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Urban DC railways have played an important role in urban transportation, and railway operation and energy management have been emphasized to be important to achieve maximum efficiency from existing infrastructure. Fast, accurate DC railway power flow analysis is required to operate a DC railway efficiently, and this paper proposes a novel methodology to analyze the DC railway power network by implementing the participation factor (PF). The PF concept consists of a correlation between the vehicle and the substation, as explained using zones based on the position of the vehicles running on the route. Then, the DC railway powerflow is calculated using the PF concept. Finally, the usability of the proposed method was validated via case studies with actual railway operation data, and we discuss some of the advantages of the suggested methodology.

This paper presents a stochastic method for an electric vehicle (EV) aggregator to coordinate EV charging schedule considering uncertainty in EV departures. The EV aggregator is responsible for managing the charging schedule of EVs while participating in the electricity markets. The managed EV charging can provide additional revenues to the aggregator from regulation market participation and charging cost reductions to EV owners. The aggregator needs to coordinate the charging schedule considering various uncertain factors such as electricity market prices and the stochastic characteristics of EVs. In this paper, the EV charging scheduling problem incorporating uncertainty in EV departures is formulated as a stochastic optimization problem. A stochastic optimization method is used to solve the EV charging scheduling problem. Latin hypercube sampling (LHS) and a scenario-reduction method are also applied to reduce the computational efforts of the proposed method. The results of a numerical example are presented to show the effectiveness of the proposed stochastic EV charging coordination method.

Demand-side management (DSM) is easy to apply to reduce system peak load by a utility and it can be a convenient way to control and change amount of electric usage by end-use customers. Planning and operating techniques for a DSM program are required to efficiently manage and operate the program. This paper is focused on planning technique for an incentive-based DSM program. This paper describes a stochastic model that can estimate the operating days, hours, and total capacity for efficiently planning a DSM program. A temperature stochastic process, from weather derivatives, is used in the proposed method. Temperature sensitivity is proposed to improve load forecasting accuracy. The generalized extreme value distribution is also proposed for estimating stochastic results. The results of case studies are presented to show the effectiveness of the proposed method.

Various load disaggregation methods have been developed for monitoring home appliance loads to save energy in a smart home. Most of load disaggregation methods are designed to focus on the on/off events of single appliance. In reality, however, multiple appliances can be turned on/off simultaneously. Load disaggregation can be complicated by the simultaneous on/off events of multiple appliances. This paper presents a cepstrum-smoothing-based load disaggregation method to effectively deal with the simultaneous on/off events of multiple appliances. Further, a data acquisition system is developed to obtain only the characteristic signals of appliances and to filter the noise inputted from the power supply. Test results are provided to demonstrate the effectiveness of the proposed cepstrumsmoothing- based load disaggregation method1.

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 target for South Korea's domestic energy consumption is to increase the proportion of the total energy consumption supplied by renewable energy to 11% by 2030. Increasing renewable energy generation has become a natural trend, thereby making it an essential factor to be considered in research for the development of a smart grid. The power system of Jeju Island is connected to the mainland (the Korean Peninsula) through HVDC #1, with another line, HVDC #2, presently under construction. The purpose of the first HVDC construction was essentially to supply a portion of the power supply to the Jeju area. However, if the construction of HVDC #2 is completed, the role of the Jeju HVDC system in the Jeju power system will be changed significantly. If flexibility through the operation of an HVDC system is increased, more wind farms can be built on Jeju Island. In addition, depending on the operation of HVDC #1 and #2, the stability of the Jeju power system will be changed greatly. In this paper, the operation of the HVDC lines and its impacts are analyzed.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.