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

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

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

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

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

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

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

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

This paper presents the Distributed Model Predictive Control (D-MPC) of a wind farm equipped with fast and short-term Energy Storage System (ESS) for optimal active power control using the fast gradient method via dual decomposition. The primary objective of the D-MPC control of the wind farm is power reference tracking from system operators. Besides, by optimal distribution of the power references to individual wind turbines and the ESS unit, the wind turbine mechanical loads are alleviated. With the fast gradient method, the convergence rate of the DMPC is significantly improved which leads to a reduction of the iteration number. Accordingly, the communication burden is reduced. Case studies demonstrate that the additional ESS unit can lead to a larger wind turbine load reduction, compared to the conventional wind farm control without ESS. Moreover, the efficiency of the developed D-MPC algorithm is independent from the wind farm size and is suitable for the real-time control of the wind farm with ESS.

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

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

A probabilistic equivalent method for doubly fed induction generator (DFIG) based wind farms is proposed in this paper. First, the wind farm equivalent model is assumed to be composed of three types of equivalent DFIGs with different dynamic characteristics. The structure of equivalent model remains constant, whereas the parameters change with the migration of different scenarios in the wind farm. Then, historical meteorological data are utilized to investigate the probability distribution of key equivalent parameters, such as capacity, wind speed and electrical impedance to the point of common coupling. Each type of equivalent DFIG is further clustered into several groups according to their active power output. Combinations are created to generate representative scenarios. The probabilistic equivalent model of wind farm is finally achieved after removing invalid combinations. Most matched representative scenarios can be predicted according to the real-time measurement. The equivalent model is applied to the probabilistic power flow calculation and the stability analysis of test systems.

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

This paper presents a dynamic discrete-time Piece- Wise Affine (PWA) model of a wind turbine for the optimal active power control of a wind farm. The control objectives include both the power reference tracking from the system operator and the wind turbine mechanical load minimization. Instead of partial linearization of the wind turbine model at selected operating points, the nonlinearities of the wind turbine model are represented by a piece-wise static function based on the wind turbine system inputs and state variables. The nonlinearity identification is based on the clustering-based algorithm, which combines the clustering, linear identification and pattern recognition techniques. The developed model, consisting of 47 affine dynamics, is verified by the comparison with a widely-used nonlinear wind turbine model. It can be used as a predictive model for the Model Predictive Control (MPC) or other advanced optimal control applications of a wind farm.

This paper presents distributed model predictive control (D-MPC) of a wind farm for optimal active power control using the fast gradient method via dual decomposition. The objectives of the D-MPC control of the wind farm are power reference tracking from the system operator and wind turbine mechanical load minimization. The optimization of the active power control of the wind farm is distributed to the local wind turbine controllers. The D-MPC developed was implemented using the clustering-based piece-wise affine wind turbine model. With the fast gradient method, the convergence rate of the D-MPC has been significantly improved, which reduces the iteration numbers. Accordingly, the communication burden is reduced. A wind farm with ten wind turbines was used as the test system. Case studies were conducted and analyzed, which include the operation of the wind farm with the D-MPC under low and high wind conditions, and the dynamic performance with a wind turbine out of service. The robustness of the D-MPC to errors and uncertainties was tested by case studies with consideration of the errors of system parameters.

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

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

EVs show great potential to cope with the intermittency of renewable energy sources (RES) and provide demand side flexibility required by the smart grid.On the other hand, the EVs will increase the electricity consumption. Large scale integration of EVs will probably have substantial impacts to the power system. This paper presents a methodology to transform driving behavior of persons into the one of cars in order to analyze the driving pattern of electric vehicles (EVs) based on the National Travel Surveys. In the proposed methodology, a statistical process is used to obtain the driving behavior of cars by grouping the survey respondents according to the driving license number and car number and mapping the households with similar characteristics. The proposed methodology was used to carry out the driving pattern analysis in the Nordic region. The detailed driving requirements and the charging/discharging availability of the vehicles along the day were obtained. Two types of EV availabilities were studied in this paper considering different charging/discharging conditions of EVs for the power system integration, i.e. EV availability all day andEV availability at home. The results show that the daily driving requirements of the Nordic region are not very intensive. The driving patterns of the vehicles in the Nordic region vary on weekdays and weekends. The two types of EV availabilities are quite different from each other.

What needs to be developed from the concept of “Smart Grid” is that: when renewable energy sources are absolutely prevailing in power generation, distributed power generation and distributed energy storage systems are widespread across the grid, and electric vehicle charging loads are prevailing in power load demands, how can the power grid support electric power as a core secondary energy source, undertake the role of a bridge between primary energy and end-use energy, and achieve the coordination and the optimization in macro energy perspective; how to guarantee the security of both macro energy and environment as well as the reliability of electricity. If a new term is needed, it should be Comprehensive Energy Network, not Energy Internet.

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

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

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

The increasing number of distributed energy resources connected to power systems raises operational challenges for the network operator, such as introducing grid congestion and voltage deviations in the distribution network level, as well as increasing balancing needs at the whole system level. Control and coordination of a large number of distributed energy assets requires innovative approaches. Transactive control has received much attention due to its decentralized decision-making and transparent characteristics. This paper introduces the concept and main features of transactive control, followed by a literature review and demonstration projects that apply to transactive control. Cases are then presented to illustrate the transactive control framework. At the end, discussions and research directions are presented, for applying transactive control to operating power systems, characterized by a high penetration of distributed energy resources. © 2016, The Author(s).

International Conference on Life System Modeling and Simulation, LSMS 2017 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017, Nanjing, China, September 22-24, 2017, Proceedings, Part III

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

China's 12th Five-Year Plan (2011–2015) identifies smart grid development as a national priority in the energy sector. It is expected that through development of a strong smart grid across the nation, overall power grid security and efficiency can be largely improved and significant growth of relevant industrial sectors can be achieved. Consequently, different research and development programs are taking place, many of which have pilot activities involved to validate and demonstrate the technical and social–economic effectiveness of new technologies. This article gives an overview of the governmental policy and strategy that drives smart grid development, followed by detailed descriptions of research activities with respect to the implementation of China's smart grid vision. Based on the analysis and evaluation of difficulties and problems in the current system, the remaining gaps are identified for future activities to enable a smooth transition toward a strong and smart power grid in China.

Network reconfiguration after complete blackout of a power system is an essential step for power system restoration. A new node importance evaluation method is presented based on the concept of regret, and maximisation of the average importance of a path is employed as the objective of finding the optimal restoration path. Then, a two-stage method is presented to optimise the network reconfiguration strategy. Specifically, the restoration sequence of generating units is first optimised so as to maximise the restored generation capacity, then the optimal restoration path is selected to restore the generating nodes concerned and the issues of selecting a serial or parallel restoration mode and the reconnecting failure of a transmission line are next considered. Both the restoration path selection and skeleton-network determination are implemented together in the proposed method, which overcomes the shortcoming of separate decision-making in the existing methods. Finally, the New England 10-unit 39-bus power system and the Guangzhou power system in South China are employed to demonstrate the basic features of the proposed method.

Primary energy has the potential to bring challenges to the reliability, economic, and eco-friendliness of global electric power systems. The concept of electric power security are proposed, including many factors that are not considered in power system reliability analysis, such as coal supply for power system, fuel price for electricity power market, carbon emissions of power generation, and so on. It is broader than power system reliability and security, which means that providing energy in the form of reliable, economical, and eco-friendly electric power. Following an account of existing blackout defence methods and systems, the processes of transforming various kinds of primary energy into electric power and the interactions between them are described, particularly with regard to flows of energy, capital, and information. Factors that limit the liquidity of these flows are defined as “generalized congestion”, and the challenges presented by primary energy are named “primary energy congestion”. China's dilemma on coal supply for power generation is presented in some detail as an example of primary energy congestion. To cope with impacts of the primary energy on electric power systems, an idea is introduced to extend the scope of existing coordinated blackout defence systems to account for primary energy, which would lead to an integrated tool to provide decision support to power system operators accounting for primary energy congestion.

The penetration of distributed generation (DG) in an active distribution system could affect the fault current level and characteristics of the original/traditional system. Thus, the protection system originally configured for a traditional distribution system should be updated to meet the operational requirement of an active distribution system. In developing a fault diagnosis method for an active distribution system, these factors should be taken into account to attain accurate fault diagnosis result. Given this background, a multi-agent system (MAS) based fault diagnosis method is presented for active distribution systems. A methodological framework is first proposed, and the communication mechanism in the MAS architecture addressed. Next, the fault diagnosis agent is designed with an existing analytical model for fault diagnosis embedded, and a correlation factor employed to represent the coupling relationship between related bus agents. The impacts of the information loss and information distortion on the accuracy of fault diagnosis results are also analyzed separately. A MAS based fault diagnosis strategy is then developed with a communication and negotiation mechanism utilized for attaining the global optimal fault diagnosis result. Test cases carried out on a modified 32-node active distribution system demonstrate the presented model and method for fault diagnosis.

As an effective measure for regulating the greenhouse gas emissions, Emission Trading Scheme (ETS) has been widely implemented. Market designers and participants are in urgent need of decision-support tools with quantitative risk analysis capability. However, one of the biggest challenges lies in that the physical laws, economic laws and human behaviors are intertwined in the emission risk prevention and control problem. While the physical laws and economic laws can be modeled mathematically, human behaviors cannot be easily modeled. In this paper, a hybrid simulation method is adopted for the sensitivity analysis of different factors influencing emission market. First, the interactions between the power system and the climate system are examined and the hybrid simulation method is introduced. Second, based on the hybrid simulation, sensitivity analysis of key influencing factors on emission price is conducted. Finally, based on the practices of a power system blackout defense system that has been developed by the authors, an emission risk prevention and control framework is proposed.

The paper presents a new control strategy to enhance the ability of reactive power support of a doubly fed induction generator (DFIG) based wind turbine during serious voltage dips. The proposed strategy is an advanced low voltage ride through (LVRT) control scheme, with which a part of the captured wind energy during grid faults is stored temporarily in the rotor's inertia energy and the remaining energy is available to the grid while the DC-link voltage and rotor current are kept below the dangerous levels. After grid fault clearance, the control strategy ensures smooth release of the rotor's excessive inertia energy into the grid. Based on these designs, the DFIG's reactive power capacity on the stator and the grid side converter is handled carefully to satisfy the new grid code requirements strictly. Simulation studies are presented and discussed.

Plug-in electric vehicles (PEVs) are increasingly popular in the global trend of energy saving and environmental protection. However, the uncoordinated charging of numerous PEVs can produce significant negative impacts on the secure and economic operation of the power system concerned. In this context, a hierarchical decomposition approach is presented to coordinate the charging/discharging behaviors of PEVs. The major objective of the upper-level model is to minimize the total cost of system operation by jointly dispatching generators and electric vehicle aggregators (EVAs). On the other hand, the lower-level model aims at strictly following the dispatching instructions from the upper-level decision-maker by designing appropriate charging/discharging strategies for each individual PEV in a specified dispatching period. Two highly efficient commercial solvers, namely AMPL/IPOPT and AMPL/CPLEX, respectively, are used to solve the developed hierarchical decomposition model. Finally, a modified IEEE 118-bus testing system including 6 EVAs is employed to demonstrate the performance of the developed model and method.

To ensure the small-signal stability of a power system, power system stabilizers (PSSs) are extensively applied for damping low frequency power oscillations through modulating the excitation supplied to synchronous machines, and increasing interest has been focused on developing different PSS schemes to tackle the threat of damping oscillations to power system stability. This paper examines four different PSS models and investigates their performances on damping power system dynamics using both small-signal eigenvalue analysis and large-signal dynamic simulations. The four kinds of PSSs examined include the Conventional PSS (CPSS), Single Neuron based PSS (SNPSS), Adaptive PSS (APSS) and Multi-band PSS (MBPSS). A steep descent parameter optimization algorithm is employed to seek the optimal PSS design parameters. To evaluate the effects of these PSSs on improving power system dynamic behaviors, case studies are carried out on an 8-unit 24-bus power system through both small-signal eigenvalue analysis and large-signal time-domain simulations.

The integration of large amount of wind power into a power system imposes a new challenge for the secure and economic operation of the system. It is necessary to investigate the impacts of wind power generation on the dynamic behavior of the power system concerned. This paper investigates the impacts of large amount of wind power on small signal stability and the corresponding control strategies to mitigate the negative effects. The concepts of different types of wind turbine generators (WTGs) and the principles of the grid-connected structures of wind power generation systems are first briefly introduced. Then, the state-of-the-art of the studies on the impacts of WTGs on small signal stability as well as potential problems to be studied are clarified. Finally, the control strategies on WTGs to enhance power system damping characteristics are presented.

In this paper, an economic dispatch model, which can take into account the uncertainties of plug-in electric vehicles (PEVs) and wind generators, is developed. A simulation based approach is first employed to study the probability distributions of the charge/discharge behaviors of PEVs. The probability distribution of wind power is also derived based on the assumption that the wind speed follows the Rayleigh distribution. The mathematical expectations of the generation costs of wind power and V2G (vehicle to grid) power are then derived analytically. An optimization algorithm is developed based on the well-established particle swarm optimization (PSO) and interior point method to solve the economic dispatch model. The proposed approach is demonstrated by the IEEE 118-bus test system.

In smart grid, a strong communication network plays a very important role, which makes the power grid more controllable and observable. It is a very tough task to study the mutual impact between these two systems due to their different characteristics. This paper proposes a generic and systematic approach for quantitatively studying the impact of communication failures on power grid's measurement and control functions. And an example is used to illustrate how to apply the approach in studying the impact of communication failure on a stability control system.

This paper presents an autonomous wind farm voltage controller based on Model Predictive Control (MPC). The reactive power compensation and voltage regulation devices of the wind farm include Static Var Compensators (SVCs), Static Var Generators (SVGs), Wind Turbine Generators (WTGs) and On-Load Tap Changing (OLTC) Transformer, and they are coordinated to keep the voltages of all the buses within the feasible range. Moreover, the reactive power distribution is optimized throughout the wind farm in order to maximize the dynamic reactive power reserve. The sensitivity coefficients are calculated based on an analytical method to improve the computation efficiency and overcome the convergence problem. Two control modes are designed for both voltage violated and normal operation conditions. A wind farm with 20 wind turbines was used to conduct case studies to verify the proposed coordinated voltage control scheme under both normal and disturbance conditions.

This paper presents a method to describe nonlinear oscillation of power system. Specifically, this paper uses the con trolling mode which is identified by the extended equal-area criterion (EEAC). The energy of center equal point (CEP) or far end point (FEP) in each swing is used to express the oscillation energy of the controlling mode, so the trend of swing energy variations can be used to show the behavior of the nonlinear oscillation. In addition, the rationality of the method is analyzed and proved by the simulation results.

An eigenstructure-based performance index is proposed in this paper to measure the dynamic performance of the system as well as control efforts. Calculation of this index is based on eigenstructure of the closed loop system and the design parameters; it does not rely on control structures. Therefore, this index can be applied for solving structurally constrained control problems. A tuning scheme based on this index is proposed for coordinating power system stabilizers (PSSs) and supplementary damping controllers (SDCs) for flexible AC transmission systems (FACTS) devices to damp inter-area oscillations of systems and to optimize their control efforts under multiple operating conditions. Both PSSs and SDCs utilize control structures as a low order single-input-single-output phase lead-lag compensator. Wide-area signals are employed to upgrade their effectiveness in damping inter-area oscillations. Time delays caused by usage of wide-area signals are also considered in the tuning scheme. Results of simulation on a four-machine two-area system and the New England and New York interconnected system show that the proposed index is effective in measuring dynamic performance of the system and the coordinatedly tuned PSSs and SDCs based on this index can robustly damp inter-area oscillations of systems with optimized control efforts.

This paper addresses the issue of improving the conventional iterative method used for integrated extended equal area criterion (IEEAC) and single machine equivalent (SIME) for multi-swing transient stability limit analysis in power system operation. Effects of the complicated phenomena on the conventional method, i.e., nonmonotonic variation of the stability margin and the isolated stability domain (ISD), are studied. Accordingly, the conventional method is improved for both efficiency and accuracy. With this knowledge, an improved sensitivity-based iterative method for searching multi-swing transient stability limit is proposed. The advantage of this method is that the complicated phenomena are fully considered and their effects are either overcome or avoided as much as possible. Case studies on the ten-generator New England power system are presented to verify the effectiveness and efficiency of this improved method.

In this paper, the inherent mechanism of social benefits associated with smart grid development is examined based on the pressure state response (PSR) model from resource economics. The emerging types of technology brought up by smart grid development are regarded as pressures. The improvements of the performance and efficiency of power system operation, such as the enhanced capability of accommodating renewable energy generation, are regarded as states. The effects of smart grid development on society are regarded as responses. Then, a novel method for evaluating social benefits from smart grid development is presented. Finally, the social benefits from smart grid development in a province in northwest China are carried out by using the developed evaluation system, and reasonable evaluation results are attained.

It has become more and more demanding to investigate the impacts of wind farms on power system operation as ever-increasing penetration levels of wind power have the potential to bring about a series of dynamic stability problems for power systems. This paper undertakes such an investigation through investigating the small signal and transient stabilities of power systems that are separately integrated with three types of wind turbine generators (WTGs), namely the squirrel cage induction generator (SCIG), the doubly fed induction generator (DFIG), and the permanent magnet generator (PMG). To examine the effects of these WTGs on a power system with regard to its stability under different operating conditions, a selected synchronous generator (SG) of the well-known Western Electricity Coordinating Council (WECC three-unit nine-bus system and an eight-unit 24-bus system is replaced in turn by each type of WTG with the same capacity. The performances of the power system in response to the disturbances are then systematically compared. Specifically, the following comparisons are undertaken: (1) performances of the power system before and after the integration of the WTGs; and (2) performances of the power system and the associated consequences when the SCIG, DFIG, or PMG are separately connected to the system. These stability case studies utilize both eigenvalue analysis and dynamic time-domain simulation methods.

An efficient and robust case sorting algorithm based on Extended Equal Area Criterion (EEAC) is proposed in this paper for power system transient stability assessment (TSA). The time-varying degree of an equivalent image system can be deduced by comparing the analysis results of Static EEAC (SEEAC) and Dynamic EEAC (DEEAC), the former of which neglects all time-varying factors while the latter partially considers the time-varying factors. Case sorting rules according to their transient stability severity are set combining the time-varying degree and fault messages. Then a case sorting algorithm is designed with the “OR” logic among multiple rules, based on which each case can be identified into one of the following five categories, namely stable, suspected stable, marginal, suspected unstable and unstable. The performance of this algorithm is verified by studying 1652 contingency cases from 9 real Chinese provincial power systems under various operating conditions. It is shown that desirable classification accuracy can be achieved for all the contingency cases at the cost of very little extra computational burden and only 9.81% of the whole cases need to carry out further detailed calculation in rigorous on-line TSA conditions.

Understanding human participants' trading behaviors is important for market designers. When analyzing the sensitivity of a control variable, it is of crucial importance to keep all other variables fixed, as well as human behaviors. In this paper, a hybrid interactive simulation methodology is introduced and utilized to model emission trading behaviors of generation companies. By extracting information from human-subjected experiments under several scenarios, a stochastic model for multi-agent is constructed and verified. Furthermore, influences of different emission reduction level on emission prices are studied based on multi-agent simulation. Combining the complementary features between experimental and agent-based computational methods, the hybrid interactive simulation can help studying human behaviors involved research problems.

With the development of strong and smart grid, power grid and communication system become highly interconnected and mutually dependent. Complex network approach is applied in this paper to describe the interactive characteristics of hybrid power and communication systems in terms of topologies. Qualitative analysis of the impact of communication system on power grid is straightforward. However, quantitative analysis is challenge. This paper proposes a generic and systematic approach for quantitatively studying the impact of communication failures on power control system's measurement and control functions.

This paper proposes algorithms for optimal siting and sizing of Energy Storage System (ESS) for the operation planning of power systems with large scale wind power integration. The ESS in this study aims to mitigate the wind power fluctuations during the interval between two rolling Economic Dispatches (EDs) in order to maintain generation-load balance. The charging and discharging of ESS is optimized considering operation cost of conventional generators, capital cost of ESS and transmission losses. The statistics from simulated system operations are then coupled to the planning process to determine the optimal siting and sizing of storage units throughout the network. These questions are investigated using an IEEE benchmark system.

Electric vehicle (EV) users' traveling and charging willingness tends to be affected by many certain and uncertain factors. An experimental economics (EE) based simulation method can reflect the game behaviors of participants. It demands enormous resources of qualified human participants, and the comparability among behaviors in a series of repeated experiments. However these 2 conditions are not easy to be satisfied. In order to solve these problems, this paper presents a multi-agent model to describe the respondents' behaviors, which is based on the extraction of multidimensional probabilistic information from their questionnaires. Based on the model, a series of agent-based Monte Carlo simulations have been run repeatedly, which enables the sensitivity analysis. The contribution degrees of EV features on the traveling willingness of the respondents are therefore compared quantitatively such as travel distance, range anxiety, and the number of charge times, etc.

The concern of the environment and energy sustainability requests a crucial target of CO2 abatement and results in a relatively high penetration of renewable energy generation in the transmission system. For maintaining system reliability and security, the transmission company (TRANSCO) has to make strategic planning to handle the uncertainty challenges from the intermittent renewable energy resources. In this paper, a stochastic multi-period multi-objective transmission planning (MPMOTP) model is proposed to reduce correlated uncertainties from renewable energy generation, conventional generation, demand-side variations, market price volatility, and transmission configuration. Three objectives, i.e. social CO2 reduction benefit, energy purchase and network expansion cost and power delivery profit, are optimized simultaneously by a developed two-phase multi-objective particle swarm optimization (MOPSO) method. The feasibility and effectiveness of the proposed uncertainty-averse MPMOTP model have been verified by the IEEE 24-bus test system.

Unstable equilibrium point (UEP) and dynamic saddle point (DSP) are completely different concepts in the power system transient stability analysis. However, some research still hold the view that UEP accurately reflects the power system transient stability and try to define and find the so-called related UEP. In this paper, the differences and correlations between UEP and DSP in reflecting system transient stability characteristics are illustrated from the perspective of theoretical analysis and simulation examples.

The controllable charging/discharging load of electric vehicles (EVs) is believed to be one of the most promising resources for flexible dispatching in the future smart grid. Power system operation could benefit from the reasonable control of EV charging behaviors. On the other hand, EV aggregators should be given some economic incentives for participating in power system optimal dispatching. In this paper, a bargaining mechanism is proposed to work out the reasonable economic compensation for EV aggregators. First, EV charging behaviors and the model for EV aggregators are presented. Secondly, the day-ahead system dispatch is addressed and the dispatching model considering the willingness and capabilities of EV charging loads presented. The bargaining strategies for both the EV aggregators and the power system dispatchers are next investigated. Finally, the proposed bargaining strategies and procedure are demonstrated through a modified IEEE 30-bus test system.