• 전기학회논문지
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• 제62권4호
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• pp.457-466
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• 2013

Wind energy is rapidly becoming significant generating technologies in electricity markets. As probabilistic nature of wind energy creates many uncertainties in the short-term scheduling, additional actions for reliable market operation should be taken. This paper presents a novel approach to evaluate ramping capability requirement for changes in imbalance energy between day-ahead market and real-time market due to uncertainty of wind generation as well as system load. Dynamic ramp rate model has been applied for realistic solution in unit commitment problem, which is implemented in day-ahead market. Probabilistic optimal power flow has been used to verify ramping capability determined by the proposed method is reasonable in economic and reliable aspects. This approach was tested on six-bus system and IEEE 118-bus system with a wind farm. The results show that the proposed approach provides ramping capability information to meet both forecasted variability and desired confidence level of anticipated uncertainty.

• KIEE International Transactions on Power Engineering
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• 제5A권3호
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• pp.300-302
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• 2005

We employ a new technique to account for extreme values when using the generalized autoregressive conditionally heteroskedastic (GARCH) methodology to forecast day-ahead electricity prices in New York City.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 A
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• pp.819-821
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• 2005

Since the System Marginal Price (SMP) is a vital factor to the market entities who intend to maximize the their profit, the short-term marginal price forecasting should be performed correctly. In a electricity market, the short-term trading between the market entities can be generally affected a short-term market price. Therefore, the exact forecasting of SMP can influence on the profit of market participants. This paper presents a methodology of day-ahead SMP foretasting using Autoregressive Integrated Moving Average (ARIMA). To show the efficiency and effectiveness of the proposed method, the numerical studies have been performed using historical data of SMP in 2004.

• 대한전기학회논문지:전력기술부문A
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• 제55권2호
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• pp.85-93
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• 2006

Since the SMP(System Marginal Price) is a vital factor to the market participants who intend to maximize the their profit and to the ISO(Independent System Operator) who wish to operate the electricity market in a stable sense, the short-term marginal price forecasting should be performed correctly. In an electricity market the short-term market price affects considerably the short-term trading between the market entities. Therefore, the exact forecasting of SMP can influence on the profit of market participants. This paper presents a new methodology for a day-ahead SMP forecasting using ARIMA(Autoregressive Integrated Moving Average) model based on the time-series method. And also the correction algorithm is proposed to minimize the forecasting error in order to improve the efficiency and accuracy of the SMP forecasting. To show the efficiency and effectiveness of the proposed method, the case studies are performed using historical data of SMP in 2004 published by KPX(Korea Power Exchange).

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2236-2244
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• 2018

We make optimal consecutive offer curves for an energy storage system (ESS) integrated wind power producer (WPP) in the co-optimized day-ahead energy and regulation markets. We build the offer curves by solving multi-stage stochastic optimization (MSSO) problems based on the scenarios of pairs consisting of real-time price and wind power forecasts through the progressive hedging method (PHM). We also use the rolling horizon method (RHM) to build the consecutive offer curves for several hours in chronological order. We test the profitability of the offer curves by using the data sampled from the Iberian Peninsula. We show that the offer curves obtained by solving MSSO problems with the PHM and RHM have a higher profitability than offer curves obtained by solving deterministic problems.

• Asia pacific journal of information systems
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• 제7권1호
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• pp.67-83
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• 1997

The utility of learning techniques in investment analysis has been demonstrated in many areas, ranging from forecasting individual stocks to entire market indexes. To date, however, the application of artificial intelligence to financial forecasting has focused largely on short predictive horizons. Usually the forecast window is a single period ahead; if the input data involve daily observations, the forecast is for one day ahead; if monthly observations, then a month ahead; and so on. Thus far little work has been conducted on the efficacy of long-term prediction involving multiperiod forecasting. This paper examines the impact of alternative procedures for extended prediction using knowledge discovery techniques. One dimension in the study involves temporal granularity: a single jump from the present period to the end of the forecast window versus a web of short-term forecasts involving a sequence of single-period predictions. Another parameter relates to the numerosity of input variables: a technical approach involving only lagged observations of the target variable versus a fundamental approach involving multiple variables. The dual possibilities along each of the granularity and numerosity dimensions entail a total of 4 models. These models are first evaluated using neural networks, then compared against a multi-input jump model using case based reasoning. The computational models are examined in the context of forecasting the S&P 500 index.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 A
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• pp.105-106
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• 2004

This paper explains a network for various participants of the electricity market to make bids on the power exchange. The power exchange accepts bids for various markets such as day-ahead, realtime, and financial over this interface. It exists on the IT plane of the market hierarchy and the participants are able to access it over the internet.

• KIEE International Transactions on Power Engineering
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• 제4A권4호
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• pp.262-267
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• 2004

With the advent of electric power systems moving from a vertically integrated structure to a deregulated environment, calculating reactive power service charges has become a new and challenging theme for market operators. This paper examines various methods for reactive power management adopted throughout various deregulated foreign and domestic markets and then proposes an innovative method to calculate reactive power service charges using a reactive power market in a wholesale electricity market. The reactive power market is operated based on bids from the generating sources and it settles on uniform prices by running the reactive OPF programs of the day-ahead electricity market. The proposed method takes into account recovering not only the costs of installed capacity but also the lost opportunity costs incurred by reducing active power output to increase reactive power production. Based on the result of the reactive OPF program, the generators that produce reactive power within the obligatory range do not make payments whereas the generators producing reactive power beyond the obligatory range receive compensation by the price determined in the market. A numerical sample study is carried out to illustrate the processes and appropriateness of the proposed method.

• Journal of Electrical Engineering and Technology
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• 제9권6호
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• pp.1851-1863
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• 2014

Many countries are increasing their investments in smart grid technology to enhance energy efficiency, address climate change, and trigger a green energy revolution. In addition to these goals, Korea also seeks to promote national competitiveness, prepare for the growth of the renewable energy industry, and export industrialization through its strategic promotion of the smart grid. Given its inherent representativeness for Korean implementation of the smart grid and its growth potential, Jeju Island was selected by the Korean government as the site for smart grid testing in June 2009. This paper presents a new design for the electricity market and an operational scheme for testing Smart Electricity Services in the Jeju smart grid demonstration project. The Jeju smart grid test-bed electricity market is constructed on the basis of day-ahead and real-time markets to provide two-way electricity transaction environments. The experience of the test-bed market operation shows that the competitive electricity market can facilitate the smart grid deployment in Korea by allowing various demand side resources to be active market players.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 추계학술대회 논문집 전력기술부문
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• pp.148-150
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• 2005

Since the SMP(System Marginal Price) is a vital factor to the market participants who intend to maximize the their profit and to the ISO(Independent System Operator) who wish to operate the electricity market in a stable sense, the short-term marginal price forecasting should be performed correctly. This paper presents a methodology of a day-ahead SMP forecasting using ARIMA(Autoregressive Integrated Moving Average) based on the Time Series. And also we suggested a correction algorithm to minimize the forecasting error in order to improve efficiency and accuracy of the SMP forecasting. To show the efficiency and effectiveness of the proposed method, the numerical studies have been performed using Historical data of SMP in 2004 published by KPX(Korea Power Exchange).