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Highlights • A method is proposed to extract the instantaneous oscillation factors. • The proposed method has a higher accuracy, compared with signal analysis methods. • A method is proposed to study the interaction of multi-eigenmodes. • Some complex phenomena can be explained by the proposed methods. • The validity and universality of the proposed method have been strictly clarified. Abstract Low-frequency oscillation is usually regarded as a small disturbance problem. Traditional methods describe oscillation through eigenvalues obtained by linearized equations at the equilibrium point. Obviously, nonlinear time-varying factors are ignored. This article introduces a trajectory section eigenvalue method that can reflect the influences of complex models and fault scenarios. Based on this theory, a trajectory reconstruction method is proposed to accomplish the decoupling of multi-eigenmodes in the time domain. On the premise that the numerical integration is accurate, the trajectory section eigenvalue and trajectory reconstruction methods can be used to study the dynamics of the oscillation and interaction of multi-eigenmodes. Simulation test results show the validity of the proposed methods. The mechanism of low-frequency oscillation is illustrated by trajectory section eigenvalues and trajectory reconstruction methods from a new perspective. These methods can be used as effective tools for theoretical research and engineering applications in power systems. [ABSTRACT FROM AUTHOR]

Highlights • A stability analysis method is proposed in consideration of fault intervals. • The proposed method chooses the most unstable interval. • The method captures the periodicity of stable margin variations with intervals. • The mechanism of the periodicity is analyzed in consideration of time-varying factors. • The proposed method is compared with common assumptions in cascading failure TSA. Abstract Cascading failure analyses are mainly based on simultaneous or quasi-steady-state (QSS) assumptions, which are inappropriate in cases of rapid cascading failure analysis and control. Fault interval is considered in the stability analysis of cascading failure for the first time in this article. Based on the extended equal area criterion (EEAC), a transient stability analysis method is proposed for cascading failure in consideration of intervals. The results show the periodicity of stable margin variations with intervals. The conclusions are validated in a Hamiltonian system, and the effects of time-varying factors are analyzed. The OMIB case shows that it may be over-optimistic to assess stability levels under simultaneous or QSS assumptions. The validity of all conclusions in this paper is verified with the New England system and with an actual power system. [ABSTRACT FROM AUTHOR]

The overall performance of emission trading (ET), a market-based emission regulation tool, strongly relies on participants' participation and responses. In order to improve market design, it is important for policy makers to understand the participants' trading behaviors in different market environments. However, human behaviors cannot be easily modeled with conventional analytical methods due to its “bounded rationality” characteristics. In this paper, based on the complementary features between experimental and agent-based computational methods, a hybrid interactive simulation methodology is proposed to solve human behaviors related problems. Human-subjected experiment based on European Union Emissions Trading System price data in 2006 is conducted, the results show that there is no fixed emission trading interval for generation companies, and the strategic behaviors of market participants are observed. Major driving factors of emission trading are categorized into emission price, emission quantity and time related factors, which are in accordance with empirical analysis results on EU ETS 2005–2006 transaction dataset. Furthermore, more human-subjected experiments are conducted under different emission price scenarios to obtain samples for quantitative analysis. Based on thousands of samples obtained, the joint influences of driving factors on emission trading behaviors are analyzed. The quantitative analysis results obtained can reflect the trading patterns of human participants, which provide basis for constructing computer agents that can act as useful substitutes for human participants. [ABSTRACT FROM PUBLISHER]

The intensification of global climate change has led to a widespread consensus on carbon reduction, with the power industry being the principal contributor to carbon emissions necessitating an inevitable transition of its energy structure. Concomitantly, the escalating frequency of natural disasters caused by extreme weather presents formidable challenges to both secure power supply and the low-carbon transition of the power system. To fulfill the developmental requirements of a low-carbon power system and address the carbon emission risks imposed by natural disasters, this paper proposes a bi-level model for generation expansion planning (GEP) that incorporates constraints on the carbon emission trajectory and the influence of natural disasters. The planning-level model optimizes investment costs of various generation technologies and energy storage (ES), as well as the overall operational expenses over the planning period, with an objective to minimize them. It incorporates carbon emission trajectory constraints and policy constraints, such as carbon peaking, carbon neutrality and renewable energy (RE) penetration rates, in order to optimize the planning installed capacity of power sources. The operational-level model aims to minimize typical daily operating costs while also simulating power unit outputs in routine and disaster scenarios. A case study is conducted in a disaster-prone province in southern China to analyze the power generation expansion planning and the trajectory of carbon emissions from 2020 to 2060 under different scenarios. The simulation results show that compared to thermal power, the planning scheme mainly focused on RE with ES is better suited to achieve the goal of a low-carbon transition of the power grid. Moreover, after considering natural disasters, the cost and carbon emissions of power system planning are higher, and the risk of carbon emissions increases with the severity of disasters. • A multi-time scale bi-level low-carbon GEP model is proposed. • Natural disaster scenarios and low-carbon policy constraints are considered in the GEP model. • Carbon emission trajectory of the planned power system is estimated and introduced as a constraint into the GEP model. • Evaluation indicators that comprehensively consider low-carbon performance and economic feasibility are proposed. [ABSTRACT FROM AUTHOR]

Many countries have set power system emissions reduction goals. However, in developing the electricity‑carbon system, regulators may prioritize final targets while overlooking the planning of pathways. The paper aims to develop a simulation framework for the long-term interactive evolution of electricity‑carbon systems and optimize a developing trajectory. To begin, the electricity system is modeled as a daily spot market over 365 days, and simulated by fast unit commitment (FUC) method. Then, considering the internal multi-player gaming, the multi-class mean field game (MMFG) theory is introduced to simulate the carbon market. Subsequently, a state transition equation for the electricity‑carbon evolution is formulated based on the concept of trajectory optimization from optimal control theory. Here, the regulator can steer the evolution by adjusting the carbon emission intensity benchmark (CEIB) in the carbon market. Finally, employing the Twin Delayed Deep Deterministic Policy Gradients (TD3) technique, the problem characterized by high-dimensional state space and continuous action space is efficiently solved. The effectiveness of the proposed method is examined by case studies on a provincial-scale grid with over 200 units, where the optimal CEIB can be achieved within a second and the control precision of trajectory evolution can be limited to 2%. • Meanfield based simulationfor continuous carbon tradingamongmulti-classgenerators. • Multi-year evolution trajectoryframeworkfor interactive electricity-carbon systems. • Efficient resolution of complexelectricity-carbon trajectory optimization. [ABSTRACT FROM AUTHOR]

Containment control is an important issue in the consensus problem of multiagent systems (MASs). This article presents an output-based containment control strategy for a class of nonaffine nonlinear MASs with uncertainies and directed topology. With the help of differential homeomorphism transform and the idea of active disturbance rejection control (ADRC), a nonaffine nonlinear MAS is transformed into an affine one with uncertainties. Two filters with extended states in each follower are designed with the idea of extended state observer to reconstruct the states of the transformed MAS. Then, uncertainties of the MAS are compensated with the help of the estimations of the extended states. Moreover, the derivative of the virtual control signal in the backstepping technique is replaced by a tracking differentiator (TD), overcoming the so-called “explosion of complexity” problem. By means of Lyapunov theory, the containment errors of the followers are proven to converge to a small neighborhood around the origin via an appropriate choice of parameters. Simulation examples are provided to demonstrate the proposed control strategy. [ABSTRACT FROM AUTHOR]

This note is concerned with bandwidth allocation-based switched dynamic triggering control under DoS attacks and time delay. To prevent DoS attacks from causing open-loop unstable operation of the system with single-channel transmission, we present a primary-redundancy (PR) communication structure on the basis of limited bandwidth allocation; the correlation of communication delay bound between PR channels is introduced. To save the limited bandwidth, we propose a dynamic the event-triggered mechanism (DETM). Then, a switched delay system model is established to describe system dynamic driven by primary channel control, redundancy channel control, and unstable operation under DoS attacks. Further, by using the convex combination method, a switching law for PR channel is designed with the aim of keeping the concealment of the redundancy channel. With the switching law, a criterion of exponential stability is obtained by using piecewise Lyapunov–Krasovskii functional method. A co-design method is proposed to obtain feedback control gains, DETM parameters and switching law parameters. Finally, two simulation examples are illustrated to verify the validness of our proposed method. [ABSTRACT FROM AUTHOR]

The acceleration of distributed energy resources and carbon pricing policies have compelled utilities to act and to prioritize carbon-constrained infrastructure augmentation in their capital programs. To implement various carbon emission reduction policies, power system transmission planning has become more challenging. The existing energy system will face massive retirement of coal-fired power plants (CFPPs), large scale integration of renewable energy and network expansion. In this paper, an electricity network planning and transformation roadmap, which has two milestones, is put forward. In the first stage, a mathematical model is proposed based on the average cost of carbon emission reduction to realize the cooperation of CFPPs retirement and renewable energy investment. It can help the network carry out the transition from a fossil-fuel dominated system to a low-carbon oriented system. Because of the promising prospect of power-to-gas (P2G) technology, in the second milestone, a method based on carbon emission flow (CEF) is employed to help the power-to-gas stations (P2GSes) to select the construction site and capacity. The gas network constraints are modeled to guarantee that P2GSes can work smoothly without energy flow congestion in both electricity and gas networks. According to the simulation results in case studies, our method can reach the emission reduction target more economically and effectively, and the P2GSes can produce and absorb clean energy. [ABSTRACT FROM AUTHOR]

With the development of energy markets and the introduction of renewable energy certificates (RECs), a regional integrated energy system (RIES) may participate in an energy wholesale market flexibly. In this paper, a bilevel optimization problem is formulated to determine the optimal management strategies of an RIES, including trading in the markets of electricity, natural gas, and RECs. In the bilevel model, an energy supplier (ES) purchases energy from energy wholesale markets and sets retail prices of energy products at the upper level to maximize its profit. At the lower level, various user aggregators (UAs) determine individual energy consumption strategies. The clearing prices of electricity, natural gas, and REC markets are determined asynchronously at the lower level. This nonlinear problem is then transformed into a mixed-integer linear programming problem using the incremental method and strong duality theory, which enables the problem to be solved efficiently. Finally, case studies are carried out to demonstrate the feasibility and effectiveness of the proposed method and the impacts of the RECs. [ABSTRACT FROM AUTHOR]

Abstract Conventional unit commitment is a mixed integer optimization problem and has long been a key issue for power system operators. The complexity of this problem has increased in recent years given the emergence of new participants such as large penetration of plug-in electric vehicles. In this paper, a new model is established for simultaneously considering the day-ahead hourly based power system scheduling and a significant number of plug-in electric vehicles charging and discharging behaviours. For solving the problem, a novel hybrid mixed coding meta-heuristic algorithm is proposed, where V-shape symmetric transfer functions based binary particle swarm optimization are employed. The impact of transfer functions utilised in binary optimization on solving unit commitment and plug-in electric vehicle integration are investigated in a 10 unit power system with 50,000 plug-in electric vehicles. In addition, two unidirectional modes including grid to vehicle and vehicle to grid, as well as a bi-directional mode combining plug-in electric vehicle charging and discharging are comparatively examined. The numerical results show that the novel symmetric transfer function based optimization algorithm demonstrates competitive performance in reducing the fossil fuel cost and increasing the scheduling flexibility of plug-in electric vehicles in three intelligent scheduling modes. Highlights • A new unit commitment model considering electric vehicles is established. • A binary symmetric based hybrid meta-heuristic method is proposed. • The impact of transfer function on unit commitment problem is evaluated. • Three flexible scheduling modes are comparatively studied. [ABSTRACT FROM AUTHOR]

With the ever-increasing penetration level of renewable generation sources, a modern power system is facing more inevitable uncertainties that could lead to weakly damped oscillations. Detecting coherency among synchronous generators is one of the key steps of situational awareness for a given power system with a very high level of renewable penetration. In this paper, a wide-area measurement system (WAMS) based coherency detection algorithm employing the kernel principal component analysis (KPCA) and clustering analysis based on affinity propagation (AP) is proposed for a power system with extensive penetration of renewable generation sources. First, several trajectory similarity indexes are presented for determining the similarity between the trajectories of any two generators in the center of inertia coordinate. Second, a KPCA-based method is presented to integrate the trajectory similarity indexes for addressing the correlations among multiple indexes. Next, the AP-based clustering analysis method is utilized to detect the coherency among synchronous generators without the need of prespecifying the number of clusters. Finally, Southern China power system and a part of northern China power system with Zhangbei wind farms included, both with very high levels of renewable generation penetration, are utilized to demonstrate the proposed WAMS-based coherency detection methodology, and the application to actual Guangdong power system in south China to verify the applicability and practicality. [ABSTRACT FROM AUTHOR]

Identification of coherent generators (CGs) in power systems is one of the key steps in determining controlled islanding strategies. In this study, a wide‐area measurement system (WAMS) and agglomerative hierarchical clustering (AHC) algorithm based coherency identification method is presented for interconnected power systems with aggregated renewable sources. First, the trajectories measured by WAMS are transformed to the centre of inertia based ones for better representing the dynamic behaviour of a given power system, and ten trajectory dissimilarity indexes are presented for determining the similarity of the trajectories of any two generators. Second, a CRITIC (CRiteria Importance Through Intercriteria Correlation) based method, in which entropy and the Spearman's rank correlation coefficient are integrated for reflecting the differences and correlations among multiple indexes, respectively, is presented to integrate the trajectory dissimilarity indexes. Next, the AHC algorithm is utilised to identify CGs. Finally, a modified New England–New York interconnected power system with a large number of renewables, a simplified actual Western Interconnection power system in North America and the eastern part of Guangdong power system in China with a recorded oscillation event happened are utilised to demonstrate the proposed wide‐area coherency identification methodology. [ABSTRACT FROM AUTHOR]

Unit commitment is a traditional mixed-integer non-convex problem and remains a key optimisation task in power system scheduling. The high penetration of intermittent renewable generations such as wind and solar as well as mass roll-out of plug-in electric vehicles (PEVs) impose significant challenges to the traditional unit commitment problem, not only by significantly increasing the complexity of the problem in terms of the dimension and constraints, but also dramatically change the problem formulation. In this paper, a new hybrid unit commitment problem considering renewable generation scenarios and charging and discharging management of plug-in electric vehicles is first formulated. To effectively solve the problem, a novel parallel-series hybrid meta-heuristic optimisation method is then proposed, which combines a hybrid topology binary particle swarm optimisation, the self-adaptive differential evolution algorithm and a lambda iteration method, to simultaneously and intelligently determine the binary on/off status of each thermal unit, the generation power of online units, as well as the demand side management of plug-in electric vehicles. The proposed parallel-series hybrid method is first assessed on a 10-unit benchmark, and then on a case where renewable generation and smart PEV management are integrated. Numerical results confirm the superiority of the proposed new algorithm in comparison with some popular meta-heuristic approaches. [ABSTRACT FROM AUTHOR]

This paper proposes a combined wind farm controller based on Model Predictive Control (MPC). Compared with the conventional decoupled active and reactive power controls, the proposed control scheme considers the significant impact of active power on voltage variations due to the low $X/R$ ratio of wind farm collector systems. The voltage control is improved. Besides, by coordination of active and reactive powers, the Var capacity is optimized to prevent potential failures due to Var shortage, especially when the wind farm operates close to its full load. An analytical method is used to calculate the sensitivity coefficients to improve the computation efficiency and overcome the convergence problem. Two control modes are designed for both normal and emergency conditions. A wind farm with 20 wind turbines was used to verify the proposed combined control scheme. [ABSTRACT FROM PUBLISHER]

Significant penetration of renewable generations (RGs) and mass roll-out of plug-in electric vehicles (PEVs) will pay a vital role in delivering the low carbon energy future and low emissions of greenhouse gas (GHG) that are responsible for the global climate change. However, it is of considerable difficulties to precisely forecast the undispatchable and intermittent wind and solar power generations. The uncoordinated charging of PEVs imposes further challenges on the unit commitment in modern grid operations. In this paper, all these factors are comprehensively investigated for the first time within a novel hybrid unit commitment framework, namely UCsRP, which considers a wide range of scenarios in renewable generations and demand side management of dispatchable PEVs load. UCsRP is however an extremely challenging optimisation problem not only due to the large scale, mixed integer and nonlinearity, but also due to the double uncertainties relating to the renewable generations and PEV charging and discharging. In this paper, a meta-heuristic solving tool is introduced for solving the UCsRP problem. A key to improve the reliability of the unit commitment is to generate a range of scenarios based on multiple distributions of renewable generations under different prediction errors and extreme predicted value conditions. This is achieved by introducing a novel multi-zone sampling method. A comprehensive study considering four different cases of unit commitment problems with various weather and season scenarios using real power system data are conducted and solved, and smart management of charging and discharging of PEVs are incorporated into the problem. Test results confirm the efficacy of the proposed framework and new solving tool for UCsRP problem. The economic effects of various scenarios are comprehensively evaluated and compared based on the average economic cost index, and several important findings are revealed. [ABSTRACT FROM AUTHOR]

Finding an optimal black-start scheme plays an important role in speeding up the restoration procedure of a power system after a blackout or a local outage. In a practical black-start decision-making procedure, uncertainties are inevitable. Some factors such as index values and indexes’ weights can be better described as uncertain interval values. So far, the black-start decision-making problem with uncertainties has not yet been systematically investigated. Given this background, a new approach for black-start decision-making based on interval values is developed. First, a decision-making matrix with interval values is normalized by using the error propagation theory. Then, a linear goal programming model is developed to seek the ideal vector of index weights and the evaluation values of all candidate black-start schemes can be obtained. A risk attitude factor based method is presented to sort the schemes. Finally, a sample example is served for demonstrating the essential feature of the proposed method, and comparisons with three existing methods are also carried out. Simulation and comparison results show that the proposed method could not only take different kinds of uncertainties into account, but also overcome several shortcomings of the existing methods. [ABSTRACT FROM AUTHOR]

The ever-increasing penetration of wind power integration into a power system can produce significant impacts on the operation of an interconnected power system. As the major energy conversion technology for large wind turbines, the doubly fed induction generator (DFIG) will play an important role in future power systems. Hence, the impacts of the DFIG on the low-frequency oscillations of interconnected power systems have become an important issue with extensive concerns. This paper examines the impacts of several factors, including the DFIG transmission distance, tie-line power of the interconnected system, DFIG capacity, with/without a power system stabilizer (PSS), on the low frequency oscillation characteristic of an interconnected power system using both eigenvalue analysis and dynamic simulations. To investigate the effects of these factors on the interarea oscillation mode, case studies are carried out on two two-area interconnected power systems, and some conclusions are obtained. [ABSTRACT FROM AUTHOR]

Parallel restoration is an efficient way to speed up the restoration process after a wide‐area outage or a complete blackout of a power system. To implement efficient parallel restoration, an appropriate network partitioning method is necessary. Given this background, a two‐step network partitioning strategy for parallel power system restoration is presented. In order to minimise the restoration time of generating units, a grouping model for non‐black‐start units is developed first. Based on the graph theory, a power network partitioning model is presented with the objectives of minimising the number of the interconnected lines and maximising the electrical distance of the interconnected lines among different restoration subsystems. The well‐established AMPL/CPLEX is employed to solve the developed optimisation models. Finally, the IEEE New England 10‐unit 39‐bus system and a part of Zhejiang provincial power system in China are employed to illustrate the feasibility and effectiveness of the developed models. [ABSTRACT FROM AUTHOR]

The applications of battery energy storage systems (BESSs) in power systems have been paid increasing attention in recent years. Apart from all the benefits that BESSs can bring to the power systems, their potential applications in assisting power system restoration after a major blackout have not yet been systematically investigated. The flexible charging and discharging characteristics of BESSs could help maintain the balance between power supply and demand during the system restoration process, thus the recovery speed of the power system concerned after a blackout could be accelerated. Given this background, the applications of BESSs in power system restoration is investigated. First, the potential applications of BESSs during power system restoration process are discussed. A multi‐objective optimisation model is next proposed, aiming at minimising the number of circuit breaker operations and outage durations of both the non‐black‐start generating units and the important loads. Meanwhile, the application of intermittent renewable energy sources for improving power system restoration is also discussed. The operation strategy of BESSs will also be optimised after the system restoration scheme is attained through the multi‐objective optimisation. The proposed method is demonstrated by numerical tests on the IEEE 30‐bus and 162‐bus test systems. [ABSTRACT FROM AUTHOR]

This study presents the distributed model predictive control (D‐MPC) of a wind farm equipped with fast and short‐term energy storage system (ESS) for optimal active power control using the fast gradient method via dual decomposition. The primary objective of the D‐MPC control of the wind farm is power reference tracking from system operators. Besides, by optimal distribution of the power references to individual wind turbines (WTs) and the ESS unit, the WT mechanical loads are alleviated. With the fast gradient method, the convergence rate of the D‐MPC is significantly improved which leads to a reduction of the iteration number. Accordingly, the communication burden is reduced. Case studies demonstrate that the additional ESS unit can lead to a larger WT load reduction, compared with the conventional wind farm control without ESS. Moreover, the efficiency of the developed D‐MPC algorithm is independent from the wind farm size and is suitable for the real‐time control of the wind farm with ESS. [ABSTRACT FROM AUTHOR]

Widespread utilization of plug-in electric vehicles (PEVs) would incur more uncertainties, and thus challenge the traditional planning strategies for distribution systems. In this paper, a scenario-based comprehensive expansion planning (SCEP) approach for distribution systems is presented to cope with future uncertainties. This approach can not only take into account the initial investment and operation costs but also consider the expected adaptation cost to other future scenarios, and in this way the flexibility of an initial planning scheme can be achieved. Future scenarios are identified in line with three uncertainties, namely the conventional load levels, penetration levels of PEVs and whether or not with successful implementation of a coordinated charging strategy. The charging profile for dump charging mode has been determined straightforwardly, while that for coordinated charging mode is obtained by solving the proposed day-ahead dispatching model (DDM). Furthermore, a priority-based real time dispatching strategy (PRTDS) is proposed to obtain direct charging instructions for PEVs, and thus to demonstrate the rationality of utilizing the charging profile acquired from DDM in distribution system expansion planning (DSEP). Finally, the performance of the proposed methodology is demonstrated by numerical studies. [ABSTRACT FROM AUTHOR]

Fast restoration of critical loads and non-black-start generators can significantly reduce the economic losses caused by power system blackouts. In a parallel power system restoration scenario, the sectionalization of restoration subsystems plays a very important role in determining the pickup of critical loads before synchronization. Most existing research mainly focuses on the startup of non-black-start generators. The restoration of critical loads, especially the loads with cold load characteristics, has not yet been addressed in optimizing the subsystem divisions. As a result, sectionalized restoration subsystems cannot achieve the best coordination between the pickup of loads and the ramping of generators. In order to generate sectionalizing strategies considering the pickup of critical loads in parallel power system restoration scenarios, an optimization model considering power system constraints, the characteristics of the cold load pickup and the features of generator startup is proposed in this paper. A bi-level programming approach is employed to solve the proposed sectionalizing model. In the upper level the optimal sectionalizing problem for the restoration subsystems is addressed, while in the lower level the objective is to minimize the outage durations of critical loads. The proposed sectionalizing model has been validated by the New-England 39-bus system and the IEEE 118-bus system. Further comparisons with some existing methods are carried out as well. [ABSTRACT FROM AUTHOR]

Traditionally, power system contingency analysis involves massive power flow calculations, which are usually based on linear approximation methods, such as dc load flow model or distribution factors based method, for fast speed but with compromised accuracy. Particularly, the accuracy of power flow results can deteriorate with the increase of k given N – k contingency analysis. Consequently, the obtained results may provide misleading information by under estimating the impact of some severe contingencies. In order to effectively implement online N – k contingency analysis, we propose a flexible framework of power flow estimation, where generalized line outage distribution factors ( GLODF s) and ac power flow model are integrated together to formulate a two-stage scheme. At first stage, the Monte Carlo sampling technique is used to generate tables of computing errors for the hybrid ac -( GLODF s/ ac ) method. The achieved error information can then provide useful references in the second stage to select either ac - GLODF s based method or ac power flow model for N – k contingency analysis. Theoretically, the framework can provide significantly enhanced accuracy as well as satisfied efficiency. Finally, comprehensive case studies with the IEEE -118 bus system are given to validate the proposed framework. [ABSTRACT FROM AUTHOR]

Traditional security-constrained unit commitment (SCUC) considers only static security criteria, which may however not ensure the ability of the system to survive dynamic transition before reaching a viable operating equilibrium following a large disturbance, such as transient stability. This paper proposes a tractable mathematical model for transient stability-constrained unit commitment (TSCUC) and a practical solution approach. The problem is modeled without explicit differential-algebraic equations, reducing the problem size to one very similar to a conventional SCUC. The whole problem is decomposed into a master problem for UC and a range of subproblems for steady-state security evaluation and transient stability assessment (TSA). Additional constraints including Benders cut and so-named stabilization cut are generated for eliminating the security/stability violations. The extended equal-area criterion (EEAC) is used for fast TSA and analytically deriving the stabilization cut, wherein multiple contingencies having common instability mode can be simultaneously stabilized by one cut. The proposed approach is demonstrated on the New England 10-machine system and the IEEE 50-machine system, reporting very high computational efficiency and high-quality solutions. [ABSTRACT FROM PUBLISHER]

China experienced a rapid economic growth in the past few decades, whereas it has been facing a critical environmental situation. It is palpable that creating a sustainable energy structure in China is urgently required. As reported in China's 12th Five-year (2011–2015) Plan (which is a five-year development plan that outlines the development focus and strategic plan for different sectors in the next five-year period, published by the State Council of the PRC every five years), integrating more renewable energy, especially from solar and wind sources, becomes an essential part of their smart grid development. This article comprehensively reviews the existing achievements of renewable technology development and current status including relevant policies from central government and some on-going demonstration projects. Promoting new energy vehicles (e.g., electric vehicles) as an effective way of reducing carbon emissions is addressed in this article as well. Finally, the difficulties at the current stage are analyzed and future trends for a subsequent phase of development are also outlined. [ABSTRACT FROM AUTHOR]

A new approach for power system transient stability preventive control is proposed by performing trajectory sensitivity analysis on the one‐machine‐infinite‐bus (OMIB) equivalence of multi‐machine systems. Exact instability time/angle are determined from the equivalent OMIB power–angle curve; the trajectory sensitivity is calculated at the instability time and the transient stability of the multi‐machine system is controlled by constraining the OMIB's angle excursion at the instability time to that of the critical OMIB which corresponds to the marginally stable condition of the system. The required preventive control action (generation rescheduling) can be efficiently solved via a linear programming model with the OMIB trajectory sensitivities‐based constraints. Simulation results on the New England 10‐machine 39‐bus system and a 285‐machine and 1648‐bus system validate its effectiveness and superiority over previous trajectory sensitivity applications to this problem. [ABSTRACT FROM AUTHOR]

The network reconfiguration is an important stage of restoring a power system after a complete blackout or a local outage. Reasonable planning of the network reconfiguration procedure is essential for rapidly restoring the power system concerned. An approach for evaluating the importance of a line is first proposed based on the line contraction concept. Then, the interpretative structural modeling (ISM) is employed to analyze the relationship among the factors having impacts on the network reconfiguration. The security and speediness of restoring generating units are considered with priority, and a method is next proposed to select the generating unit to be restored by maximizing the restoration benefit with both the generation capacity of the restored generating unit and the importance of the line in the restoration path considered. Both the start-up sequence of generating units and the related restoration paths are optimized together in the proposed method, and in this way the shortcomings of separately solving these two issues in the existing methods are avoided. Finally, the New England 10-unit 39-bus power system and the Guangdong power system in South China are employed to demonstrate the basic features of the proposed method. [ABSTRACT FROM AUTHOR]

An elaborately designed integrated power distribution and electric vehicle (EV) charging system will not only reduce the investment and operation cost of the system concerned, but also promote the popularization of environmentally friendly EVs. In this context, a multi-objective collaborative planning strategy is presented to deal with the optimal planning issue in integrated power distribution and EV charging systems. In the developed model, the overall annual cost of investment and energy losses is minimized simultaneously with the maximization of the annual traffic flow captured by fast charging stations (FCSs). Additionally, the user equilibrium based traffic assignment model (UETAM) is integrated to address the maximal traffic flow capturing problem. Subsequently, a decomposition based multi-objective evolutionary algorithm (MOEA/D) is employed to seek the non-dominated solutions, i.e., the Pareto frontier. Finally, collaborative planning results of two coupled distribution and transportation systems are presented to illustrate the performance of the proposed model and solution method. [ABSTRACT FROM PUBLISHER]

• Establish a quantitative technical-economic model for energy transition simulation. • Propose a mathematic approach to describe the energy transition target and pathway. • Evaluate China's energy strategy in terms of energy, economic, and emission by 2050. • Analyze the uncertain range of China's cumulative CO 2 emission and allowance by 2050. The quantitative analysis of the low-carbon energy transition is crucial for providing decision-support for energy planning and policy-making. This study presented an approach for quantitative analysis of energy transition, involving dynamic simulation model, mathematical methods of describing the energy transition targets and pathways, boundary condition, and evaluation indicators. The transition targets of Baseline scenario were set following the Energy Production and Consumption Revolution Strategy (2016 – 2030) (hereinafter referred to as the Energy Strategy) promulgated by the Chinese government. Four comparative scenarios were generated by adjusting the targets of the total primary energy consumption and the proportion of power generation. The carbon emissions and economic costs were calculated based on the quantitative dynamic simulation model. In the study, an attempt was made to explain whether China's cumulative carbon emissions following the Energy Strategy match the emission quotas under the global 2 °C temperature rise target. Furthermore, the future research direction for low-carbon transition was also analyzed. [ABSTRACT FROM AUTHOR]