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Jufri, Fauzan Hanif,Widiputra, Victor,Jung, Jaesung ELSEVIER 2019 APPLIED ENERGY -BARKING THEN OXFORD- Vol.239 No.-
<P><B>Abstract</B></P> <P>The rise of power outages caused by extreme weather events and the frequency of extreme weather events has motivated the study of grid resilience. This paper presents a state-of-the-art review of existing research on the study of grid resilience, which focuses on the point of view of power system engineering with respect to extreme weather events. Firstly, it investigates confounding terminologies used in the study of grid resilience, such as the definitions, the differences with grid reliability, the extreme weather events, and their extreme impact on the power systems. Secondly, it presents a grid resilience framework as a general provision to understand the subjects in the study of grid resilience. Thirdly, it describes several methodologies of grid resilience assessment and some quantitative indices. Finally, various grid resilience enhancement strategies implementations are discussed.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Different definitions of power grid resilience are studied and clarified. </LI> <LI> Relation of extreme event and extreme impact to power grid resilience are discussed. </LI> <LI> Power grid resilience framework is explained. </LI> <LI> Existing power grid resilience assessment methodologies and indices are described. </LI> <LI> Power grid resilience enhancement strategies are presented. </LI> </UL> </P>
Jufri, Fauzan Hanif,Oh, Seongmun,Jung, Jaesung Elsevier 2019 ENERGY Vol.176 No.-
<P><B>Abstract</B></P> <P>It is essential to monitor and detect the abnormal conditions in Photovoltaic (PV) system as early as possible to maintain its productivity. This paper presents the development of a PV abnormal condition detection system by combining regression and Support Vector Machine (SVM) models. The regression model is used to estimate the expected power generation under the respective solar irradiance, which is used as the input for the SVM model. The SVM model is then used to identify the abnormal condition of a PV system. The proposed model does not require installing additional measurement devices and can be developed at low cost, because the data that is used as the input variable for the model is retrieved from the Power Conversion System (PCS). Furthermore, the accuracy of the detection system is improved by taking into consideration the daylight time and the interactions between the independent variables, as well as the implementation of the multi-stage k-fold cross-validation technique. The proposed detection system is validated by using actual data retrieved from a PV site, and the results show that it can successfully distinguish the normal condition, as well as identify the abnormal condition of a PV system by using the basic measurements.</P> <P><B>Highlights</B></P> <P> <UL> <LI> PV abnormal condition detection system is developed. </LI> <LI> The model does not require to install any additional measurement devices. </LI> <LI> Regression analysis is employed to estimate the ideal PV generation. </LI> <LI> Support Vector Machine (SVM) algorithm is used to identify PV abnormal condition. </LI> <LI> The proposed detection system is validated by using the actual data. </LI> </UL> </P>
Fauzan Hanif Jufri,오성문,정재성 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.2
Day-ahead system marginal price (SMP) forecasting constitutes essential information in the competitive energy market. Hence, this paper presents the development of a day-ahead SMP forecasting model via implementing an artifi cial neural network (ANN) algorithm. The accuracy of the ANN-based model is improved by including long-term historical data in addition to short-term historical data and by applying the k -fold cross-validation optimization algorithm. The selection of the short-term type input variable applies the Pearson correlation coeffi cient. Whereas the long-term type input variable is selected by applying the discrete Fréchet distance in conjunction with the information related to the season and type of the day to fi nd the Similar-Days Index. In order to verify the model, the forecasted load and actual SMP for 15 years of historical data are used. The results indicate that the proposed model can forecast SMP with higher accuracy than the conventional forecasting model.
Kong, Junhyuk,Jufri, Fauzan Hanif,Kang, Byung O,Jung, Jaesung The Korean Institute of Electrical Engineers 2018 Journal of Electrical Engineering & Technology Vol.13 No.6
Under the current policies and compensation rules in South Korea, Photovoltaic (PV) generation supplier can maximize the profit by combining PV generation with Energy Storage System (ESS). However, the existing operational strategy of ESS is not able to maximize the profit due to the limitation of ESS capacity. In this paper, new ESS scheduling algorithm is introduced by utilizing the System Marginal Price (SMP) and PV generation forecasting to maximize the profits of PV generation supplier. The proposed algorithm determines the charging time of ESS by ranking the charging schedule from low to high SMP when PV generation is more than enough to charge ESS. The discharging time of ESS is determined by ranking the discharging schedule from high to low SMP when ESS energy is not enough to maintain the discharging. To compensate forecasting error, the algorithm is updated every hour to apply the up-to-date information. The simulation is performed to verify the effectiveness of the proposed algorithm by using actual PV generation and ESS information.
Junhyuk Kong,Fauzan Hanif Jufri,Byung O Kang,Jaesung Jung 대한전기학회 2018 Journal of Electrical Engineering & Technology Vol.13 No.6
Under the current policies and compensation rules in South Korea, Photovoltaic (PV) generation supplier can maximize the profit by combining PV generation with Energy Storage System (ESS). However, the existing operational strategy of ESS is not able to maximize the profit due to the limitation of ESS capacity. In this paper, new ESS scheduling algorithm is introduced by utilizing the System Marginal Price (SMP) and PV generation forecasting to maximize the profits of PV generation supplier. The proposed algorithm determines the charging time of ESS by ranking the charging schedule from low to high SMP when PV generation is more than enough to charge ESS. The discharging time of ESS is determined by ranking the discharging schedule from high to low SMP when ESS energy is not enough to maintain the discharging. To compensate forecasting error, the algorithm is updated every hour to apply the up-to-date information. The simulation is performed to verify the effectiveness of the proposed algorithm by using actual PV generation and ESS information.