Search

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]

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]

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]

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]

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]

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]

Seven centrality indexes are first presented for identifying important nodes with topological features of a complex network and electrical characteristics of a power system considered. Then, a criteria importance through intercriteria correlation-based multi-index decision-making 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 comprehensively identify the importance degrees of nodes in a given power grid. Finally, the proposed indexes and method are applied to actual Guangdong power system in China, and the results are compared with those attained by single-attribute and multi-attribute evaluation methods. [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]

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]

The overall performance of emission trading (ET), a market-based emission regulation tool, strongly relies on participants' participation and responses. In order to improve market design, it is important for policy makers to understand the participants' trading behaviors in different market environments. However, human behaviors cannot be easily modeled with conventional analytical methods due to its “bounded rationality” characteristics. In this paper, based on the complementary features between experimental and agent-based computational methods, a hybrid interactive simulation methodology is proposed to solve human behaviors related problems. Human-subjected experiment based on European Union Emissions Trading System price data in 2006 is conducted, the results show that there is no fixed emission trading interval for generation companies, and the strategic behaviors of market participants are observed. Major driving factors of emission trading are categorized into emission price, emission quantity and time related factors, which are in accordance with empirical analysis results on EU ETS 2005–2006 transaction dataset. Furthermore, more human-subjected experiments are conducted under different emission price scenarios to obtain samples for quantitative analysis. Based on thousands of samples obtained, the joint influences of driving factors on emission trading behaviors are analyzed. The quantitative analysis results obtained can reflect the trading patterns of human participants, which provide basis for constructing computer agents that can act as useful substitutes for human participants. [ABSTRACT FROM PUBLISHER]

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]

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]

A fast islanding detection tool can help power dispatchers monitor and control power system operations. Frequency monitoring network (FNET) is a low cost and quickly deployable wide-area phasor measurement system at the distribution system level. The frequency disturbance recorder (FDR) in FNET is actually a single-phase phasor measurement unit (PMU) installed at ordinary 120 V outlets in the sense that it measures the voltage phase angle, amplitude, and frequency from a single-phase voltage source. Based on the data collected by the FDRs deployed in the North American power grid, two islanding detection methods, the frequency difference method and the change of angle difference method, are proposed. The nine real cases recorded, including islanding cases, generation trip cases, load shedding cases and oscillation cases, are presented to verify the proposed methods of islanding detection. Sensitivity analysis on the thresholds of the frequency deviation and angle deviation is done based on the real measurement data for obtaining the insensitive interval of two thresholds. The results show that the proposed methods can correctly detect power system islanding, and will not be falsely triggered by generation trips, load shedding and system oscillations. [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]

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]

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]

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]

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.

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