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PSO알고리즘을 이용한 전력계통의 발전기 예방정비계획 수립
박영수(Youngsoo Park),정희명(Heemyung Jeong),박종국(Jongkook Park),김진호(Jinho Kim),박준호(Juneho Park) 대한전기학회 2007 대한전기학회 학술대회 논문집 Vol.2007 No.11
This paper focuses on maintenance scheduling using PSO algorithm for power plants from an economic (operating cost) and reliable(variance of operating reserve margin) point of view. We also apply regional reserve margin and transfer capability to maintenance scheduling problem. The proposed method has been applied to IEEE-RTS(1996) with 32-generators and a real -world large scale power system with 291 generators.
마이크로그리드에서 SOC균형을 고려한 ESS의 충·방전 전력배분 방법
이상욱(Sang-Wook Lee),박준호(Juneho Park) 대한전기학회 2017 전기학회논문지 Vol.66 No.2
In this paper, multiple ESS(Energy Storage System) control strategy for microgrids is presented. Installation of ESS becomes mandatory when microgrids are used to supply high quality power to the loads. The one of main functions of the ESS is to maintain power balance. However ESS has limitation of its capacity and instantaneous injecting power. Power allocation method based on SOC(State Of Charge) of each ESS is proposed. P-Q control is employed as the basic control strategy for the distributed ESSs. By using the proposed method, the coefficients in the conventional P-Q control method are modified. The ESSs with higher SOC inject more active power, while those with lower SOC inject less, leading to more balanced SOC levels among the ESSs. The proposed method is demonstrated by simulation using PSCAD/EMTDC.
구본길(Bon-gil Koo),김형수(Hyoung-su Kim),이흥석(Heung-seok Lee),박준호(Juneho Park) 대한전기학회 2015 전기학회논문지 Vol.64 No.8
Accurate and robust load forecasting model is very important in power system operation. In case of short-term electric load forecasting, its result is offered as an standard to decide a price of electricity and also can be used shaving peak. For this reason, various models have been developed to improve forecasting accuracy. In order to achieve accurate forecasting result for summer season, this paper proposes a forecasting model using corrected effective temperature based on Heat Index and CDH data as inputs. To do so, we establish polynomial that expressing relationship among CDH, load, temperature. After that, we estimate parameters that is multiplied to each of the terms using PSO algorithm. The forecasting results are compared to Holt-Winters and Artificial Neural Network. Proposing method shows more accurate by 1.018%, 0.269%, 0.132% than comparison groups, respectively.