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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 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]

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]

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]

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]

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 novel generator start-up sequence optimization strategy is proposed in this work, taking the integration time of wind-solar hybrid systems into account. • The proposed model requires fewer variables compared with other models, therefore the computational efficiency can be greatly improved. • The confidence gap decision theory based generator start-up sequence strategy is proposed to address the generation uncertainty of wind-solar hybrid systems. • Better generator start-up sequence solutions with higher computational efficiency and less conservatism can be attained by employing the proposed strategy. A reasonable start-up sequence of generators considering renewable energy generation integration can effectively improve the restoration efficiency of the power system concerned. The restoration strategy of generators is time-sensitive, however, the computation procedure of existing methods is not fast enough to meet the requirements of practical applications. In this paper, the generator start-up sequence optimization (GSSO) problem is addressed with the integration of wind-solar hybrid systems (WSSs) considered in bulk power systems. Specifically, a deterministic model for GSSO is formulated to maximize the total generation capability during the restoration procedure, in which fewer auxiliary variables are required to promote the computational efficiency. On this basis, in order to deal with the inherent and significant uncertainties of WSSs, a confidence gap decision theory (CGDT) based approach is proposed combining the advantages of stochastic optimization and the information gap decision theory based method while considering the worst-case realization of unknown probability distributions, so that a chance-constrained robust generator start-up solution is thereby attained. Moreover, the formulated CGDT based model is transformed into a mixed integer linear programming problem. Finally, the IEEE 39-bus power system and a modified version of an actual power system in China are employed to demonstrate the feasibility and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

• A load restoration scheme is proposed to coordinate the transmission, distribution, and gas systems. • The coordinated load restoration is achieved in a distributed manner to respect the information privacies. • The line-packs caused by slow gas slow dynamics is exploited to assist in load restoration. • An efficient algorithm is proposed to solve the distributed load restoration scheme with excellent calculation performance. Recent electricity systems have witnessed many power outage events, and the interrupted loads must be restored as soon as possible to reduce the enormous outage losses. However, the ever-increasing interdependencies of the transmission, distribution, and gas systems cause the restoration behaviors of a system to inevitably influence the others, which brings about new impacts on the load restoration decisions. Moreover, the different velocities of power and gas flows cause additional complexities in making load restoration decisions for the whole system. To address these problems, this paper proposes a new load restoration scheme coordinating transmission, distribution, and gas systems. Firstly, to respect the information privacy of individual energy subsystems, a distributed load restoration coordination structure is proposed with the interactions of different energy systems and corresponding load restoration models considering pipeline gas storage capacity caused by slow gas flow dynamics. Based on the distributed restoration coordination structure and considering that discrete variables in load restoration models hinder the efficient calculation, a projection function-based adaptive alternating direction method of multipliers is then proposed with iterative relaxation and projection of discrete variables. Finally, simulation results in the T118-D33-N56 test system and a real-world system in China demonstrate that coordination of transmission, distribution, and gas systems and considering pipeline gas storage capacity can shorten the load restoration time. The proposed solution method also shows good computational performance. [ABSTRACT FROM AUTHOR]

In recent years, the ever-increasing charging demand of electric vehicles (EVs) imposes challenges on both power supply security and reliability in the distribution system. In this paper, an EV accommodation capability evaluation model of a distribution system, with high penetrations of flexible resources, is established. Firstly, according to the actual classifications of EVs and transportation rules, a Monte Carlo simulation is used to simulate the charging behaviors of EVs so as to obtain the relevant parameters of EV charging. Then, a coordinated charging optimization model for various types of EVs is proposed based on the charging characteristics of EVs. The presented model comprises a mixed-integer linear programming problem and a constrained optimization problem which are respectively solved by CPLEX (the Simplex method implemented in the °C programming language) and the particle swarm optimization (PSO) algorithm. Last of all, a real-life distribution system in the coastal areas of China is served for demonstrating the feasibility and efficiency of the proposed approach. Moreover, the impacts of flexible resources, distribution network zoning rules, and EV growth on the EV accommodation capability of a distribution system are also discussed. [ABSTRACT FROM AUTHOR]

An energy deviation settlement (EDS) mechanism has been proposed to settle the deviation between consumption and energy contracts in China's forward electricity market. Due to high EDS costs of electricity retailers, a deviation mutual insurance (DMI) mechanism is put forward, representing an option for electricity retailers to reduce the deviation through contract transfer. Given this background, an optimal alliance strategy is proposed to maximize the utility of different types of individual electricity retailers. The alliance income model considers the EDS cost reduction and the cooperation cost analyzed based on transaction cost theory (TCT). An improved income allocation method, which considers distribution indexes selected by game theory and resource dependence theory (RDT), is proposed to ensure the comprehensive and reasonable allocation of alliance income. Considering the expected income and financial risk, an evaluation model using conditional value at risk (CVaR) as the financial risk assessment indicator is presented to measure the stability of alliances. Simulations based on data from a provincial electricity market in China illustrate that the EDS costs of retailers can be reduced by selecting appropriate partners for alliance. In addition, the proposed alliance income allocation method is more reasonable and more targeted than the cooperative game-based methods. [ABSTRACT FROM AUTHOR]

Unbalanced operation of a three-phase distribution system could incur more power losses, compared with the balanced operation. So far, the phase information of users in low voltage areas is not recorded by power utilities and the three-phase unbalance degree is very significant in some low voltage distribution systems. Given this background, this letter presents an integrated method for solving related issues including user phase identification based on spectral clustering and three-phase unbalance mitigation, and a Mixed Integer Linear Programming (MILP) model is then formulated. Some actual cases in Zhejiang province, China, are utilized to verify the effectiveness of the presented phase identification algorithm, and simulation results show that the three-phase unbalance can be significantly mitigated. [ABSTRACT FROM AUTHOR]