Abstract
Due the high penetration of intermittent renewable energy sources (IRESs), transmission line currents show large fluctuations and thus significant uncertainty. This makes it difficult to operate a power system without violating transmission capacity constraints. This paper evaluates the dynamic line ratings (DLRs) of overhead lines based on changes in the line current owing to the high penetration of intermittent renewable energy sources. In particular, by focusing on extremely large (but rare) forecasting errors in the intermittent renewable energy source output, which are generally inevitable in most forecasting methods, a model for representing the forecasting error in line with current variation due to intermittent renewable energy source output is developed. The model is based on a shape parameter that represents the equivalent current variation required for the same temperature increase as that due to the extremely large forecasting error. Finally, based on the annual minute-by-minute irradiance data, preventive control of the transmission network with dynamic line ratings is evaluated using worst-case parameter values.
Highlights
Electric power generation from intermittent renewable energy sources (IRESs), including photovoltaic (PV) systems and wind power generators, exhibits significant output fluctuations
This paper proposes an evaluation method of dynamic line ratings (DLRs) based on a continuous-time line current variation model for representing extremely large forecasting errors in IRES output in order to estimate the rise in line temperature accurately, assuming that the transmission line current fluctuates significantly because of the high penetration of IRESs
The continuous-time model for paper, we propose an equivalent change in the line current that can result in the same temperature extremely large forecasting errors shown in Figure 2 can be developed as: increase as those caused by extremely large forecasting errors
Summary
Electric power generation from intermittent renewable energy sources (IRESs), including photovoltaic (PV) systems and wind power generators, exhibits significant output fluctuations. Given the increased penetration of IRESs, these output fluctuations may cause large power flow variations in transmission lines. Various methods for predicting IRES output have been developed using different time scales, spatial scales, and procedures [1,2,3,4,5,6]. In all these methods, it is critical to ensure the secure operation of power systems with IRESs. it has been noted that the distribution of the forecasting error is a heavy-tailed one instead of a normal one [7,8]. Even in the case of short-term predictions (e.g., those for less than 30 min) [9], extremely large forecasting errors must be considered during the operation of power systems, even though such errors occur rarely
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