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Present trends indicate that electrical vehicles (EVs) are favourable technology for road network transportation. The lack of easily accessible charging stations will be a negative growth driver for EV adoption. Consequently, the charging station placement and scheduling of charging activity have gained momentum among researchers all over the world. Different planning and scheduling models have been proposed in the literature. Each model is unique and has both advantages and disadvantages. Moreover, the performance of the models also varies and is location specific. A model suitable for a developing country may not be appropriate for a developed country and vice versa. This paper provides a classification and overview of charging station placement and charging activity scheduling as well as the global scenario of charging infrastructure planning. Further, this work provides the challenges and solutions to the EV charging infrastructure demand and deployment. The recommendations and future scope of EV charging infrastructure are also highlighted in this paper. [ABSTRACT FROM AUTHOR]

This paper focuses on transactive energy management in local energy communities and proposes a coalitional game model which considers the presence and utilization of flexible loads and the power output uncertainties from renewable energy sources. The superadditivity and balancedness of the proposed game model are proved rigorously by demonstrating that a nucleolus-based solution leads to a stable and fair payoff distribution scheme for all players. More specifically, the objective function is proved concave with its analytical expression, derived through an approximated piecewise linear function. The proposed allocation of realized payoffs is then proved to converge consistently to a nucleolus-based solution. It is demonstrated by numerical simulations that the grand coalition effectively increases the global payoff and the proposed allocation scheme contributes to peak shaving and valley filling in the overall load profile. [ABSTRACT FROM AUTHOR]

In the context of the global low-carbon energy development strategy, the massive influx of renewable energy leads to the instability of the power system, so it is necessary to carry out effective scheduling strategy and management mechanism for energy. On this basis, the Integrated Energy System including Electricity/Heat/Storage (EHSIES) is discussed in this paper. First, an ordered clustering method using a combination of simulated annealing and improved profile coefficients for load evolution number approximation is proposed. Second, a hybrid load clustering algorithm based on hierarchical clustering algorithm and K-means++ is presented and four types of load scenarios are obtained. Third, an Improved Harris Hawk Optimization (IHHO) algorithm based on the hybrid strategy is proposed and solved to the model of EHSIES. Finally, under the premise of the dynamic carbon trading mechanism considering historical compliance and load aggregator management, the scheduling strategies of flexible electric loads and thermal loads participating in EHSIES are discussed and solved by CPLEX. The results show that the solution time of IHHO is 50% and 33.3% lower than that of particle swarm optimization (PSO) and Harris hawk optimization (HHO), and the number of iterations is 76.6% and 56.25% lower than that of PSO and HHO, respectively. In addition, considering a flexible load aggregator and a compliance-based dynamic carbon trading mechanism in EHSIES scheduling can effectively reduce the actual operating cost by 27.37%, the source-side energy spill rate by 68.75%, and the carbon emission by 51.92% and improve the operating efficiency and economic benefits of EHSIES. [ABSTRACT FROM AUTHOR]

With the ever-increasing penetration level of renewable generation sources, a modern power system is facing more inevitable uncertainties that could lead to weakly damped oscillations. Detecting coherency among synchronous generators is one of the key steps of situational awareness for a given power system with a very high level of renewable penetration. In this paper, a wide-area measurement system (WAMS) based coherency detection algorithm employing the kernel principal component analysis (KPCA) and clustering analysis based on affinity propagation (AP) is proposed for a power system with extensive penetration of renewable generation sources. First, several trajectory similarity indexes are presented for determining the similarity between the trajectories of any two generators in the center of inertia coordinate. Second, a KPCA-based method is presented to integrate the trajectory similarity indexes for addressing the correlations among multiple indexes. Next, the AP-based clustering analysis method is utilized to detect the coherency among synchronous generators without the need of prespecifying the number of clusters. Finally, Southern China power system and a part of northern China power system with Zhangbei wind farms included, both with very high levels of renewable generation penetration, are utilized to demonstrate the proposed WAMS-based coherency detection methodology, and the application to actual Guangdong power system in south China to verify the applicability and practicality. [ABSTRACT FROM AUTHOR]

Identification of coherent generators (CGs) in power systems is one of the key steps in determining controlled islanding strategies. In this study, a wide‐area measurement system (WAMS) and agglomerative hierarchical clustering (AHC) algorithm based coherency identification method is presented for interconnected power systems with aggregated renewable sources. First, the trajectories measured by WAMS are transformed to the centre of inertia based ones for better representing the dynamic behaviour of a given power system, and ten trajectory dissimilarity indexes are presented for determining the similarity of the trajectories of any two generators. Second, a CRITIC (CRiteria Importance Through Intercriteria Correlation) based method, in which entropy and the Spearman's rank correlation coefficient are integrated for reflecting the differences and correlations among multiple indexes, respectively, is presented to integrate the trajectory dissimilarity indexes. Next, the AHC algorithm is utilised to identify CGs. Finally, a modified New England–New York interconnected power system with a large number of renewables, a simplified actual Western Interconnection power system in North America and the eastern part of Guangdong power system in China with a recorded oscillation event happened are utilised to demonstrate the proposed wide‐area coherency identification methodology. [ABSTRACT FROM AUTHOR]

Aiming at the distributed resources of electric vehicles with photovoltaics (PVs) on the user side, a trading mode of surplus energy sharing for electric vehicles based on the user-side PVs is proposed by utilizing the bidirectional mobility of information and energy. Power transfer can be implemented between different electric vehicle users through vehicle-to-grid (V2G) technology with a reasonable distribution of benefits taken into account. First, the operational framework of electric energy trading is presented, and the transmission architecture of each body of interest in the system is analyzed. Second, the portraits of EV users' charging behaviors are established considering their different charging habits, and electric vehicle users are divided into electricity buyers and sellers in each trading time period. An electricity transaction model based on "multi-seller–multi-buyer" is established, and all electricity transactions are realized through blockchain-based decentralized technology. Finally, the benefit to each interest group is maximized using the improved Northern Goshawk Optimization (NGO) algorithm. Simulation results of a sample system indicate that the new power trading mode proposed in this study could lead to reasonable reuse of the electric energy of private electric vehicles and can achieve a win–win situation for all stakeholders. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the progressive exhaustion of fossil energy and the enhanced awareness of environmental protection, more attention is being paid to electric vehicles (EVs). Inappropriate siting and sizing of EV charging stations could have negative effects on the development of EVs, the layout of the city traffic network, and the convenience of EVs' drivers, and lead to an increase in network losses and a degradation in voltage profiles at some nodes. Given this background, the optimal sites of EV charging stations are first identified by a two-step screening method with environmental factors and service radius of EV charging stations considered. Then, a mathematical model for the optimal sizing of EV charging stations is developed with the minimization of total cost associated with EV charging stations to be planned as the objective function and solved by a modified primal-dual interior point algorithm (MPDIPA). Finally, simulation results of the IEEE 123-node test feeder have demonstrated that the developed model and method cannot only attain the reasonable planning scheme of EV charging stations, but also reduce the network loss and improve the voltage profile. [ABSTRACT FROM AUTHOR]

Some uncertainties, such as the uncertain output power of a plug-in electric vehicle (PEV) due to its stochastic charging and discharging schedule, that of a wind generation unit due to the stochastic wind speed, and that of a solar generating source due to the stochastic illumination intensity, volatile fuel prices, and future uncertain load growth could lead to some risks in determining the optimal siting and sizing of distributed generators (DGs) in distribution system planning. Given this background, under the chance constrained programming (CCP) framework, a new method is presented to handle these uncertainties in the optimal siting and sizing of DGs. First, a mathematical model of CCP is developed with the minimization of the DGs' investment cost, operating cost, maintenance cost, network loss cost, as well as the capacity adequacy cost as the objective, security limitations as constraints, and the siting and sizing of DGs as optimization variables. Then, a Monte Carlo simulation-embedded genetic-algorithm-based approach is employed to solve the developed CCP model. Finally, the IEEE 37-node test feeder is used to verify the feasibility and effectiveness of the developed model and method, and the test results have demonstrated that the voltage profile and power-supply reliability for customers can be significantly improved and the network loss substantially reduced. [ABSTRACT FROM AUTHOR]

China has made great achievements in wind power generation. By the end of 2009, the installed capacity of wind power totaled 25.8053 GW, 13.8032 GW of which was added in 2009. Although the industry has developed at an impressive rate, its scale is still comparatively small, not fully matching the country's economic size and great demand for green power. It is widely believed that the wind power market in China can develop at an even faster rate over the next decade. A stable market for wind power investment relies on both the cost of wind power and an effective pricing policy to motivate growth in the wind industry. This paper presents corresponding analysis of the costs of wind power generation and the current pricing policies implemented in China. The composition of wind power costs is elaborated through comparing it with that of conventional power resources. The localization of wind turbine manufacturing, which aims to decrease the wind generation costs and promote the development of the local wind power industry, is introduced. Two current pricing systems applied in China are examined: the price committed by the wind power concession bidding projects and the price authorized by the provincial governments. An inflation-adapted model is proposed to analyze the risk of concession prices. Finally, the paper concludes with discussions on the potential benefits and the future strategies for the development of wind power in China. [ABSTRACT FROM AUTHOR]

The alarm-processing problem is to interpret a large number of alarms under stress conditions, such as faults or disturbances, by providing summarized and synthesized information instead of a flood of raw alarm data. Alarm timestamps represent the temporal relationship among event occurrences and consist of rich and useful information for alarm processing. However, the temporal information has not been well utilized in existing alarm-processing methods. The temporal constraint network (TCN) is a type of directed acyclic graph suitable for representing temporal logics. Based on TCN, a new analytic model is developed for alarm processing with temporal information taken into account. Three major modules are included in the developed approach or alarm processor (i.e., alarm selection, event analysis, and result evaluation). In the alarm selection module, reported alarms are divided into related groups. The function of the event analysis module is to find out what events cause the reported alarms and to estimate when these events happen. The result evaluation module is used to identify abnormal or missing alarms. Finally, two alarm-processing scenarios of an actual power system are served for demonstrating the feasibility and efficiency of the developed approach. [ABSTRACT FROM PUBLISHER]

Optimization of schemes for black-start is one of the most important factors determining the speed of power system restoration after either a global blackout or a local outage. In this paper a novel method employing the entropy weight-based decision-making theory is proposed for this application. By using the method for the decision making in black start, the subjective weights acquired from knowledge and experience of experts could be used in conjunction with the objective weights (entropy weighs) deduced from some closely related indices. Obviously, the developed method can greatly improve the performance in power system restoration by overcoming the shortcomings of conventional methods that only use either the subjective or the objective weights. Furthermore, the proposed method is applicable in power systems with different levels of black-start experience for assisting the optimal black-start decision making. It is shown that the proposed method is suitable for power systems with different levels of black-start experience. [ABSTRACT FROM AUTHOR]

Presents a framework for building bidding strategies for power suppliers in an electricity market. Monte Carlo based method; Optimization based method; Test results with inelastic load.

In the context of modern power system development to support the evolution towards green energy and carbon-neutral emission goals, many existing problems and even challenges demand new technical solutions. In recent years, decentralized blockchain technology has been employed to address some problems in power systems, and many papers have been published. In this paper, the concept of blockchain is first introduced. A brief survey of the existing publications regarding the applications of blockchain in power systems, including power system dispatching, microgrid operation, energy trading, electricity trading settlement, transmission, and distribution system operation, is then carried out. In addition, several application scenarios of blockchain technology in power systems are also introduced. Through the discussion, we found that we still need to weigh the advantages and disadvantages, overcome its leakage, and bring its value into play if we apply blockchain technology in modern power systems in support of zero carbon goals. [ABSTRACT FROM AUTHOR]

In a Cyber-Physical Power System (CPPS), the interaction between the power cyber system and the power physical system becomes more extensive and more in-depth. The failure of a cyber component could have an impact on the security and reliability of the power physical system. Existing publications have focused on the impacts of the power cyber network on the power physical network, while a general CPPS model considering the mutual impacts of these two networks is less studied. Given this background, a game-theoretic approach for a cyber-physical power system vulnerability analysis is proposed. First, a CPPS interactive model framework is structured, consisting of five types of elements: P-nodes, PP-links, C-nodes, CC-links and CP-links. The interactions among these elements are considered. On this basis, the system cascading failure under potential attacks is analyzed, followed with an optimal load curtailment operation when in an emergency. To further illustrate the system vulnerability, a bi-level optimization model under a game-theoretic framework is presented to describe the interactions between a CPPS attacker and a system defender. Optimal resource allocation by the system defender for maintaining system reliability can be obtained by solving the problem. The feasibility and effectiveness of the proposed method are demonstrated by a revised version of the IEEE 14-bus power system. [ABSTRACT FROM AUTHOR]

The ever-increasing penetration of wind power generation and plug-in electric vehicles introduces stochastic continuous disturbances to the power system. This paper proposes an analytical approach to analyze the influence of stochastic continuous disturbances on power system small signal stability. The noise-to-state stability (NSS) and NSS Lyapunov function (NSS-LF) are adopted for stability analysis with respect to the magnitude of uncertainties in a power system. The power system is modeled as a set of stochastic differential equations (SDEs). The supremum of the norm of the covariance is employed to characterize the influence of magnitudes of uncertainties on the power system. Then the relationship between the magnitudes of stochastic variations and probabilistic stability is explicitly identified by NSS. The proposed method can assess the stochastic stability of the power system by checking some algebraic expressions. Hence, it has high computation efficiency compared with the well-established Monte Carlo based method. Besides, since the magnitudes of the stochastic variations are integrated into the definition of the stochastic stability, the proposed method provides theoretical explanations for the impacts of uncertainties. [ABSTRACT FROM AUTHOR]

With the ever-intensive utilization of distributed generators (DGs) and smart devices, distribution networks are evolving from a hierarchal structure to a distributed structure, which imposes significant challenges to network operators in system dispatch. A distributed energy-management method for a networked microgrid (NM) is proposed to coordinate a large number of DGs for maintaining secure and economic operations in the electricity-market environment. A second-order conic programming model is used to formulate the energy-management problem of an NM. Network decomposition was first carried out, and then a distributed solution for the established optimization model through invoking alternating-direction method of multipliers (ADMM). A modified IEEE 33-bus power system was finally utilized to demonstrate the performance of distributed energy management in an NM. [ABSTRACT FROM AUTHOR]

The spatial-temporal distribution of electric vehicle (EV) charging loads can be improved by implementing optimized charging scheduling, and by the joint planning of a distribution network (DN) and a charging network of EVs. In this paper, such a joint planning scheme was studied and modeled as two-level optimization model. In the upper level of the optimization, a joint planning model of a DN and a charging network of EVs with distributed photovoltaic (PV) systems and/or energy storage (ES) units is constructed to minimize the total cost. Then the optimized results of the DN and the EV charging network construction plan are used as the inputs of the lower-level operation model. In the lower-level operation optimization, an optimal charging scheduling approach is proposed to minimize the sum of the DN's operation cost and the traffic congestion cost. The spatial-temporal distribution of the charging demand, under an optimized charging strategy, is used as feedback for the upper-level optimization model, which effectively can reduce the scale of the DN and the charging network construction. The proposed joint planning model was demonstrated through applications in a city in China. [ABSTRACT FROM AUTHOR]

With the development of energy markets and the introduction of renewable energy certificates (RECs), a regional integrated energy system (RIES) may participate in an energy wholesale market flexibly. In this paper, a bilevel optimization problem is formulated to determine the optimal management strategies of an RIES, including trading in the markets of electricity, natural gas, and RECs. In the bilevel model, an energy supplier (ES) purchases energy from energy wholesale markets and sets retail prices of energy products at the upper level to maximize its profit. At the lower level, various user aggregators (UAs) determine individual energy consumption strategies. The clearing prices of electricity, natural gas, and REC markets are determined asynchronously at the lower level. This nonlinear problem is then transformed into a mixed-integer linear programming problem using the incremental method and strong duality theory, which enables the problem to be solved efficiently. Finally, case studies are carried out to demonstrate the feasibility and effectiveness of the proposed method and the impacts of the RECs. [ABSTRACT FROM AUTHOR]

Due to the development of new technologies, change of generation mix and appearance of newly formed energy supply hubs, there is a large year‐on‐year change in the marginal loss factors in power systems. Since any change of marginal loss factors could have significant impacts on payment of loads and profitability of generators, it is necessary to carry out a comparative study on the loss factor‐based locational marginal pricing methods. Considering that a systematic comparison of various locational marginal pricing methods has not been reported in existing publications, this work presents a comparative study of the loss factor‐based locational marginal pricing methods that are widely adopted in electricity markets. Advantages and disadvantages of each locational marginal pricing method are explored in detail, and could serve as references in selecting appropriate locational marginal pricing methods in practice. The selected five locational marginal pricing models are tested in two standard power systems, that is, the IEEE 5‐bus and 39‐bus systems. Then, through numerical experiments and detailed analysis, key findings about the reference point dependency of loss factors, accuracy of loss estimation, load payment, generation income, and market settlement surplus are summarised and elaborated. It is found that marginal loss factors‐based locational marginal pricing methods tend to produce a higher market settlement surplus and can lead to a lower generation income than other locational marginal pricing methods. [ABSTRACT FROM AUTHOR]

With the ever-increasing penetration of electric vehicles (EVs), extreme fast charging stations (XFCSs) are being widely deployed, wherein battery energy storages (BESs) are also installed for reducing the peak charging power. However, integrating the XFCS with a high-capacity power converter into the power distribution network (PDN) is difficult and uneconomical due to the restrictions regarding urban planning and high investment in PDN expansion. Considering the fluctuation in the EV charging demand and the limited capacity of the power converter, a collaborative policy for real-time EV charging power allocation and BES discharging power control is proposed based on Markov Decision Process (MDP), which is solved by the constraint deep deterministic policy gradient (CDDPG). The proposed model makes it possible to integrate the XFCS with reduced capacity power converter into the PDN with a minimal negative impact on the quality of service (QoS) of EV owners. Finally, the experimental evaluation with real-word data sets demonstrates that the proposed approach is more effective than benchmark methods in dynamically allocating charging power for XFCS. • The charging process and satisfaction for extreme fast charging are analyzed. • An MDP-based power allocation policy is proposed to enhance charging satisfaction. • A constraint DDPG algorithm is proposed to avoid excessive extra service time. • A cooperation policy for XFCS and battery energy storages is proposed. [ABSTRACT FROM AUTHOR]

With ever-growing interconnections of various kinds of energy sources, the coupling between a power distribution system (PDS) and a district heating system (DHS) has been progressively intensified. Thus, it is becoming more and more important to take the PDS and the DHS as a whole in energy flow analysis. Given this background, a steady state model of DHS is first presented with hydraulic and thermal sub-models included. Structurally, the presented DHS model is composed of three major parts, i.e., the straight pipe, four kinds of local pipes, and the radiator. The impacts of pipeline parameters and the environment temperature on heat losses and pressure losses are then examined. The term “heat-power flow” is next defined, and the optimal heat-power flow (OHPF) model formulated as a quadratic planning problem, in which the objective is to minimize energy losses, including the heat losses and active power losses, and both the operational constraints of PDS and DHS are respected. The developed OHPF model is solved by the well-established IPOPT (Interior Point OPTimizer) commercial solver, which is based on the YALMIP/MATLAB toolbox. Finally, two sample systems are served for demonstrating the characteristics of the proposed models. [ABSTRACT FROM AUTHOR]

A microgrid (MG) is always prone to the uncertainties of its demand variation and the generation of its non-dispatchable renewable sources when operating in islanded mode. Such variation events can lead to the voltage or frequency (VF) violation in the MG. However, there are some techniques available in the literature that can predict these events a few minutes ahead. Using such techniques, the VF violation can also be predicted and prevented with the introduction of a suitable preventive controller. Hence, this paper proposes a look-ahead controller that uses the short-horizon prediction data of demand and renewable generation to determine any prospective VF violation. If a violation is predicted, the proposed technique will aim to define the most optimal generation level of dispatchable sources, the MG's best network configuration and the engagement level of the supportive actions, such as exchanging power with neighboring microgrids, utilizing energy storages, a demand response and renewable energy curtailment (if and when available). The technical, reliability and environmental aspects of the MG are considered within the proposed technique along with the operational cost. The determined optimal control variables are then sent to the local controllers to apply the proper arrangements in the system to retain the VF within the desired range. The performance of the developed technique is validated through extensive numerical analyses in MATLAB. [ABSTRACT FROM AUTHOR]

The increasing penetration of distributed energy resources (DERs) into distribution networks has changed the energy trading pattern in traditional electricity markets to some degree, and this will possibly cause network congestion and nodal voltage violations. This paper proposes a two-stage modeling framework for peer-to-peer (P2P) energy trading with voltage regulation services provision considered. In the first stage, direct P2P trading among prosumers, considering network congestion management, is enabled. In the second stage, prosumers provide voltage regulation services to address possible voltage violations. Aiming at maximizing social welfare, the alternative direction method of multipliers (ADMM) is applied to solve the two-stage problem. On the basis of the optimal energy solution of the two-stage problem, the energy prices of P2P transactions and the price of voltage regulation services are settled based on the Nash bargaining model. Finally, simulation results of the IEEE 33-bus power system with six prosumers included demonstrate the effectiveness of the proposed models. [ABSTRACT FROM AUTHOR]

To address the challenges of integrating ever-increasing renewable energy generation such as wind power and photovoltaic (PV) to the power system concerned, the wind-PV-thermal-bundled (WPTB) transmission mode could be employed to mitigate the intermittent power output from wind and PV units. In this work, based on the mathematical models of thermal generation units, wind turbines and PV units, the WPTB transmission system model is developed. Each load demand is modeled as a random variable under various operating conditions of the concerned power system. The Lyapunov linearization method is used to obtain the system matrix of each operation mode, and then all eigenvalues of the system can be attained. According to the generator participation factor, the oscillation modes of the power system are classified. The eigenvalue and damping ratio of each oscillation mode are employed to formulate the objective function of the optimal strategy for enhancing probabilistic small-signal stability, and the particle swarm optimization algorithm is used for global optimization. The secure operation of the power system can be ensured in a wider range by the designed optimization strategy in this work. The effectiveness of the method is demonstrated by simulation results of a sample power system which can operate stably under some uncertain operation conditions. [ABSTRACT FROM AUTHOR]

Presenta a discussion on the paper, "Gas-Electricity Coordination in Competitive Markets Under Renewable Energy Uncertainty." [ABSTRACT FROM PUBLISHER]

The problem of designing customized pricing strategies for different residential users is investigated based on the identification results of residential electric appliances and classifications of end-users according to their consumption behaviors. This study is based on the following assumptions: 1) Each retailer purchases electricity from the forward contract market, day-ahead spot market, and real-time market; 2) the competition among retailers is modeled by a market share function; 3) each retailer adopts fixed time-of-use prices for end-users; 4) the price fluctuations in day-ahead and real-time spot markets as well as uncertainty of electricity consumption behaviors are considered as main sources of risk. Under these assumptions, a pricing framework for retailers is established based on the bilevel programming framework and the optimal clustering in a time sequence. Meanwhile, profit risk is considered by taking conditional value at risk as the risk measure. The proposed bilevel optimization model is finally reformulated into a mixed-integer nonlinear programming problem by solving Karush–Kuhn–Tucker conditions. The online optimization solvers provided by the network-enabled optimization system server and the commercial solver AMPL/GUROBI are used to solve the developed models, respectively. Finally, a case study is employed to demonstrate the feasibility and efficiency of the developed models and algorithms. [ABSTRACT FROM PUBLISHER]

A letter to the editor is presented in response to the article "Distribution Locational Marginal Pricing for Optimal Electric Vehicle Charging Management" by R. Li, Q. Wu and S. S. Oren in the January 2014 issue.

Power system stability has been suffering increasing threats with the ever-growing penetration of intermittent renewable generation such as wind power and solar power. Battery energy storage systems (BESSs) could mitigate frequency fluctuation of the power system because of their accurate regulation capability and rapid response. By dividing the Area Control Error (ACE) signal and the State of Charge (SOC) of battery energy storage systems into different intervals, the frequency control task of BESSs could be determined by considering the frequency control demand of the power grid in each interval and SOC self-recovery. The well-developed model predictive control can be employed to attain the optimal control variable sequence of BESSs in the following time, which can determine the output depths of BESSs and action timing at different intervals. The simulation platform MATLAB/Simulink is used to build two secondary frequency control scenarios of BESSs for providing frequency regulation service. The feasibility of the presented strategy is demonstrated by simulation results of a sample system. [ABSTRACT FROM AUTHOR]

Efficient and well‐timed investment in distributed generation (DG) in distribution networks in the electricity market environment presents a big challenge for distribution companies. In this study, a real option valuation framework is proposed to determine the optimal investment strategies for DG including the investment location, size, and timing. Within the proposed framework, the profit from investing in DG is modelled, where the benefits include the operation cost savings and capacity update deferral benefit compared with a no‐DG‐investment scenario over the study period. Future power demands and electricity prices are modelled as stochastic variables. The candidate DG investment plans are considered as multiple mutually exclusive options, and the corresponding managerial flexibility for seizing opportunities and mitigating risk of loss upon an unfavourable unfolding of future uncertainties is assessed with real option analysis using the extended least square Monte Carlo method. The distribution of future investment strategies and optimal initial investment threshold levels under various scenarios are analysed in a case study to demonstrate the characteristics of the proposed framework and methodology. [ABSTRACT FROM AUTHOR]

In modern transmission systems, duplicated protections are widely employed to enhance reliability, and global positioning system (GPS) substation clocks are set up to deliver a higher degree of synchronization. Thus, redundant alarms tagged with synchronized timestamps would be received during a fault. Both redundancy and timestamps of alarms consist of rich and useful information. Given this background, an effort is made to develop a novel analytic model for power system fault diagnosis so as to well utilize redundancy and temporal information of alarms. An alarm preprocessing procedure is proposed to preserve such properties without impairing diagnosis accuracy and efficiency. A new fault hypothesis is established to systematically take account of malfunctions of protective devices, missing and distorted alarm messages, and timestamp errors in the proposed model. An objective function is developed for fault diagnosis, and could accommodate various protection schemes and bus configurations. Based on the presented fault diagnosis method, a software package is developed. Several fault scenarios occurred in an actual power system in China are served for demonstrating the correctness and efficiency of the developed software package. [ABSTRACT FROM PUBLISHER]

The well-known dispatcher training simulator (DTS), as a good tool to train power system dispatchers, has been widely used for over 40 years. However, with the high-speed development of the smart grid, the traditional DTSs have struggled to meet the power industry's expectations. To enhance the effectiveness of dispatcher training, technical innovations in DTSs are becoming more and more demanding. Meanwhile, the ever-advancing artificial intelligence (AI) technology provides the basis for the design of intelligent DTSs. This paper systematically reviews the traditional DTS in terms of its origin, structure, and functions, as well as limitations in the context of the smart grid. Then, this paper summarizes the AI techniques commonly used in the field of power systems, such as expert systems, artificial neural networks, and the fuzzy set theory, and employs them to develop intelligent DTSs. Regarding a less studied aspect of DTSs, i.e., intelligent training control, we introduce the Adaptive Learning System (ALS) to develop a personalized training program, which will also be an important aspect of future research. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Three-phase unbalance could have significant impacts on the secure and economic operation of a low-voltage distribution system. With the deployment of smart meters and the emergence of phase switch devices (PSDs), more efficient schemes for mitigating three-phase unbalance are expected by utilizing numerous measurement data and the installed PSDs in low-voltage areas. Therefore, a bi-level optimal phase switch device placement model for mitigating three-phase unbalance is proposed in this work. In the upper-level model, the PSD placement strategy is optimized by minimizing the total costs of PSD installation and operation costs of low-voltage areas determined in the lower-level model. In the lower-level model, the operation costs of low-voltage areas for typical days are minimized based on the PSDs and their locations determined in the upper-level model. The proposed bi-level model is tested by an actual low-voltage area of Zhejiang province, China, and the effectiveness of the optimal PSD placement model for reducing the total investment and operation costs of the low-voltage area demonstrated. [ABSTRACT FROM AUTHOR]

During the transition towards a low carbon energy system, and as a result of the increasing penetration of renewable energy generation, ancillary services play an important role in ensuring the security and economics of power system operation. An integrated energy service provider (IESP), with the energy coupling and storage devices inside, can flexibly participate in electric energy markets and ancillary service markets. In this paper, a mixed-integer optimization problem is formulated to determine the optimal operating strategy for the IESP with ancillary service provision. First, the mathematical model of a regional integrated energy system (RIES) is established, including energy coupling devices and energy storage devices. Then, an optimization model for the IESP operation is formulated with the objective of maximizing daily operation profit, and the corresponding optimal capacity of ancillary service provision from the IESP is attained. Finally, case studies of an RIES are carried out to demonstrate the feasibility and effectiveness of the proposed method, and the impacts of the IESP with ancillary service provision on operational characteristics and economic benefits are analyzed. [ABSTRACT FROM AUTHOR]

With the target of carbon peaking and carbon neutrality, renewable energy generation (REG) develops rapidly. The increasing penetration of REG brings along the problems of fluctuation in power flow and the possible abandonment of wind and photovoltaics (PV) generation. In this context, the so-called integrated energy system (IES) becomes a promising solution to the accommodation of REG thanks to energy storage systems and coupling devices inside. In this paper, the optimal operation model of an IES is first presented, with the schemes of green certificate trading and carbon emission right trading included to provide economic incentives for accommodating REG. Next, in order to address the problem of uncertainty in REG, the devices in the IES are divided into three types based on regulation flexibility, and a multi-time period optimal dispatching scheme is proposed, including day-ahead optimal scheduling, rolling optimal dispatching, and real-time control strategy. Finally, it is demonstrated by simulation results of a numerical example that the proposed method not only promotes the accommodation capability for REG but can also cope well with contingencies. [ABSTRACT FROM AUTHOR]

Electricity theft behaviors have caused great harm to the economic benefits of power companies and the secure operation of power systems, thus electricity theft detection is paid much attention in the actual power supply management. In this work, a two-step electricity theft detection strategy is proposed to identify electricity theft users and predict potentially stolen electricity (PSE) for maximizing economic return. In the first step, a neural network model called convolutional autoencoder (CAE) is proposed for electricity theft identification, and the convolutional layer is adopted in CAE to extract and identify the abnormalities of electricity theft users against the uniformity and periodicity of normal power consumption features. In the second step, the PSE of each identified electricity theft user is predicted by the improved regression algorithm named Tr-XGBoost, which combines the extreme gradient boosting (XGBoost) algorithm and transfer adaptive boosting (TrAdaBoost) training strategy. The propsoed Tr-XGBoost could learn the relationship between the extracted electricity features and the PSE of each electricity theft user, and then the predicted PSE can be used to determine the list of electricity theft users to be inspected for maximizing economic return. Case studies on both the IEEE 33-bus test system and a low-voltage distribution system of a province in China show that the proposed two-step electricity theft detection strategy can improve the accuracy of electricity theft identification, and obtain a larger economic return because of a more accurate result of PSE prediction than other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

With the ever‐increasing penetration level of renewable energy generation in a power system, more uncertainties are introduced and hence risk management in the electricity market associated is becoming a more difficult issue for a market participant in the context of optimising his/her portfolio. Among a lot of risk factors in the competitive electricity market environment, the highly volatile electricity price contributes most to the financial risk of the power portfolio, especially in a short‐term risk management scenario such as the spot market and real‐time balancing market. Some research work has shown that the fluctuations of electricity prices exhibit multifractal characteristics, but less work has been done on the price volatility risk evaluation based on the multifractal theory. This study hence examines the feasibility of applying the multifractal theory to analyse the electricity price fluctuation, and applies the multifractal theory for evaluating the financial risk caused by electricity price volatility. A modified return interval approach considering the parameters of multifractal characteristics is employed to estimate the value‐at‐risk (VaR) of the electricity price. The fluctuant electricity price data series in the Pennsylvania‐New Jersey‐Maryland energy market are employed to demonstrate the effectiveness of the proposed VaR estimation method for short‐term electricity price volatility risk evaluation. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization model for the joint planning of sectionalizing switches (SSs), including remote-controlled switches (RCSs) and manual switches (MSs), and tie lines among distribution feeders. Considering a flexible installation of SSs and tie lines (i.e., deployment of SSs on both sides of a line and construction of external and internal tie lines on of a feeder), this model is conducive to developing an economic investment scheme while preserving the expected energy not supplied (EENS) reliability criterion. First, the minimum total cost of investment, maintenance and penalty for EENS is chosen as the objective function. Next, a route analysis method is introduced to analyze particular performances of different deployment schemes for RCSs, MSs, external tie lines and internal tie lines. Considering the differences between overhead lines and underground cables, the lower bound of the restoration time of each load is estimated based on optimal restoration strategies in different fault scenarios. Then, the joint planning problem is formulated as a mixed-integer linear programming (MILP) model with the aid of algebraic operations to calculate the global optimal solution. The effectiveness of the proposed model is verified for several cases in a 54-node distribution system. Numerical results indicate that the proposed planning solution merits higher economy and lower EENS as compared to the schemes offered by other prevalent methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

Smart-grid development calls for effective solutions, such as electric vehicles (EVs), to meet the energy and environmental challenges. To facilitate large-scale EV applications, optimal locating and sizing of charging stations in smart grids have become essential. This paper proposes a multiobjective EV charging station planning method which can ensure charging service while reducing power losses and voltage deviations of distribution systems. A battery capacity-constrained EV flow capturing location model is proposed to maximize the EV traffic flow that can be charged given a candidate construction plan of EV charging stations. The data-envelopment analysis method is employed to obtain the final optimal solution. Subsequently, the well-established cross-entropy method is utilized to solve the planning problem. The simulation results have demonstrated the effectiveness of the proposed method based on a case study consisting of a 33-node distribution system and a 25-node traffic network system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

Restoring a large-scale power system has always been a complicated and important issue. A lot of research work has been done on different aspects of the whole power system restoration procedure. However, more time will be required to complete the power system restoration process in an actual situation if accurate and real-time system data cannot be obtained. With the development of the wide area monitoring system (WAMS), power system operators are capable of accessing to more accurate data in the restoration stage after a major outage. The ultimate goal of the system restoration is to restore as much load as possible while in the shortest period of time after a blackout, and the restorable load can be estimated by employing WAMS. Moreover, discrete restorable loads are employed considering the limited number of circuit-breaker operations and the practical topology of distribution systems. In this work, a restorable load estimation method is proposed employing WAMS data after the network frame has been reenergized, and WAMS is also employed to monitor the system parameters in case the newly recovered system becomes unstable again. The proposed method has been validated with the New England 39-Bus system and an actual power system in Guangzhou, China. [ABSTRACT FROM PUBLISHER]

As the first stage of power system restoration after a blackout, an optimal black‐start scheme is very important for speeding up the whole restoration procedure. Up to now, much research work has been done on generating or selecting an optimal black‐start scheme by a single round of decision‐making. However, less attention has been paid for improving the final decision‐making results through a multiple‐round decision‐making procedure. In the group decision‐making environment, decision‐making results evaluated by different black‐start experts may differ significantly with each other. Thus, the consistency of black‐start decision‐making results could be deemed as an important indicator in assessing the black‐start group decision‐making results. Given this background, an intuitionistic fuzzy distance‐based method is presented to analyse the consistency of black‐start group decision‐making results. Moreover, the weights of black‐start indices as well as the weights of decision‐making experts are modified in order to optimise the consistency of black‐start group decision‐making results. Finally, an actual example is served for demonstrating the proposed method. [ABSTRACT FROM AUTHOR]

A flood of alarm messages in an automatic digital substation makes the monitoring task a significant challenge for the operators in a remote control center, especially under fault scenarios. An online intelligent alarm-processing system is developed based on the architecture of the digital substation. First, real-time alarms are classified according to the IEC 61850 standard in order to provide synthesized and organized alarms for the alarm-processing procedure in the next step. Then, a new and systematic alarm-processing approach for digital substations is developed. Two modules (i.e., the generation of candidate hypotheses and the truth evaluation for the hypotheses) are included in the developed approach, and these two modules are operating in parallel in online implementation. This approach could not only determine the fault/disturbance cause but also the missing or false alarms as well as the causes of the false alarms. According to actual application requirements, an online intelligent alarm-processing system is developed and applied in the Xingguo substation—the first digital substation in Jiangxi Province, China. Finally, an actual alarm-processing scenario serves to demonstrate the presented alarm-processing method as well as the developed software system. [ABSTRACT FROM AUTHOR]

One of the major ways of delivering renewable energy generation from large-scale energy bases in northeast, northwest, and northern China, is to bundle the power from wind-photovoltaic-thermal units and then transmit it by alternating current/direct current (AC/DC) systems. The interactions among wind power units, photovoltaic (PV) power units, and high voltage direct current (HVDC) systems make the stability of the interconnected power system more complicated. In this paper, a framework model of wind-PV-thermal-bundled power transmitted by AC/DC systems is constructed based on the mathematical models of thermal power units, wind power units, PV power units, and HVDC transmission systems. Considering the fluctuations of wind power outputs and PV power outputs, a small-signal stability model of an interconnected power system with wind-PV-thermal-bundled power transmitted by AC/DC systems is established based on an existing stability theorem. The interactions among various forms of energy and their impacts on wind-PV-thermal-bundled power transmitted by AC/DC systems are studied through theoretical analysis and dynamic simulation. A sample interconnected power system is used to demonstrate the proposed method. It is shown by simulation results that the wind-PV-thermal-bundled power transmission distance; the electrical distance among wind power units, PV power units, and thermal power units; the grid-connected capacities of wind, PV, and thermal power units; and the power loading ratios of AC lines over DC lines all have various degrees of impacts on the stability of the interconnected power system. [ABSTRACT FROM AUTHOR]

• A joint energy-frequency regulation power market is proposed for energy transition. • Environmental cost of fossil-fueled generation is incorporated into electricity trading. • Renewable energy generation is motivated to actively manage uncertainty and intermittency of outputs. • Extensive numerical simulations are conducted to validate the proposed system. Striving to develop renewable energy generation (REG) has become a global trend. Due to high carbon emissions, fossil-fueled generation units (FFGUs) in an electrical energy market such as the day-ahead and real-time electricity markets are facing unprecedented constraints. From the perspective of FFGUs, an effective way is to change their market roles from energy suppliers with high environmental costs to secure and reliable frequency regulation service providers. To help FFGUs successfully complete the transition, a bi-level optimization model for the joint energy and regulation service market is proposed in this paper. Different from existing joint markets, both the environmental cost of FFGUs and the frequency regulation service (FRS) cost of REG units are considered in this research for market operators to determine the scheduling priorities of submitted bids from generation units. In the frequency regulation service market model, a dynamic approach to determining the FRS demand is applied, which not only incentivizes REG units to initiatively manage their outputs, but also derives the price signals through the equilibrium of the FRS demand and supply to ensure the revenue of FFGUs in the FRS market. Meanwhile, the decision-making model of the FFGUs under the proposed market mechanism is also presented. Finally, extensive numerical experiments demonstrate the feasibility and efficiency of the mechanism. Simulation results show that the proposed mechanism can stimulate a smooth transition of FFGUs by adapting to actual situations and policy changes. [ABSTRACT FROM AUTHOR]

• A fully decentralized P2P power market with AC modelled network constraints is proposed. • Network operational security is guaranteed under proposed P2P market mechanism. • User privacy is protected as nodal voltage variables are the only public variables shared among participants. • Extensive numerical simulations are conducted to validate proposed models and solving algorithm. The rapid growth of Distributed Energy Resources (DERs) and demand response in modern power grids calls for decentralized electricity trading mechanisms to effectively induce and coordinate responses from massive DERs and load entities. This paper proposes a fully decentralized peer-to-peer (P2P) distribution electricity trading mechanism backboned by a modified version of Alternating Direction Method of Multipliers (ADMM). Comparing with the state-of-the-art P2P electricity markets, the proposed electricity trading mechanism comprehensively incorporates power network constraints formulated by an alternating current power flow model. In the proposed method, nodal voltages are designed as the only public variables shared among participants. Privacy of each participant is thus sufficiently preserved without exposing the bid data to the others. Meanwhile, the proposed P2P method can also ensure the global optimality of social welfare in electricity transactions. Comprehensive case studies are carried out using the IEEE 33-node distribution system to demonstrate the feasibility and efficiency of proposed P2P electricity trading mechanism. The results indicate that the proposed model can efficiently integrates network constraints in the market clearing process while maintaining computational performance, ensuring stable convergence regardless of distribution system congestion. The proposed P2P electricity market can achieve global maximization of social welfare, with a relative difference of no more than 5.0% compared to the conventional centralized market. Even in worst-case scenarios, the relative difference remains within 7.32%. Meanwhile, the method accurately accounts for network losses, guaranteeing the feasibility of trading plans within the physical network. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]