디지털융복합연구 (Journal of Digital Convergence)

  • 제18권12호
  • /
  • Pages.267-278
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  • 2020
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  • 2713-6434(pISSN)
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  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
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부하예측 및 태양광 발전예측을 통한 ESS 운영방안(Guide-line) 연구

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)
  • 투고 : 2020.09.14
  • 심사 : 2020.12.20
  • 발행 : 2020.12.28
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⟨ 이전 논문 다음 논문 ⟩

초록

에너지 패러다임이 격변하는 시점에서 ESS는 전력부족 및 전력수요관리의 해소와 재생에너지의 증진에 필수적인 요건이다. 이에 본 논문에서는 부하 및 태양광 발전 예측량을 통하여 비용효과적인 ESS Peak-Shaving 운영방안을 제안한다. ESS 운영방안을 위해 통계적 척도인 RMS을 통해 부하 및 태양광 발전 예측하였으며 예측된 부하 및 태양광 발전량을 통해 한 시간 단위의 목표 부하 절감량 Guide-line을 설정하였다. 대상 수용가의 1년 실데이터를 활용한 부하 및 태양광 발전 예측 시뮬레이션으로 2019년 5월 6일 ~ 10일의 부하 및 태양광 발전량을 예측 하였으며 시간별 Guide-line을 설정하였다. 부하 예측 평균오차율은 7.12%였으며, 태양광 발전량 예측 평균오차율은 10.57%를 나타냈다. ESS 운영방안을 통한 시간별 Guide-line 제시를 통해 수용가의 Peak-shaving 최대화에 기여하였음을 확인하였다. 본 논문의 결과를 통해 태양광과 연계하여 화석에너지로 발생하는 환경적인 영향을 최소화하며 신재생에너지를 최대 활용하여 에너지 문제를 줄일 수 있다고 기대한다.

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.

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