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Risk preference is an important factor in electricity market strategy analysis and decision-making. The existing methods of risk preference analysis need to design and execute questionnaires or experiments on the subjects, and hence are costly and time-consuming for bidding in electricity markets. This article proposes a new method of data-driven risk preference analysis for power generation plants based on historical data and inverse reinforcement learning. Historical data are transformed to the transition function model according to the specific market mechanism. An adjusted inverse reinforcement learning model is thereafter proposed along with the optimization objective and technical constraints. The proposed method is tested in a simulated electricity market environment using the Australian Energy Market Operator (AEMO) day-ahead bidding data. Simulation results show that 1) thermal power plants prefer to adjust risk preferences within the day; 2) apart from the thermal power plants, the rest types of power plants are risk-neutral; 3) the daily risk preference trend of the thermal power plants varies in different seasons and is closely related to the load level.

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.

Online Decision Support System (DSS) for restorative control can be used to track the restoration process of power system dynamically and optimize the restoration plans online to support the decision-making of dispatchers’ online restorative control. Parallel restoration can improve the restoration efficiency effectively, but the division of the restoration partitions should match the real-time conditions of the power grid. This paper proposes a dynamic partitioning method for power system restoration considering the restoration capabilities of partitions, which can be used to refresh the partitioning results dynamically based on the power system restoration process so as to cope with the restoration-related uncertainties. Furthermore, the restoration targets (outage power plants and outage substations) of partitions will be filtered and evaluated based on its restoration values to maximize the overall restoration revenues. A decision support system for restorative control is developed and a case study using data of a real regional grid in China is conducted to illustrate the flexibility and effectiveness of the proposed method in dealing with restoration-related uncertainties. Keywords: Restorative control, Decision support system, Dynamic partitioning, Parallel restoration

Traditional isolated modeling and calculation methods for cyber systems and power systems cannot satisfy the requirements for the analysis and control of cyber physical power systems (CPPSs). In this paper, a matrix-based modeling method for the coupled behavior of a CPPS is presented to describe the complex interactions of the information and energy flows. A hybrid calculation architecture is proposed for the reliability and security analysis of CPPSs. On that basis, the CPPS security defense system is established. Case studies verify the effectiveness of this CPPS modeling approach and hybrid calculation architecture.

With the rapid deployment of the advanced metering infrastructure (AMI) and distribution automation (DA), self-healing has become a key factor to enhance the resilience of distribution networks. Following a permanent fault occurrence, the distribution network operator (DNO) implements the self-healing scheme to locate and isolate the fault and to restore power supply to out-of-service portions. As an essential component of self-healing, service restoration has attracted considerable attention. This paper mainly reviews the service restoration approaches of distribution networks, which requires communication systems. The service restoration approaches can be classified as centralized, distributed, and hierarchical approaches according to the communication architecture. In these approaches, different techniques are used to obtain service restoration solutions, including heuristic rules, expert systems, metaheuristic algorithms, graph theory, mathematical programming, and multi-agent systems. Moreover, future research areas of service restoration for distribution networks are discussed.

Affected by the nonlinear time-varying factors due to fault scenarios, protection relaying, and control measures, the dynamic behaviors of a power system may be significantly different from the results of previous methods. In order to analyze the oscillation characteristics of complex power systems more accurately and suppress the low frequency oscillation more effectively, this paper improves the trajectory section eigenvalue method. Firstly, the time response of a system is obtained by numerical simulation in a given fault scenario. Secondly, the algebraic variables are substituted to the differential equations along the trajectory. Thus, the original time-varying differential-algebraic equations are approximated by a set of linear ordinary differential equations, which can be updated along the trajectory. On this basis, this paper proposes a method to extract instantaneous features of the oscillation from the micro perspective. The non-equilibrium points with strong nonlinearity or critical eigenmodes are identified by the proposed method. The simulation test results of the IEEE 3-machine 9-bus system and the New England system illustrate the validity of the proposed method.

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.

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.

The clean transition of GenCo is affected by various uncertainties such as strategies of other participants, power grid development, policies, and power demand, among which, being the most direct stakeholders in electricity market, other participants’ generation investment is crucial. This paper simulates the dynamic trajectories of installed capacity, power generation, as well as annual average coal consumption rate of China’s power supply structure from 1985 to 2015, and the dynamic transition process of a traditional coal-dominated GenCo from 2016 to 2030. Based on simulation, this paper comprehensively evaluates the impact of other participants’ generation investment on clean transition of GenCo, and quantitatively analyzes the robustness of a given clean transition strategy in different scenarios. The results achieved by quantitative analysis of simulation can provide decision support for GenCos’ decision makers.

A cyber physical distribution power system (CPDS) is a large and complex infrastructure that coordinates the cyber communication system and the physical distribution power system. Because of the increasingly advanced information communication technology, the development of cyber physical distribution power system has caused key cyber security issues related to system operation. This paper is focused on realizing a unified system attack modeling and security assessment of an active distribution power system. In this paper, first we present an overview of the system operation from the fusion system perspective. The significant effects of network intrusion attacks on operational security are evaluated. A new unified cyber physical network model is established using a limited stochastic Petri net graph theory that considers refined firewalls and password components. Then, a security effectiveness evaluation method is proposed to analyze channel throughput variation and system robustness. Overall CPDS security risk values are determined based on physical influence coefficients. Finally, simulations of an improved IEEE-33 bus distribution power system and security assessment under intrusion attacks are described. The research work could raise awareness of the cyber intrusion threats and provide the basis for security defense.

The clean transition of traditional coal-dominated GenCos plays critical role in national energy transition. However, the formulation of strategy is dominated by qualitative analysis, which lacks the ability to quantify key operation indicators in a complex transition process. This paper develops a computer simulation tool for the study of medium and long-term transition of GenCo to simulate the dynamic process of its clean transition. Under the background of clean transition in China’s power sector, it quantitatively analyzes the dynamic performance such as business structure, profit, and carbon emission of a traditional GenCo according to its 2020 and 2030 clean development strategic targets, and compares the differences between different pathways under the same strategic targets. And then, this paper analyzes the impact of uncertainties such as coal prices and renewable energy subsidies.

The low-carbon transition of the power system plays critical role in energy system transition. In 2016, the Chinese government proposed an ambitious low-carbon energy transition strategy by 2030. However, relevant researches remain at the level of qualitative analysis. This paper proposes a quantitative analysis approach for assessing China’s power sector transition strategy based on a dynamic simulation model. This appraisal method translates the transition target of non-fossil energy generation in 2030 into a series of plausible pathways. Through the dynamic simulation model, the economic costs and carbon emissions of each transition pathway are evaluated. The simulation results give a range of the total system costs and carbon emissions of China’s transition to a low-carbon power system during the transition period of 2016 to 2030.

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.

In this paper, a new dynamic pricing scheme is proposed for demand response aggregators (DRAs) to maximize their profits. A Stackelberg game is established to model the interactions between a load serving entity (LSE) and DRAs, and a bi-level optimization formulation attained. In the upper-level problem, the energy procurement from the wholesale electricity market is determined for the LSE based on the electricity price forecast, while DRAs adjust their consumption patterns with respect to the dynamic pricing scheme. The proposed model is transformed into a mixed-integer, quadratically-constrained quadratic programming (MIQCQP) problem, and the corresponding Karush-Kuhn-Tucker (KKT) conditions are attained to represent the optimality of the lower-level problem. The complementary constraints in the KKT conditions are linearized using big-M reformulation. Finally, the effectiveness of the proposed approach is demonstrated by numerical examples.

Consensus control has recently been employed to regulate active power output of distributed generators (DGs) of a virtual power plant. However, such networked control systems, integrating information and wireless communication technologies, are subject to cyberattacks. This paper investigates the risk of jamming attacks on the coordination of DGs in a distribution network. A binary-valued stochastic process is employed to model packet losses caused by jamming attacks and nonmalicious communication failures. Using an attack rate to characterize the strategy of an attacker, the proposed method highlights the vulnerability of the cooperative control of DGs subject to cyberattacks. Furthermore, a probabilistic instability analysis provides the conditions under which jamming attacks can prevent active power outputs of DGs from reaching a consensus to track a dispatch command. Accordingly, this paper shows how an attacker might launch malicious attacks to impair consensus-based control of DGs so that energy companies can take precautions. Finally, the theoretical results are verified on the IEEE 34-node test feeder under two different attack strategies.

This paper proposes a hierarchical control of Thermostatically Controlled Loads (TCLs) to provide primary frequency control support. The control architecture is comprised of three levels. At the high level, an aggregator coordinates multiple distribution substations and dispatches the primary reserve references. At the middle level, distribution substations estimate the available power of TCLs based on the aggregated bin model, and dispatch control signals to individual TCLs. At the local level, a supplementary frequency control loop is implemented at the local controller, which makes TCLs respond to the frequency event autonomously. Case studies show that the proposed controller can efficiently respond to frequency events and fulfill the requirement specified by the system operator. The users’ comforts are not compromised and the short cycling of TCLs is largely reduced. Due to the autonomous control, the communication requirement is minimized.

Driven by its fast-growing high-tech industry during the 2010s, China has witnessed an upsurge in data rates from online shopping, mobile Internet services, and industrial informatization, among other uses. As awareness of big data has increased, multiple industries have begun to embrace analytics to exploit value from all these data. The electric power industry, using modern information and communication technologies and millions of newly deployed smart meters, is an important forerunner in the big data analytics field.

The wide-area measurement system provides a new data acquisition and supervisory control tool for a power system, and the data acquisition level is increased dramatically with its development in the smart grid environment. Huge data associated with the power system operation are acquired, which are beneficial for enhancing situational awareness of a power system concerned. Identifying the coherency among synchronous generators using real-time signals from phasor measurement units (PMUs) is one of the major tasks of situational awareness in power system operation. Given this background, a data-driven coherency identification methodology is proposed based on the spectral clustering algorithm. First, several trajectory dissimilarity indices for the rotor angle and rotor speed trajectories of generators as measured by PMUs are presented based on the trajectory similarity theory. Second, a decision-making method based on the Gini coefficient and Kendall rank correlation coefficient is presented for integrating multiple indices describing trajectory dissimilarities. Third, the spectral clustering algorithm is presented to identify the coherency of synchronous generators, and silhouette is presented for determining a reasonable number of coherent groups. Finally, oscillation events happened/simulated in two actual power systems, i.e., Guangdong power system in China and Western Interconnection power system in North America, are utilized to demonstrate the effectiveness of the proposed data-driven coherency identification methodology.

In recent years, the increasing penetration level of renewable generation and combined heat and power (CHP) technology in power systems is leading to significant changes in energy production and consumption patterns. As a result, the integrated planning and optimal operation of a multi-carrier energy (MCE) system have aroused widespread concern for reasonable utilization of multiple energy resources and efficient accommodation of renewable energy sources. In this context, an integrated demand response (IDR) scheme is designed to coordinate the operation of power to gas (P2G) devices, heat pumps, diversified storage devices and flexible loads within an extended modeling framework of energy hubs. Subsequently, the optimal dispatch of interconnected electricity, natural gas and heat systems is implemented considering the interactions among multiple energy carriers by utilizing the bi-level optimization method. Finally, the proposed method is demonstrated with a 4-bus multi-energy system and a larger test case comprised of a revised IEEE 118-bus power system and a 20-bus Belgian natural gas system.

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.

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.

Regarded as a long-term, large capacity energy storage solution, commercialized power-to-gas (PtG) technology has attracted much research attention in recent years. PtG plants and natural gas-fired power plants can form a close loop between an electric power system and a natural gas network. An interconnected multi-energy system is believed to be a solution to the future efficient and environmental friendly energy systems. However, some crucial issues require in-depth analysis before PtG plants can be economically implemented. This paper discusses current development status and potential application of PtG plants in the future interconnected multi-energy systems, and further analyzes the costs and benefits of PtG plants in different application scenarios. In general, the PtG plants are not economical efficient based on current technologies and costs. But the situation is likely to change with the development of PtG technologies and interconnected operation of gas-electricity energy system.

This paper develops a network-constrained transactive control method to integrate distributed energy resources (DERs) into a power distribution system with the purpose of optimizing the operational cost of DERs and power losses of the distribution network as well as preventing grid problems including power transformer congestion and voltage violations. In this method, a price coordinator is introduced to facilitate the interaction between the distribution system operator (DSO) and aggregators in the smart grid. Electric vehicles are used to illustrate the proposed network-constrained transactive control method. Mathematical models are presented to describe the operation of the control method. Finally, simulations are presented to show the effectiveness of the proposed method. To guarantee its optimality, we also checked the numerical results obtained with the network-constrained transactive control method and compared them with the one solved by centralized control, and found a good performance of the proposed control method.

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.

Study the influence of generator-tripping time on transient stability is of great significance for guiding the setting of the optimal generator-tripping time. Based on the extended equal area criterion (EEAC), the paper points out that the generator-tripping operation affects transient power angle stability margin of the system by changing the equivalent electromagnetic power, the equivalent mapping rotor angle and its corresponding energy accumulation interval, and clarifies accordingly the paths of generator-tripping time affects transient power angle stability margin. For the first swing of angle instability of the Hamiltonian system, the complex influences of generator-tripping time on the control effect are revealed. As generator-tripping time increases, the transient power angle stability margin may present an evolution situation that first increases and then decreases, and reaches a maximum value when generator-tripping time equals to the fault clearing time, and may also present decreases monotonically. The analysis conclusions are verified by simulation experiments.

Power electronic dominated power system has brought difficulties to the research of transient stability analysis. With the great progress in time-domain simulation technology, electromagnetic transients on power system can be calculated. There’s still a lack of ability to assess the transient stability margin considering electromagnetic transient model. This article builds a framework to quantitively analyze the transient stability of AC/DC system based on the EEAC theory. Electromechanical-electromagnetic transient hybrid simulation and trajectory stability assessment are combined in this framework. Some key technologies such as trajectory aggregation and parameter calculation are also discussed. Besides, the sensitivity analysis is used to find the critical clearing time in hybrid system. Case studies in test system demonstrate the validity of the proposed method.

Probabilistic wind power forecasting has become an important tool for optimal economic dispatch and unit commitment of modern power systems with significant renewable energy penetrations. Ensemble forecasting based on Monte Carlo simulation is commonly used by many grid operators, but other probabilistic approaches, such as multi-step iterative wind power forecasting have not yet been fully explored. The associated uncertainty analysis is an important yet challenging issue in this area. This paper proposes to use an analytic interval forecasting framework to estimate the forecasting uncertainty of a wind farm in Ireland based on the Temporally Local Gaussian Process (TLGP) model and evaluates the probabilistic forecasting metrics of reliability and sharpness. The key findings confirm that TLGP not only has better forecasting accuracy but is also less sensitive to uncertainty propagation than Gaussian Process (GP). This work provides an effective analytical framework for iterative multi-step interval forecasting.

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.

Coherency identification among generators plays an important role in controlled islanding and wide-area monitoring of a power system with renewable energy sources. So far, wide-area measure...

Investment for renewables has been growing rapidly since the beginning of the new century, and the momentum is expected to sustain in order to mitigate the impact of anthropogenic climate change. Transition towards higher renewable penetration in the power industry will not only confront technical challenges, but also face socio-economic obstacles. The connected between environment and energy systems are also tightened under elevated penetration of renewables. This paper will provide an overview of some important challenges related to technical, environmental and socio-economic aspects at elevated renewable penetration. An integrated analytical framework for interlinked technical, environmental and socio-economic systems will be presented at the end.

Since a microgrid consists of various distributed energy resources and local loads, integration of a distributed grid and multiple microgrids leads to an integrated hybrid energy system. This paper focuses on improving profit, economy, security, and dynamic performance of the integrated hybrid energy system by means of developing different areas of management and control strategies upon four-level hierarchical multiagent system. The level 1 agent implements optimal price bidding strategies for high profit of the overall system. The level 2 agent optimizes the energy management strategies for economic operation of each microgrid. The level 3 agent executes coordinated switching control for maintaining security of each microgrid. The level 4 agent facilitates local hybrid control of each distributed energy resource for guaranteeing dynamic performance. Finally, validity of the proposed scheme is tested by means of simulation study.

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.

Restorative self-healing is one of the most important features of smart grids. The major purpose of a restorative self-healing control strategy is to steer a power system to secure operating states. In this paper, a self-healing transmission network reconfiguration algorithm based on the complex network theory is proposed. The capacities of generators and the amounts of important loads (i.e., the high priority loads), as well as the distribution and importance of each transmission line in the outage area are considered in the proposed method using the presented electrical “betweenness.” Next, the optimal restoration paths and the optimal restorative self-healing strategy can be attained automatically. Finally, the New England 10-unit 39-bus system and a part of the actual Guangdong power system in China are employed to illustrate the features of the proposed method.

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.

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.

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.

This paper 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 and the ESS unit, the wind turbine mechanical loads are alleviated. With the fast gradient method, the convergence rate of the DMPC 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 wind turbine load reduction, compared to 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.

An ultra high voltage (UHV) ac and dc interconnection will become the foundation of China’s future smart grid. Due to the wide spread of interconnected regions, the distance between control stations will increase dramatically. Therefore, the communication system’s reliability and real-time performance will become increasingly crucial. However, failures of the communication system, such as interruptions, latency, and bit error, are inevitable. This paper uses the UHV grid security and stability defense system (SSDS) as an example to analyze its requirements for communication and the impact of communication failure on the system’s performance. The effect of communication latency on the power system’s stability is analyzed quantitatively and qualitatively. Based on this analysis, a framework of an UHV grid SSDS considering the risk of the communication system is proposed. A preliminary power system and communication system co-simulation tool is developed to perform a case study. The case study demonstrates that communication latency in the UHV grid changes the control strategy’s effectiveness due to a delay in executing the control strategy. Furthermore, communication latency will negatively affect the power grid’s stability.

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.

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.

The coupling between a power system and a traffic system concerned is strengthened by the increasing penetration of electric vehicles (EVs) as well as ever-growing employment of fast charging stations (FCSs). FCSs play an important role in linking these two systems, and their charging scheduling could have significant impacts on the security of power system operation, traffic flows and experience of EV drivers. The real-time traffic flow plays a critical role in the charging scheduling for EVs. In this paper, the driving behaviors of EVs are simulated by the microscopic traffic flow model (MTFM) with geographic distances among FCSs and EVs well taken into account, and then a bi-level dynamic charging scheduling model presented to determine the real-time charging strategy for EVs with objectives of optimally tracking the day-ahead scheduling and minimizing the waiting time cost of EVs. Finally, a coupled system including a modified 33-node distribution system and a 48-node traffic system is employed to demonstrate the essential features of the proposed method.

Power systems and natural gas systems have been tightly coupled and turn into integrated gas-electricity energy systems with the development of energy conversion technology. Demand side management (DSM) contributes to load leveling in power systems, and then is beneficial to the secure and economic operation of the integrated gas-electricity energy system. In this paper, a collaborative planning model with DSM in an integrated gas-electricity energy system is established. The operation framework of an energy coupling center is first proposed with three DSM schemes and the customer satisfaction well taken into account. Next, the optimal collaborative planning model is presented, aiming to minimize the total cost of construction, operation and DSM compensation. Finally, an integrated gas-electricity energy system including a 39-bus power system and a 20-bus natural gas system is employed to illustrate the feasibility and effectiveness of the proposed optimal planning model.

With the close integration of cyber system and physical system in smart grid, traditional isolated modeling and calculation methods for information and communication system and power system cannot meet the needs of the analysis and control of the Cyber Physical Power System (CPPS). In this paper, a hybrid calculation architecture, which is on demand response and layered, is proposed to realize the hybrid calculation of the CPPS model and the security analysis of CPPS. Case study verifies the effectiveness of this CPPS hybrid calculation architecture.

The coupling between electricity and natural gas systems can be promoted with the emergence of power-to-gas (P2G) technology and natural gas generation units (NGGUs), which offers a potential alternative to accommodate the surplus renewable energy generation (REG). Given this background, an optimal coordinated schedule model for electricity and natural gas distribution networks with P2G facilities is first proposed. In this presented model, the objective is to minimize the overall operation costs of the integrated energy system, while the constraints include the operation ones in both the electricity distribution network and natural gas distribution network. The commercial solver IPOPT is then utilized in the AMPL environment to solve this developed optimization model. Finally, an integrated energy system consisting of a modified 54-bus electricity distribution network and a 19-bus natural gas distribution network is employed to demonstrate the feasibility of the presented model.

In recent years, the coupling between the power system and the natural gas system has been strengthened with the development of the power to gas (PtG) technology, and the flexibility of power system operation is improved with the application of the demand side management (DSM). The emerging energy hub (EH) plays an important role in an integrated electricity-gas energy system with PtG equipment and natural gas-fired plants (NGFP) units included. Given this background, an optimal planning model of EHs is presented with the DSM of both industrial load and resident load considered in an integrated electricity-gas energy system. The continuity of each task and the definite sequence between tasks are considered in the DSM. The objective of this model is to maximize the potential profits of the planning scheme in the given future years, including the electricity and natural gas sales revenue and the environmental benefit, with the investment cost, operation cost and DSM compensation cost taken into account and solved by AMPL/CPLEX. Finally, an integrated electricity-gas energy system containing a 39-bus power system and a 20-bus natural gas system is employed to illustrate the feasibility and effectiveness of the proposed optimal planning model.

In this paper, a two-stage optimal charging scheme based on transactive control is proposed for the aggregator to manage day-ahead electricity procurement and real-time electric vehicles (EV) charging management in order to minimize its total operating cost. The day-ahead electricity procurement considers both the day-ahead energy cost and expected real-time operation cost. In the real-time charging management, the cost of employing the charging flexibility from the EV owners is explicitly modeled. The aggregator uses a transactive market to manage the real-time charging demand to provide the regulating power. A model predictive control-based method is proposed for the aggregator to clear the transactive market. The real-time charging decisions of the EVs are determined by the clearing of the proposed transactive market according to the real-time requests and preferences of the EV owners. As such, the aggregators decisions in the real-time EV charging management and regulating power markets can be optimized. At the same time, the charging requirements and response preferences of the EV owners are respected. Case studies using real world driving data from the Danish National Travel Surveys were conducted to verify the proposed framework.

Power system restoration comprises of three stages, i.e., the preparation stage, system restoration stage, and load restoration stage. This paper addresses the first two stages by a new skeleton-network restoration strategy considering the generator start-up sequence (GSUS) and load pickup. The proposed restoration strategy consists of three mathematical models: 1) GSUS model; 2) transmission line restoration (TLR) model; and 3) load pickup (LDP) model. The GSUS model aims to maximize the overall system generated energy considering the actual power system topology. The TLR model is to attain an optimal skeleton-network by identifying the restoration sequence of transmission lines. Critical loads are restored to keep the voltage within a specified respective threshold, and the load amounts are optimized in the LDP model. The proposed GSUS model and TLR model are both solved by the branch-and-bound method, while the LDR model is solved by the interior point method as well as the branch-and-cut method. Finally, the performance of the proposed method is demonstrated by two case studies on the New England 10-unit 39-bus system and an actual power system in Guangdong, China, respectively. MOE (Min. of Education, S’pore)

An electric vehicle (EV) centred ecosystem has not yet been formed, the existing limited statistic data are far from enough for the analysis of EV users’ travel and charge behaviors, which however tends to be affected by many certain and uncertain factors. An experimental economics (EE) based simulation method can be used to analyze the behaviors of key participants in a system. However, it is restricted by the system size, experimental site and the number of qualified human participants. Therefore, this method is hard to be adopted for the behavioral analysis of a large number of human participants. In this paper, a new method combining a questionnaire statistics and the EE-based simulation is proposed. The causal relationship is considered in the design of the questionnaires and data extraction, then a multi-agent modeling integration method is introduced in the EE-based simulation, which enables the integration of causal/statistical/behavioral models into the multi-agent framework to reflect the EV users’ travel willingness statistically. The generated multi-agents are used to replace human participants in the EE-based simulation in order to evaluate EV users’ travel demands in different scenarios, and compare the differences of simulated or measured travel behaviors between potential EV users and internal combustion engine (ICE) vehicle users.

Participation of flexible loads in microgrid operation provides an effective approach for enhancing the efficiency of intermittent renewable energy utilization. The advanced automatic control level in an industrial microgrid makes it a good choice for dispatching generation and flexible loads cooperatively. This paper presents an economic operation model for industrial microgrids with multiple kinds of flexible loads, including industrial manufacturing loads, building thermal loads and electric vehicle (EV) charging loads. Detailed models of flexible loads are first formulated respectively and then integrated to an optimization model with an objective of minimizing the energy cost. A highly efficient solver GUROBI is next utilized in the MATLAB environment to solve the developed optimization model. Finally, a case study of a typical industrial microgrid is employed to demonstrate the essential features of the proposed model.

Splitting time plays an important role for a successful controlled splitting, and the wide-area measurement system provides an effective means for determining optimal controlled splitting time. Given this background, a method for determining controlled splitting time based on WAMS data and a combined prediction algorithm is proposed in this paper. Firstly, the criteria used to determine the splitting time is established from two aspects, namely, the power angle and the operating frequency. Secondly, the WAMS data are utilized to examine the dynamic process of the power system in real time, and the prediction algorithm is adopted to estimate the disturbance trajectory, aiming to determine the optimal splitting time. Furthermore, in order to avoid the drawbacks of the traditional prediction algorithm, a combined linear prediction algorithm is presented to improve the prediction performance. Finally, the IEEE 39-bus test system is employed to demonstrate the feasibility and effectiveness of the proposed method.