In view of the challenges of new energy generation, load growth and obsolete distribution facilities, it is imperative to improve the utilization of transmission line capacity [
1]. However, due to the scarcity of space and land, there is great difficulty in building new transmission corridors [
2]. Therefore, it is particularly important to accurately evaluate the maximum allowable ampacity and tap the transmission potential of existing lines to make full use of power grid resources [
3].
The transmission capacity is limited by thermal load, so the research on the thermal load capability of transmission lines is of great significance [
4]. The traditional static thermal rating (STR) uses severe meteorological conditions to determine the maximum allowable ampacity of the line [
5], whose result tends to be conservative and reduces the utilization of the line. With the rapid development of sensor technology, the meteorological data obtained by the meteorological measurement devices are used to determine the real-time capacity [
6]. Therefore, dynamic thermal rating (DTR) has gradually become a research hotspot [
7]. Subsequently, meteorological numerical prediction technology has gradually gained widespread attention. In [
8], the meteorological prediction technology applied to DTR is introduced. Compared with STR, DTR makes full use of the hidden capacity of transmission lines under the safe operation of the power grid, and the utilization of capacity is greatly increased [
9]. However, the high cost of the monitoring equipment makes it difficult to implement [
10]. Moreover, due to the randomness of meteorological data, there are some errors between the data obtained from the meteorological numerical prediction devices and real-time data, which often leads to the calculation results of DTR deviate from the actual operation [
11]. In addition, the time variant of DTR increases the complexity of system operation and control [
12]. Therefore, the concept of quasi-dynamic thermal rating (QDR) was proposed in [
13]. QDR is constant in a certain time scale. Compared with DTR, although QDR is slightly conservative, it is more reliable and less costly to implement [
14]. In [
15], the historical data of key meteorological parameters were analyzed statistically to determine their thresholds in different confidence levels and time scales, and then the QDR in corresponding time scales were calculated by using the thresholds. However, the thresholds at the same confidence level cannot occur simultaneously, so the method is conservative. Therefore, based on the change of key meteorological parameters, a method for determining QDR of long time scale by statistical analysis of line ampacity is proposed in this paper. The results show that QDR can effectively improve the utilization of the line capacity. The conclusion can provide a basis for decision-making of line thermal rating method in power sector, and give some important suggestions for the selection of time scale and confidence level.
The rest of this paper is organized as follows: in
Section 2, the heat balance equation based on CIGRE (International Council on Large Electric systems) standard, the concept of QDR, and the assessment method of operation risk and tension loss are introduced. In
Section 3, based on the heat balance equation, control variate method is used to analyze the influence degree of conductor parameters and meteorological parameters on ampacity and identify the key parameters. The necessity of dividing time scales (year, season, month) is illustrated by statistical analysis of historical data of key parameters in
Section 4. In
Section 5, based on the historical key data in corresponding time scales, the maximum allowable ampacity is calculated, and the QDR under different confidence levels is determined by statistically analysis of the ampacity. The operating annual meteorological data is used to evaluate operation risk and tension loss of QDR. Discussion and conclusions are given in
Section 6 and
Section 7, respectively.