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산업용 수용가의 에너지저장장치(ESS)를 이용한 수요자원 거래시장 참여 전략
강병오(Byung O Kang),황병국(Byung Guk Hwang),권균(Kyoon Kwon),정재성(Jaesung Jung) 한국신재생에너지학회 2017 신재생에너지 Vol.13 No.2
An operational strategy of an Energy Storage System (ESS) to participate in the Demand Response (DR) market for industrial customers is proposed. First, two ESS operations currently installed in different industries are compared. The paper then shows the different benefits of peak-shaving and arbitrage based on their load pattern. In addition, the additional cost-benefit when ESS participates in the DR market is estimated. Finally, the operational strategy is proposed to effectively respond to the DR command in conjunction with its own operations, including peak-shaving and arbitrage. The simulation result shows that the proposed operation increases the annual cost-benefit of the ESS by successfully participating in the DR market.
e단기 및 단기 다변수 시계열 결합모델을 이용한 24시간 부하예측
이원준(WonJun Lee),이문수(Munsu Lee),강병오(Byung-O Kang),정재성(Jaesung Jung) 대한전기학회 2017 전기학회논문지 Vol.66 No.3
This paper proposes a combined very-short-term and short-term multi-variate time-series model for 24 hour load forecasting. First, the best model for very-short-term and short-term load forecasting is selected by considering the least error value, and then they are combined by the optimal forecasting time. The actual load data of industry complex is used to show the effectiveness of the proposed model. As a result the load forecasting accuracy of the combined model has increased more than a single model for 24 hour load forecasting.
ARIMA모델 기반 생활 기상지수를 이용한 동 · 하계 최대 전력 수요 예측 알고리즘 개발
정현철(Hyun Cheol Jeong),정재성(Jaesung Jung),강병오(Byung O Kang) 대한전기학회 2018 전기학회논문지 Vol.67 No.10
This paper proposes Autoregressive Integrated Moving Average (ARIMA)-based forecasting algorithms using meteorological indices to predict seasonal peak load. First of all, this paper observes a seasonal pattern of the peak load that appears intensively in winter and summer, and generates ARIMA models to predict the peak load of summer and winter. In addition, this paper also proposes hybrid ARIMA-based models (ARIMA-Hybrid) using a discomfort index and a sensible temperature to enhance the conventional ARIMA model. To verify the proposed algorithm, both ARIMA and ARIMA-Hybrid models are developed based on peak load data obtained from 2006 to 2015 and their forecasting results are compared by using the peak load in 2016. The simulation result indicates that the proposed ARIMA-Hybrid models shows the relatively improved performance than the conventional ARIMA model.