Based on the load survey study, a stratified sampling method is proposed to select the proper size of customers so that the load patterns derived can represent the load behavior of whole customer population. In this study there are 1315 customers out of Taipower customers over various service classes are selected for the installation of intelligent meters in the field to measure the power consumption within every 15 minutes. The bad data detection is performed to identify the abnormal power consumption by executing the Chi-square test. The standardized daily load pattern of each customer class has been derived with the mean per-unit method of customer load. The billing data are retrieved from the customer information system and applied to derive the customer daily load pattern by considering the customer load patterns. According to the total power consumption by all customers within the same class and considering the corresponding daily load pattern, the daily load profile of the customer class is then determined. By aggregating the load profiles of all customer classes, the daily load composition and load model of each service district can therefore be solved. By the same manner, the daily load pattern of whole Taipower system can be derived and it can be used to support the proper design of tariff structure according to the respective contribution of system power demand by each customer class.
To investigate the overloading of distribution main transformers during the summer season, the correlations analysis of customer power consumption and temperature is performed. The effect of temperature change to the power consumption of each customer class is solved by multiple regression analysis with 95% confidential level. Based on the temperature sensitivity and the corresponding load composition, the load change due to temperature rise for various customer classes can be estimated. To demonstrate the impact of temperature change to distribution system operation, considering the temperature sensitivity of power consumption and load composition solves the power demand at each load bus. By updating the bus load demand due to temperature change, the feeder loading and power loss is therefore derived. To resolve the over loading problem of distribution feeders and main transformers during the summer season, a temperature adaptive switching operation has been proposed to perform the proper load transfer among the feeders/main transformers.
In this dissertation, the effect of temperature change to the time varying characteristics of load buses and power transmission in Taipower is investigated. The dc circuit model of Taipower system and the temperature effect of customer power consumption are considered in the stochastic load flow analysis. With the temperature rise, the power demand of northern buses is increased dramatically and more power has to be transmitted from the southern region. The large voltage angle difference is significantly various between system buses during the summer peak period. It is suggested that the safety margin assessment of system operation has to be executed by considering the temperature effect to the bus loading of power systems.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0706101-112612 |
| Date | 06 July 2001 |
| Creators | Kang, Meei-Song |
| Contributors | Ching-Tsai, Sheng-Nian Yeh, Chan-Nan Lu, Hong-Tzer Yang, Chao-Shun Chen |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0706101-112612 |
| Rights | off_campus_withheld, Copyright information available at source archive |
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