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Power Systems, IEEE Transactions on

Issue 1 • Date Feb. 2005

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Displaying Results 1 - 25 of 76
  • Table of contents

    Page(s): c1 - 2
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    Freely Available from IEEE
  • IEEE Transactions on Power Systems publication information

    Page(s): c2
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    Freely Available from IEEE
  • A general-purpose symbolically assisted numeric computation environment as a support in power engineering education

    Page(s): 3 - 12
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (792 KB) |  | HTML iconHTML  

    This paper describes and illustrates a Windows-based, general-purpose, symbolically assisted numeric computation environment within power engineering education applications. The examples considered include fundamental problems derived from three areas: power system analysis and optimization, electric machinery, and feedback control systems. View full abstract»

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  • Modeling and forecasting electricity prices with input/output hidden Markov models

    Page(s): 13 - 24
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    In competitive electricity markets, in addition to the uncertainty of exogenous variables such as energy demand, water inflows, and availability of generation units and fuel costs, participants are faced with the uncertainty of their competitors' behavior. The analysis of electricity price time series reflects a switching nature, related to discrete changes in competitors' strategies, which can be represented by a set of dynamic models sequenced together by a Markov chain. An input-output hidden Markov model (IOHMM) is proposed for analyzing and forecasting electricity spot prices. The model provides both good predictions in terms of accuracy as well as dynamic information about the market. In this way, different market states are identified and characterized by their more relevant explanatory variables. Moreover, a conditional probability transition matrix governs the probabilities of remaining in the same state, or changing to another, whenever a new market session is opened. The model has been successfully applied to real clearing prices in the Spanish electricity market. View full abstract»

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  • A decentralized implementation of DC optimal power flow on a network of computers

    Page(s): 25 - 33
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    This work presents a decentralized implementation of the DC Optimal Power Flow (OPF) problem on a network of workstations. Each workstation is assigned to a regional Transmission System Operator (TSO), who manages the operation of the transmission system of his own region, as well as cross-border exchanges with neighboring regions. The workstations take part in an iterative process, exchanging information with each other and solving regional OPF sub-problems, until the global OPF solution is reached. Tie-line related information is exchanged between workstations assigned to neighboring regions. A master workstation, assigned to a "Super-TSO", checks for the convergence of the algorithm. The parallel processing system is tested on various test systems, including a large real-world system, the Balkan power system. View full abstract»

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  • A particle swarm optimization for economic dispatch with nonsmooth cost functions

    Page(s): 34 - 42
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    This work presents a new approach to economic dispatch (ED) problems with nonsmooth cost functions using a particle swarm optimization (PSO) technique. The practical ED problems have nonsmooth cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult using any mathematical approaches. A modified PSO (MPSO) mechanism is suggested to deal with the equality and inequality constraints in the ED problems. A constraint treatment mechanism is devised in such a way that the dynamic process inherent in the conventional PSO is preserved. Moreover, a dynamic search-space reduction strategy is devised to accelerate the optimization process. To show its efficiency and effectiveness, the proposed MPSO is applied to test ED problems, one with smooth cost functions and others with nonsmooth cost functions considering valve-point effects and multi-fuel problems. The results of the MPSO are compared with the results of conventional numerical methods, Tabu search method, evolutionary programming approaches, genetic algorithm, and modified Hopfield neural network approaches. View full abstract»

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  • Power transmission network design by greedy randomized adaptive path relinking

    Page(s): 43 - 49
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    This work presents results obtained by a new metaheuristic approach called greedy randomized adaptive path relinking (GRAPR), applied to solve static power transmission network design problems. This new approach uses generalized GRASP concepts to explore different trajectories between two "high-quality" solutions previously found. The results presented were obtained from two real-world case studies of Brazilian systems. View full abstract»

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  • Bidding strategies in oligopolistic dynamic electricity double-sided auctions

    Page(s): 50 - 58
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    In this paper, the problem of developing bidding strategies in oligopolistic dynamic electricity double-sided auctions is studied. We model electricity double-sided auctions as dynamic systems and use Nash-Cournot strategies for the market participants (generating firms and load serving entities). Through simulation studies, we compare the efficiency and competitiveness of electricity double-sided auctions to those of electricity supplier-only auctions (using the developed bidding strategies). View full abstract»

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  • Neural network-based market clearing price prediction and confidence interval estimation with an improved extended Kalman filter method

    Page(s): 59 - 66
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    Market clearing prices (MCPs) play an important role in a deregulated power market, and good MCP prediction and confidence interval (CI) estimation will help utilities and independent power producers submit effective bids with low risks. MCP prediction, however, is difficult, since MCP is a nonstationary process. Effective prediction, in principle, can be achieved by neural networks using extended Kalman filter (EKF) as an integrated adaptive learning and CI estimation method. EKF learning, however, is computationally expensive because it involves high dimensional matrix manipulations. This work presents a modified U-D factorization method within the decoupled EKF (DEKF) framework. The computational speed and numerical stability of this resulting DEKF-UD method are significantly improved as compared to standard EKF. Testing results for a classroom problem and New England MCP predictions show that this new method provides smaller CIs than what provided by the BP-Bayesian method developed by the authors. Testing also shows that our new method has faster convergence, provides more accurate predictions as compared to BP-Bayesian, and our DEKF-UD MCP predictions are comparable in quality to ISO New England's predictions. View full abstract»

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  • A strategic production costing model for electricity market price analysis

    Page(s): 67 - 74
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    Production costing models (PCMs) have been extensively used to analyze traditional power systems for decades. These tools are based on the costs of production, but in oligopolistic electricity markets market prices can not be explained attending just to marginal costs but instead bid prices have to be considered, since market participants seize their dominant position in the market looking for higher profits. Thus, the merit order composition criteria applied in traditional PCMs has to be somehow reconsidered in order to be able to represent the agents' strategic bidding. The objective of the strategic production costing model (SPCM) presented in this paper is to evolve the PCM approach to adapt it to the actual wholesale electricity markets without losing its typical advantages. The generalization proposed allows to represent an oligopolistic hydrothermal electricity market and provides the system price-duration curve as well as the income and expected costs of every generating agent. Compared with other oligopolistic models, the main advantage of the SPCM is its potential computational speed, which makes it very suitable for risk analysis studies that require considering a large number of scenarios. View full abstract»

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  • A reliability-centered asset maintenance method for assessing the impact of maintenance in power distribution systems

    Page(s): 75 - 82
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    This paper proposes a method for comparing the effect of different maintenance strategies on system reliability and cost. This method relates reliability theory with the experience gained from statistics and practical knowledge of component failures and maintenance measures. The approach has been applied to rural and urban distribution systems. In particular, a functional relationship between failure rate and maintenance measures has been developed for a cable component. The results show the value of using a systematic quantitative approach for investigating the effect of different maintenance strategies. View full abstract»

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  • Games with incomplete and asymmetric information in poolco markets

    Page(s): 83 - 89
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    Strategic equilibrium solutions as a result of gaming by electricity market participants have been the subject of various recent studies. Yet, in many of the tools developed for finding those strategic solutions, either information is regarded as being complete, or the complexities of the transmission system are disregarded. Since the assumption of complete information is neither absolutely correct nor absolutely necessary for the more complex models, these models need to be extended. The strategic solutions attained in these incomplete information or Bayesian games are Bayesian Nash equilibria. The central thrust of the paper is on bringing the representation of incomplete and asymmetric information in strategic games together with some of the transmission system constraints. For this purpose, the paper focuses on a specific strategic game model referred to as the IWM. Numerical examples, using the IEEE 57-bus system, that deal with the power flow equality constraints are presented as an illustration of the concepts behind the proposed Bayesian games. View full abstract»

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  • Reliability analysis for systems with large hydro resources in a deregulated electric power market

    Page(s): 90 - 95
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    This work describes a procedure that determines the optimal allocation for the yearly energy resulting from random water inflows to the different subperiods of a year so that the expected benefits are maximized. Its main idea is to distribute the energy stored in reservoirs in each period into two parts: one is directly sold in the energy market, while the other is made available to cover any unplanned outages of thermal units. The method proposed fulfills two objectives, to distribute the hydro energy optimally according to economic criteria and to assess the impact of new market rules on the reliability of an electric system. The procedure will be illustrated by an example based on the Spanish generating system. View full abstract»

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  • Short-term load forecasting for the holidays using fuzzy linear regression method

    Page(s): 96 - 101
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    Average load forecasting errors for the holidays are much higher than those for weekdays. So far, many studies on the short-term load forecasting have been made to improve the prediction accuracy using various methods such as deterministic, stochastic, artificial neural net (ANN) and neural network-fuzzy methods. In order to reduce the load forecasting error of the 24 hourly loads for the holidays, the concept of fuzzy regression analysis is employed in the short-term load forecasting problem. According to the historical load data, the same type of holiday showed a similar trend of load profile as in previous years. The fuzzy linear regression model is made from the load data of the previous three years and the coefficients of the model are found by solving the mixed linear programming problem. The proposed algorithm shows good accuracy, and the average maximum percentage error is 3.57% in the load forecasting of the holidays for the years of 1996-1997. View full abstract»

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  • Next day load curve forecasting using hybrid correction method

    Page(s): 102 - 109
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (264 KB) |  | HTML iconHTML  

    This work presents an approach for short-term load forecast problem, based on hybrid correction method. Conventional artificial neural network based short-term load forecasting techniques have limitations especially when weather changes are seasonal. Hence, we propose a load correction method by using a fuzzy logic approach in which a fuzzy logic, based on similar days, corrects the neural network output to obtain the next day forecasted load. An Euclidean norm with weighted factors is used for the selection of similar days. The load correction method for the generation of new similar days is also proposed. The neural network has an advantage of dealing with the nonlinear parts of the forecasted load curves, whereas, the fuzzy rules are constructed based on the expert knowledge. Therefore, by combining these two methods, the test results show that the proposed forecasting method could provide a considerable improvement of the forecasting accuracy especially as it shows how to reduce neural network forecast error over the test period by 23% through the application of a fuzzy logic correction. The suitability of the proposed approach is illustrated through an application to actual load data of the Okinawa Electric Power Company in Japan. View full abstract»

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  • Real-time simulation of voltage source converters based on time average method

    Page(s): 110 - 118
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    When a real-time digital simulator, emulating a switched circuit such as a voltage source converter, is interfaced with a digital controller, the controller's firing signals may not be in synchronism with the simulation time step. Several methods to minimize the resulting inaccuracies have been proposed in the past. However, some of these approaches introduce unnecessary delays and/or generate artificial harmonics. A method based on time averaging is presented. The algorithm is shown to be fast, easy to implement in a programmable logic device, and highly accurate. The method is applied in a hardware-in-the-loop simulation, and the simulation results are compared with experimental results. View full abstract»

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  • An oligopolistic power market model with tradable NOx permits

    Page(s): 119 - 129
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    Models formulated as complementarity problems have been applied previously to assess the potential for market power in transmission-constrained electricity markets. Here, we use the complementarity approach to simulate the interaction of pollution permit markets with electricity markets, considering forward contracts and the operating reserve market. Because some power producers are relatively large consumers of permits, there could be interaction between market power in the permits and energy markets. Market power in the energy market is modeled using a Cournot game, while a conjectured price response model is used in the permits market. An illustrative application is made to Pennsylvania-New Jersey-Maryland Interconnection (PJM), which we represent by a 14-node dc load-flow model, and the USEPA Ozone Transport Commission NOx Budget Program. The results show that forward contracts effectively mitigate market power in PJM energy market and both simulated solutions of perfect and Cournot (oligopoly) competition are a good approximation to actual prices in 2000, except that the Cournot model yielded higher peak prices. The NOx market influences the Cournot energy market in several ways. One is that Cournot competition lowers the price of NOx permits, which in turn affects on low- and high-emission producers differently. In general, because pollution permits are an important cost, high concentration in the market for such permits can exacerbate the effects of market power in energy markets. View full abstract»

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  • A predictor/corrector scheme for obtaining Q-limit points for power flow studies

    Page(s): 130 - 137
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    This work proposes a new continuation power flow method tracing QV constraint exchange points (CEP), at which generators regulating voltages hit the reactive power limits. The proposed method is based on a predictor/corrector scheme to obtain CEPs in succession. The condition for Q-limit immediate instability is derived and used in the algorithm, where the stability of the obtained CEP is checked in each iteration. The point of collapse method is also combined in the algorithm to detect a saddle node bifurcation. The effectiveness of the proposed method is demonstrated through numerical examinations in IEEE 118 bus systems. View full abstract»

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  • Solving several problems of power systems using spectral and singular analyses

    Page(s): 138 - 148
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    The paper is of tutorial nature, covering a wide range of the spectral and singular analyses of the electric power systems' (EPS) nodal admittance matrix and Jacobian matrix. Results of the analyses are applied to detect sensors and weak places in EPS and to visualize them. This allows for the solution to many important problems related to power system operation like estimation of network reinforcement and determination of sites for power quality metering. In this paper the theoretical background of the approach and its above mentioned applications are presented and discussed in the scope of their usefulness for the power system operation, planning and control. View full abstract»

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  • A novel approach to trace time-domain trajectories of power systems in multiple time scales

    Page(s): 149 - 155
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (320 KB) |  | HTML iconHTML  

    This work presents a novel approach for multitime-scale power system dynamic simulation based on differential and algebraic equations (DAEs). By properly selecting the continuation (local) parameter, the approach can avoid the numerical divergence encountered by a conventional approach, due to the singularity of the algebraic part of the equations. Meanwhile, by setting step size in a local parameterization, the approach varies its time step size according to the first- or second-order sensitivity of the solution to the variation of local parameter. It employs larger time step sizes for slow dynamics and smaller ones for fast dynamics. The approach presents an advanced tool to study voltage dynamics in multiple time scales and is easily adaptable for quasisteady-state analysis (QSS). The applicability of the approach to multiple time scales and QSS is demonstrated through a 39-bus New England system. View full abstract»

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  • Fuzzy interrupted energy assessment rate based on actual system performance

    Page(s): 156 - 163
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    In competitive environment, customer outage cost may be taken into account for the delivery of energy services. Based on a probabilistic approach the outage cost can be evaluated from the Interrupted Energy Assessment Rate (IEAR) which normally requires customer perception on the damages occurred from an electricity interruption. In general the IEAR is only calculated based on mean values, even though the obtained customer damage information usually contains fairly large deviation. To cope with the large deviation of the data, this paper employs fuzzy arithmetic to model the customer damage function which is consequently used in association with power interruption statistics, rather than conventional probabilistic reliability calculation, to calculate the Fuzzy Interrupted Energy Assessment Rate (FIEAR). The proposed method has been tested with a distribution system of which more than 40,000 actual interruptions were recorded. The results show that the FIEAR normally covers the IEAR and provides flexibility to allow the customer outage cost to be implemented in real competitive environment. View full abstract»

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  • Load estimation for load monitoring at distribution substations

    Page(s): 164 - 170
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (464 KB) |  | HTML iconHTML  

    This paper addresses the issues related to the real-time monitoring of loads at distribution substations. A method is proposed for estimating the measurements that become unavailable due to metering problems. To address the low redundancy associated with the load monitoring scheme, the method uses a regression-based model and makes use of the strong correlation between the loads that are geographically close to each other. The method was tested with actual field data. The results indicate that the method has the acceptable performance for measurement loss of up to a week. The average estimation error varies 2-7.5%, depending on which measurement is lost and how up-to-date the historical data is. View full abstract»

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  • Dispatchable transmission in RTO markets

    Page(s): 171 - 179
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    We consider transmission owners that bid capacity, under appropriate Regional Transmission Organization (RTO) market rules, at a positive price into forward and spot (dispatch) auctions to derive congestion revenues. This can encompass daily, monthly, or multimonthly auctions, allowing for commitment of transmission to reflect market needs in different time periods, e.g., seasons. We provide two and three node examples and a general formulation of the auction model. View full abstract»

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  • Strategic bidding under uncertainty: a binary expansion approach

    Page(s): 180 - 188
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (488 KB) |  | HTML iconHTML  

    This work presents a binary expansion (BE) solution approach to the problem of strategic bidding under uncertainty in short-term electricity markets. The BE scheme is used to transform the products of variables in the nonlinear bidding problem into a mixed integer linear programming formulation, which can be solved by commercially available computational systems. The BE scheme is applicable to pure price, pure quantity, or joint price/quantity bidding models. It is also possible to represent transmission networks, uncertainties (scenarios for price, quantity, plant availability, and load), financial instruments, capacity reinforcement decisions, and unit commitment. The application of the methodology is illustrated in case studies, with configurations derived from the 80-GW Brazilian system. View full abstract»

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  • Feature extraction via multiresolution analysis for short-term load forecasting

    Page(s): 189 - 198
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (496 KB) |  | HTML iconHTML  

    The importance of short-term load forecasting has been increasing lately. With deregulation and competition, energy price forecasting has become a big business. Bus-load forecasting is essential to feed analytical methods utilized for determining energy prices. The variability and nonstationarity of loads are becoming worse, due to the dynamics of energy prices. Besides, the number of nodal loads to be predicted does not allow frequent interactions with load forecasting experts. More autonomous load predictors are needed in the new competitive scenario. This paper describes two strategies for embedding the discrete wavelet transform into neural network-based short-term load forecasting. Its main goal is to develop more robust load forecasters. Hourly load and temperature data for North American and Slovakian electric utilities have been used to test the proposed methodology. View full abstract»

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Aims & Scope

Covers the requirements, planning, analysis, reliability, operation, and economics of electric generating, transmission, and distribution systems for general industrial, commercial, public, and domestic consumption.

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Meet Our Editors

Editor-in-Chief
Antonio J. Conejo
The Ohio State University