| Summary: | 碩士 === 國立中山大學 === 電機工程學系研究所 === 91 === This paper is to investigate the impact of temperature sensitivity to the load profiles of power system by artificial neural networks (ANN). The load survey study is performed to derive the typical load patterns of the residential, commercial, and industrial customers respectively. By executing the training process of customer power consumption and temperature, the ANN model is created to derive the temperature sensitivity of power consumption for each customer class, which is then used to solve the impact of temperature rise to system power profiles. According to the system load composition and temperature sensitivity of power consumption by each customer class, the hourly increase of system power loading due to 1℃ temperature rise is solved.
To study the temperature effect to the system reliability, the “IEEE Reliability Test System” is selected as test system for power system reliability analysis. Based on the temperature sensitivity of power consumption for each customer class and load composition of each load bus. The power demand is updated with the temperature rise. The temperature sensitivity of commercial customers is very significant because of the high air conditioner loading. When the system load composition is most composed of commercial customers, the power demand are due to temperature rise will have very critical impact to system reliability. On the other hand, the tempearture rise will have less impact of reliability analysis for the system which serves high percentage of industrial customers.
It is concluded that the research of temperature sensitivity on power consumption can provide important information for system reliability analysis. Better substation planning and system capacity expansion can be obtained to meet system reliability criterion by taking into account the temperature effect to system loading.
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