전기요금 절감용 ESS를 활용한 Particle Swarm Optimization 기반 Peak Shaving 제어 방법
Particle Swarm Optimization-Based Peak Shaving Scheme Using ESS for Reducing Electricity Tariff
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초록
본 논문에서는 전기요금 절감용 ESS를 활용한 Particle swarm optimization(PSO) 기반 Peak shaving 제어 방법을 제안한다. 제안한 방식은 실제 부하와 예상되는 부하의 소비를 비교하여 피크 절감을 위해 ESS의 추가 유효전력값을 계산하여 입력을 더한다. 또한 추가로 증가시킨 유효전력을 보상하기 위해, 유효전력을 할당하는 과정을 수행하며 유효전력 할당치가 피크 부하에영향을 주지 않도록 유효전력 할당 지점에 예상되는 부하의 평균을 최소화하는 최적화 해를 PSO를 통해 찾는다. 제안한 방식의성능 검증을 위해 실제 부하 데이터와 예측 알고리즘을 반영하여 예측 오차가 적은 경우와 큰 경우의 사례 연구를 수행하였다. 사례 연구 수행 결과 제안한 방식을 전기요금 절감을 위한 충ㆍ방전 제어 방식과 같이 수행한 경우 예측 오차가 큰 경우에도성공적으로 피크 부하 절감을 수행하였으며, 17.8%의 피크 부하 절감 효과와 6.02%의 전기요금 절감 효과를 보였다.
Abstract
This paper proposes a particle swarm optimization (PSO)-based peak shaving scheme using energy storage system(ESS) for electricity tariff reduction. The proposed scheme compares the actual load with the estimated load consumption,calculates the additional output power that the ESS needs to discharge additionally to reduce peak load, and adds theinput. In addition, in order to compensate for the additional power, the process of allocating power to the determined pointis performed, and an optimization that minimizes the average of the load expected at the active power allocations usingPSO so that the allocated value does not affect the peak load. To investigated the performance of the proposed scheme,case study of small and large load prediction errors was conducted by reflecting actual load data and load predictionalgorithm. As a result, when the proposed scheme is performed with the ESS charge and discharge control to reduceelectricity tariff, even when the load prediction error is large, the peak load is successfully reduced, and the peak loadreduction effect of 17.8% and electricity tariff reduction effect of 6.02% is shown.
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