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Power systems are undergoing fundamental changes with increasing penetration of renewable energies. A reactive power ancillary service market is of significance to incentivize private providers, such as thermal power units and renewable power plants (RPPs), to participate in maintaining voltage security. Because of the local nature of reactive power, one of the recognized issues is that monopolistic participants may have great market power and strategically manipulate their costs. To pursue economic efficiency, this paper proposes an incentive-compatible mechanism based on Vickery-Clarke-Groves (VCG) auctions for the day-ahead reactive power ancillary service market. The obligations of reactive power ancillary service are quantified in the VCG-based market mechanism. Depending on the expected performance of participants compared with the corresponding obligations, payments are obtained for thermal power units, RPPs, and loads. Hence, providers have the incentive to declare their true costs and undertake obligations. Furthermore, a scarcity price is employed to reward the provision of scarce reactive power resources and ensure reliable market clearing. The proposed mechanism is illustrated using the IEEE 24-bus and 118-bus systems. The results verify the effectiveness of the proposed mechanism. Moreover, high rewards or penalties are observed for voltage weak areas, thereby inducing appropriate investments in reactive power capacities. [ABSTRACT FROM AUTHOR]

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

• Anovel analytical approach for the frequency stability constraint is proposed. • A frequency-constraint unit commitment model is established. • Adecomposition-based solution of the proposed model is introduced. • The effectivenessof the proposed modelisvalidatedin a real-life system. Modern power systems are confronted with great challenges of frequency instability due to the conventional generators constantly being replaced by renewable energy source. Therefore, it is necessary to consider the frequency stability constraints in unit commitment processes to obtain safer operation plans. In this paper, an analytical approach for the frequency stability constraint is proposed. This approach is given by estimating the largest disturbance the system can withstand. Then, a novel frequency constrained unit commitment model is proposed by incorporating the proposed approach into a unit commitment model. The diverse governor response characteristics are considered in the proposed frequency constrained unit commitment model, and the essential inertia and reserve level are accurately estimated. The proposed model is formulated as a mixed-integer nonlinear programming problem, and generalized Benders decomposition is adopted to facilitate the solution generation. The convergence of the decomposition-based solution is proved. Case studies on the New England 39-bus system and a regional system are carried out where the performance of the proposed model is compared with two existing frequency constrained unit commitment models. The results show that the proposed model can guarantee frequency security while preserving the economic factors in power systems with high renewable energy penetration. [ABSTRACT FROM AUTHOR]

Currently, most district heating networks in China are running in the heat-setting mode, which limits the adjustments of combined heat and power (CHP) units. By considering the electrical power system and district heating system together, the peak regulatory capabilities of CHP units can be improved, and renewable energy accommodation can be promoted. In this paper, a tractable integrated heat and electricity dispatch (IHED) model is described that addresses the thermal inertia characteristics of pipelines and buildings to increase flexibility. A water mass method (WMM) for pipeline thermal inertias is proposed to simplify the IHED model by removing the integer variables and differential equations. Benefiting from the WMM, the proposed IHED model is a single-level nonlinear model that is tractable. An efficient iterative algorithm based on the generalized Benders decomposition and a sequential approach combined with the iterative algorithm and Interior Point OPTimizer (IPOPT) are proposed to solve the IHED model. The WMM is tested in thermal inertia simulations compared to an existing node method (NM) and commercial simulation software. The proposed solution strategy is verified in a small-scale system and a practical system. The simulation results are discussed to demonstrate the feasibility and economy of the IHED model proposed here. • A water mass method (WMM) for pipeline thermal dynamics is proposed. • An integrated heat and electricity dispatch (IHED) model considering thermal dynamics is proposed. • A decomposition method and an efficient iterative algorithm are proposed to solve IHED. • A sequential approach combined with iterative algorithm and IPOPT is proposed to improve the optimality of the solution. • The WMM is tested in the simulations compared to an existing node method (NM) and a commercial simulation software. [ABSTRACT FROM AUTHOR]

In this paper, we review the emerging challenges and research opportunities for voltage control in smart grids. For transmission grids, the voltage control for accommodating wind and solar power, fault-induced delayed voltage recovery, and measurement-based Thévenin equivalent for voltage stability analysis are reviewed. For distribution grids, the impact of high penetration of distributed energy resources is analyzed, typical control strategies are reviewed, and the challenges for local inverter Volt–Var control is discussed. In addition, the motivation, state-of-the-art, and future directions of the coordination of transmission system operators (TSO) and distribution system operators (DSO) are also thoroughly discussed. [ABSTRACT FROM AUTHOR]

The decentralized unit commitment problem (DUCP) solved by autonomous distributed agents has been studied by many researchers. Several methods based on traditional distributed optimization approaches have been proposed for solving the DUCP. However, these existing methods do not determine the unit commitment (UC) decisions in a distributed manner or cannot theoretically guarantee convergence and optimality. This paper proposes a method for formulating and solving the DUCP using a recently published parallelizable augmented Lagrangian method, referred to as SDM-GS-ALM. SDM-GS-ALM is a parallelizable method for solving large-scale non-convex problems, and under mild conditions, its optimality and convergence have been proven. The DUCP can be modelled as a mixed-integer linear programming problem, and in this case, its global optimal solution can be obtained via SDM-GS-ALM. This paper focuses on the DUCP in integrated heat and electricity systems. The proposed model and solution method are both general and can be applied directly to solve the DUCP in power systems. The optimality and convergence of the proposed method are demonstrated in case studies. In three test systems, the gaps between the optimal objective values of the centralized UC problem and the DUCP are less than 0.001%. [ABSTRACT FROM AUTHOR]

Highlights • CNN is designed for transient stability prediction with high-dimension dynamic data. • Ensemble method is used to avoid the diverse errors. • Active learning and fine-tuning are integrated to rapidly update the classifier. • The updating scheme can obtain an accurate classifier by using small dataset. • The computational time of data and classifier updating are greatly reduced. Abstract Data driven methods are playing an increasingly important role in transient stability assessment, primarily because of the availability of large annotated datasets. Nevertheless, training data cannot cover all the possible operating conditions of a modern power system with variable power generations and loads. The classifier should adjust to the near-future operation condition in limited time, and this adjustment may be hindered by the computational time of the simulations and classifier training. To dramatically reduce the computational cost, this paper presents a systematic approach for building and updating an accurate transient stability classifier. First, the time-series trajectories of generators after disturbance are used as the inputs, and then a convolutional neural network (CNN) ensemble method is proposed to generate the transient stability predictor using these multi-dimensional data. To reduce the misclassification of instability, different cost weights are considered for the stable and unstable instances in the loss function. When the operating condition changes substantially and makes the pre-trained classifier unavailable, the active learning and fine-tuning techniques are integrated to update the classifier with good performance using fewer labelled instances and short computational time. The simulation results of two power systems illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Power system security assessment is an important and challenging problem. Large variations in loads and power generation present increased risks to the secure operation of power systems. This study proposes a distributed deep network structure for power system security knowledge discovery based on multitask learning to monitor and control power grids more properly and effectively. First, a deep neural network structure based on the deep belief network (DBN) is designed to non‐linearly extract deep and abstract features layer‐by‐layer for total transfer capability (TTC) regression tasks. Then, a distributed training algorithm for the deep structure is developed to accelerate the training process. Furthermore, multitask learning is adopted by grouping and training‐related tasks together to improve the task performance. Finally, the accuracy and efficiency of the deep structure are evaluated using the Guangdong Power Grid in China. The simulation results demonstrate that the proposed model can outperform the existing shallow models in terms of accuracy and stability and can meet the requirements of online computing efficiency. [ABSTRACT FROM AUTHOR]

Highlights • A model for detecting power system security weak spots is proposed. • Microclimates are considered in the proposed model. • A deep neural network is designed to extract deep features. • Parallelism is applied to enhance the training efficiency. • The proposed model can greatly improve the task accuracy. Abstract With the integration of renewable energy and microclimate-sensitive loads, secure and economic power system operation is becoming an increasingly important and complex problem. Therefore, based on big data from power systems and meteorological systems, a deep spatial-temporal data-driven model is proposed to predict and detect power system security weak spots during a future period. First, microclimates are considered in the proposed model. Then, a deep neural network structure is designed to extract deep features layer by layer for security weak spot detection. Furthermore, model simplification and parallelism as well as data parallelism are applied. Finally, the proposed model is evaluated based on the Guangdong Power Grid in China. The simulation results demonstrate that (1) power system security weak spots have spatial-temporal and microclimate-sensitive characteristics; (2) the deep model considering microclimates can greatly improve the task accuracy of online applications; and (3) simplification and parallelism can significantly enhance the training efficiency. [ABSTRACT FROM AUTHOR]

Short‐term voltage stability (SVS) is a serious issue in modern power systems. In China, the East China Power Network is especially vulnerable to short‐term voltage instability due to its increasing dependence on electrical power from external through high‐voltage direct current (HVDC) transmission lines. To study the SVS, a criterion/index is first required to evaluate the SVS of power systems. However, the currently used practical criteria cannot effectively evaluate the influence of controlling strategies (such as regulating dynamic VAR reservation) of power systems. Therefore, a practical and continuous SVS index (SVSI) based on voltage curves is proposed in this study. The proposed SVSI can quantify the extent of SVS in power systems, thus can be used in the optimisation target of preventive control for improving the SVS of power systems. Moreover, the SVSI consists of three components, and each component reflects a feature of the voltage curve in terms of SVS. The SVSI is essentially a model‐free method for assessing the SVS of power systems, so it is robust. Three verifying cases and one application case are presented to show the validity of the SVSI and the feasibility of the SVSI in practical applications. [ABSTRACT FROM AUTHOR]

With the rapid increase in the proportion of natural gas for power generation, the operations of the power system are increasingly relevant to the supply of natural gas. To improve the reliability of power generation, some additional gas storage equipment should be set up in gas power plants. This paper considered different constraints, objective functions and gas flow/power flow equations at different time scales and built multi-time period optimized configuration and scheduling of gas storage models for gas power plants. Case studies demonstrated the following: (1) The most economical gas storage capacity could be obtained by the first optimal configuration model considering the long-term seasonal fluctuations of gas loads and gas prices. (2) The second model can not only obtain the minimum gas storage configuration capacity to meet the short-term peak load demand but also obtain the day-ahead gas storage schedule. That is, the remaining gas storage capacity at the initial and final points of each day. Moreover, the results can provide some suggestions on the reconstruction of natural gas pipelines. (3) The charge scheme of the gas storage obtained from the third scheduling model considering the uncertainty of the power load forecast can adapt to a certain fluctuating load interval. [ABSTRACT FROM AUTHOR]

This paper proposes a new distance-based distributionally robust unit commitment (DB-DRUC) model via Kullback–Leibler (KL) divergence, considering volatile wind power generation. The objective function of the DB-DRUC model is to minimize the expected cost under the worst case wind distributions restricted in an ambiguity set. The ambiguity set is a family of distributions within a fixed distance from a nominal distribution. The distance between two distributions is measured by KL divergence. The DB-DRUC model is a “min-max-min” programming model; thus, it is intractable to solve. Applying reformulation methods and stochastic programming technologies, we reformulate this “min-max-min” DB-DRUC model into a one-level model, referred to as the reformulated DB-DRUC (RDB-DRUC) model. Using the generalized Benders decomposition, we then propose a two-level decomposition method and an iterative algorithm to address the RDB-DRUC model. The iterative algorithm for the RDB-DRUC model guarantees global convergence within finite iterations. Case studies are carried out to demonstrate the effectiveness, global optimality, and finite convergence of a proposed solution strategy. [ABSTRACT FROM AUTHOR]

The recent rapid development of distributed generation (DG) necessitates the consideration of transmission–distribution interactions in voltage stability assessment (VSA), and how to perform a transmission–distribution-coupled static VSA (TDVSA) has become a pressing problem. To resolve this urgent issue, this paper first theoretically analyzes the impacts of the voltage-maintaining capacity and low-voltage tripping of DG units on the static voltage stability of an integrated transmission–distribution system and then proposes a distributed TDVSA method that fully considers these impacts when assessing the voltage stability of the integrated system. As the core of the method, a distributed continuation power flow algorithm is designed that includes a distribution-equivalencing-based predictor, a distributed transmission–distribution corrector, and a step-length regulator and requires only a limited amount of data to be exchanged between the transmission system operator (TSO) and the distribution system operator (DSO). Numerical simulations show that even in the case of low DG penetration, a cascading low-voltage tripping event can significantly degrade an integrated system's voltage stability, and only the proposed distributed TDVSA method can produce an accurate assessment. In addition, the number of iterations between the TSO and DSO is shown to be limited, indicating the potential of applying the proposed method to future grids. [ABSTRACT FROM PUBLISHER]

To prevent the wind units from cascading trip faults, the concept of an autonomous voltage security region (AVSR) has been discussed in previous studies. However, the AVSR is sometimes not sufficiently large due to the insufficient dynamic reactive power reserve (DRPR) under normal conditions, resulting in secure operation problems and a large amount of wind curtailment. Therefore, reservation of an appropriate capacity of dynamic reactive power is a crucial issue in wind farms, so a DRPR optimisation model to enlarge the AVSR is proposed in this work. The computation of the optimal DRPR includes six steps and involves the exchange of information between wind farms and the control centre. The computation is rapid and can be applied to real wind systems online. The proposed method demonstrated good performance in a simple test case and a real system in Northern China. [ABSTRACT FROM AUTHOR]

The stochastic fluctuation characteristics of intermittent renewable energy sources and energy loads, as well as their multi-energy interactions and dependencies, have negligible effects on the operation and analyses of integrated energy systems. Determining how to model the probability characteristics of such systems with high calculation accuracy using limited scenarios is a major difficulty of uncertainty description. This study proposes the use of an optimum quantile method based on the Wasserstein distance metric to generate a typical scenario set in an integrated energy system considering energy correlations based on weather conditions. The use of discrete variables, as opposed to continuous variables based on sampling techniques such as Monte Carlo simulations, sets this study apart from other studies. The uncertainties of a typical network containing power, heat, and gas are analysed, and the results show that the proposed method can produce a typical scenario set with good precision. [ABSTRACT FROM AUTHOR]

The wide application of renewable energy sources in power systems has a significant influence on both electrical and tightly coupled systems. The aim of this paper is to build a comprehensive system model of a natural gas and electricity coupled network. The concept of distributed stack nodes was introduced to overcome the shortcoming of adjusting active power by a single gas-fired unit to achieve power balance. On this basis, the impact of the active power output uncertainty of wind farms was studied, and the interval flow of the natural gas system was analyzed by two proposed methods. The results were compared with Monte Carlo stochastic simulation. Case studies demonstrated the effectiveness of the proposed method and led to the conclusion that the uncertainty of wind power has a significant impact on the steady-state operation of natural gas systems. Interval solutions could provide great insights into the operating and planning of coupled systems with wind power uncertainty. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the impacts of intermittent renewable energy sources (RESs) and stochastic energy loads on the operation and uncertainties of an integrated energy system (IES). In our analysis, we use a first order reliability method (FORM) to estimate the failure probabilities, which are crucial for ensuring the reliability of the gas supply and surplus power absorption. The Hasofer Lind and Rackwitz Fiessler (HLRF) algorithm is introduced to solve the FORM optimization model while considering the stochastic behaviours and dependencies of multiple energy sources. A mathematical case is presented to demonstrate the use of the FORM in the estimation of failure probability, and the results are validated using the Latin hypercube sampling theories, including the Iman and Stein methods. The results of a failure probability analysis for an ideal IES are provided to illustrate the proposed technique. The failure probability can be used to improve IES operation and planning and ensure better reliability. [ABSTRACT FROM AUTHOR]

Mobile charging is proposed as a brand-new charging solution in response to the relatively slow construction of charging facilities. Operating a mobile charging service system involves scheduling mobile charging vehicles (MCVs) and batteries owned by the mobile charging service operator (MCSO), which is important to improve its economic efficiency and has a nonnegligible impact on the power system. In this paper, a bilevel optimization framework for logistics and electricity is developed for MCSOs to achieve joint optimization of planning and operation of the mobile charging service system. For transportation logistics, the upper level plans the size of the MCV fleet and routes MCVs in the dynamic traffic network, which can accommodate dynamic changes in the traffic network and make a reasonable route arrangement. For battery energy management, the lower level plans the battery number and optimizes battery charging and discharging at energy service stations, which can track the electric quantity and the state of each battery during the whole process and accurately describe the matching relationship between batteries in a battery swapping scenario. The upper and lower levels are coupled through the battery swapping behavior between MCVs and energy service stations. Through the iteration and adjustment of the two levels, the results optimize the MCSO's total net profit as much as possible and provide assistance to the power system using service capacity margins. Numerical experiments of a certain scale are used to verify the validity of the proposed framework. • A bilevel optimization framework combines vehicle routing with battery scheduling. • A mobile charging vehicle routing model considers the dynamic traffic network. • An optimization model for battery scheduling tracks each battery's behavior. • Comparison of the proposed framework and benchmarking method using a case study. [ABSTRACT FROM AUTHOR]

Integrated heat and electricity dispatch is crucial to exploit synergistic benefits from integrated energy systems. However, this requires information from both electricity systems and district heating systems (DHSs), which are managed by an electricity control center (ECC) and district heating control centers (DHCCs), respectively. For reasons pertaining to privacy, communication, dimension, and compatibility, it is not practical for DHCCs to send detailed models to the ECC. Therefore, a new feasible region method is proposed for formulation of new DHS models, which exploit the flexibility of DHSs with consideration of building thermal inertia. A greedy method is developed to solve the new modified feasible region models by calculating a series of linear programming problems efficiently. Then the new models are sent to the ECC to be used in central dispatch considering DHS operation constraints, i.e. integrated heat and electricity dispatch. The modified models are similar to conventional power plants and storages, and are thus compatible with current dispatch programs. Case studies verify the effectiveness of the method. Although some conservativeness exists, the total cost and wind energy curtailment are both decreased compared to conventional decoupled dispatch. [ABSTRACT FROM AUTHOR]

Because distributed generations are extensively integrated into active distribution grids (ADGs), the transmission and distribution coordinated economic dispatch (TDCED) should be investigated to optimally dispatch the generation resources and evaluate the locational marginal prices (LMPs) of the entire system. In this paper, the TDCED problem is formulated, and a new heterogeneous decomposition (HGD) algorithm is proposed. In the HGD algorithm, the transmission LMP at the boundary bus and ADGs' power are exchanged among transmission and ADGs. The optimality and convergency of HGD are proven and numerically verified. In addition, several related issues are further discussed: a) methods to improve the convergency of HGD, b) considerations regarding security issues and voltage constraints in TDCED and corresponding modified HGD algorithms, and c) the practicality of HGD in either separate or transparent interaction modes of transmission and distribution in future grids. Numerical simulations indicate that the generation resources are optimally utilized and that LMPs are reasonably evaluated in TDCED. Congestion in the traditional isolated dispatch mode can also be prevented. Moreover, simulations on several systems from T6D2 to T300D60 verify that the HGD algorithm is an efficient and robust algorithm with limited communication burdens to solve TDCED. [ABSTRACT FROM PUBLISHER]

Integrated energy systems (IESs) are under development for a variety of benefits. District electricity and heating systems (DEHSs) deliver electricity and heat, the most common energy demands, to end-users. This paper studies the interactions in a DEHS considering the time-scale characteristics. Interaction mechanisms of a DEHS are analyzed. A disturbance in one system influences another system through coupling components, depending on the disturbance, operating characteristics, and control strategies. A model of the main components in DEHSs is presented. The time scale characteristics are studied based on a dynamic comparison of the different components. Then the interaction process is divided into four stages; each is a quasi-steady state. A quasi-steady multi-energy flow model is proposed and calculated, with a heating network node type transformation technique developed. A case study with detailed results and discussion of 3 types of disturbance is presented to verify the methods. The results present the interactions between the electricity and the system. It is suggested that attention should be paid both on the fast hydraulic process and slow thermal process for system security and economic operation. [ABSTRACT FROM AUTHOR]

A multivariate statistical analysis (MSA)‐based power‐grid‐partitioning method is proposed. Considering the effectiveness of automatic voltage regulation of generators, network modelling is based on the quasi‐steady‐state sensitivity of voltage with respect to reactive power. The MSA‐based power‐grid‐partitioning method contains two stages: a quantitative method for determining the appropriate number of zones and allocation of nodes into a number of zones. The proposed method is applied to an automatic voltage control system and an N − 1 robust pilot node selection method is presented. Simulation studies on IEEE 14‐bus, 39‐bus systems and a real provincial power system in China are described to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Storage-like devices (SLDs), which include energy storage systems as well as devices with similar properties such as electric vehicles, can be exploited for load leveling. However, to prevent simultaneous charging and discharging of an SLD, complementarity constraints should be included in the optimization model, which makes the problem strongly non-convex. Mixed-integer programming (MIP) methods are commonly used to solve such problems; however, this results in long solution time to achieve an approximate optimal solution. Therefore, a method to efficiently find optimal solutions of load-leveling problems with SLDs is desirable. Here, we report a load-leveling optimization model for a system with SLDs and show that the complementarity constraints can be exactly relaxed under two sufficient conditions so that a convex relaxed model can be solved instead. Moreover, the exactness of the relaxation can be determined prior to solving the relaxed model, and the sufficient conditions are usually satisfied in practical situations. The numerical studies verify the theoretical analysis and show that an equally good optimal solution of the load-leveling problem with SLDs can be obtained far more efficiently by using the proposed method than a commonly used MIP method. [ABSTRACT FROM AUTHOR]

In this letter, an improved real-time, short-term voltage stability monitoring method is introduced. The impact of voltage magnitude oscillation on the calculation of the Lyapunov exponent is analyzed, and a phase rectification method to eliminate the negative influence of oscillation is proposed. The simulation work was conducted on the provincial power grid at Guangdong in China. Based on our simulation results, the proposed method is expected to improve the effectiveness of short-term voltage stability monitoring. [ABSTRACT FROM PUBLISHER]

• Data products can reduce uncertainty in decision-making problems with uncertainty. • Data revenues and costs are defined by uncertainty reduction and privacy exposure. • Electricity-side and data-side transactions are linked with data sharing framework. • The profit allocation mechanism ensures the profitability of electricity retailer. Information incompletion always forces market participants to make decisions under uncertainty in energy transactions, while obtaining related data is a feasible way for them to reduce uncertainty and gain profits. However, the application of data transactions is not yet mature. To extend the application range of data transactions and concretize the data transaction model, a novel framework of electricity-side and data-side transactions linked with data sharing is proposed from the electricity retail perspective in this paper. The necessity and processes of data sharing between the electricity retailer and data suppliers are elaborately illustrated in the framework. Data revenues and data costs are analyzed according to uncertainty reduction and information provision. Considering the widely used two-settlement system of electricity markets, data revenues and data costs can be expressed in closed forms and their differences are net profits that are regarded to drive the data flow. Furthermore, an ex-post profit allocation mechanism is matched to appropriately allocate the net profits between the electricity retailer and data suppliers in the data sharing model. By comparison with the Shapley value method, the mechanism is less time-consuming and will ensure the profitability of the electricity retailer. Finally, a practical case with real data is employed to realize the results proposed in the theoretical analysis, and the feasibility of the data sharing model and profit allocation mechanism is validated. [ABSTRACT FROM AUTHOR]

Integration of high levels of wind power penetration is an important feature of the emerging smart transmission grid. To address the dramatic voltage fluctuations and wind turbine generators (WTGs) cascading trip faults, which have been greatly challenged the wind pool area's operation, a hierarchical automatic voltage control system to support wind power integration (Wind-AVC) is designed and presented in this paper. For each wind farm, an autonomous wind farm voltage controller (WFVC), which takes the details of the wind farm side grid into account, is applied to optimize the voltage and reactive power distribution inside the wind farm. Three different control modes are implemented to consider all the terminal voltages of WTGs as well as the dynamic Var reserves. A control center voltage controller (CCVC) is designed to coordinate all the distributed WFVCs. A security-constrained optimal power flow (SCOPF) based preventive control is presented, which ensures all the wind farms’ voltage satisfying the operational constraints for all the possible N − 1 scenarios. The Wind-AVC system has been put into operation in Northern China and was found to be effective in mitigating the voltage fluctuations and reducing the cascading trip risks. [ABSTRACT FROM AUTHOR]

Inverter-based microgrids will be vital components of future energy systems with large-scale integration of renewable energy resources. To cope with the low inertia problem induced by inverters, fully distributed control has been shown to effectively improve system stability owing to its fast response capability. Investigating the property of multiple delays in distributed control architecture is critical to guide its practical implementation in cyber–physical systems. Compared with power systems equipped with decentralized or centralized controllers, inverter-based cyber–physical microgrids under fully distributed control have a more complicated and heterogeneous delay environment. Existing, proven delay properties in centralized systems, such as the delay merge property that measurement and control delays of decentralized power system stabilizers can be merged for small-signal stability analysis, are not applicable to distributed systems To investigate the novel delay properties in distributed systems, we first propose a block-matrix-based cyber–physical model considering heterogeneous delays for inverter-based microgrids under fully distributed control. Then, we derive and prove the novel properties of heterogeneous delay in such systems. Finally, we perform numerical tests based on a real-world 7-bus islanded microgrid and a modified PG&E 69-bus microgrid under fully distributed frequency control. Our findings show that the impacts of measurement delay and control delay of one distributed controller on system stability are equivalent only if all distributed controllers are identical. Moreover, as the diversity of distributed controllers increases, the heterogeneous delay property becomes more significant. • Inverter-based microgrid under fully distributed control is considered. • Heterogeneous delays are formulated in the proposed cyber–physical model. • Novel properties of distributed microgrids are found and proven. • Aggregated delay property is satisfied only if specific conditions hold. [ABSTRACT FROM AUTHOR]

The digital transformation of power systems into cyber-physical systems (CPSs) is the inevitable trend of modern power systems with the integration of large-scale renewable energy. The in-depth interdependence of cyber and physical spaces leads to more complicated external environments for such cyber-physical power systems (CPPSs) and brings great challenges to the resilience of CPPSs. A resilient CPS imposes strict requirements for its ability to cope with high-impact, low-probability cyber-physical disturbances. To better study the vulnerability and resilience of CPPSs, several representative blackouts from the past two decades are reviewed from the cyber-physical perspective. Inspired by general system theory, this study offers a framework with three key features of a CPPS and presents the three-layer interdependences from facilities to functions. The differences between CPPS resilience and conventional power system resilience are also emphasized. Thereafter, the study discusses the influence of cyber-physical disturbances from natural hazards, cyberattacks, and human-in-the-loop on the resilience of CPPSs. Accordingly, a survey of the state-of-the-art resilience-oriented techniques for CPPS in the face of natural hazards is organized based on quantitative metrics as well as planning and operation attributes. Regarding the resilience against cyberattacks, relevant cutting-edge research is reviewed in terms of prevention, detection, and mitigation strategies. Furthermore, from the cyber-physical-social perspective, the exploitation of social behaviors to inform the design of the physical system and the cyber system to ultimately enhance the resilience of CPPSs is also studied. Based on the findings from this research, the remaining challenges and the broad prospects of cyber-physical resilience enhancement techniques are also discussed. • A CPPS has three-layer interdependences on facilities, topologies, and functions. • External environments consist of hazards, cyberattacks, and human behaviors. • Resilience-oriented techniques for CPPSs in three categories are reviewed. • Cyber-physical collaborative techniques have broad prospects. [ABSTRACT FROM AUTHOR]

Inverter-based microgrids under fully synchronous distributed control have broad prospects in multi-agent-based energy systems. Such a system is a typical cyber–physical system. Studying the impact of a synchronous control period on system stability is the pre-requisite for practical deployment of a distributed control architecture with a proper synchronous period. However, simulation-based methods provide little theoretical insight into how synchronous control period influences system stability. Additionally, control-period determination methods based on the convergence analysis of a distributed algorithm cannot sufficiently consider the dynamic characteristic of a physical inverter-based microgrid. In this paper, a synchronized cyber–physical state-transition model of distributed inverter-based microgrids considering multiple time delays from a cyber–physical perspective is proposed. The stability criterion of the system under certain synchronous control periods with multiple time delays is obtained. Furthermore, considering multiple stochastic switching delays with infinite combinations of states, the corresponding stability criterion is also obtained based on switched system theory. The stable interval of the synchronous control period for such cyber–physical power systems can be determined based on the proposed stability criteria and algorithm. A real-world microgrid under fully distributed frequency control is tested and simulated by Simulink simulations to verify the accuracy of the obtained stable synchronous control period. • Theoretical insight into how synchronous period influences microgrids is provided. • A synchronized cyber–physical model for time-delay microgrids is proposed. • Stability criterion are provided to identify unstable synchronization with delays. • Relative values of multiple delays affect the microgrid stability. • Switching delay leads to a stricter stable period compared with constant delay. [ABSTRACT FROM AUTHOR]

• Power-traffic pricing interaction modeled as a multi-leader Stackelberg game. • Divergent pricing goals and imperfect information of network operators considered. • A decentralized coordinated pricing framework proposed considering privacy issues. • Influences of charging electric vehicle penetration and traffic demand discussed. The growing demand for the fast charging of electric vehicles has created stronger interdependency between the operation of urban traffic networks and power distribution networks. This paper investigates the necessity and feasible mechanism of coordinated pricing in coupled urban power-traffic networks. The retail charging pricing of the power distribution network operator and congestion pricing of the urban traffic network operator will simultaneously affect the routing and charging decision choices for electric vehicles. Then, the response of multiclass vehicles—i.e., the aggregated traffic flow and charging load—will in return affect the operation performance—i.e., power loss and traffic congestion—of the coupled networks. However, due to operators' differing pricing goals and limited access to user information, the actual response for vehicles might deviate from the respective expectation of the two operators under isolated pricing. With the growing penetration of electric vehicles, such deviation might become more evident, calling for a focus on pricing coordination. To investigate the necessity of coordinated pricing, the Stackelberg game framework is employed to model the pricing interactions between operators (leaders) and vehicles (followers). Accounting for privacy issues, a decentralized coordination framework of the two networks is proposed for information sharing. The numerical test results confirm the negative impacts of isolated pricing and demonstrate the effectiveness of coordinated pricing. Sensitivity analysis is conducted to examine the influence of critical parameters: e.g., charging electric vehicle penetration and total travel demand. [ABSTRACT FROM AUTHOR]

The power industry in China has grown significantly over the past decade, spurring the adoption of system-wide automatic voltage control (AVC) technology to meet stricter requirements for security and economical power system operation. To cope with the rapidly developing and frequently changing Chinese power grid, an AVC scheme based on adaptive zone division is introduced in this paper. Logically, this type of system has a three-level hierarchical structure, but, here, both secondary voltage control (SVC) and tertiary voltage control (TVC) are implemented via software at the same control center. The control zones are no longer fixed but are reconfigured online and updated in accordance with variations in the grid structure. The technical details of these procedures are presented here. The implementation of TVC (which is based on an online reactive optimal power flow) and SVC are also discussed, together with some technical details on improvements to their reliability and robustness. Some results obtained from field-site applications based on similar-days testing rather than simulations are employed to evaluate the performance and improvements of the AVC system. This system has been installed at over 20 control centers in China. [ABSTRACT FROM PUBLISHER]

• Data products can reduce uncertainty in decision-making problems with uncertainty. • Data value is defined to describe profit enhancement through uncertainty reduction. • Closed-form formulas of data value rates can be directly used for data valuation. • Parametric and nonparametric estimation can meet different needs in data valuation. Decision-making in energy transactions should consider inherent uncertainty. Obtaining data from data transactions and sharing can reduce the uncertainty, therefore mitigate risks in decision-making, and enhance the economic benefits of market participants. In this paper, the quantitative relationships between the uncertainty reduction and the profit enhancement are proposed and solved, which could be regarded as the contribution of the data. Regarding the electricity retailers as the core market participants, a typical optimization model with load demand uncertainty according to the two-settlement market system is constructed, the optimal solution is analyzed under different standard deviations of the load. Considering that data products, which come from end users' private data, such as their historical electricity consumption data and future electricity consumption schedule, can contribute to the uncertainty reduction, a data valuation paradigm and an explicit expression of data value are proposed. Hence, closed-form formulas of data value rate are derived, and their guidance and assistance in the decision-making of electricity retailers are illustrated. To improve the comprehensiveness and accuracy of the valuation, parametric estimation and nonparametric estimation methods are investigated in the distribution fitting of load forecast errors, and the corresponding data valuation can be conducted directly and quickly. Finally, a numerical case is studied using load data from the Commission for Energy Regulation to demonstrate the generality and feasibility of the data valuation and the effectiveness of theoretical results. [ABSTRACT FROM AUTHOR]

• A dynamic gas flow model using GPM is proposed. • The GPM is linear and accurate. • The GPM has fewer constraints and variables than the finite difference method. • A GPM-based integrated electricity-gas dispatch (IEGD) is proposed. • The GPM-based IEGD has a shorter calculation time than the traditional IEGD. In this paper, a generalized phasor method (GPM) for modeling gas flow dynamics is proposed and applied in integrated electricity-gas dispatch (IEGD). An accurate dynamic model of natural gas systems is described by partial differential equations (PDEs). In existing studies, PDEs are often approximated by using the finite difference method (FDM), but many variables and constraints are required in that approach. The proposed GPM transforms PDEs into ordinary differential equations (ODEs) by using the Fourier method and then eliminates spatial variables by analytically solving the transformed ODEs. Since spatial differences are avoided, the GPM has fewer constraints and variables than the traditional FDM. Moreover, the GPM contains only linear complex algebraic equations, so IEGD problems based on the GPM can be efficiently solved. In case studies, IEGD models based on the GPM and FDM are compared for two integrated electricity-gas systems. The results illustrate that the proposed GPM and FDM yield similar accuracies, but the GPM has a shorter calculation time than the FDM. [ABSTRACT FROM AUTHOR]

This paper solves the vehicle assignment problem of an operating entity with dispatchable vehicles (e.g., an urban Integrated Energy-Traffic System (IETS) coordinator or an e-hailing company) in the context of energy-traffic coordinated modeling and analysis. Current research on electric vehicle (EV) scheduling and optimization has broadly assumed that human beings are rational decision-makers and obey the scheduling plan. In contrast, we proposed a user-centric dynamic pricing scheme that improves the system performance by incentivizing human participants to follow the expected behavioral patterns from a Cyber-Physical-Human System (CPHS) perspective. The interdisciplinary fields of power systems, traffic systems, and human decision-making were used to study the interactions in the CPHS. The proposed incentive scheme was applied to the urban IETS scenarios based on real-world Global Positioning System (GPS) data of EV taxis, and the results showed that it effectively optimized the integrated system. • Dynamic pricing scheme proposed for dispatching electric vehicles in an urban area. • Proposed scheme combines energy and traffic systems. • Unlike current research, human users are not assumed to be rational. • Proposed scheme incentivizes users to follow expected behavioral patterns. • Proposed scheme applied to real-world EV taxi data for validation. [ABSTRACT FROM AUTHOR]

• An Information Masking method for MIQP problem is proposed. • The proof for the precise recovery of the optimal solution is given. • Information masking mechanism for MIQP is designed in a cloud-edge framework. • Privacy preservation and computation cost are elaborated to adapt to real applications. The increasing development of information and communication technologies in power grid makes it possible to optimize the energy management of numerous physical users with cloud-based service, while users' concerns about privacy security have attracted increasingly more attention. To attack this challenge, this paper proposes the information masking (IM) method for the energy management problem in the form of mixed-integer quadratic programming (MIQP). Additionally, the feasibility and optimality of the recovered solution in the IM of MIQP is proven and given in the form of two lemmas. Next, the implementation mechanism of IM is designed based on a cloud-edge framework. Furthermore, the general requirements of the IM of MIQP are elaborated with respect to privacy security and computational cost to better accommodate practical applications. Finally, the optimal schedule of microgrid with on-site generators and flexible demand resources is modelled and simulated to demonstrate the feasibility and effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

• The heat recovery steam generator (HRSG) is modeled and integrated in the modeling of combined heat and power units. • An economic dispatch model considering HRSG characteristics is formulated. • An efficient algorithm for solving the economic dispatch model is provided. • The benefits of considering HRSG characteristics in economic dispatch are analyzed. The installation of combined heat and power (CHP) units has rapidly increased due to their energy efficiency and environmental advantages. The broad application of CHP units requires the joint dispatch of power and heating systems, in which the modeling of CHP units plays a pivotal role. However, existing CHP models face the contradiction between accuracy and solvability. In this paper, we propose a novel CHP model that well balances these two issues. The heat exchange theory is employed to derive the models of HRSGs. Based on the models, an economic dispatch model for CHP systems that considers the characteristics of heat recovery steam generators (HRSGs) is formulated. A corresponding algorithm for solving the proposed model is presented as well. In case studies, the impacts of the HRSG characteristics on the CHP operation region and dispatch results are studied, and the performances of the proposed model and the conventional model are compared. The results indicate that the proposed model outperforms the conventional model in terms of the level of optimality achieved during economic dispatch, benefiting from the more accurate description of the electro-thermal coupling relationship of CHP units. [ABSTRACT FROM AUTHOR]

• DGOL simplified model is proposed for short-term voltage stability (STVS) analysis. • The mechanism of short-term voltage instability is analyzed based on DGOL model. • A model and data hybrid driven real-time STVS monitoring method is introduced. • A two-dimensional index is proposed to evaluate the STVS margin. • Quantitative evaluation of the influence of rotor angle and motor on STVS. This paper proposes a model and data hybrid-driven short-term voltage stability assessment method. First, a double-generator-one-load (DGOL) simplified model for short-term voltage stability analysis is introduced, and the mechanism of the occurrence of short-term voltage instability is analysed based on a DGOL model. Then, a model and data hybrid-driven online application algorithm is proposed, which includes a pre-fault analysis module and a real-time monitoring module. At last, a two-dimensional index is proposed to evaluate the short-term voltage stability margin. Simulation work is first conducted based on the DGOL system to illustrate the advantage of the proposed simplified model, and on the EPRI 36-bus system to demonstrate the effectiveness of online application algorithm. [ABSTRACT FROM AUTHOR]

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

With the rapid growth of distributed generators, the distribution static voltage stability assessment (D-VSA) becomes as important as the transmission voltage stability assessment (T-VSA). However, the T-VSA and D-VSA normally treat the transmission and distribution separately, neglecting the interactions between transmission and distribution (T-D). This letter studies the impact of coupled T-D on VSA via physical analysis and numerical simulation. We observe that without considering the T-D interaction, D-VSA may exaggerate the actual load margin significantly, and T-VSA may either exaggerate or underestimate the actual load margin, both of which are unreliable. Hence, the coupled T-D interactions must be considered in both T-VSA and D-VSA. [ABSTRACT FROM AUTHOR]

Storage-concerned economic dispatch (ED) problems with complementarity constraints are strongly non-convex and hard to solve. In this letter, an exact relaxation method is proposed to relax the non-convex ED to a convex form under two sufficient conditions, which can be further generalized for other problems of similar structures. A mathematical proof is provided, and the numerical tests verify the effect of this method. [ABSTRACT FROM PUBLISHER]

This letter presents a real-time electric vehicle (EV) smart-charging method (N-RT), that not only considers currently connected EVs, but also uses a prediction of the EVs that are expected to plug in in the future. Numerical tests show that the N-RT method improves valley-filling under various levels of prediction accuracy. [ABSTRACT FROM AUTHOR]

Transmission–distribution coordinated energy management (TDCEM) is recognized as a promising solution to the challenge of high distributed energy resource (DER) penetration, but there is a lack of a distributed computation method that universally and effectively works for the TDCEM. To bridge this gap, this paper presents a generalized master–slave-splitting (G-MSS) method. This method is based on a general-purpose transmission–distribution coordination model called G-TDCM, which enables the G-MSS to be applicable to most of the central functions of the TDCEM. In this G-MSS method, a basic heterogeneous decomposition (HGD) algorithm is first derived from the heterogeneous decomposition of the coupling constraints in the optimality conditions of the G-TDCM. Its optimality and convergence properties are proved. Then, inspired by the sufficient conditions for convergence, a modified HGD algorithm that utilizes the subsystem's response function is developed and demonstrated to converge faster. The distributed G-MSS method is then demonstrated to successfully solve a series of central functions of the TDCEM, e.g., power flow, contingency analysis, voltage stability assessment, economic dispatch, and optimal power flow. The severe issues of over-voltage and erroneous assessment of the system security that are caused by DERs are thus resolved by the G-MSS method with modest computation cost. [ABSTRACT FROM AUTHOR]

• Impact of information superiority among charging service operators is analysed. • Nash-Stackelberg-Nash game describing EV charging service operator competition. • Stochastic user equilibrium model considering heterogeneous customer preferences. • Parameter- and sample-driven charging service operator pricing models are proposed. • Influence of endogenous and exogenous scenario factors is comprehensively discussed. The rapid development of electric vehicles (EVs) has increased the demand for public fast-charging stations (FCSs). Multiple self-interested charging service operators (CSOs) in an urban transportation network compete with each other through pricing signals to maximize their respective profits, where the information about customer demands and heterogeneous preferences work as important decision criteria. With the prevailing data transaction, the information superiority differences between CSOs may significantly affect the competitive pattern in the charging market. In this study, a Nash-Stackelberg-Nash game framework is established to investigate the strategic pricing of CSOs under different information distribution scenarios. The customer response pattern, namely, the routing and charging choices of EV drivers to pricing signals, is described via a computationally efficient extended stochastic user equilibrium model comprehensively considering heterogeneous customer preferences and decision stochasticity. Then, aiming at different data forms, both parameter-driven and sample-driven CSO pricing models are proposed. The pricing models are reformulated as a tractable single-level mixed-integer linear program, and the game equilibrium is solved alternately via Gauss-Seidel iteration. Numerical simulations are conducted to analyze the impact of single CSO information superiority and double CSO information distribution on CSO profitability and customer experience in both data forms. The influences of endogenous factors like CSO data amount and processing ability, as well as exogenous scenario parameters like customer preference distribution and perception error distribution, are discussed. [ABSTRACT FROM AUTHOR]

With the growing application of electric vehicles (EVs), it is of great significance to have a deep understanding of EV users driving and charging patterns for charging forecasting. However, the rapid growth scale of EV taxis with charging patterns that are closely coupled with human behaviours of temporal–spatial charging choices was not compatible with most previous coordinated strategies. Unlike the majority of existing approaches, a large volume of second‐level EV global positioning system (GPS) data was used to study the behaviour patterns of EV users. In practise, a mobile crowdsensing system that records GPS data and transmits information to the server was deployed in a fleet of electric‐taxi cabs in Shenzhen, China, making it possible to record the exact behaviour of each vehicle. Travelling and charging statuses of EVs were recorded and analysed into different characteristics of behaviour for each user. The load forecast methods proved to be more effective with more knowledge of both history and real‐time data. [ABSTRACT FROM AUTHOR]

This paper presents methods for assessing the statistical interdependencies between solar irradiance and the outside temperature, which are used to assess the correspondence between the operation of technologies to meet the heat demands and the PV output. It is important to be able to assess the impact of heating technology changes and increased PV penetration on power system requirements. Considering the correlation between the PV outputs and heat loads, there exist multiple effects of weather variations on the “Source-Grid-Load.” Heat load, photovoltaic (PV), electric air conditioner (EC), natural gas-fuelled combined heat and power (CHP), and central heating boiler models are established to study the influences of the solar irradiance and the temperature of the outdoors. The energy dependence impacts on the “Source-Grid-Load” are simulated in a 41-bus distribution network, and the paper's results show how the correspondence between the temperature and solar irradiance can impact the system's requirements. [ABSTRACT FROM AUTHOR]

Currently, a custom-designed configuration of different renewable technologies named the integrated energy system (IES) has become popular due to its high efficiency, benefiting from complementary multi-energy technologies. This paper proposes an information entropy approach to quantify uncertainty in an integrated energy system based on a stochastic model that drives a power system model derived from an actual network on Barry Island. Due to the complexity of co-behaviours between generators, a copula-based approach is utilized to articulate the dependency structure of the generator outputs with regard to such factors as weather conditions. Correlation coefficients and mutual information, which are effective for assessing the dependence relationships, are applied to judge whether the stochastic IES model is correct. The calculated information values can be used to analyse the impacts of the coupling of power and heat on power flows and heat flows, and this approach will be helpful for improving the operation of IES. [ABSTRACT FROM AUTHOR]