Sung Kwan Joo

Electric vehicle charging method for smart homes/buildings with a photovoltaic system

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.

Coherency and aggregation techniques incorporating rotor and voltage dynamics

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.

Social Welfare Maximization in Transmission Enhancement Considering Network Congestion

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.

Smart heating and air conditioning scheduling method incorporating customer convenience for home energy management system

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.

Holiday Load Forecasting Using Fuzzy Polynomial Regression With Weather Feature Selection and Adjustment

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.

Empirical Analysis of the Impact of 2003 Blackout on Security Values of U.S. Utilities and Electrical Equipment Manufacturing Firms

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.

Customer baseline load (CBL) calculation using exponential smoothing model with weather adjustment

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.

Home appliance load disaggregation using cepstrum-smoothing-based 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 cepstrum-smoothing- based load disaggregation method1.

Stochastic Method for the Operation of a Power System With Wind Generators and Superconducting Magnetic Energy Storages (SMESs)

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.

A market-based analysis on the generation expansion planning strategies

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