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Abstract The carbon market plays a critical role in promoting the transition toward renewable energy sources and reducing greenhouse gas emissions in the electricity generation and transmission. Extant research has overlooked the dynamic bilateral causality that exists between electricity and carbon markets. Moreover, these studies have frequently treated the macroeconomic effect as exogenous. To bridge this research gap, this paper presents a holistic modeling framework that comprehensively captures the intertwined nature of electricity and carbon markets and their concomitant interactions with the overarching economy. The suggested modeling framework is an integration of three principal modules, namely, a carbon market, an electricity market, and economic system. This synergistic blend provides an exhaustive understanding of the entire market operation cycle. It offers detailed clearance rules, and most importantly, it adopts a macroeconomic systematic modeling approach for evaluating the impact emanating from the interconnected electricity and carbon markets. To illustrate the practicality and effectiveness of the proposed approach, a case study anchored on empirical data sourced from the electricity and carbon markets in China is conducted. The empirical findings underscore the fact that incorporating a green certificate market into the modeling framework can precipitate a reduction in greenhouse gas emissions. Additionally, the results indicate that expanding the scale of the green certificate market from 1.9% in 2021 to 33% by 2023 will increase the generation of green electricity by 10%.

The nuclear event risk (NER) is an important and disputed factor that should be reasonably considered when planning the pathway of nuclear power development (NPD) to assess the benefits and risks of developing nuclear power more objectively. This paper aims to explore the impact of nuclear events on NPD pathway planning. The influence of nuclear events is quantified as a monetary risk component, and an optimization model that incorporates the NER in the objective function is proposed. To optimize the pathway of NPD in the lowcarbon transition course of power supply structure evolution, a simulation model is built to deduce alternative NPD pathways and corresponding power supply evolution scenarios under the constraint of an exogenously assigned carbon emission pathway (CEP); moreover, a method is proposed to describe the CEP by superimposing the maximum carbon emission space and each carbon emission reduction (CER) component, and various CER components are clustered considering the emission reduction characteristics and resource endowments of different power generation technologies. A case study is conducted to explore the impact of NER and its risk valuation uncertainty on NPD pathway planning. The method presented in this paper allows the impact of nuclear events on NPD pathway planning to be quantified and improves the level of coordinated optimization of benefits and risks.

Abstract The effective use of wind energy is an essential part of the sustainable development of human society, in particular, at the recent unprecedented pressure in shaping a low carbon energy environment. Accurate wind resource and power forecasting play a key role in improving the wind penetration. However, it has not been well adopted in the real‐world applications due to the strong stochastic characteristics of wind energy. In recent years, the application boost of deep learning methods provides new effective tools in wind forecasting. This paper provides a comprehensive overview of the forecasting models based on deep learning in the field of wind energy. Featured approaches include time‐series‐based recurrent neural networks, restricted Boltzmann machines, convolutional neural networks as well as auto‐encoder‐based approaches. In addition, future development directions of deep‐learning‐based wind energy forecasting have also been discussed.

The decreasing cost of solar photovoltaics (PVs) and battery storage systems is driving their adoption in the residential distribution system, where more consumers are becoming prosumers. Accompanying this trend is the potential roll-out of home energy management systems (HEMSs), which provide a means for prosumers to respond to externalities such as energy price, weather, and energy demands. However, the economic operation of prosumers can affect grid security, especially when energy prices are extremely low or high. Therefore, it is paramount to design a framework that can accommodate the interests of the key stakeholders in distribution systems—namely, the network operator, prosumer, and aggregator. In this paper, a novel transactive energy (TE)-based operational framework is proposed. Under this framework, aggregators interact with the distribution grid operator through a negotiation process to ensure network security, while at the lower level, prosumers submit their schedule to the aggregator through the HEMS. If network security is at risk, aggregators will send an additional price component representing the cost of security (CoS) to the prosumer to stimulate further response. The simulation results show that the proposed framework can effectively ensure the economic operation of aggregators and prosumers in distribution systems while maintaining grid security.

Abstract Non‐intrusive load monitoring (NILM) is essential for understanding consumer power consumption patterns and may have wide applications such as in carbon emission reduction and energy conservation. Determining NILM models requires massive load data containing different types of appliances. However, inadequate load data and the risk of power consumer privacy breaches may be encountered by local data owners when determining the NILM model. To address these problems, a novel NILM method based on federated learning (FL) called Fed‐NILM is proposed. In Fed‐NILM, instead of local load data, local model parameters are shared among multiple data owners. The global NILM model is obtained by averaging the parameters with the appropriate weights. Experiments based on two measured load datasets are performed to explore the generalization capability of Fed‐NILM. In addition, a comparison of Fed‐NILM with locally trained NILM models and the centrally trained NILM model is conducted. Experimental results show that the Fed‐NILM exhibits superior performance in terms of scalability and convergence. Fed‐NILM out performs locally trained NILM models operated by local data owners and approaches the centrally trained NILM model, which is trained on the entire load dataset without privacy protection. The proposed Fed‐NILM significantly improves the co‐modelling capabilities of local data owners while protecting the privacy of power consumers.

With the growing interdependence between the electricity system and the natural gas system, the operation uncertainties in either subsystem, such as wind fluctuations or component failures, could have a magnified impact on the reliability of the whole system due to energy interactions. A joint reserve scheduling model considering the cross-sectorial impacts of operation uncertainties is essential but still insufficient to guarantee the reliable operation of the integrated electricity and natural gas system (IEGS). Therefore, this paper proposes a day-ahead security-constrained unit commitment (SCUC) model for the IEGS to schedule the operation and reserve si-multaneously considering reliability requirements. Firstly, the multi-state models for generating units and gas wells are established. Based on the multi-state models, the expected unserved energy cost (EUEC) and the expected wind curtailment cost (EWC) criteria are proposed based on probabilistic methods considering wind fluctuation and random failures of components in IEGS. Furthermore, the EUEC and EWC criteria are incorporated into the day-ahead SCUC model, which is noncon-vex and mathematically reformulated into a solvable mixed-integer second-order cone programming (MISOCP) problem. The proposed model is validated using an IEEE 30-bus system and Belgium 20-node natural gas system. Numerical results demonstrate that the proposed model can effectively schedule the energy reserve to guarantee the reliable operation of the IEGS considering the multiple uncertainties in different subsystems and the cross-sectorial failure propagation.

Aiming at the high-dimensional uncertainties of restoration process, an optimization model for distribution system restoration control is proposed considering expected restoration benefits, expected restoration costs, and security risks of the overall restoration scheme. In the proposed model, the effect of security control on restoration process is actively analyzed considering the security control costs of preventive, emergency, and correction controls. A two-layer decision support framework for distribution system restoration decision support system (DRDSS) is also designed. The upper layer of the proposed framework generates the pre-adjustment schemes of operation mode for energized power grid by load transfer and selects the optimal pre-adjustment scheme and the corresponding partitioning scheme based on the partition adjustment results of each pre-adjustment scheme. In addition, it optimizes the spatial-temporal decision-making of the inter-partition connectivity. For each partition, the lower layer of the proposed framework pre-selects the units and loads to be restored according to the pre-evaluated restoration income, generates the table of alternative restoration scheme for coping with uncertain events through simulation and deduction, and evaluates the risk and benefit of each scheme. For the uncertain events in the actual restoration process, the current restoration scheme can be adaptively switched to a sub-optimal scheme or re-optimized if necessary. Meanwhile, the proposed framework provides an information interaction interface for collaborative restoration with the related transmission system. A 123-node test system is built to evaluate the effectiveness and adaptability of the proposed model and framework.

Nuclear power development is a complex issue spanning cyber, physical, and social systems that is essential to achieving energy security and climate goals. With the ongoing worldwide trend towards carbon neutrality, the positioning of nuclear power in energy mix should be reconsidered. This paper aims to present a systematic review of current research on optimization of nuclear power development. The concept of cyber – physical – social system in energy (CPSSE) is adopted, which provides a suitable perspective and enables the review of relevant studies to achieve some novel insights. Based on the CPSSE, firstly, a research framework is established and the main research elements in optimization are identified, followed by a proposed conceptual risk-based optimization model. Secondly, current studies are analyzed and classified into four categories according to the research boundary. The status quo and limitations are discussed. It is found that the research results of nuclear-specific issues have not been well integrated into the optimization of nuclear power. As a relatively reliable power supply, nuclear power is capable of maintaining power and electricity adequacy of the whole system, especially in the case of power shortage caused by long-period low output of renewable energy or extreme external disasters. This superiority should not be ignored in the optimization. Other critical factors that should be further considered include disruptive technologies, nuclear safety, energy policies, and stakeholder behaviors. Finally, suggestions are given for future research.

Abstract The ever‐increasing competition in electricity retail markets has become one of the pivotal causes for electricity retailers to provide attractive packages to consumers for enhancing their market shares. Based on the current user‐side time‐of‐use electricity price mechanism in China, a bi‐level optimization model for designing a time‐phased and quantity‐incentive electricity package is proposed for electricity retailers considering bounded rationality of consumers. In the upper‐level model, the optimization objective is to maximize the electricity purchase and sale revenue of a specified retailer. In the lower‐level model, consumers are modelled as bounded rational decision‐makers whose objective is to purchase the electricity package with the highest value, considering the value of the excess electricity of the package. In addition, consumer psychology theory is introduced to describe the bounded rational responses of consumers to electricity packages. Simulations based on data of residential consumers in a city of China illustrate that by properly determining package quotas in different time periods, electricity retailers can guide consumers to change consumption time and volume, and hence, increase their revenues by increasing trading volume or reducing electricity procurement costs in forward bilateral transactions and spot markets.

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

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

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

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

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

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

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

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

Complex phenomena such as prolongedly undamped ultra-low frequency oscillation (ULFO) and eigenmode re-excitation are observed in the simulations of hydroelectric power systems. Emphases are put on nonlinearities and mode interactions, which cannot be analyzed by traditional eigen-analysis methods. In order to investigate the mechanism of the evolvement of the nonlinear dynamic process in ULFO, this paper proposes a method to analyze the mode interactions quantificationally. First, a disturbed trajectory is decoupled into a set of time-varying components. Second, transfer matrices of eigenmodes are extracted along the trajectory. Third, consecutive sequences of eigenvalues and trajectories of components are formed by a proposed technique. Based on the decoupled components and transfer matrices, the mechanisms of mode interactions and inheritance relationships between eigenmodes are analyzed. The causes and developments of the above complex phenomena are revealed by the proposed method in a test two-machine system. Meanwhile, the accuracy of the eigenmode matching technique is verified in the New England system.

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

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

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

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

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

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

Abstract Climate change has become one of the most important issues for the sustainable development of social well-being. China has made great efforts in reducing CO2 emissions and promoting clean energy. Pilot Emission Trading Systems (ETSs) have been launched in two provinces and five cities in China, and a national level ETS will be implemented in the third quarter of 2017, with preparations for China’s national ETS now well under way. In the meantime, a new round of China’s electric power system reform has entered the implementation stage. Policy variables from both electricity and emission markets will impose potential risks on the operation of generation companies (GenCos). Under this situation, by selecting key variables in each domain, this paper analyzes the combined effects of different allowance allocation methods and power dispatching models on power system emission. Key parameters are set based on a provincial power system in China, and the case studies are conducted based on dynamic simulation platform for macro-energy systems (DSMES) software developed by the authors. The selected power dispatching models include planned dispatch, energy saving power generation dispatch and economic dispatch. The selected initial allowance allocation methods in the emission market include the grandfathering method based on historical emissions and the benchmarking method based on actual output. Based on the simulation results and discussions, several policy implications are highlighted to help to design an effective emission market in China.

Abstract 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.

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.

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

In [1], the complete affiliation of Yusheng Xue is as follows: Y. Xue (corresponding author) is with the School of Electrical Engineering, Southeast University, Nanjing, China, and he is also with State Grid Electric Power Research Institute (NARI Group Corporation), Nanjing, China, and State Key Laboratory of Smart Grid Protection and Operation Control, Nanjing, China.

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.

A reasonable islanding strategy of a power system is the final resort for preventing a cascading failure and/or a large-area blackout from occurrence. In recent years, the applications of wide area measurement systems (WAMS) in emergency control of power systems are increasing. Therefore, a new WAMS-based controlled islanding scheme for interconnected power systems is proposed. First, four similarity indexes associated with the trajectories of generators are defined, and the weights of these four indexes are determined by using the well-developed entropy theory. Then, a coherency identification algorithm based on hierarchical clustering is presented to determine the coherent groups of generators. Secondly, an optimization model for determining controlled islanding schemes based on the coherent groups of generators is developed to seek the optimal cutset. Finally, a 16-generator 68-bus power system and a reduced WECC 29-unit 179-bus power system are employed to demonstrate the proposed WAMS-based controlled islanding schemes, and comparisons with existing slow coherency based controlled islanding strategies are also carried out.

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.

Traditional experimental economics methods often consume enormous resources of qualified human participants, and the inconsistence of a participant's decisions among repeated trials prevents investigation from sensitivity analyses. The problem can be solved if computer agents are capable of generating similar behaviors as the given participants in experiments. An experimental economics based analysis method is presented to extract deep information from questionnaire data and emulate any number of participants. Taking the customers' willingness to purchase electric vehicles (EVs) as an example, multi-layer correlation information is extracted from a limited number of questionnaires. Multi-agents mimicking the inquired potential customers are modelled through matching the probabilistic distributions of their willingness embedded in the questionnaires. The authenticity of both the model and the algorithm is validated by comparing the agent-based Monte Carlo simulation results with the questionnaire-based deduction results. With the aid of agent models, the effects of minority agents with specific preferences on the results are also discussed.

Electic vehicles (EVs) show great potential to cope with the intermittency of renewable energy sources (RES) and provide demand side flexibility required by the smart grid. Furthermore, EVs will increase the electricity consumption. Large scale integration of EVs will probably have substantial impacts on power systems. This paper presents a methodology to transform driving behavior of person into one of the cars in order to analyze the driving pattern of 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 charging/discharging availability of 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 and EV availability at home. The results show that the daily driving requirements of the Nordic region are not very intensive. The driving patterns of vehicles in the Nordic region vary on weekdays and weekends. The two types of EV availabilities are quite different from each other.

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.

In the last two decades, the wind power generation has been rapidly and widely developed in many regions and countries for tackling the problems of environmental pollution and sustainability of energy supply. However, the high share of intermittent and fluctuating wind power production has also increased the burden of system operator for securing power system reliability during the operational phase. Moreover, the power system restructuring and deregulation have not only introduced the competition for reducing cost but also changed the strategy of reliability evaluation and management of power systems. The conventional long-term reliability evaluation techniques have been well developed, which have been more focused on planning and expansion rather than operation of power systems. This paper proposes a new technique for evaluating operational reliabilities of restructured power systems with high wind power penetration. The proposed technique is based on the combination of the reliability network equivalent and time-sequential simulation approaches. The operational reliability network equivalents are developed to represent reliability models of wind farms, conventional generation and reserve provides, fast reserve providers and transmission network in restructured power systems. A contingency management schema for real time operation considering its coupling with the day-ahead market is proposed. The time-sequential Monte Carlo simulation is used to model the chronological characteristics of corresponding reliability network equivalents. A simplified method is also developed in the simulation procedures for improving the computational efficiency. The proposed technique can be used to evaluate customers' reliabilities considering high penetration of wind power during the power system operation in the deregulated environment.

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling 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 continuum model is a key paradigm describing the behavior of electromechanical transients in power systems. In the past two decades, much research work has been done on applying the continuum model to analyze the electromechanical wave in power systems. In this work, the uniform and non-uniform continuum models are first briefly described, and some explanations borrowing concepts and tools from other fields are given. Then, the existing approaches of investigating the resulting wave equations are summarized. An application named the zero reflection controller based on the idea of the wave equations is next presented.

This paper presents a series of operating schedules for Battery Energy Storage Companies (BESC) to provide peak shaving and spinning reserve services in the electricity markets under increasing wind penetration. As individual market participants, BESC can bid in ancillary services markets in an Independent System Operator (ISO) and contribute towards frequency and voltage support in the grid. Recent development in batteries technologies and availability of the day-ahead spot market prices would make BESC economically feasible. Profit maximization of BESC is achieved by determining the optimum capacity of Energy Storage Systems (ESS) required for meeting spinning reserve requirements as well as peak shaving. Historic spot market prices and frequency deviations from Australia Energy Market Operator (AEMO) are used for numerical simulations and the economic benefits of BESC is considered reflecting various aspects in Australia's National Electricity Markets (NEM).

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.

The Indian power system underwent two separate massive blackouts on 30th and 31st of July 2012. These blackouts exposed a series of inherent problems underlying the power system of India, such as its generation planning, transmission planning, grid infrastructure, management systems, primary energy resources, environmental constraints, electricity market, and information technology, which together aggravate the massive blackouts. These factors which affect the adequacy and security of the power supply in a similar manner as the transmission congestion of physical grids does, can be called by a generic term as generalized congestion. Competitive manipulations and “game-playing” between various interested parties further intensifies these congestions. Here, from the perspective of generalized congestions, the paper reflects the inevitability of India's massive blackouts, discusses occasional effects of trigger events, inherent evolution law of blackouts and the enlightenment on power industry in China, and explores risk control measures to defend blackouts in China. The positive role of strong smart grid in insuring energy and environment security is highlighted.

The increasing number of distributed energy resources such as electric vehicles and heat pumps connected to power systems raises operational challenges to the network operator, for example, introducing grid congestion and voltage deviations in the distribution network level if their operations are not properly coordinated. Coordination and control of a large number of distributed energy resources requires innovative approaches. In this paper, we follow up on a recently proposed network-constrained transactive energy (NCTE) method for scheduling of electric vehicles and heat pumps within a retailer’s aggregation at distribution system level. We extend this method with: (1) a new modeling technique that allows the resulting congestion price to be directly interpreted as a locational marginal pricing in the system; (2) an explicit analysis of the benefits and costs of different actors when using the NCTE method in the system, given the high penetration of distributed energy resources. This paper firstly describes the NCTE-based distribution system that introduces a new interacting scheme for actors at the distribution system level. Then, technical modeling and economic interpretation of the NCTE-based distribution system are described. Finally, we show the benefits and costs of different actors within the NCTE-based distribution system.

β3-adrenergic receptor (AR) and the downstream signaling, nitric oxide synthase (NOS) isoforms, have been emerged as novel modulators of heart function and even potential therapeutic targets for cardiovascular diseases. However, it is not known whether β3-AR plays cardioprotective effects against myocardial infarction (MI) injury. Therefore, the present study was designed to determine the effects of β3-AR on MI injury and to elucidate the underlying mechanism. MI model was constructed by left anterior descending (LAD) artery ligation. Animals were administrated with β3-AR agonist BRL37344 (BRL) or β3-AR inhibitor SR59230A (SR) respectively at 0.1 mg/kg/hour one day after MI operation. The scar area, cardiac function and the apoptosis of myocardial were assessed by Masson's trichrome stain, echocardiography and TUNEL assay respectively. Western blot analysis was performed to elucidate the expressions of target proteins. β3-AR activation with BRL administration significantly attenuated fibrosis and decreased scar area after MI. Moreover, BRL also preserved heart function, and reduced the apoptosis of cardiomyocyte induced by MI. Furthermore, BRL treatment altered the phosphorylation status of endothelial NOS (eNOS) and increased the expression of neuronal NOS (nNOS). These results suggested that β3-AR stimulation has a substantial effect on recovery of heart function. In addition, the activations of both eNOS and nNOS may be associated with the cardiac protective effects of β3-AR.