Abstract
With the large number of distributed generation (DG) access to the distribution network, the traditional distribution network with a single-supply radial structure has been transformed into an active distribution system (ADS) with source and bidirectional currents. This transformation makes the calculation of the power supply capacity (PSC) of the ADS face new challenges, and the uncertainty of the DG output increases the difficulty in calculating the PSC. At the same time, the power market transaction check needs to meet the safety constraints of the distribution network operation, and is required to know the PSC information of the ADS more quickly and accurately. Therefore, in order to quickly evaluate the PSC of the ADS, this paper proposes a fast evaluation method of the PSC based on the DG output rolling prediction and the information gap decision theory (IGDT). The method first establishes a rolling prediction model of the DG output, and calculates the PSC of the ADS at the corresponding time. Next, it establishes a risk avoidance model (RAM) and a risk speculation model (RSM) for the PSC of the ADS based on the IGDT. These models further calculate the probability of the range of the PSC at the corresponding time, so as to better evaluate the PSC of the ADS. Finally, the improved IEEE-14 node is used to verify that the model can consider the influence of the DG output uncertainty and quickly calculate the information of PSC.
Highlights
In recent years, the distributed generation (DG) industry has experienced rapid growth due to its broad prospects in addressing energy demand and environmental issues
Propose a DG output rolling prediction model based on similar day selection and error correction, which provides a basis for analyzing the power supply capacity (PSC) of the active distribution system (ADS)
When the DG output is lower than the predicted value, the distribution network needs to transmit more power from the upper-level power grid, which will inevitably lead to a decrease in the maximum
Summary
The distributed generation (DG) industry has experienced rapid growth due to its broad prospects in addressing energy demand and environmental issues. By establishing a photovoltaic annual output forecasting model for the whole year, the sampling error of the predicted values is superimposed to obtain the full-year Photovoltaic (PV) output This method fails to analyze the influence of the DG output fluctuation on the PSC. This paper hopes to further analyze the uncertainty of the DG output and the impact on the PSC by combining the rolling prediction algorithm and information gap decision theory (IGDT) theory. Reference [10] proposes a short-term power rolling prediction model for photovoltaic power generation based on the Particle Swarm Optimization Support Vector Machine (PSO-SVM). This paper establishes the DG output rolling prediction model based on the similar day selection error correction. Propose a DG output rolling prediction model based on similar day selection and error correction, which provides a basis for analyzing the PSC of the ADS. The following is a brief introduction to the prediction model using photovoltaics as an example
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