Showing papers in "IEEE Transactions on Power Systems in 2002"

ARIMA models to predict next-day electricity prices

Abstract: Price forecasting is becoming increasingly relevant to producers and consumers in the new competitive electric power markets. Both for spot markets and long-term contracts, price forecasts are necessary to develop bidding strategies or negotiation skills in order to maximize benefit. This paper provides a method to predict next-day electricity prices based on the ARIMA methodology. ARIMA techniques are used to analyze time series and, in the past, have been mainly used for load forecasting, due to their accuracy and mathematical soundness. A detailed explanation of the aforementioned ARIMA models and results from mainland Spain and Californian markets are presented.

General model for representing variable speed wind turbines in power system dynamics simulations

Abstract: A tendency to erect ever more wind turbines can be observed in order to reduce the environmental consequences of electric power generation. As a result of this, in the near future, wind turbines may start to influence the behavior of electric power systems by interacting with conventional generation and loads. Therefore, wind turbine models that can be integrated into power system simulation software are needed. In this contribution, a model that can be used to represent all types of variable speed wind turbines in power system dynamics simulations is presented. First, the modeling approach is commented upon and models of the subsystems of which a variable speed wind turbine consists are discussed. Then, some results obtained after incorporation of the model in PSS/E, a widely used power system dynamics simulation software package, are presented and compared with measurements.

Forecasting next-day electricity prices by time series models

Abstract: In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper provides two highly accurate yet efficient price forecasting tools based on time series analysis: dynamic regression and transfer function models. These techniques are explained and checked against each other. Results and discussions from real-world case studies based on the electricity markets of mainland Spain and California are presented.

Optimal power flow by enhanced genetic algorithm

Abstract: This paper presents an enhanced genetic algorithm (EGA) for the solution of the optimal power flow (OPF) with both continuous and discrete control variables. The continuous control variables modeled are unit active power outputs and generator-bus voltage magnitudes, while the discrete ones are transformer-tap settings and switchable shunt devices. A number of functional operating constraints, such as branch flow limits, load bus voltage magnitude limits, and generator reactive capabilities, are included as penalties in the GA fitness function (FF). Advanced and problem-specific operators are introduced in order to enhance the algorithm's efficiency and accuracy. Numerical results on two test systems are presented and compared with results of other approaches.

An Improved Tabu Search for Economic Dispatch with Multiple Minima

Abstract: This paper develops an improved tabu search algorithm (ITS) for economic dispatch (ED) with noncontinuous and nonsmooth cost functions. ITS employs a flexible memory system to avoid the entrapment in a local minimum, and developed the ideal of "distance" to the fitness to accelerate optimization. The new approach extends simple tabu search algorithm (STS) to real valued optimization problem, and applies parallelism to weaken the dependence of the convergence rate of modified tabu search algorithm (MTS) on the initial condition. Effectiveness of the method was compared with many conventional methods. Results show that the proposed algorithm can provide accurate solutions with reasonable performance, and has a great potential for other applications in the power system.

Oligopolistic competition in power networks: a conjectured supply function approach

Abstract: Conjectured supply function (CSF) models of competition among power generators on a linearized DC network are presented. As a detailed survey of the power market modeling literature shows, CSF models differ from previous approaches in that they represent each generation company's (GenCo) conjectures regarding how rival firms will adjust sales in response to price changes. The CSF approach is a more realistic and flexible framework for modeling imperfect competition than other models for three reasons. First, the models include as a special case the Cournot conjecture that rivals will not change production if prices change; thus, the CSF framework is more general. Second, Cournot models cannot be used when price elasticity of demand is zero, but the proposed models can. Third, unlike supply function equilibrium models, CSF equilibria can be calculated for large transmission networks. Existence and uniqueness properties for prices and profits are reported. An application shows how transmission limits and strategic interactions affect equilibrium prices under forced divestment of generation.

A hybrid particle swarm optimization for distribution state estimation

Abstract: This paper proposes a hybrid particle swarm optimization (HPSO) for a practical distribution state estimation. The proposed method considers nonlinear characteristics of the practical equipment and actual limited measurements in distribution systems. The method can estimate load and distributed generation output values at each node by minimizing the difference between measured and calculated voltages and currents. The feasibility of the proposed method is demonstrated and compared with an original particle swarm optimization-based method on practical distribution system models. Effectiveness of the constriction factor approach of particle swarm optimization is also investigated. The results indicate the applicability of the proposed state estimation method to the practical distribution systems.

Neural network load forecasting with weather ensemble predictions

Abstract: In recent years, a large amount of literature has evolved on the use of artificial neural networks (ANNs) for electric load forecasting. ANNs are particularly appealing because of their ability to model an unspecified nonlinear relationship between load and weather variables. Weather forecasts are a key input when the ANN is used for forecasting. This paper investigates the use of weather ensemble predictions in the application of ANNs to load forecasting for lead times from one to ten days ahead. A weather ensemble prediction consists of multiple scenarios for a weather variable. We use these scenarios to produce multiple scenarios for load. The results show that the average of the load scenarios is a more accurate load forecast than that produced using traditional weather forecasts. We use the load scenarios to estimate the uncertainty in the ANN load forecast. This compares favorably with estimates based solely on historical load forecast errors.

A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function

Abstract: Dynamic economic dispatch (DED) is one of the main functions of power generation operation and control. It determines the optimal settings of generator units with predicted load demand over a certain period of time. The objective is to operate an electric power system most economically while the system is operating within its security limits. This paper proposes a new hybrid methodology for solving DED. The proposed method is developed,in such a way that a simple evolutionary programming (EP) is applied as a based level search, which can give a good direction to the optimal global region, and a local search sequential quadratic programming (SQP) is used as a fine tuning to determine the optimal solution at the final. A ten-unit test system with nonsmooth fuel cost function is used to illustrate the effectiveness of the proposed method compared with those obtained from EP and SQP alone.

A multi-agent approach to power system restoration

Abstract: This paper proposes a multi-agent approach to power system restoration. The proposed system consists of a number of bus agents (BAGS) and a single facilitator agent (FAG). BAG is developed to decide a suboptimal target configuration after a fault occurrence by interacting with other BAGS based on only locally available information, while FAG is to act as a manager in the decision process. The interaction of several simple agents leads to a dynamic system, allowing efficient approximation of a solution. Simulation results have demonstrated that this method is able to reach suboptimal target configurations, which are favorably compared with those obtained by a mathematical programming approach.

Transmission loss allocation: a comparison of different practical algorithms

Abstract: A pool-operated electricity market based on hourly auctions usually neglects network constraints and network losses while applying its market-clearing mechanism. This mechanism determines the accepted and nonaccepted energy bids as well as the hourly market-clearing prices. As a result, ex post procedures are needed to resolve network congestions and to allocate transmission losses to generators and demands. This paper focuses on transmission loss allocation procedures and provides a detailed comparison of four alternative algorithms: (1) pro rata (PR); (2) marginal allocation; (3) unsubsidized marginal allocation; and (4) proportional sharing. A case study based on the IEEE RTS is provided. Different load scenarios covering a whole year are analyzed. Finally, conclusions and recommendations are stated.

Online risk-based security assessment

Abstract: The work described in this paper was motivated by a perceived increase in the frequency at which power system operators are encountering high stress in bulk transmission systems and the corresponding need to improve security monitoring of these networks. Online risk-based security assessment provides rapid online quantification of a security level associated with an existing or forecasted operating condition. One major advantage of this approach over deterministic online security assessment is that it condenses contingency likelihood and severity into indices that reflect probabilistic risk. Use of these indices in control room decision making leads to increased understanding of potential network problems, including overload, cascading overload, low voltages, and voltage instability, resulting in improved security-related decision making. Test results on large-scale transmission models retrieved from the energy-management system of a U.S. utility company are described.

One-Hour-Ahead Load Forecasting Using Neural Networks

Abstract: Load forecasting has always been the essential part of an efficient power system planning and operation. Several electric power companies are now forecasting load power based on conventional methods. However, since the relationship between load power and factors influencing load power is nonlinear, it is difficult to identify its nonlinearity by using conventional methods. Most of papers deal with 24-hour-ahead load forecasting or next day peak load forecasting. These methods forecast the demand power by using forecasted temperature as forecast information. But, when the temperature curves changes rapidly on the forecast day, load power changes greatly and forecast error would going to increase. In conventional methods neural networks uses all similar day's data to learn the trend of similarity. However, learning of all similar day's data is very complex, and it does not suit learning of neural network. Therefore, it is necessary to reduce the neural network structure and learning time. To overcome these problems, we propose a one-hour-ahead load forecasting method using the correction of similar day data. In the proposed prediction method, the forecasted load power is obtained by adding a correction to the selected similar day data.

Voltage-stability protection and control using a wide-area network of phasor measurements

Abstract: This paper presents a concept for local monitoring of the onset of voltage collapse, protective, and emergency control in the presence of voltage-sensitive loads. The onset of voltage collapse point is calculated based on the load characteristics and simulated voltage and current phasors measurements, which are provided by a network of phasor-measurement units. If the stability margin is small and the reactive-power reserves are nearly exhausted, then controls to steer the power system away from the critical point will be activated.

Trading wind generation in short term energy markets

Abstract: Even with state of the art forecasting methods, the short-term generation of wind farms cannot be predicted with a high degree of accuracy. In a market situation, these forecasting errors lead to commercial risk through imbalance costs when advance contracting. This situation is one that needs to be addressed due to the steady increase in the amount of grid connected wind generation, combined with the rise of deregulated, market orientated electricity systems. In the presence of imbalance prices and uncertain generation, a method is required to determine the optimum level of contract energy to be sold on the advance markets. Such a method is presented here using Markov probabilities for a wind farm and demonstrates substantial reductions in the imbalance costs. The effect of market closure delays and forecasting window lengths are also shown.

Nondominated Sorting Genetic Algorithm for Optimal Phasor Maesurement Placement

Abstract: This paper considers a phasor measurement unit (PMU) placement problem requiring simultaneous optimization of two conflicting objectives, such as minimization of the number of PMUs and maximization of the measurement redundancy. The objectives are in conflict, for the improvement of one of them leads to deterioration of another. Consequently, instead of a unique optimal solution, there exists a set of the best trade-offs between competing objectives, the so-called Pareto-optimal solutions. A specially tailored nondominated sorting genetic algorithm (NSGA) for the PMU placement problem is proposed as a methodology to find these Pareto-optimal solutions. The algorithm is combined with the graph-theoretical procedure and a simple GA to reduce the initial number of the PMU candidate locations. The NSGA parameters are carefully set by performing a number of trial runs and evaluating the NSGA performances based on the number of distinct Pareto-optimal solutions found in the particular run and the distance of the obtained Pareto front from the optimal one. Illustrative results on the 39-bus and 118-bus IEEE systems are presented.

Self-scheduling of a hydro producer in a pool-based electricity market

Abstract: This paper addresses the self-scheduling of a hydro generating company in a pool-based electricity market. This company comprises several cascaded plants along a river basin. The objective is to maximize the profit of the company from selling energy in the day-ahead market. This paper proposes a 0/1 mixed-integer linear programming model to account, in every plant, for the nonlinear and nonconcave three-dimensional (3-D) relationship between the power produced, the water discharged, and the head of the associated reservoir. Additionally, start-up costs due mainly to the wear and tear are considered. Finally, different realistic case studies are analyzed in detail.

Modeling of wind turbines for power system studies

Abstract: In this paper, the modeling of wind turbines for power system studies is investigated. Complexities of various parts of a wind turbine model, such as aerodynamic conversion, drive train, and generator representation, are analyzed. The results are verified by field measurements made on a stall-regulated fixed-speed wind turbine. The modeling focuses on deriving a representation that is suitable for use in grid simulation programs.

Price-taker bidding strategy under price uncertainty

Abstract: This paper provides a framework to obtain the optimal bidding strategy of a price-taker producer. An appropriate forecasting tool is used to estimate the probability density functions of next-day hourly market-clearing prices. This probabilistic information is used to formulate a self-scheduling profit maximization problem that is solved taking advantage of its particular structure. The solution of this problem allows deriving a simple yet informed bidding rule. Results from a realistic case study are discussed in detail.

Customer characterization options for improving the tariff offer

Abstract: This paper deals with the classification of electricity customers on the basis of their electrical behavior. Starting from an extensive field measurement-based database of customer daily load diagrams, the authors searched for the most appropriate indices or sets of indices to be used for customer classification. They propose two original measures to quantify the degree of adequacy of each index. Using the indices as distinguishing features, they adopt an automatic clustering algorithm to form customer classes. Each customer class is then represented by its load profile. They use the load profiles to study the margins left to a distribution company for fixing dedicated tariffs to each customer class. They take into account new degrees of freedom available in the competitive electricity markets, which increase flexibility in the tariff definition under imposed revenue caps. Results of a case study performed on a set of customers of a large distribution company are presented.

Assessment of available transfer capability and margins

Abstract: Available transfer capability (ATC) calculation is a complicated task that involves the determination of total transfer capability (TTC) and two margins: transmission reliability margin (TRM) and capacity benefit margin (CBM). Three currently used methods of TTC determination are presented and compared in this paper. Besides these methods, the transfer-based security constrained OPF (TSCOPF) method is proposed in this paper as a replacement of the conventional SCOPF method for use in the deregulation environment. Both TRM and CBM, which account for reliability of the system, are seldom mentioned in papers associated with ATC. This paper presents a probabilistic method to assess TRM and proposes rules and a procedure to allocate CBM and two methods of incorporating CBM into ATC. A modified IEEE RTS is utilized to demonstrate the proposed methods, and the results show that the values of ATC are quite different when margins are taken into account and the methods of incorporating ATC affect the ATC value significantly.

A market approach to long-term security of supply

Abstract: The problem of ensuring that there is enough generation capacity to meet future demand has been an issue in market design since the beginning of the deregulation process. Although ideally the market itself should be enough to provide adequate investment incentives, there are several factors that prevent this result from being achieved, and some actual markets have already experienced problems related with a lack of generation capacity. A regulatory framework to address this question is presented. The procedure is based on an organized market where reliability contracts (based on financial call options) are auctioned, so both their price and their allocation among the different plants are determined through competitive mechanisms. This results in a stabilization of the income of the generators and provides a clear incentive for new generation investment, with a minimum of regulatory intervention. Additionally, the method represents a market-compatible mechanism to hedge demand from the occurrence of high market prices.

Self-healing in power systems: an approach using islanding and rate of frequency decline-based load shedding

Abstract: This paper provides a self-healing strategy to deal with catastrophic events when power system vulnerability analysis indicates that the system is approaching an extreme emergency state. In the authors' approach, the system is adaptively divided into smaller islands with consideration of quick restoration. Then, a load shedding scheme based on the rate of frequency decline is applied. The proposed scheme is tested on a 179-bus, 20-generator sample system and shows very good performance.

Towards a Competitive Market for Reactive Power

Abstract: This paper presents the design of a competitive market for reactive power ancillary services. Generator reactive power capability characteristics are used to analyze the reactive power costs and subsequently to construct a bidding framework. The reactive power market is settled on uniform price auction, using a compromise programming approach based on a modified optimal power flow model. The paper examines market power issues in these markets and identifies locations where strategic market power advantages are present that need to be removed through investments in reactive power devices.

A new strategy for transmission expansion in competitive electricity markets

Abstract: It will be important to develop a transmission network capable of handling future generation and load patterns in a deregulated, unbundled, and competitive electricity market. A new strategy for transmission expansion under a competitive market environment is therefore presented in this paper. In the proposed strategy, a new transmission planning model is developed to consider a variety of market-driven power-flow patterns while a decision analysis scheme is incorporated to minimize the risk of the selected plan. Numerical examples are given to illustrate the potential of the proposed strategy to make a significant contribution to transmission planning in competitive markets.

Windmill modeling consideration and factors influencing the stability of a grid-connected wind power-based embedded generator

Abstract: Generation of electricity using wind power has received considerable attention worldwide in recent years. In order to investigate the impacts of the integration of wind farm into utilities' network, various windmill models have been developed. One such impact is related to the critical clearing time (CCT) of the wind power based embedded generators (WPBEGs). The work in this paper has shown that oversimplification of the modeling of windmill mechanical drive train could introduce significant error in the value of the CCT that defines the stability limit of an integrated wind farm. This paper also reports investigation into the factors that influence the dynamic behavior of the WPBEGs following network fault conditions. It is shown that wind farm CCT can be affected by various factors contributed by the host network. Results obtained from several case studies are presented and discussed. This investigation is conducted on a simulated grid-connected wind farm using EMTP.

Power system stability agents using robust wide area control

Abstract: Conventional power system stabilizers (PSSs) are major local damping controllers acting through generator excitation systems. There are studies that suggest that remote signals could increase damping beyond that attainable by local signals. In this paper, a supervisory level power system stabilizer (SPSS) using wide area measurements is proposed. The robustness of the proposed controller is capable of compensating for the nonlinear dynamic operation of power systems and uncertain disturbances. The coordination of the robust SPSSs and local PSSs is implemented based on the principles of multiagent system theory. This theory is an active branch of applications in distributed artificial intelligence (DAI). The performance of the robust controller as a power system stability agent is studied using a 29-machine 179-bus power system example.

Unit Connuitment Solution Methodology Using Genetic Algorithm

Abstract: Solution methodology of unit commitment (UC) using genetic algorithms (GA) is presented. Problem formulation of the unit commitment takes into consideration the minimum up and down time constraints, start up cost and spinning reserve, which is defined as minimization of the total objective function while satisfying the associated constraints. Problem specific operators are proposed for the satisfaction of time dependent constraints. Problem formulation, representation and the simulation results for a 10 generator-scheduling problem are presented.

Value of security: modeling time-dependent phenomena and weather conditions

Abstract: Deterministic security criteria provide a degree of security that may be insufficient under some operating conditions and excessive for others. To determine an appropriate level of security, one should perform a probabilistic cost/benefit analysis that balances the cost of the security margin against its benefits, i.e., the expected societal cost of the avoided outages. This paper shows how a previously published method based on Monte Carlo simulation can be enhanced to take into account time-dependent phenomena (TDP) such as cascade tripping of elements due to overloads, malfunction of the protection system, and potential power system instabilities. In addition, the importance of using failure rates that reflect the weather conditions is discussed. Studies based on the South-Western part of the transmission network of England and Wales demonstrate the validity of the models that have been developed.

Network-constrained economic dispatch using real-coded genetic algorithm

Abstract: A genetic algorithm (GA) solution to the network-constrained economic dispatch problem is presented. A real-coded GA has been implemented to minimize the dispatch cost while satisfying generating unit and branch power flow limits. A binary-coded GA was also developed to provide a means of comparison. GA solutions do not impose any convexity restrictions on the dispatch problem. The proposed method was applied on the electrical grid of Crete Island with satisfactory results. Various tests with both convex and nonconvex unit cost functions demonstrate that the proposed GA locates the optimum solution, while it is more efficient than the binary-coded GA.