flexibility. The focus of this paper is on VSC-HVDC technologies for urban power grid enhancement and modernization. First, a potential technical scheme is proposed for converting an existing AC circuit to DC operation, which could boost the power transfer capability of the critical transmission corridor and increase network operational flexibility. Second, this paper proposes three operation modes for the VSC-HVDC interties in urban power grids corresponding to normal, emergency and island operating conditions, respectively. An integrated, adaptive emergency control strategy (AEC) is proposed that can enable adaptive power flow responses of the VSC-HVDC intertie under varying system operating conditions and critical contingencies. The flexibility and effectiveness of the proposed operational principles of urban VSC-HVDC intertie and the corresponding control strategies are verified in PSCAD/EMTDC using a realistic urban power grid in China. [ABSTRACT FROM AUTHOR]

High renewable energy penetration increases the electricity seasonal imbalance in the long-term timescale. Power system planning needs to consider the optimal configuration of various flexibility resources and electricity balance in different timescales. The coupling of multiple timescales largely increases the computation complexity of the power system planning problem. Thus, this paper presents an efficient source-grid-storage co-planning model which incorporates a year-round hourly operation simulation. To improve the computation efficiency of the planning model, from the temporal scale, a self-adaptive compact panorama time series (CPTS) model is applied, which greatly reduces the number of variables related to short-term decisions. From the spatial scale, a network-constrained relaxed clustered unit commitment (NC-RCUC) model is introduced, which significantly reduces the number of variables related to unit commitment decisions. Case studies on the modified Garver’s 6-node system and HRP-38 system prove the validation and efficiency of the proposed model (“HRP” stands for high renewable penetration). The studies on the China power grid in 2035 demonstrate the future planning results of generation, transmission and storage in China power systems based on the proposed model. [ABSTRACT FROM AUTHOR]

Construction of the linearized state-space model is the key step of eigenvalue analysis for SSO study of power systems with large-scale renewable power generation (RPG) integrated. This paper proposes a novel method to construct the state matrix of the AC/DC hybrid power system with RPG within the discrete-time domain. The continuous-time state-space model of each AC/DC component as well as RPG units in the power system, is discretized together with their corresponding control. The discretized equation of each component is then represented by an equivalent circuit consisting of parallel historical current and conductance. According to the power system configuration, the discrete-time equivalent circuit of the whole AC/DC hybrid system is established, and the nodal analysis method is applied for the construction of the discrete-time state-space model of the power system. In this way, the state matrix can be acquired much more conveniently than that of continuous-time state-space method, which needs topology analysis to find independent state variables of the network from the proper tree. The proposed method has been applied in the eigenvalue analysis for the SSO study of a case system adapted from the AC/DC hybrid transmission system in northwest China. Eigenvalue results are in high accordance with time-domain simulation results in PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

The high penetration of renewable energy will substantially change the power system operation. Traditionally, the annual operation of a power system can be represented by some typical operation modes and acts as the basis for the power-system-related analysis. The introduction of highly penetrated renewable energy will make the power system operation mode highly diversified and variable. These modes may not follow traditional empirical patterns. In this paper, we propose a data-driven method based on high-dimensional power system operation data (including power flow, unit generation, and load demand) to identify the pattern of the operation modes and analyze the impact of high renewable penetration. Specifically, the proposed data-driven method is composed of simulation, preprocessing, clustering, dimension reduction, and visualization with the aim to provide an intuitive understanding of the operation mode variety under high renewable penetration. In addition, several indices are introduced to quantify the space dispersion, time variation, and seasonal consistency of operation modes. A case study on actual Qinghai provincial power system in China validates the effectiveness of the proposed data-driven method and indicates that the dispersion and time variation of operation mode will significantly increase in the beginning and then saturate with the increase in renewable penetration level. The operation mode is also less correlated with seasons in renewable energy dominated power system. [ABSTRACT FROM AUTHOR]

Oscillations have been observed in wind farms with weak grid interconnections. While Texas observes 4 Hz low-frequency oscillations, the west region in China observes subsynchronous oscillations at 30 Hz. Furthermore, this oscillation mode caused torsional interactions with a remote synchronous generator and led to shutdown of the power plant. Inspired by those real-world events, this paper aims to present an analytical model of type-4 wind in weak grids that can demonstrate both low-frequency and subsynchronous frequency oscillations. Critical factors, e.g., the parameters of the phase-locked loop (PLL), are examined using small-signal analysis. The analysis results are validated using a testbed built in MATLAB/SimPowerSystems. Except power electronic switching sequences, the testbed has controls (e.g., wind turbine pitch control, maximum power point control, converter controls), machine dynamics and power system dynamics modeled. This testbed includes a 100 MW Type-4 wind farm, a 600 MW synchronous generator, a long transmission line and a grid. The testbed successfully demonstrates two types of dominant oscillations under different PLLs. In addition, the testbed demonstrates torsional interactions due to the proximity of the subsynchronous mode and one of the torsional modes. [ABSTRACT FROM AUTHOR]

While many machine learning approaches have been widely applied to power system online dynamic stability assessment, how to sufficiently learn spatial-temporal correlations from system transients without losing the interpretability is still a challenging issue. In this paper, a novel networked time series shapelet learning approach is proposed to learn spatial-temporal correlations for transient stability assessment (TSA) in an interpretable manner. Specifically, a network impedance-based adjacency matrix is first introduced to characterize spatial networked correlations. Based on graph structural regularization, this matrix is effectively incorporated into the subsequent learning procedure as spatial constraints. Taking time series trajectories acquired from multiple buses as the inputs, networked shapelet learning is heuristically performed to learn critical sequential features, i.e., networked shapelets, for TSA model derivation. With the learning procedure strategically guided by inherent spatial-temporal correlations of the system, the obtained data-driven TSA model is able to perform highly reliable and interpretable online TSA. Numerical test results on the IEEE 39-bus test system and the realistic GD Power Grid in China verify the superior performances of the proposed approach. [ABSTRACT FROM AUTHOR]

The uncertain nature of renewable energy resources (RERs) and fast demand response lead to recurring voltage violations in the power systems, which causes frequent transformer tap shifting and capacitor switching. Therefore, this paper resorts to the static var compensators (SVCs) to manage the bus voltages based on the multi-agent deep reinforcement learning (MA-DRL) algorithm. The proposed scheme includes several system agents and SVC agents to collaboratively adjust the injected reactive power to restrict the bus voltages within the normal range. All the agents are trained centrally and executed separately, which requires minimum communication cost. The IEEE 14-bus system, IEEE 300-bus system, and China 157-node urban power grid are used to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The rotational inertia and primary frequency response capabilities of power systems are declining as synchronous generators (SGs) are replaced by inverter-based variable renewable energy (VRE) resources. Therefore, when a high VRE proportion is present, it becomes essential to include frequency security constraints in power system planning. Under this circumstance, the decline in SGs would deteriorate the system frequency response performance; thus, inverter-based resources are supposed to provide frequency response support. This paper presents a frequency-constrained stochastic planning method for high VRE share systems. Frequency response support from wind farms is considered in the planning process for the first time, and the unique characteristics of wind power support are addressed. Specifically, the uncertainty of and variations in wind power support capabilities are modeled from the field-measured data, and the optimal combination of various support schemes can be determined by introducing binary variables with novel linear constraints into the planning process. Moreover, the nonlinear frequency constraints are transformed and embedded in the planning model by the proposed computationally efficient adaptive piecewise linearization method. The planning is formulated as a stochastic optimization model to consider the VRE power uncertainty. Finally, case studies on the IEEE 30-bus system validate the proposed method, with results indicating time savings of 22%. Further, compared with the case that only SGs provide frequency support, the results based on the East China system demonstrate that considering wind power support can improve the VRE share from 49.5% to 79.7%. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of how to effectively smooth the short-term tie-line power fluctuations for grid-connected industrial microgrids (IMGs) from demand side. A wide-area closed-loop control architecture is proposed by directly and continuously modulating load power consumption, e.g., the aluminum electrolysis load. To optimally design the load smoothing controllers, an output regulation approach combined with LQ optimal control is developed. In particular, dominant frequencies of wind power fluctuations are analyzed and incorporated into the exogenous system that models time-varying disturbances. Simulations are conducted for a real industrial microgrid in the Inner Mongolia, China, to validate the practical feasibility and effectiveness of the proposed smoothing control strategy. It is demonstrated that the demand side control based on output regulation is able to achieve not only zero steady error in rejecting the time-varying wind power disturbance but also guarantee the satisfactory dynamic performance of modulated loads for the grid-connected IMG. [ABSTRACT FROM AUTHOR]

Due to the geographic features and fruitful water resources, the distributed generation of Small Hydropower Generator Stack (SHGS) are vigorously developed in southwest China. As the more concerns about security and stability of power grids with the large amount of SHGS feed into the grid, the accurate models are critical to analyze those potential impacts. With the development of online monitoring technologies in smart grid, PMU based dynamic equivalent modeling for SHGS drawn many attentions. However, field applications reveal that traditional SHGS equivalent model may not be robustness enough to adapt the different disturbances. To overcome this deficiency, method to improve the robustness of SHGS dynamic equivalent modeling is proposed. In the paper, a coherency identification based multi-machine modeling method is introduced to enhance the accuracy of equivalent model. Sensitivity and correlation analysis based key parameters selection scheme is implemented to select the critical parameters, meanwhile avoiding the multiple solutions problem. Moreover, multi-objective optimization algorithm is designed for key parameters identification of the equivalent model. Additionally, hybrid dynamic simulation has adopted into the equivalent process to improve the computational efficiency. The effectiveness of the proposed method has been validated with an actual case from China Southwest grids. [ABSTRACT FROM AUTHOR]

With the advantage of quick response and flexible ramp, energy storage system (ESS) offers a promising capability of fast frequency control for power systems, especially under a severe disturbance. This paper proposes an ESS control strategy using local measurement in order to provide fast frequency support right after a sufficiently severe disturbance is observed. The proposed strategy includes online frequency characteristic estimator and online optimization controller. Distinguished from existing approaches, no prior knowledge of power system model is required in this work, including generator configuration, operation condition, and frequency response of each component. Instead, the overall system frequency characteristic is approximated online by the proposed estimator. Then, the power setpoint of ESS is determined adaptively to participate in frequency control by the proposed model predictive controller. Finally, the effectiveness of the proposed method is verified via real-world data of a provincial power grid in China. [ABSTRACT FROM AUTHOR]

This paper develops a hierarchical deep learning machine (HDLM) to efficiently achieve both quantitative and qualitative online transient stability prediction (TSP). For the sake of improving its online efficiency, multiple generators’ fault-on trajectories as well as the two closest data-points in pre-/post-fault stages are acquired by PMUs to form its raw inputs. An anti-noise graphical transient characterization technique is tactfully designed to transform multiplex trajectories into 2-D images, within which system-wide transients are concisely described. Then, following the divide-and-conquer philosophy, the HDLM trains a two-level convolutional neural network (CNN) based regression model. With stability margin regressions hierarchically refined, it manages to perform reliable and adaptive online TSP almost immediately after fault clearance. Test results on the IEEE 39-bus test system and the real-world Guangdong Power Grid in South China demonstrate the HDLM's superior performances on both stability status and stability margin predictions. [ABSTRACT FROM AUTHOR]

Accurate and reliable low-voltage load forecasting is critical to optimal operation and control of distribution network and smart grid. However, compared to traditional regional load forecasting at high-voltage level, it faces tough challenges due to the inherent high uncertainty of the low-capacity load and distributed renewable energy integrated in the demand side. This paper proposes a novel hybrid ensemble deep learning (HEDL) approach for deterministic and probabilistic low-voltage load forecasting. The deep belief network (DBN) is applied to low-voltage load point prediction with the strong ability of approximating nonlinear mapping. A series of ensemble learning methods including bagging and boosting variants are introduced to improve the regression ability of DBN. In addition, the differencing transformation technique is utilized to ensure the stationarity of load time series for the application bagging and boosting methods. On the basis of the integrated thought of ensemble learning, a new hybrid ensemble algorithm is developed via integrating multiple separate ensemble methods. Considering the diversity in various ensemble algorithms, an effective K nearest neighbor classification method is utilized to adaptively determine the weights of sub-models. Furthermore, HEDL based probabilistic forecasting is proposed by taking advantage of the inherent resample idea in bagging and boosting. The effectiveness of the HEDL method for both deterministic and probabilistic forecasting has been systematically verified based on realistic load data from East China and Australia, indicating its promising prospective for practical applications in distribution networks. [ABSTRACT FROM AUTHOR]

Demand-side management (DSM) increases the complexity of forecasting environment, which makes traditional forecasting methods difficult to meet the firm's need for predictive accuracy. Since deep learning can comprehensively consider various factors to improve prediction results, this paper improves the deep belief network from three aspects of input data, model and performance, and uses it to solve the short-term load forecasting problem in DSM. In the data optimization stage, the Hankel matrix is constructed to increase the input weight of DSM data, and the gray relational analysis is used to select strongly correlated data from the data set. In the model optimization stage, the Gauss-Bernoulli restricted Boltzmann machine is used as the first restricted Boltzmann machine of the deep network to convert the continuity feature of input data into binomial distribution feature. In the performance optimization stage, a pre-training method combining error constraint and unsupervised learning is proposed to provide good initial parameters, and the global fine-tuning of network parameters is realized based on the genetic algorithm. Based on the actual data of Tianjin Power Grid in China, the experimental results show that the proposed method is superior to other methods. [ABSTRACT FROM AUTHOR]

Frequency drop due to loss of massive generation is a threat to power system frequency stability. Under-frequency load shedding (UFLS) is the principal measure to prevent successive frequency declination and blackouts. Based on traditional stage-by-stage UFLS scheme, a new continuous UFLS scheme is proposed in this paper to shed loads proportional to frequency deviation. The characteristic of the proposed scheme is analyzed with a closed-form solution of frequency dynamics. Frequency threshold and time delay are added to make the proposed scheme practical. A line-by-line scheme based on precise load control is introduced to implement the continuous scheme for systems without enough continuously controllable loads. The load shedding scale factor of the proposed scheme is tuned with an analytical method to achieve adaptability to different operating conditions. The adaptability of the proposed scheme is validated with 39-bus New England model and simplified Shandong Power Grid of China. [ABSTRACT FROM AUTHOR]

Machine learning approaches have exhibited high potential in power system transient stability assessment (TSA), yet their initial preparation stages of stability knowledge base generation (SKBG) based on time-domain simulations often undergo high computational costs. In fact, how to ease the heavy computational burden of SKBG without sacrificing the reliability is still a significant challenge. To address this problem, this paper develops a semi-supervised ensemble learning (SSEL) framework for reliable SKBG acceleration, without additional need for computing hardware upgrading. In particular, it performs detailed simulations for a minority of cases and fast simulations for the majority ones to reduce the total computation time. Considering the absence of stability status information of those fast simulated cases, an SSEL scheme is systematically designed to reliably label their stability status. Before implementing SSEL, two concise feature descriptors are first introduced to efficiently extract transient features from multiplex system trajectories. Then, all the cases are characterized in a unified feature space, whereby a series of semi-supervised support vector machines are trained in randomly formed subspaces. Afterward, these single machines are systematically combined to derive an enhanced SSEL model, which is able to make reliable and robust labeling decisions. Further, a backtrace strategy is carefully devised for SSEL, so as to maintain the high reliability of SKBG. Test results on the IEEE 39-bus system and the realistic GD Power Grid in South China illustrate the excellent performances of the proposed framework on SKBG acceleration. [ABSTRACT FROM AUTHOR]

Power system planning is essential for ensuring development of a safe and stable power system. Articles on power system planning have been published in recent decades. In this paper, bibliometric analysis is applied to power system planning to identify its basic characteristics, and to summarize the research hotspots and trends. A total of 4562 publications were obtained from the Web of Science during 1971–2020. The 4562 publications cover 8136 keywords, 1526 journals, 107 countries/territories, and 2519 institutions. IEEE Transactions on Power Systems is the key journal in power system planning; USA, China, and England are the countries that dominate publication production. Research hotspots and trends were analyzed using author-keyword co-occurrence analysis and keyword burst detection. Four clusters were identified and topics from Cluster I (central nodes as power system stability and transient stability) and Cluster IV (central nodes as power system simulation and economics) are important for power system planning research. However, their relative popularities have declined over the past two decades. This indicates that Cluster II (central nodes as renewable energy, wind power, and energy storage) and Cluster III (central nodes as distributed generation and smart grids) will be the focus of future research. [ABSTRACT FROM AUTHOR]

With the sequential Monte Carlo simulation (sMCs), the duration of the system state is obtained by the difference of the state sequences of related equipment. The state probability is not the explicit function of the reliability parameters. Therefore, it is difficult to derive the sensitivity directly. A new sensitivity model for the sMCs is proposed in this paper, which is based on the derivative of the state duration to the reliability parameters, and the sensitivity model is applied to the AC/UHVDC system. The states and durations of the related equipment from the beginning of the simulated years to the generated time of the system state are considered and differentiated to describe the derivative. To obtain the sequence of the UHVDC line, the possible reached state, mean duration, and probability for each state transition are considered. By combining the sensitivity model of the UHVDC line itself, the equivalent transition rates are applied to connect the system state duration and reliability parameters of the UHVDC components. The numerical results based on a provincial system in China are provided to validate the feasibility and accuracy of the proposed model. [ABSTRACT FROM AUTHOR]

Converting wind energy into ammonia (WtA) has been recognized as a promising pathway to produce “green” ammonia compared with traditional coal-based technologies. As the key part of WtA, Power-to-Ammonia (PtA) has great potential to facilitate the usage of wind generation. This paper proposes a co-planning approach for regional wind resources-based ammonia industry and the electric network (EN). To this end, PtA is first modeled as a flexible power load of power systems with spatial and temporal constraints on hydrogen supply chains (HSC). Then a novel co-planning model of WtA and EN is established to optimize the WtA configuration and the EN expansion. An alternating direction method of multipliers (ADMM) based algorithm is introduced to effectively solve this model. Real data of Inner Mongolia Province in China is adopted to verify the effectiveness and significance of the proposed approach. It is shown that the siting and operation flexibility of PtA with HSC can reduce the expansion burden of EN. The co-planning of WtA and EN can significantly enhance wind power utilization and reduce total investment costs. Furthermore, feasibility analysis on WtA in comparison with coal-to-ammonia (CtA) and ultra-high voltage transmission (UHV) provides helpful guidelines for the realization of WtA. [ABSTRACT FROM AUTHOR]

With the largest installed capacity in the world, wind power in China is experiencing a \sim 20\% curtailment during operation. The large portion of the generation capacity from inflexible combined heat and power (CHP) is the major barrier for integrating this variable power source. This paper explores opportunities for increasing the flexibility of CHP units using electrical boilers and heat storage tanks for better integration of wind power. A linear model is proposed for the centralized dispatch for integrated energy systems considering both heat and power, with detailed modeling of the charging processes of the heat storage tanks. The model balances heat and power demands in multiple areas and time periods with various energy sources, including CHP, wind power, electrical boilers, and heat storage. The impact of introducing electrical boilers and heat storage systems is examined using a simple test system with characteristics similar to those of the power systems in Northern China. Our results show that both electrical boilers and heat storage tanks can improve the flexibility of CHP units: introducing electrical boilers is more effective at reducing wind curtailment, whereas heat storage tanks save more energy in the energy system as a whole, which reflect a different heating efficiency of the two solutions. [ABSTRACT FROM PUBLISHER]

In practical power systems, it is still very challenging to figure out a cost-effective undervoltage load shedding (UVLS) scheme that can reliably and adaptively react to the short-term voltage stability (SVS) problem. Faced with this challenge, this paper develops an intelligent data-driven predictive UVLS scheme for online SVS enhancement. Inspired by valuable ideas in model predictive control and supplementary excitation control in power systems, a novel deep feedback learning machine (DFLM) is designed to precisely predict future voltage violations after UVLS execution. With the help of the DFLM, the UVLS scheme is aware of potential effects of various candidate UVLS actions. Owing to this desirable nature, it can adaptively respond to diverse SVS conditions and optimize UVLS decisions in a non-iterative way. Further, two well-designed strategies, i.e., stepwise constraint relaxation and incremental DFLM adaptation, are introduced to enhance the scheme's reliability and adaptability during online application. Numerical test results on the Nordic test system and the realistic North GZ Power Grid in China showcase the excellent performances of the proposed scheme. [ABSTRACT FROM AUTHOR]

Time delays are inevitable in the power systems with wide-area control signals, which may compromise the control pe-rformance and thus jeopardize the stability of the power systems. To analyze the impacts of system parameters on critical oscillation modes of a time-delayed power system (TDPS), a new method for eigenvalue trajectory tracking of TDPS is proposed in this paper based on the continuation theory of invariant subspaces. When small changes are imposed on system parameters, such as parameters of wide-area damping controllers (WADCs) and time delays, the invariant subspaces are continuously predicted and corrected, followed by the eigenvalues and eigenvectors. In the process, the predictor and corrector are computed by solving linear equations sparsely and in blocks, which is highly scalable and computationally efficient for large-scale power system applications. The 16-generator 68-bus test system and two real-world power grids in China are used to test and demonstrate the proposed method. The results show that the proposed method can accurately and efficiently track eigenvalue trajectories for TDPS, especially for large-scale power grids. In addition, the sensitivities of eigenvalues and eigenvectors with respect to WADCs’ parameters and time delays are obtained as by-products of the approach. [ABSTRACT FROM AUTHOR]

With the increasing development of AC/DC hybrid power systems, local synchronous generators in receiving-end grids are being replaced by high-voltage direct current (HVDC) power. The risk of short-term voltage instability exists because of the decline in dynamic reactive power resources from generators, the presence of high-proportioned induction motors and the demand for vast reactive power of HVDC under AC faults. In this situation, unit commitment (UC) should take short-term voltage stability into account. Hence, crucial generators can be retained to provide sufficient dynamic reactive power. This paper proposes a UC model incorporating short-term voltage stability constraints (SVSCUC). Furthermore, a decomposition-based solution approach is presented, which involves an alternate iterative solving of UC and differential-algebraic equations (DAEs). Time-domain simulation (TDS) is utilized to quantify the stability with an indicator. Linear stable cuts are formulated through trajectory sensitivity analysis and added into the UC. The UC is decomposed into a master problem based on mixed-integer linear programming (MILP) and subproblems. The SVSCUC is illustrated using benchmark systems and a real system in eastern China. The results show the effectiveness and efficiency of the proposed methods. Violations of short-term voltage stability for credible contingencies are eliminated under the commitment of SVSCUC. [ABSTRACT FROM AUTHOR]

The irregular forbidden zone (FZ) is a common phenomenon with giant hydropower plants developed in the past two decades in China. Irregular shapes of FZs significantly challenge hydro unit commitment (HUC). This paper proposes a novel MILP based framework for HUC considering irregular FZ related constraints including the FZ constraint, effects of linearization errors in both the net head and the output, and the FZ crossing constraint. In the framework, the FZ constraint is handled by the optimal convex partitioning algorithm and the common structure-based formulation method. Inspired by the planar translating robot placement problem, linearization errors are considered by the Minkowski sum method. To handle the FZ crossing constraint, we then propose a graph theory-based approximate formulation method. The framework is integrated into a HUC model with an objective of peak shaving. The model is then tested with a batch of real-world instances of a cascade hydropower system formed by ten giant units with highly irregular FZs. The results show our framework can effectively consider the irregular FZ related constraints. The major advantage of our framework is its ability to handle the irregular shapes of FZs without any tedious and error-prone manual processing. [ABSTRACT FROM AUTHOR]

A batch of huge hydropower plants with multiple vibration zones have been developed in China. These vibration zones varying with net head, i.e., head-sensitive, seriously challenge the hydro unit commitment (HUC). This paper presents a MILP model for HUC with a head-sensitive reservoir and multiple vibration zones. The focus of the study is mainly on two net head related constraints, namely the unit performance curves and multiple vibration zones. These constraints are linearized through the use of binary 0-1 integer variables. The practical behavior of the MILP model is demonstrated using real-world case studies. The results demonstrate that the proposed model is computationally efficient and suitable for large hydropower plants with multiple vibration zones, showing its potential practicability and validity for solving the HUC problem. [ABSTRACT FROM PUBLISHER]

Currently, the increasing wind power penetration, with consequent randomness and variability, presents great challenges to power system planning and operation. Probabilistic power flow (PPF) has been developed to calculate the power flow under uncertain circumstances. However, the current wind power models are subject to specific probability distributions, limiting their accuracies in wider applications. Additionally, the cumulant method (CM)-based PPF, if nonlinear relationship is considered in, would face an impractically high computational complexity. To address these problems in modeling and cumulant calculation, this article proposes a novel generalized density/distribution fitting method (GDFM) combining with the Copula function to establish a joint probability model for wind power generation. A special impulse- mixed probability density (IMPD) integration method is also introduced to derive the input cumulants from the model. Finally, a fast cumulant method (FCM) is proposed to reduce the computational burden of output cumulant calculation while retaining a high accuracy in a nonlinear context. Case study on the IEEE-118 test system validates the effectiveness of the proposed methods, and a real application to a provincial power grid in China provides some useful power flow risk information for decision making. The whole FCM-based PPF scheme can be helpful for future power flow examination in power system planning and operation. [ABSTRACT FROM AUTHOR]

In the analysis of cascading outages and blackouts in power systems, risky cascading fault chains should be accurately identified in order to do further block or alleviate blackouts. However, the huge computational burden makes online analysis difficult. In this paper, an online search method for representative risky fault chains based on reinforcement learning and knowledge transfer is proposed. This method aims at promoting efficiency by exploiting similarities of adjacent power flow snapshots in operations. After the “representative risky fault chain” is defined, a framework of tree search based on Markov Decision Process and Q-learning is constructed. The knowledge in past runs is accumulated offline and then applied online, with a mechanism of knowledge transition and extension. The proposed learning based approach is verified on an illustrative 39-bus system with different loading levels, and simulations are carried out on a real-world 1000-bus power grid in China to show the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

To make better use of local generators' dynamic var reserve to improve short-term voltage stability (STVS), this paper proposes an integrated high side var-voltage control (IHSV^2C) for power plants in receiving-end power systems. The IHSV^2C consists of a plant-level multi-machine var coordinator (MMVC) and several unit-level high side voltage controllers (HSVCs). MMVC coordinates the reactive power output among generators and provides control parameters for HSVCs, while HSVCs can maintain the voltage of a pre-defined voltage control point (VCP) by regulating the voltage reference of the excitation control of each generator. Therefore, when the system suffers a serious fault, IHSV^2C can drive the generators to provide stronger var support. Consequently, the voltage stability can be improved. A conceptual model of the receiving-end power system in China Southern Power Grid (CSG) is established, and the IHSV^2C has been validated through a simulation analysis on the system. Both time-domain simulation results and the voltage sag severity index (VSSI) have fully demonstrated its performance. Therefore IHSV^2C offers a new and effective approach to improve STVS of receiving-end power systems. [ABSTRACT FROM AUTHOR]

This paper presents an approach for integrated generation and transmission maintenance scheduling model (IMS) that takes into consideration N-1 contingencies. The objective is to maximize the maintenance preference of facility owners while satisfying N-1 security and other constraints. To achieve this goal, Benders decomposition is employed to decompose the problem into a master problem and several sub-problems. A Relaxation Induced (RI) algorithm is proposed to efficiently solve the large mixed integer programming (MIP) master problem. This algorithm is based on the solution of the linear relaxed problem. It is demonstrated that the proposed algorithm can efficiently reach a near-optimal solution that is usually satisfactory. If this near-optimal solution is not acceptable, it is used as the initial solution to fast start the solution of the original IMS problem. The performance of the proposed method is demonstrated using a modified version of the IEEE 30-bus system and a model of the power system of a Chinese province. Case studies show that the proposed algorithm can improve the computational efficiency by more than an order of magnitude. [ABSTRACT FROM AUTHOR]

This paper proposes a resilience enhancement strategy for power transmission system against ice storms by the optimal coordination of power system schedule with the pre-positioning and routing of mobile dc de-icing devices (MDIDs). A two-stage robust optimization model is established to accommodate the variable ice thickness on transmission lines. The first stage coordinates the pre-positioned MDIDs and unit commitment in day-ahead. These decisions, which are based on a robust approach, can accommodate the variable ice thickness in which the coordinated real-time schedule would always be feasible with respect to day-ahead decisions. At the second stage, the real-time operation, which integrates the power system dispatch, de-icing schedule, and MDID routing, is scheduled according to the real-time ice thickness. Auxiliary variables are adopted to convert the proposed nonconvex nonlinear model to a mixed-integer second-order cone programming (MISOCP) problem. The nested column-and-constraint generation algorithm is utilized to solve the two-stage robust MISOCP problem. Several computational enhancement strategies including Lagrangian relaxation are proposed to improve the performance of the proposed resilience enhancement strategy. Numerical results for an integrated 6-bus 6-node electricity-road network and a real-world example employed in China show the effectiveness of the proposed model and solution technique for enhancing the transmission grid resilience. [ABSTRACT FROM AUTHOR]

Due to the uneven geographical distribution between load and wind power, currently it is difficult to integrate the large-scale wind power into the State Grid in China. There are significant wind curtailments for wind farms due to system restrictions. This paper proposes a new way of utilizing wind power, i.e., in-situ consumption, as an alternative to the costly development of long-distance transmission of large-scale wind power. Electrolytic aluminum load, which is one of the most typical high energy consuming loads, is employed to absorb the excess wind power in western China. An actual isolated industrial power system for aluminum production driven by wind power and coal-fired power is described. The penetration level of wind power in the isolated power system is up to 48.8%. The power imbalance between generation and load demand caused by wind power fluctuation or the tripping of coal-fired generators can dramatically impact the frequency stability of the isolated power system, which is of small inertia. An online identification method of power imbalance based on Wide Area Measurement System (WAMS) is presented. According to the characteristics of the electrolytic aluminum load, a system frequency control method by regulating the bus voltages of aluminum loads to eliminate the power imbalance is introduced. The simulation is done in Real Time Digital Simulator (RTDS) and the results verify the validity of the proposed frequency control method. [ABSTRACT FROM PUBLISHER]

With the largest installed capacity in the world, wind power in China is experiencing a ∼20% curtailment. The inflexible combined heat and power (CHP) has been recognized as the major barrier for integrating the wind source. The approach to reconcile the conflict between inflexible CHP units and variable wind power in Chinese energy system is yet unclear. This paper explores the technical and economic feasibility of deploying the heat storage tanks and electric boilers under typical power grids and practical operational regulations. A mixed integer linear optimization model is proposed to simulate an integrated power and heating energy systems, including a CHP model capable of accounting for the commitment decisions and nonconvex energy generation constraints. The model is applied to simulate a regional energy system (Jing–Jin–Tang) covering 100-million population, with hourly resolution over a year, incorporating actual data, and operational regulations. The results project an accelerating increase in wind curtailment rate at elevated wind penetration. Investment for wind breaks even at 14% wind penetration. At such penetration, the electric boiler (with heat storage) is effective in reducing wind curtailment. The investment in electric boilers is justified on a social economic basis, but the revenues for different stakeholders are not distributed evenly. [ABSTRACT FROM AUTHOR]

Recently, sustained power oscillation at subsynchronous frequency was captured in direct-drive permanent magnetic synchronous generator (PMSG) based wind farms in Xinjiang Uygur Autonomous Region, China. This new type of subsynchronous interaction (SSI) detected in practical systems has never been reported and analyzed before. Therefore, its mechanism and characteristics are not yet clearly clarified. In this paper, a simplified but representative system model with multiple PMSGs interfaced with AC networks is established first based on the actual system and the PMSG model provided by the manufacturer. Then, small-signal eigenanalysis, time-domain simulation, and impedance model analysis are carried out to investigate the interactive dynamics between them. The results show that such interaction between direct-drive PMSG wind farms and weak ac grids characterized by low short-circuit ratio would cause negative-resistance effect for the SSI mode, leading to unstable oscillation. In such cases, the controller of PMSG would saturate soon, resulting in sustained power oscillation in the system. If unfortunately the oscillation frequency matches the torsional mode of any nearby turbogenerator, severe torsional vibration would be excited on the shaft of the latter. The analysis results are finally validated with field measurements of an actual SSI event. To address the problem, a supplementary subsynchronous damping control loop is attached to the controllers of PMSGs to reshape the impedance and thus to stabilize the SSI. [ABSTRACT FROM PUBLISHER]

In this study, a locally weighted linear regression (LWLR) method is proposed to predict damping ratio of a dominant mode online. The LWLR method, which is nonparametric and data-oriented, is essentially proposed for nonlinear data fitting; therefore, it can track the nonlinear power system operations and help damping ratio prediction in real power systems, which is hardly achieved by the conventional linear regression. To successfully implement this method, the measurement of weighting value and the choice of weighting function as well as its parameter setting, related to prediction accuracy and numerical conditions, are extensively discussed. Simulations are carried out in a two-area four-machine system and a large complex system, China Southern Grid. Both results validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In order to accurately and effectively diagnose the transmission line faults of the power system, a genetic-algorithm support vector machine and the D-S evidence theory based fault diagnostic model is proposed. Two genetic-algorithm support vector machine based diagnosis boxes separately identify the fault types to get two kinds of preliminary diagnostic results according to the voltage and current phasors data from the phasor measurement units. Then, a deviation coefficient is derived to represent the diagnostic conflicts existing in the two kinds of preliminary diagnostic results. The mass function of the D-S evidence theory is updated by using the deviation coefficient to eliminate the diagnostic conflicts and improve the diagnosis accuracy. Finally, the proposed diagnostic model was applied in an actual power system in Hubei province in China. The simulation results and practical results indicated the feasibility and the effectiveness of the diagnostic model. [ABSTRACT FROM AUTHOR]

A frequency-dependent network equivalent (FDNE) is essential to capture wide-band frequency dynamics in the hybrid simulation of large-scale modular multi-level converter based ac–dc grids. The FDNE model must be enforced to be passive, ensuring the numerical stability in time-domain simulations. However, existing passive enforcement techniques based on global optimization perturbations cannot guarantee convergence, accuracy, and efficiency simultaneously. To address the issues, a two-layer FDNE (T-FDNE) model is developed for the ac grids. The two layers, namely, detailed layer and equivalent layer, have their admittances derived by perturbation test and analytical approach, respectively. The passivity of the T-FDNE model is guaranteed by the proposed local passivity compensation technique using auxiliary rational functions. Since no optimization is required and the passivity is enhanced locally, the convergence, accuracy, and efficiency can be improved considerably. By incorporating the T-FDNE model into the interface model of transient (TS) and electromagnetic transient hybrid simulations, wide-band frequency interactions, especially those of very high frequency, can be reflected effectively. The performance (efficiency and accuracy) of the T-FDNE model as well as of the hybrid simulation method has been validated on a modified and practical ac–dc system in China. [ABSTRACT FROM AUTHOR]

In modern transmission systems, duplicated protections are widely employed to enhance reliability, and global positioning system (GPS) substation clocks are set up to deliver a higher degree of synchronization. Thus, redundant alarms tagged with synchronized timestamps would be received during a fault. Both redundancy and timestamps of alarms consist of rich and useful information. Given this background, an effort is made to develop a novel analytic model for power system fault diagnosis so as to well utilize redundancy and temporal information of alarms. An alarm preprocessing procedure is proposed to preserve such properties without impairing diagnosis accuracy and efficiency. A new fault hypothesis is established to systematically take account of malfunctions of protective devices, missing and distorted alarm messages, and timestamp errors in the proposed model. An objective function is developed for fault diagnosis, and could accommodate various protection schemes and bus configurations. Based on the presented fault diagnosis method, a software package is developed. Several fault scenarios occurred in an actual power system in China are served for demonstrating the correctness and efficiency of the developed software package. [ABSTRACT FROM PUBLISHER]

Three-phase unbalance could have significant impacts on the secure and economic operation of a low-voltage distribution system. With the deployment of smart meters and the emergence of phase switch devices (PSDs), more efficient schemes for mitigating three-phase unbalance are expected by utilizing numerous measurement data and the installed PSDs in low-voltage areas. Therefore, a bi-level optimal phase switch device placement model for mitigating three-phase unbalance is proposed in this work. In the upper-level model, the PSD placement strategy is optimized by minimizing the total costs of PSD installation and operation costs of low-voltage areas determined in the lower-level model. In the lower-level model, the operation costs of low-voltage areas for typical days are minimized based on the PSDs and their locations determined in the upper-level model. The proposed bi-level model is tested by an actual low-voltage area of Zhejiang province, China, and the effectiveness of the optimal PSD placement model for reducing the total investment and operation costs of the low-voltage area demonstrated. [ABSTRACT FROM AUTHOR]

Electricity theft behaviors have caused great harm to the economic benefits of power companies and the secure operation of power systems, thus electricity theft detection is paid much attention in the actual power supply management. In this work, a two-step electricity theft detection strategy is proposed to identify electricity theft users and predict potentially stolen electricity (PSE) for maximizing economic return. In the first step, a neural network model called convolutional autoencoder (CAE) is proposed for electricity theft identification, and the convolutional layer is adopted in CAE to extract and identify the abnormalities of electricity theft users against the uniformity and periodicity of normal power consumption features. In the second step, the PSE of each identified electricity theft user is predicted by the improved regression algorithm named Tr-XGBoost, which combines the extreme gradient boosting (XGBoost) algorithm and transfer adaptive boosting (TrAdaBoost) training strategy. The propsoed Tr-XGBoost could learn the relationship between the extracted electricity features and the PSE of each electricity theft user, and then the predicted PSE can be used to determine the list of electricity theft users to be inspected for maximizing economic return. Case studies on both the IEEE 33-bus test system and a low-voltage distribution system of a province in China show that the proposed two-step electricity theft detection strategy can improve the accuracy of electricity theft identification, and obtain a larger economic return because of a more accurate result of PSE prediction than other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Deep learning is considered to be a promising method for total transfer capability (TTC) evaluation due to its high speed and accuracy for nonlinear and complicated problems. However, the operating scenarios of power systems are complex and ever changing. It is difficult to train a single deep model using an overly complex dataset containing operating scenarios with high variability. In addition, making the deep model transparent to validate for operators is another challenge to its application in the power system. To enable the practical application of deep models in TTC evaluation, the operating sample space is clustered into simpler spaces via a two-stage clustering method, and the idea of neighborhood model for the deep TTC evaluation network (DTEN) is proposed. Then, the local post hoc interpretability for the DTEN based on first-order control variable sensitivity is presented. During the interpretability analysis, the quasi-steady state is introduced to assess the coupling effects among different inputs. Case studies on the IEEE 39-bus system and a real-world regional system in China validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Robust look-ahead dispatch (RLAD) is essential to manage uncertainties in power systems. As its key step, the worst-case scenario identification (WCSI) problem is cast to a max-min program. This computationally intensive procedure has to be performed repeatedly, impairing the computational efficiency of RLAD. To address this issue, an efficient RLAD scheme incorporating critical region preparation in gap time is proposed. The computation burden is mostly transferred from the online decision stage to the gap time via a customized technique based on multi-parametric programming. The accuracy of computing the critical regions is validated by the test in a six-bus system, and the proposed method is shown to cut down 21% of total iterations and save computational time dramatically by the test in a practical-scale system in Northeastern China. [ABSTRACT FROM AUTHOR]

An energy deviation settlement (EDS) mechanism has been proposed to settle the deviation between consumption and energy contracts in China's forward electricity market. Due to high EDS costs of electricity retailers, a deviation mutual insurance (DMI) mechanism is put forward, representing an option for electricity retailers to reduce the deviation through contract transfer. Given this background, an optimal alliance strategy is proposed to maximize the utility of different types of individual electricity retailers. The alliance income model considers the EDS cost reduction and the cooperation cost analyzed based on transaction cost theory (TCT). An improved income allocation method, which considers distribution indexes selected by game theory and resource dependence theory (RDT), is proposed to ensure the comprehensive and reasonable allocation of alliance income. Considering the expected income and financial risk, an evaluation model using conditional value at risk (CVaR) as the financial risk assessment indicator is presented to measure the stability of alliances. Simulations based on data from a provincial electricity market in China illustrate that the EDS costs of retailers can be reduced by selecting appropriate partners for alliance. In addition, the proposed alliance income allocation method is more reasonable and more targeted than the cooperative game-based methods. [ABSTRACT FROM AUTHOR]

Unbalanced operation of a three-phase distribution system could incur more power losses, compared with the balanced operation. So far, the phase information of users in low voltage areas is not recorded by power utilities and the three-phase unbalance degree is very significant in some low voltage distribution systems. Given this background, this letter presents an integrated method for solving related issues including user phase identification based on spectral clustering and three-phase unbalance mitigation, and a Mixed Integer Linear Programming (MILP) model is then formulated. Some actual cases in Zhejiang province, China, are utilized to verify the effectiveness of the presented phase identification algorithm, and simulation results show that the three-phase unbalance can be significantly mitigated. [ABSTRACT FROM AUTHOR]

This letter proposes a novel method to solve the environmental/economic dispatch (EED) problem with DC power flow constraints. A parameterized KKT condition is introduced to characterize the Pareto front. Then Nash's bargain criterion is employed to determine a compromise solution on the Pareto front. The proposed method is applied to the EED of a regional power grid in China. Numerical studies demonstrate that it is valid and promising. [ABSTRACT FROM PUBLISHER]