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전력망 연계형 마이크로그리드 최적운영을 위한 분산에너지자원 에너지관리시스템

DER Energy Management System for Optimal Management of Grid-Connected Microgrids

  • Choi, Jongwoo (Electronics and Telecommunications Research Institute IoT Research Division) ;
  • Shin, Youngmee (Electronics and Telecommunications Research Institute IoT Research Division) ;
  • Lee, Il-Woo (Electronics and Telecommunications Research Institute IoT Research Division)
  • 투고 : 2017.01.19
  • 심사 : 2017.03.27
  • 발행 : 2017.04.30

초록

본 논문에서는 전력망 연계형 마이크로그리드의 분산에너지자원을 위한 에너지관리시스템의 구조에 대해 서술한다. 전력망 연계형 마이크로그리드의 분산에너지자원 에너지관리시스템은 분산에너지자원들의 상태나 시간대별 차등요금제와 같은 마이크로그리드 내외의 각종 정보들을 다양한 프로토콜들을 통해 수집한다. 에너지관리시스템은 수집한 정보들을 바탕으로 예측과 최적화 계산을 수행하고, 전기요금 절감이라는 마이크로그리드 최적운영 목표를 달성할 수 있도록 분산에너지자원들의 운전 스케줄을 도출한다. 최적운영 달성을 위하여 에너지관리시스템은 내부적으로 효과적 스케줄 도출을 위한 알고리즘을 포함하고 있어야하며, 도출한 스케줄을 외부의 분산에너지자원에 전달할 수 있는 프로토콜을 갖추어야 한다. 예측 과정에서 발생하는 실제와의 오차를 줄이기 위하여 에너지관리시스템은 rolling horizon controller로 작동한다. 도출된 운전 스케줄은 국제표준프로토콜을 통하여 실시간으로 분산에너지자원에 전달되어 마이크로그리드 최적운영을 가능하도록 한다.

This paper presents the structure of an energy management system for distributed energy resources of a grid-connected microgrid. The energy management system of a grid-connected microgrid collects information of the microgrid such as the status of distributed energy resources and the time varying pricing plan through various protocols. The energy management system performs forecasting and optimization based on the collected information. It derives the operation schedule of distributed energy resources to reduce the microgrid electricity bill. In order to achieve optimal operation, the energy management system should include an optimal scheduling algorithm and a protocol that transfers the derived schedule to distributed energy resources. The energy management system operates as a rolling horizon controller in order to reduce the effect of a prediction error. Derived control schedules are transmitted to the distributed energy resources in real time through the international standard communication protocol.

키워드

참고문헌

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피인용 문헌

  1. 미래 기상정보를 사용하지 않는 LSTM 기반의 피크시간 태양광 발전량 예측 기법 vol.24, pp.4, 2019, https://doi.org/10.7838/jsebs.2019.24.4.119