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In a demand response (DR) market run by independent system operators (ISOs), load aggregators are important market participants who aggregate small retail customers through various DR programs. A load aggregator can minimize the allocation cost by efficiently allocating its demand response resources (DRRs) considering retail customers' characteristics. However, the uncertain response behaviors of retail customers can influence the allocation strategy of its DRRs, increasing the economic risk of DRR allocation. This paper presents a risk-based DRR allocation method for the load aggregator that takes into account not only the physical characteristics of retail customers but also the risk due to the associated response uncertainties. In the paper, a conditional value-at-risk (CVaR) is applied to deal with the risk due to response uncertainties. Numerical results are presented to illustrate the effectiveness of the proposed method.

Periodic preventive maintenance of generators is required to maintain the reliable operation of a power system. However, generators under maintenance cannot supply electrical energy to the power system; therefore, it is important to determine an optimal generator maintenance schedule to facilitate efficient supply. The schedule should consider various constraints of the reliability-based demand response program, power system security, and restoration. Determining the optimal generator maintenance schedule is generally formulated as a non-linear optimization problem, which leads to difficulties in obtaining the optimal solution when the various power system constraints are considered. This study proposes a generator maintenance scheduling (GMS) method using transformation of mixed integer polynomial programming in a power system incorporating demand response. The GMS method is designed to deal with various system requirements and characteristics of demand response within a power system. A case study is conducted using data from the Korean power system to demonstrate the effectiveness of the proposed method for determining the optimal maintenance schedule. The results show that the proposed GMS method can be used to facilitate the efficient and reliable operation of a power system, by considering the applicable system constraints.

Recently, many studies have suggested that an electric vehicle (EV) is one of the means for increasing the reliability of power systems in new energy environments. EVs can make a contribution to improving reliability by providing frequency regulation in power systems in which the Vehicle-to-Grid (V2G) technology has been implemented and, if economically viable, can be helpful in increasing power system reliability. This paper presents a stochastic method for optimal coordination of charging and frequency regulation decisions for an EV aggregator using the Least Square Monte-Carlo (LSMC) with modeling of electricity price uncertainty. The LSMC can be used to assess the value of options based on electricity price uncertainty in order to simultaneously optimize the scheduling of EV charging and regulation service for the EV aggregator. The results of a numerical example show that the proposed method can significantly improve the expected profits of an EV aggregator.

A Home Energy Management System (HEMS) is expected to be vital for saving energy costs considering the time-varying price of electric power in a smart home environment. Studies on various energy resources such as energy storage systems and fuel cells in a smart home environment are required for HEMS development. In the area of the HEMSs, however, there exists very limited research on heating and air conditioning scheduling incorporating customer convenience. This paper presents a smart heating and air conditioning scheduling method for HEMS that considers customer convenience as well as characteristics of thermal appliances in a smart home environment. The prototype software based on the proposed method for HEMS is also implemented.

Due to the increased penetration of electric vehicles (EVs) and photovoltaic (PV) systems, additional application for home/building energy management system (EMS) is needed to determine when and how much to charge an electric vehicle in an individual home/building. This paper presents a smart EV charging method for smart homes/buildings with a PV system. The paper consists of two parts: EV charging scheduling algorithm for smart homes/buildings and implementation of prototype application for home/building EMS. The proposed EV charging algorithm is designed to determine the optimal schedules of EV charging based on predicted PV output and electricity consumption. The implemented prototype application for home/building EMS can provide EV charging schedules according to user preferences. Numerical results are provided to demonstrate the effectiveness of the proposed smart EV charging method.

The power industry is currently facing many challenges, due to the new environment created by the introduction of smart grid technologies. In particular, the large-scale deployment of electric vehicles (EVs) may have a significant impact on demand for electricity and, thereby, influence generation and transmission system planning. However, it is difficult to deal with uncertainties in EV charging loads using deterministic planning methods. This paper presents a two-stage stochastic decomposition method with Latin-hyper rectangle sampling (LHRS) to solve the integrated generation and transmission planning problem incorporating EV deployment. The probabilistic distribution of EV charging loads is estimated by Latin-hyper rectangle sampling (LHRS) to enhance the computational performance of the proposed method. Numerical results are presented to show the effectiveness of the proposed method.

The load forecasting problem is a complex nonlinear problem linked with social considerations, economic factors, and weather variations. In particular, load forecasting for holidays is a challenging task as only a small number of historical data is available for holidays compared with what is available for normal weekdays and weekends. This paper presents a fuzzy polynomial regression method with data selection based on Mahalanobis distance incorporating a dominant weather feature for holiday load forecasting. Selection of past weekday data relevant to a given holiday is critical for improvement of the accuracy of holiday load forecasting. In the paper, a data selection process incorporating a dominant weather feature is also proposed in order to improve the accuracy of the fuzzy polynomial regression method. The dominant weather feature for selection of historical data is identified by evaluating mutual information between various weather features and loads from season to season. The results of case studies are presented to show the effectiveness of the proposed method.

Demand side management is used to maintain the reliability of power systems and to increase the economic benefits by avoiding power plant construction. This paper presents a systematic method to calculate the quantity of seasonal demand side management using time series. A numerical example is presented to calculate the quantity of demand side management in winter season using time series.

During an emergency due to a shortage of power, a load aggregator (LA) can use the demand response operation system to deploy demand response resources (DRRs) through various demand response (DR) programs. This paper introduces the demand response operation system for a load aggregator to manage various demand response resources in a smart grid environment.

The addition of new generation capacity increases the fault current levels in power systems. New generation and transmission capacity additions can be limited by the fault current constraints in power systems. The superconducting fault current limiter (SFCL) has great potential as a special protection device to limit the fault current in the event of a fault in power systems. Installations of SFCLs can alter fault current levels in power systems, thereby influencing the power system planning. This paper presents fault current constrained decentralized optimal power flow (OPF) for generation system planning incorporating a SFCL. The numerical simulation results are presented to show the effectiveness of the proposed method.

Various energy storage systems have been adopted in power systems for economic and reliability reasons. In order to maintain the reliability of power systems, reserve power needs to be scheduled against possible generation unit outages. The variation of wind power outputs also requires additional reserve power in addition to the reserve for the possible generation unit outages. Superconducting Magnetic Energy Storage (SMES) has a potential for providing regulation power to meet the reserve requirements in power systems involving wind power generators. This paper presents a stochastic method for the optimal operation of a power system with wind generators and SMES systems. Numerical simulation results are presented to illustrate the potential application of SMES systems to power systems.

The economic value of a wind farm project is influenced by various risk factors such as wind power output and electricity market price. In particular, there is uncertainty in the economic evaluation of a wind farm project due to uncertain wind power outputs, which are fluctuated by weather factors such as wind speed, and volatile electricity market prices. This paper presents a systematic method to assess the economic value and payback period of a wind farm project using Least Square Monte-Carlo (LSMC) simulation. Numerical example is presented to validate the effectiveness of the proposed economic assessment method for a wind farm project.

Occasionally, wind curtailments may be required to avoid an oversupply when wind power, together with the minimum conventional generation, exceed load. By curtailing wind power, the forecast uncertainty and short-term variations in wind power can be mitigated so that a lower spinning reserve is sufficient to maintain the operational security of a power system. Additionally, the electric vehicle (EV) charging load can relieve the oversupply of wind power generation and avoid uneconomical wind power curtailments. This paper presents a stochastic generation scheduling method to ensure the operation security against wind power variation as well as against forecast uncertainty considering the stochastic EV charging load. In the paper, the short-term variations of wind power that are mitigated by the wind curtailment are investigated, and incorporated into a generation scheduling problem as the mixed-integer program (MIP) forms. Numerical results are also presented in order to demonstrate the effectiveness of the proposed method.

The International Electrotechnical Commission (IEC) flicker standard 61000-4-15, which was originally proposed for 230 V/50 Hz systems in Europe and further revised with an amendment to include the necessary modifications for 120 V/60 Hz systems in North America, is becoming more internationally adopted for evaluating the flicker severity. Since the IEC flicker standard is restricted to work on systems with 230 V/50 Hz or 120 V/60 Hz, however, the IEC standard cannot be directly applied to the other power systems beyond its coverage (e.g. 220 V/60 Hz systems in South Korea). A new weighting filter derived from lamp tests, new reference points and a new performance test specification to accommodate 220 V/60 Hz systems in South Korea, complying with the IEC standard, are proposed. The proposed weighting filter, reference points and performance test specification provide a practical guide to develop flicker meters for 220 V/60 Hz systems in South Korea.

The non-separable quadratic penalty terms create an inherent difficulty when applying the standard augmented Lagrangian relaxation (ALR) method for decomposing the unit commitment problem into independent subproblems. This paper presents a separable augmented Lagrangian relaxation method for solving the unit commitment problem. The proposed method is designed to have a separable structure by introducing the quadratic terms with additional auxiliary terms in the augmented Lagrangian function. Numerical results are presented to validate the effectiveness of the proposed method.

This letter presents a novel approach for traffic light detection in a video frame captured by an in-vehicle camera. The algorithm consists of rotated principal component analysis (RPCA), modified amplitude thresholding with respect to the histograms of the PC planes and final filtering with a neural network. The proposed algorithm achieves an average detection rate of 96% and is very robust to variations in the image quality.

Recent large-scale blackouts in North America, Europe, and other countries raised great concerns over the reliability of our electric energy infrastructure and the economic impacts of blackouts. These blackouts were caused by a cascading sequence of events involving line outages, overloading of other lines, malfunctions of protection systems, power oscillations and voltage problems, and system separation and collapse. In this paper, common characteristics of blackouts are identified by analyzing the cascaded events of the blackouts. It is important to take appropriate control actions to alleviate overload and emergency conditions in a power system in order to avoid catastrophic power outages. This paper discusses available control procedures and emergency control systems needed to help prevent catastrophic outages. Economic losses from these blackouts in the U.S., Europe, and other countries were significant. An evaluation of the economic costs of blackouts can be used to estimate the benefits of emergency control systems that can be installed to prevent blackouts. This paper provides an overview of the assessment methods and procedures for evaluation of the economic costs of blackouts. This paper also describes the generic procedure of an event study to measure the economic impact of blackouts on the values of the firms in financial markets. Copyright © 2008 John Wiley & Sons, Ltd.

Transmission enhancement in a competitive electricity market is analyzed in terms of its impact on the social welfare. A formulation of social welfare and the utility functions are derived in this research. Variations of supply and demand during a year are considered in transmission enhancement decisions. The proposed social welfare optimization problem is solved to determine the transmission congestion status under the optimal condition of the existing system. Transmission enhancement options are compared quantitatively to demonstrate their ability to increase social welfare, which provides valuable information for long-term transmission. The proposed method provides a systematic and quantitative tool for social welfare evaluation that is not available in industry today.

A switch in congestion management nile in an electricity market may have positive or negative impact on the surplus of a market participant. As a number of wholesale electricity markets around the world either have already adopted or plan to implement Locational Marginal Pricing (LMP) for energy and congestion management, it is necessary to study the economic impact of a change in congestion management in the Korean electricity market. However, the impact of congestion management on consumer costs and generator surplus has not been studied for the electricity market in Korea. This paper examines how a change in congestion management affects consumer costs and generator surplus in the Korean electricity market.

This paper presents an asynchronous decentralized method to solve the optimization problem of interconnected electricity markets. The proposed method decomposes the optimization problem of combined electricity markets into individual optimization problems. The impact of neighboring markets’ information is included in the objective function of the individual market optimization problem by the standard Lagrangian relaxation method. Most decentralized optimization methods use synchronous models of communication to exchange updated market information among markets during the iterative process. In this paper, however, the solutions of the individual optimization problems are coordinated through an asynchronous communication model until they converge to the global optimal solution of combined markets. Numerical examples are presented to demonstrate the advantages of the proposed asynchronous method over the existing synchronous methods.

This paper presents a new method to determine the locations and sizes of Distributed Generations (DGs) for loss reduction and voltage profile enhancement in distribution systems. The strategic placement of DG can help reduce power losses and improve feeder voltage profile. Fuzzy Goal Programming (FGP) is adopted to handle the multiobjective DG placement problem incorporating the voltage characteristics of each individual load component. The original objective functions and constraints are transformed into the multiobjective function with fuzzy sets by FGP. The transformed multiobjective function with fuzzy sets represents the imprecise natures for criterion of loss reduction and voltage profile enhancement, and the number and total capacities of DGs. The solution of the transformed multiobjective function with fuzzy sets is searched by Genetic Algorithm (GA). The proposed method is tested on the IEEE 34-bus system to demonstrate its effectiveness. Copyright © 2008 John Wiley & Sons, Ltd.

On August 14, 2003, the U.S. faced the largest blackout in history, which left over 50 million people without electricity in eight U.S. states and part of Canada. This paper investigates the effects of the blackout on the security values of the U.S. electric utilities and manufacturing firms in the electric power equipment industry, using an event study method. The results of this empirical study show that the electric utilities were negatively affected, but the electrical equipment manufacturing firms were significantly, positively affected.

The recent movement towards an open, competitive market environment introduced new optimization problems such as market clearing mechanism, bidding decision and Available Transfer Capability (ATC) calculation. These optimization problems are characterized by the complexity of power systems and the uncertainties in the electricity market. Accurate evaluation of the transfer capability of a transmission system is required to maximize the utilization of the existing transmission systems in a competitive market environment. The transfer capability of the transmission networks can be limited by various system constraints such as thermal, voltage and stability limits. The ability to incorporate such limits into the optimization problem is a challenge in the ATC calculation from an engineering point of view. In the competitive market environment, a power supplier needs to find an optimal strategy that maximizes its own profits under various uncertainties such as electricity prices and load. On the other hand, an efficient market clearing mechanism is needed to increase the social welfare, i.e. the sum of the consumers’ and producers’ surplus. The need to maximize the social welfare subject to system operational constraints is also a major challenge from a societal point of view. This paper presents new optimization techniques motivated by the competitive electricity market environment. Numerical simulation results are presented to demonstrate the performance of the proposed optimization techniques.

Dynamic equivalents have been widely used to reduce computational efforts when a large number of scenarios have to be studied to evaluate the stability of interconnected power systems. Most coherency methods developed for model reduction do not consider the effect of voltage variations on the coherency of generators due to weak coupling between real power and voltage. In reality, however, the coherency of generators can be influenced by voltage related factors such as location and speed of AVR controls, generator terminal voltage variation, and load bus voltage variation. The reduced dynamic equivalent is not accurate if the voltage characteristics of the original system are not included in the model. This paper presents coherency identification techniques incorporating rotor and voltage dynamics in order to improve the accuracy of dynamic equivalents. This paper also presents a new method to match the power flow conditions for generator aggregation. This new technique is shown to be critical for the accuracy of aggregated dynamic models. Numerical results based on the model of an actual power system in Asia are presented to demonstrate the performance of the proposed method.

This paper presents Cepstrum smoothing-based feature extraction method to reduce the variation of the characteristic signals of home appliance for Nonintrusive Load Monitoring (NILM). In this paper, a data acquisition system is also developed to obtain only characteristic signals of appliances and to filter the noise inputted from the power supply. Testing results are provided to demonstrate the effectiveness of the proposed method.

In this paper, a scheduling method of air-conditioner is proposed for reducing the energy purchase cost in a smart home/building. The proposed method is designed to determine the air-conditioning schedule by using occupancy prediction in an energy management system for a smart home/building. A numerical simulation example is presented to illustrate the effectiveness of the proposed method.

The effective use of energy is emphasized in a smart home environment because of concerns regarding growing energy demands. To save energy in a smart home, detailed energy consumption information at the appliance level needs to be monitored and delivered to consumers. Unlike distributed direct sensing, non-intrusive load monitoring (NILM) is receiving increased attention as a low-cost, low-complexity load motoring technique. This paper presents an electrical event identification technique for monitoring home appliance load using appliance load signatures. The load signature acquisition system is also introduced in the paper.

Photovoltaic (PV) generation is emerging as an important part of the electric energy system. Various interconnection and operation technologies are needed to accommodate the PV systems into power grid operation and control. Due to the volatility of PV power outputs, it is a challenging task to enhance the overall operational efficiency of grid interconnected PV systems. This paper presents a cross-entropy (CE) based technique to optimize the generation schedule of the grid interconnected PV systems incorporating the volatility of the PV power outputs. Numerical simulation results are presented to demonstrate the effectiveness of the proposed technique.

A novel idea is presented to locate intersections in a video sequence captured from a moving vehicle. More specifically, we propose a Bayesian network approach to combine evidence extracted from a video sequence and evidence from a database, maximizing evidence from various sensors in a systematic manner and locating intersections robustly. the causal relationship between events happening at intersections. Second, we construct a BN based on the analyzed causal relationship to successfully combine feature evidence extracted from a video sequence and evidence from a database to locate intersections robustly.

A home energy management system (HEMS) is expected to play an important role in saving energy costs under time-varying electricity price in a smart home environment. The development of HEMS requires studies on various energy resources in a home, such as energy storage system, and fuel cell. However, in HEMS, very limited research about heating and air conditioning scheduling with customer convenience has been conducted. This study presents a smart heating and air conditioning scheduling method that considers customer convenience and characteristics of the thermal device and an optimization-based approach to minimize the cost in HEMS.

In this paper, a smart electric vehicle charging method is proposed for smart home/building with a photovoltaic system. The proposed method is designed to determine the charging schedule of an electric vehicle based on the predicted photovoltaic output and electricity consumption. Numerical results are provided to demonstrate the effectiveness of the method.

The objective of this study is to develop a deformable 3D image registration algorithm for breast PET-CT and MR images based on perturbation-weighted feature information. As a preprocessing, breast regions were separated by regional masks from the abdomen CT and MR. To remove the jig frame that was used for fixing patient posture during the PET-CT scan, we performed the breast segmentation by region growing. Then a point set was extracted from each modality for surface matching. The point set of CT was used as a target model, and the point set of contrast-enhanced MR was used as a reference model. The surface of a breast does not have many features, but we thought that the nipples and scars can be used as salient features to improve registration performance. The proposed algorithm imposes more weights on those features according to the degree of surface perturbation. Furthermore, we used the hierarchical block-wise operation to minimize the error of the local part. After matching the point sets of the global region, we divide the region to N by N blocks and match the point sets of local blocks. We repeat this step by increasing N until the similarity hit the maximum value. Finally, the PET image is transformed with the optimized transformation parameters and is overlaid onto the MR image. The preliminary results show that the surface of breasts from MR and CT matched well, thereby alignment of the clinically meaningful lesions of the MR and PET slices were improved. For future works, we will investigate more features other than surface and improve the performance of algorithm by adjustment of the weights.

With an increasing concern about environmental pollution and rising price of fossil fuels, electric vehicles (EVs) are becoming an important alternative energy source in a transportation sector. A rapid deployment of EVs may have significant impacts on demand for electricity in a power system. Also, a large-scale deployment of EVs can introduce greater uncertainty in EV charging patterns and loads. This paper presents a Stratified Latin Hypercube Sampling (SLHS)-based probabilistic load flow (PLF) method incorporating electric vehicle charging load. In this paper, probabilistic EV charging load is modeled by using the EV penetration level, hourly traffic patterns, and EV charging scenarios. The Monte Carlo Simulation (MCS)-based PLF requires a significant amount of computation time to obtain accurate results. In this paper, SLHS technique is also applied to reduce the computation time of the PLF. A numerical example is presented to show the performance of the proposed method.

This paper presents a stochastic optimization method to solve an integrated generation and transmission planning problem under uncertainty. The two-stage decomposition method is combined with gradient radar step (GRS) to enhance the computational efficiency. The numerical results are presented to validate the effectiveness of the proposed method.

The objective of this research is to develop 2D/3D registration algorithm for PET/CT and CT or MR images acquired by different systems at different times. We matched two anatomical images first (one from PET/CT and the other from standalone CT or MR) that contains affluent anatomical information. Then we geometrically transformed PET image according to the result of transformation parameters calculated by previous step. We developed two stages of registration algorithm. The first stage is global registration. It is consists of 4 independent steps. After selection of reference and target images different data types and ROI of images have been normalized in the preprocessing step. As a next step, target image is geometrically transformed then the similarity between two images has been measured quantitatively. The optimization step updates transformation parameters to find the best matched parameter set efficiently. In second stage that is called fine adjustment, we introduce feature based registration algorithm. The features of each image slices are extracted by independent component analysis(ICA) and the extracted feature plane is used to measure the similarity. B-spline based freeform deformation is done to form a registered image as a final step. The result of proposed algorithm shows good agreement of images that between PET/CT to CT and PET/CT to MR. We will expand the application of the algorithm to different imaging modalities.

This paper presents a risk assessment method to measure the Conditional Value at Risk (CVaR) of a transmission expansion project using the Cross-Entropy (CE) method. In the transmission investment, there are various uncertainties such as demand, fuel costs and electricity prices. These uncertain factors can influence the future cash flow of a transmission expansion project and become risk factors for an investor. The CVaR can be used to give an investor an accurate indication of the potential losses for the transmission investment. However, the use of the Crude Monte Carlo (CMC) method in CVaR calculation requires a large computational effort to obtain reliable results. In this paper, the CE method is applied to reduce the computational effort in the CVaR calculation. A numerical example is presented to illustrate the effectiveness of the proposed CE-based CVaR method.

This paper proposes CBL calculation method that uses exponential smoothing model with weather adjustment multiplicative. It can estimate the daily electric load patterns of customers who participate in a demand response program. The proposed method is designed to estimate CBL using exponential smoothing model. The proposed method adopts weather adjustment for compensating CBL because the weather has strong influence on CBL. The numerical test results show that the proposed method can improve the accuracy of CBL.

We investigate a method to improve the position decoding for thick crystal versions (i.e., ≥8 mm) of the continuous miniature crystal element (cMiCE) PET detector by more accurately modeling the detector light response function (LRF). The LRF for continuous detectors varies with the depth of interaction (DOI) of the detected photon. This variation in LRF can result in a positioning error for two-dimensional positioning algorithms. We explore a method to improve positioning performance by deriving two lookup tables, corresponding to the front and back regions of the crystal. The DETECT2000 simulation package was used to investigate the light response characteristics for a 48.8 mm by 48.8 mm by 10 (8) mm slab of LSO coupled to a 64-channel, flat-panel PMT. The data are then combined to produce the two-dimensional light collection histograms. Light collection histograms that have markedly non-Gaussian distributions are characterized as a combination of two Gaussian functions, where each Gaussian function corresponds to a DOI region of the crystal. The results indicate that modest gains in positioning accuracy are achieved near the central region of the crystal. However, significant improvements in spatial resolution and positioning bias are achieved for the corner section of the detector.

The demise of the native franchise markets and emergence of competitive markets for electricity generation service is changing the way that electricity is and will be priced and is making increasingly difficult for market participants to appraise the prospects for the future electricity market. As a result, conventional generation expansion planning (GEP) problems determining the least-cost capacity addition plan that meets forecasted demand within pre-specified reliability criteria over a planning horizon (typically 10 to 20 years) is becoming no more valid in these competitive market environments. Therefore, it is necessary to develop a new methodology for generation investment, which is applicable to changed business environments and is able to address the post-privatization situation where individual generation firms seek to maximize their return on investment against market prices. In this paper, we present a new formulation of generation expansion planning problems for privatized generation firms, which is applicable in competitive electric power industries. Based on the game-theoretic approach for the evaluation of future incomes of individual generation firms, we propose a new mathematical formulation and efficient solution scheme considering changed electric power industry environments

Improved market efficiency from combining markets through inter-regional market coordination can lead to significant changes in the consumer and producer surplus of an individual market since inter-regional energy trading can significantly alter energy prices and market shares across various regions. It is necessary to study the impacts of inter-regional energy trading on the net welfare of an individual market as the volumes of inter-regional energy trading continue to grow. Energy trading between regions increases the overall social welfare of markets unless uneconomic power flow occurs between those regions. Inter-regional energy trade may have different implications for the consumers and generators of an individual market, depending whether they are in an importing market or in an exporting market. This paper investigates how inter-regional energy trade affects the net welfare of an individual market in interconnected electricity markets. A numerical example is presented to validate the analytical results developed in the paper

Neural Networks (NN) have been applied to the security assessment of power systems and have shown great potential for predicting the security of large power systems. The curse of dimensionality states that the required size of the training set for accurate NN increases exponentially with the size of input dimension. Thus, an effective feature reduction technique is needed to reduce the dimensionality of the operating space and create a high correlation of input data with the decision space. This paper presents a new feature reduction technique for NN-based power system security assessment. The proposed feature reduction technique reduces the computational burden and the NN is rapidly trained to predict the security of power systems. The proposed feature reduction technique was implemented and tested on IEEE 50-generator, 145-bus system. Numerical results are presented to demonstrate the performance of the proposed feature reduction technique.

Feasibility of Nash equilibrium in generation markets is analyzed using a game theory for simplified competitive electricity markets. Generation companies in the markets are classified into two groups by the state of each company at equilibrium and a new method for obtaining Nash equilibrium in N-company spot markets is explored. Spot markets are assumed to be operated as uniform pricing auctions and generation companies are assumed to submit their bids into the auctions in the form of a seal-bid. The uniform pricing auctions in this analysis are formulated as games in which generation companies correspond to the players. The coefficient of the bidding function of company-n is assigned to the strategy of the player-n (company-n) and the payoff of the player-n is defined by his profit from the uniform price auction. Based on the concept of residual demand, best response functions of each generation company in the N-company auctions are analytically derived. Finally, an efficient way to obtain all the possible equilibrium set pairs and to examine their feasibilities as Nash equilibriums are suggested. A simple numerical example with three generation companies is demonstrated to show the basic ideas of the proposed method.

The presentation focuses on the economic analysis of congestion management rules in terms of the social cost of an individual market in interconnected power grids. There is an increasing need for such studies as electricity markets continue to extend beyond traditional state or national borders with the objective of optimizing the social cost and benefits of the markets. In particular, the presentation shows how congestion management rules can be designed in such a way that it gives preference to the local generators over out-of-region generators in neighboring markets. Case studies was presented to illustrate that the more profitable market rules can change depending on whether the out-of-region generators contribute to congestion or relieve congestion inside its own region.

In dynamic studies of large electric power systems, it is often necessary to reduce a large power network to a smaller equivalent system while keeping the dynamic characteristics of the full system model within the desired accuracy. This paper proposes new coherency identification techniques using the K-means algorithm for construction of dynamic equivalent models. Numerical simulation results based on a NPCC 48-machine test system are presented to demonstrate the performance of the proposed method.

Power flow patterns and fault current levels are influenced by the introduction of distributed generations (DGs) in an electric power system. In particular, the change in the fault current levels caused by DG installation may require a change in the coordination of relays to prevent their misoperation. When DGs are installed in an electric power system, superconducting fault current limiters (SFCLs) can be used to help reduce the fault currents within the breaking capacity of the protective devices. In this paper, multiple criteria such as the number of SFCLs, fault current reduction, and the total operating time of the relays are considered in order to determine the optimal placement of SFCLs for protection of an electric power system with DGs, and a scenario optimization based approach is used to solve the multi-criteria SFCL placement problem. Numerical simulations are also carried out to demonstrate the effectiveness of the proposed approach.

An increase in the generation capacity can lead to an increase in the fault current levels in a power system. The increased fault currents can then impose additional operation costs in power system operation. When new generators are installed, the appropriate use of a superconducting fault current limiter (SFCL) not only reduces the maximum fault current but also improves the efficiency of power system operation. Therefore, there is an increasing need to evaluate the economics of an SFCL by considering the operation costs of a power system and the investment costs of an SFCL. In this paper, a systematic method to assess the economic benefits of an SFCL as an alternative to the bus-splitting method is proposed. Numerical results are presented to illustrate the effectiveness of the proposed SFCL economic assessment method.

As the size of a power system continues to expand, generation capacity and transmission network expansion may be restricted by the fault current limit. Superconducting fault current limiters (SFCLs) are one of the promising fault current limiting technologies that could have potential for greater use in power systems. The installation of SFCLs in a power system is expected to help reduce the fault current levels. This paper presents a fault current constrained planning method for integrated generation and transmission expansion considering SFCLs. In this paper, the fault current constrained planning problem of the integrated generation and transmission expansion is formulated as a co-optimization problem considering SFCLs. Results of a numerical simulation are presented to show the effectiveness of the proposed integrated generation and transmission expansion planning method considering SFCLs.

We developed a high precision position decoding method for a positron emission tomography (PET) detector that consists of a thick slab scintillator coupled with a multichannel photomultiplier tube (PMT). The DETECT2000 simulation package was used to validate light response characteristics for a 48.8 mm×48.8 mm×10 mm slab of lutetium oxy- orthosilicate coupled to a 64 channel PMT. The data are then combined to produce light collection histograms. We employed a Gaussian mixture model (GMM) to parameterize the composite light response with multiple Gaussian mixtures. In the training step, light photons acquired by N PMT channels was used as an N-dimensional feature vector and were fed into a GMM training model to generate optimal parameters for M mixtures. In the positioning step, we decoded the spatial locations of incident photons by evaluating a sample feature vector with respect to the trained mixture parameters. The average spatial resolutions after positioning with four mixtures were 1.1 mm full width at half maximum (FWHM) at the corner and 1.0 mm FWHM at the center section. This indicates that the proposed algorithm achieved high performance in both spatial resolution and posi- tioning bias, especially at the corner section of the detector. C � 2011 Society