There are several methods of peak-shaving, which reduces grid power demand, electricity bought from electricity utility, through lowering “demand spike” during On-Peak period. An optimization method using linear programming is proposed, which can be used to perform peak-shaving of grid power demand for grid-connected PV+ system. Proposed peak shaving method is based on the forecast data for electricity load and photovoltaic power generation. Results from proposed method are compared with those from On-Off and Real Time methods which do not need forecast data. The results also compared to those from ideal case, an optimization method which use measured data for forecast data, that is, error-free forecast data. To see the effects of forecast error 36 error scenarios are developed, which consider error types of forecast, nMAE (normalizes Mean Absolute Error) for photovoltaic power forecast and MAPE (Mean Absolute Percentage Error) for load demand forecast. And the effects of forecast error are investigated including critical error scenarios which provide worse results compared to those of other scenarios. It is shown that proposed peak shaving method are much better than On-Off and Real Time methods under almost all the scenario of forecast error. And it is also shown that the results from our method are not so bad compared to the ideal case using error-free forecast.

1. 서 론
2. 그리드에 연결된 배터리 에너지 저장 시스템을갖는 태양광 시스템
3. Peak-Shaving
    3.1 Peak-Shaving
    3.2 본 연구에서 제안한 Peak-Shaving 최적화 모형
4. 최적화 모형을 토대로 한 Peak-Shaving 방법
    4.1 선형 계획 모형
    4.2 전체적인 Peak-Shaving 방법
5. 예제 및 결과 분석
    5.1 데이터
    5.2 결과
6. 결 론
References

• Gi-hwan Lee(서울과학기술대학교 산업공학과) | 이지환
• Kang-won Lee(서울과학기술대학교 산업공학과) | 이강원 Corresponding Author