Probabilistic Dynamic Assessment for Operating Reserve Requirements of Power System with High Penetrated Renewables

doi:10.12204/j.issn.1000-7229.2023.06.013

Abstract

Abstract:

The current criterion for the configuration of operating reserve capacity is based on the load percentage and maximum single-unit capacity, which can result in insufficient or excessively abundant reserve capacity in cases with a high proportion of renewable energy access. To address the challenge of accurately assessing the reserve capacity demand for high proportions of renewable energy grid operation during current dispatching operations, this study proposes a probabilistic dynamic assessment method for operating reserve requirements based on kernel density estimation. Furthermore, a dynamic confidence selection strategy considering the renewable energy penetration rate and evaluation period is proposed, which can facilitate the rolling differential evaluation of the up- and down-regulation of reserve capacity demand. Based on this proposal, a strategy for formulating conventional power supply startup capacity that considers dynamic reserve capacity demands was developed. This strategy maximizes the space for renewable energy consumption while ensuring sufficient operation reserve capacity. An application test was conducted using a provincial power grid with a high proportion of renewable energy sources, and the effectiveness of the proposed method was verified.

Key words: operational reserve, reserve capacity, kernel density estimation, unit commitment, renewable energy

CLC Number:

TM73

WU Sijia, CHI Fangde, YE Xi, KUANG Li, WANG Ze, WEN Yunfeng. Probabilistic Dynamic Assessment for Operating Reserve Requirements of Power System with High Penetrated Renewables[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(6): 126-134.

Figures/Tables 8

Fig.1 Probability fitting of day-ahead renewable generation prediction error

Fig.2 Day-ahead operational reserve capacity requirement assessment framework

Fig.3 Confidence level dynamic selection process in Kernel density estimation method

Fig.4 Impact of traditional unit online capacity on the accommodation range of renewable generation

Fig.5 Evaluation results of different methods for testing grid operation reserve capacity requirements

Table 1 Adaptability analysis of different assessment methods on operational reserve capacity requirement

Fig.6 Evaluation results of different methods for testing grid operation reserve capacity requirements

Fig.7 The online capacity of the conventional power supply of the grid on the next day tested by different methods

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