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http://dx.doi.org/10.14400/JDC.2020.18.12.267

Through load prediction and solar power generation prediction ESS operation plan(Guide-line) study  

Lee, Gi-Hyun (Department of Industrial Engineering, Ajou University)
Kwak, Gyung-il (Department of Industrial Engineering, Ajou University)
Chae, U-ri (Department of Industrial Engineering, Ajou University)
KO, Jin-Deuk (Department of Industrial Engineering, Ajou University)
Lee, Joo-Yeoun (Department of Industrial Engineering, Ajou University)
Publication Information
Journal of Digital Convergence / v.18, no.12, 2020 , pp. 267-278 More about this Journal
Abstract
ESS is an essential requirement for resolving power shortages and power demand management and promoting renewable energy at a time when the energy paradigm changes. In this paper, we propose a cost-effective ESS Peak-Shaving operation plan through load and solar power generation forecast. For the ESS operation plan, electric load and solar power generation were predicted through RMS, which is a statistical measure, and a target load reduction guideline for one hour was set through the predicted electric load and solar power generation amount. The load and solar power generation amount from May 6th to 10th, 2019 was predicted by simulation of load and photovoltaic power generation using real data of the target customer for one year, and an hourly guideline was set. The average error rate for predicting load was 7.12%, and the average error rate for predicting solar power generation amount was 10.57%. Through the ESS operation plan, it was confirmed that the hourly guide-line suggested in this paper contributed to the peak-shaving maximization of customers.Through the results of this paper, it is expected that future energy problems can be reduced by minimizing environmental problems caused by fossil energy in connection with solar power and utilizing new and renewable energy to the maximum.
Keywords
ESS; RMS; Load Prediction; Solar Power Generation Forecast; ESS Operation Plan;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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1 Lee, Wongoo, KIM, Kang-Won, KIM, Balho H. (2016). A Research on PV-connected ESS dissemination strategy considering the effects of GHG reduction. Journal of energy engineering, 25(2), 94.0-100.0.   DOI
2 Korea Energy Economics Institute, "Analyzing the effect of demand management for energy storage systems (ESS) and a study on market creation," Basic Research Report 14-23, 2014.
3 S. Singh, S. Hussain and M. A. Bazaz, "Short term load forecasting using artificial neural network," 2017 Fourth International Conference on Image Information Processing (ICIIP), Shimla, 2017, pp. 1-5.
4 Q. Wang, B. Zhou, Z. Li and J. Ren, "Forecasting of short-term load based on fuzzy clustering and improved BP algorithm," 2011 International Conference on Electrical and Control Engineering, Yichang, 2011, pp. 4519-4522.
5 J.B Park, S.H Moon, S.H Lee, H.M Hwang, Y.G Park. (2014). Development of Building Electricity Load Forecasting Algorithm for Economic EMS Operations. Journal of The Korean Data Analysis Society, 16(5), 2457-2468.
6 J.W Lee, G.J Kim, S.C Yoon, S.D Jang. (2017). Development and verification of a solar power generation system prediction model. Journal of the Korean Society of Facility Engineering, (), 454-457.
7 D.H Lee, K.H Kim. (2019). A deep learning-based long-term solar power generation prediction technique using climate and seasonal information. The Korean Journal of Electronic Commerce, 24(1), 1-16.
8 D.H Kim, K.H Cho, H.A Park, E.S Kim. (2017). Optimal Operation Research Followed by Pattern Analysis of Charging and Discharging ESS of Industrial customer. Proceedings of the Korean Institute of Electrical Engineers, (), 646-647.
9 J.W Im, D.H Han, S.Y Kim, C.H Ban, J.M Choi, G.H Choi. (2012). PV System with Battery Storage using Peak-cut Algorithm. Proceedings of the Power Electronics Society Conference, (), 135-136.
10 W.J Lee, J.S Jung. (2017). Development of optimal ESS charging/discharging algorithm using load prediction and solar power generation prediction. Proceedings of the Korean Institute of Electrical Engineers, (), 638-639.
11 S.H Park, G.H Lee, M.S Chung, U-ri Chae, J.Y Lee. (2019). Solar ESS Peak-cut Simulation Model for Customer. Journal of Digital Convergence, 17(7), 131-138.   DOI
12 C. Pan and J. Tan, "Day-Ahead Hourly Forecasting of Solar Generation Based on Cluster Analysis and Ensemble Model," in IEEE Access, vol. 7, pp. 112921-112930, 2019.   DOI
13 S. D Park. (2006). Climate change convention and technical countermeasures against global warming. Journal of Energy and Climate Change, 1(1), 1-12.
14 S.W Park (2016). Post-2020 Climate Regime and Paris Agreement - Key Issues and Agreed Results of UNFCCC COP 21 -. Environmental laws and policies, 16(), 285-322.   DOI
15 Colak, M. Yesilbudak, N. Genc and R. Bayindir, "Multi-period Prediction of Solar Radiation Using ARMA and ARIMA Models," 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, 2015, pp. 1045-1049.
16 Yanting Li, Yan Su, Lianjie Shu, "An ARMAX model for forecasting the power output of a grid connected photovoltaic system", Renewable Energy, Volume 66, 2014, Pages 78-89, ISSN 0960-1481   DOI
17 M. Detyniecki, C. Marsala, A. Krishnan and M. Siegel, "Weather-based solar energy prediction," 2012 IEEE International Conference on Fuzzy Systems, Brisbane, QLD, 2012, pp. 1-7.