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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.

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.

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.

Addressed to the $N-k_{1}-k_{2}$ cascading outages, it is computationally burdensome for the reliable calculation of active and reactive power flows. This paper builds a comprehensive framework with three algorithms, including the distribution factor (DF), the Newton-Raphson (NR), and the first iteration of NR algorithm (termed as 1J). Classifiers are designed to determine whether the NR algorithm should be employed for accuracy. Classifier features are extracted upon the analytical error of 1J. As reactive power is partially considered in the 1J but neglected in the DF algorithm, the deviation between the solutions is taken as one crucial feature. The support vector machine (SVM) is then utilized for classifier training. As the deep integration of the causal inference and the statistical paradigm, this framework calculates active and reactive power flows rapidly, reliably, and robustly. The effectiveness and robustness are fully validated in three typical IEEE systems.

The power system restoration control has a higher uncertainty level than the preventive control of cascading failures. In order to ensure the feasibility of the decision support system of restoration control, a decision support framework for adaptive restoration control of transmission system is proposed, which can support the coordinated restoration of multiple partitions, coordinated restoration of units and loads, and coordination of multi-partition decision-making process and actual restoration process. The proposed framework is divided into two layers, global coordination layer and partition optimization layer. The upper layer partitions the transmission system according to the power outage scenario, constantly and dynamically adjusts the partitions during the restoration process, and optimizes the time-space decision-making of inter-partition connectivity. For each partition, the lower layer pre-selects restoration targets according to the estimated restoration income, optimizes the corresponding restoration paths, and evaluates the restoration plans according to the expected net income per unit of power consumption. During the restoration process, if the restoration operation such as energizing the outage branch fails, the current restoration plan will be adaptively switched to the sub-optimal one or re-optimized if necessary. The framework includes two operation modes, i.e., the on-line operation mode and training simulation mode, and provides an information interaction interface for collaborative restoration with related distribution systems. The effectiveness and adaptability of the proposed framework is demonstrated by simulations using the modified IEEE 118-bus system.

In order to reduce the risk of commutation failure (CF) in the AC/DC hybrid power system, the quantitative analysis on CF is required for on-line assessment and optimal control. This paper presents an accurate and reliable method to quantify the commutation security based on the trajectory due to the complexity of the high-voltage direct current (HVDC) model. Firstly, the characteristics of the extinction angle trajectory are analyzed under both commutation success and failure conditions. The commutation security margin index (CSMI) is then proposed for the HVDC systems. Moreover, a search strategy for parameter limits is put forward based on the sensitivity analysis of CSMI to accelerate the search speed with a guaranteed accuracy level. A modified IEEE 39-bus power system and an actual large-scale power system with 46 generators and 821 buses are utilized to verify the validity and robustness of the proposed index and strategy.

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.

The development of renewable energy and the increasing peak-valley difference of load demand lead to an increasing requirement of spinning reserve (SR). However, the traditional operation reliability analysis of power system mainly focuses on the up SR and neglects the down SR. Therefore, the operation reliability of power system considering the impacts of down SR is investigated in this paper. Firstly, the constraints of down SR are incorporated into the day-ahead unit commitment (UC) model to obtain the generation scheduling and reserve allocation of power systems. Based on the dispatch results of UC model, the re-dispatched energy and load interruption can be determined using optimal power flow (OPF) model in the real-time operation in various contingency states. Operation reliability indices are calculated based on the load curtailment to represent the reliability performances of power systems. The proposed approaches are validated using the modified IEEE reliability test system. Case studies demonstrate that down SR can improve the operation reliability of power systems.

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.

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.

Cascading failure may incur catastrophic consequences. The high order and non-simultaneousness properties are the leading causes for the massive power flow (PF) calculations of the online static security assessment (SSA). Current PF models fail to satisfy the requirements for accuracy, speed, and robustness. This paper proposes a novel method where line outage distribution factor (LODF) model and AC model are coordinated by a binary classifier. The classifier determines the feasibility of LODF model in a specific case by evaluating its potential error. Cases are preferred to be analyzed by LODF model, while the ones with large potential errors will be analyzed by AC model. Case studies of three IEEE systems and the Texas 2000-bus system are given to verify the effectiveness, flexibility, and robustness of the proposed method.

With continuous development of smart grid technology, the interaction between the power system and user-side resources is constantly strengthening. Among various users who can participate in demand response (DR), industrial users have the characteristics of large power consumption and high degree of equipment automation, and hence can be scheduled more flexibly. In this paper, an optimal power management strategy for industrial users based on the state task network (STN) is proposed to assist industrial users specifically scheduling electrical equipment with an objective of minimizing electricity costs. Taking into account the user preferences for production equipment, the electricity consumption of users during the peak electricity price period could be significantly reduced through optimized operation of the schedulable task nodes of industrial users. Case studies are carried out to demonstrate the feasibility and effectiveness of the proposed method and to examine the impacts of applying the proposed method on industrial production processes and economic benefits of industrial users.

As typical prosumers, commercial buildings equipped with electric vehicle (EV) charging piles and solar photovoltaic panels require an effective energy management method. However, the conventional optimization-model-based building energy management system faces significant challenges regarding prediction and calculation in online execution. To address this issue, a long short-term memory (LSTM) recurrent neural network (RNN) based machine learning algorithm is proposed in this paper to schedule the charging and discharging of numerous EVs in commercial-building prosumers. Under the proposed system control structure, the LSTM algorithm can be separated into offline and online stages. At the offline stage, the LSTM is used to map states (inputs) to decisions (outputs) based on the network training. At the online stage, once the current state is input, the LSTM can quickly generate a solution without any additional prediction. A preliminary data processing rule and an additional output filtering procedure are designed to improve the decision performance of LSTM network. The simulation results demonstrate that the LSTM algorithm can generate near-optimal solutions in milliseconds and significantly reduce the prediction and calculation pressures compared with the conventional optimization algorithm.

The big obstacles for residential energy transition are from lacks of consumption behavior model and relative information system. A four-tier residential energy demand model is developed to explore the cognition-consciousness-demand-behavior evolution process; then residential energy behavior analysis data sources are investigated and necessity of questionnaire survey system is analyzed. To have a theory-supported information system, extension of Engel-Kollat-Blackwell consumption behavior model has been developed, and design of questionnaire survey system for residential customer is introduced with simple analysis of real implementation.

The big obstacle for residential energy transition is the lack of a consumption behavior model. At first, special characteristics of residential customer are discussed, including their fundamental purpose of energy use and attitudes against constraints and energy prices, as well as derived demand characteristics on appliance in energy consumption, etc. Then, a four-tier energy consumption behavior model of residential customers is developed with “virtual appliance” being generalized into three time scales as current, near future, future, and the behavior evolution process is sectionalized into cognition, consciousness, demand and behavior. The methodology and technology to implement the four-tier model in real applications will be dominated by the emerging cyber physical social system in energy, and a customized questionnaire survey system is demanded to investigate the consumption behaviors of residential customers.

The available main method for energy transition in the energy consumption side is the electric energy substitution. With respect to user-side energy transition, an energy co-pricing method for a park-level integrated energy system (IES) is addressed in this work. First, an energy co-pricing mechanism in the IES is introduced, with a park operator (PO) and user agents (UAs) included. A Stackelberg game model is next established with a PO and UAs involved, and a bi-level optimization formulation attained. The PO (the leader) sets prices of different kinds of energy products such as coal and electricity, with an objective to maximize its revenue of energy sales at the upper level, where penalty factors for non-electric energy consumption is added to the objective function of the PO. At the lower level, the UAs (the follower) could coordinate its coal-fired boiler and electric boiler to minimize its energy cost, given its heat demand satisfied. In this way, energy prices are attained and employed to provide the incentive for UAs to participate in electric energy substitution. The developed bi-level optimization problem is then transformed into a mixed integer liner programming problem with the Karush-Kuhn-Tucker optimality condition and duality theory employed. Finally, case studies of a park-level IES with the electric load demand, residential heat load demand and industrial heat load demand included are carried out to demonstrate the proposed model.

Identification of coherent generators (CGs) in power systems is one of the key steps in determining controlled islanding strategies. In this chapter, 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 center of inertia (COI) based ones for better representing the dynamic behavior of a given power system, and ten dissimilarity indexes are presented for determining the similarity between 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 utilized to identify CGs. Finally, a modified New England-New York interconnected power system (NENYPS) with a large number of renewables, a simplified actual Western Interconnection power system (WECCPS) in North America, and the eastern part of Guangdong power system in China with a recorded oscillation event happened are utilized to demonstrate the proposed wide-area coherency identification methodology.

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.

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.

An effective power system restoration strategy plays an important role in speeding up the recovery of electricity supply in cases of major blackouts. Determining the start-up sequences of non black-start units (NBSUs) is a major concern in power system restoration and is usually formulated as an optimization problem and solved by optimization algorithms including evolutionary algorithms which usually suffer from slow convergence. So far, analytical methods have not yet been widely adopted in formulating power system restoration strategies, and the majority of which are unit-oriented. However, the operation of a transmission network can hardly be integrated into the unit-oriented restoration strategies, thus the iterations between determining the start-up sequences of NBSUs and optimizing the structure of the transmission network concerned become inevitable. In this paper, a graph-theory-based path-oriented power system restoration strategy is proposed for fast, dynamic and highly adaptive power system restoration. A specified minimum spanning tree (SMST) is introduced to model the path-oriented restoration problem considering practical power system constraints, and a modified Prim's algorithm is proposed to generate the SMST. The optimality of the proposed SMST strategy and the performance of the modified Prim's result are also demonstrated. Finally, the feasibility and efficiency of the developed model and method are verified with a modified version of the New England 10-unit 39-bus power system and an actual power system in Guangdong, China.

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.

The fast optimisation of transient stability emergency control (TSEC) strategy is a key factor of online safeguard for AC–DC interconnected systems. An engineering applicable TSEC algorithm has to obtain the disturbed trajectories of the system by numerical integration, and then do quantised stability knowledge mining. Theoretical analysis and simulation verification reveal that the premise is to identify the unstable mode correctly, and then emergency control decision can be optimised according to the cost performance of the actions. Small integral step is usually needed for accuracy. In order to coordinate the accuracy and computational burden intelligently, this study proposes an algorithm for optimising generator-tripping control, which can adaptively reduce computational burden. If the unstable mode is insensitive to the change of fault parameters (e.g. fault clearing time), this case's time-varying property is more likely to be weak. Only for such cases, large-step Taylor series expansions (LSTSE) can be taken to replace small-step numerical integration (SSNI) and search for the optimal result. Based on its time-varying property, each case can get disturbed trajectories using LSTSE or SSNI adaptively and obtain optimal solution. The excellent performance of this algorithm is verified by simulation of nine Chinese regional power systems under various operating conditions.

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...

Smart grids (SGs) are electric networks that use innovative and intelligent monitoring, control, communication, and self-healing technologies to deliver better connections and operations for generators and distributors, flexible choices for prosumers, and reliability and security of electricity supply. SGs are a complex cyber–physical system by their very nature, and this has impacted the way energy is generated, transported and used. In our 2016 paper [1] , we examined the SG concept in the context of cyber–physical systems (CPSs), and outlined the challenges ahead alongside with fast development of advanced technologies such as Internet of Things, cloud computing, big data and complex networks.

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.

Considering the communication resources limitation, the hybrid-triggered mechanism based distributed control of islanded microgrids cluster is proposed, which can restore the frequency to the rated value and realize the active power sharing when the disturbance occurs. The hybrid-triggered mechanism consists of the self- and event-triggered mechanisms, which are configured at each leader and follower distributed generation to determine the inter-microgrids and intra-microgrid information transmission, respectively. The communication burdens can be sharply reduced since the information is transmitted aperiodically only when the proposed triggering conditions are satisfied under the hybrid-triggered mechanism. Moreover, Zeno behavior is analyzed to be avoided to make the hybrid-triggered mechanism reasonable and practicable for practical islanded microgrids cluster. The simulation verifies the effectiveness of theoretical results.

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.

With the development of smart grid technology, each entity in a power system, from power sources to end users, could have more intensive interactions with some others. In this paper, flexible loads and emerging concentrating solar power (CSP) plants are considered as generalized demand response resources and dispatched cooperatively to mitigate the problem of insufficient power supply during peak load periods. Numerous commercial buildings have great potential to provide dispatching flexibility for a power system through generalized demand response management. Given this background, an optimal operation model is presented with an objective of minimizing the total operational cost of a given commercial building with a CSP plant, a heat pump, inflexible loads, electric vehicles (EVs), lighting systems and air conditioners included. A highly efficient solver YALMIP is next utilized in the MATLAB environment to solve the developed optimization model. Finally, an office building is employed to demonstrate the proposed optimal operation model.

The uncertain output of a photovoltaic (PV) system imposes a problem for the power system concerned to accommodate the PV generation. The energy-stored capability of electric vehicles (EVs) can be utilized to enhance the capability of accommodating PV generation locally. Given this background, an optimal scheduling strategy for EVs and PVs in dynamic time-of-use (TOU) tariff environment is proposed in this paper. The charging/discharging schedules are optimized by EV users/agents with the objective to minimize the charging cost and maximize the discharging profit. Then, the regional operator adjusts the schedule in order to enhance the usage of the PV power output locally. TOU tariffs are updated at set intervals, and can guide charging/discharging schedules of EVs so as to implement valley filling and peak shaving. Finally, a distribution system with 3 residential communities is employed to demonstrate the effectiveness of the proposed method.

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.

Carbon market, as an efficient emission reduction mechanism, has been widely implemented around the world. However, if not being designed properly, carbon market cannot fulfill its original intention of design under the influences of both internal and external disturbances. There is an urgent need for models or tools for ex-ante simulation analysis of the carbon market. Since in a market environment, disturbances will influence the carbon market dynamics through trading behaviors, it is important to accurately model carbon trading behaviors. In this paper, an improved carbon trading behavioral modelling method combining discretized statistical analysis and extreme learning machine (ELM) is proposed. Based on the trading samples of human participants, stochastic behavioral model can be constructed by using discretized statistical analysis method. However, the unevenly distributed number of samples make it difficult to obtain reliable statistical results in some discretized value intervals. Thus, based on the results of discretized statistical analysis, ELM is used to further improve modeling accuracy by utilizing its generalization capability. The case studies validate the feasibility of the proposed method.

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.