Calculate of Probabilistic Power of RERs Participating into Power Balance Considering Loss Load Probability

With the participating of renewable energy resources (RERs) into power balance, power system is confronted with loss load probability due to the inherent fluctuation and uncertainty of RERs. In this paper, a probabilistic power model of RERs considering loss load probability is proposed to calculate the power of RERs which participating day-ahead generation scheduling. Firstly, the Information Entropy (IE) theory is introduced to quantify the uncertainty of the system at each moment. Leveraging the level of uncertainty, historical data is divided into multiple time periods and the loss load probability is selected. Secondly, the equivalent power model of RERs is developed to reduce supply risk, with a full consideration of derived power balance constraint which was transformed into a probabilistic form after taking into account the stochastic characteristic of load and RERs. Finally, the simulation result reveals that proposed model can effectively calculate the equivalent power of RERs participating day-ahead power balance, which greatly improves power supply reliability and reduces the supply risk of power system.

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Publication Details

Published
2024-09-27
DOI
https://doi.org/10.1109/icepg63230.2024.10775489
Primary Topic
Power Systems and Technologies
Type
article
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article

Calculate of Probabilistic Power of RERs Participating into Power Balance Considering Loss Load Probability

Fei Lv, Hongyu He, Xiaobi Teng, Fan Yang et al.
Power Systems and Technologies
article

Calculate of Probabilistic Power of RERs Participating into Power Balance Considering Loss Load Probability

Fei Lv, Hongyu He, Xiaobi Teng, Fan Yang, Dongdong Li, Pan Wu
article en

Abstract

With the participating of renewable energy resources (RERs) into power balance, power system is confronted with loss load probability due to the inherent fluctuation and uncertainty of RERs. In this paper, a probabilistic power model of RERs considering loss load probability is proposed to calculate the power of RERs which participating day-ahead generation scheduling. Firstly, the Information Entropy (IE) theory is introduced to quantify the uncertainty of the system at each moment. Leveraging the level of uncertainty, historical data is divided into multiple time periods and the loss load probability is selected. Secondly, the equivalent power model of RERs is developed to reduce supply risk, with a full consideration of derived power balance constraint which was transformed into a probabilistic form after taking into account the stochastic characteristic of load and RERs. Finally, the simulation result reveals that proposed model can effectively calculate the equivalent power of RERs participating day-ahead power balance, which greatly improves power supply reliability and reduces the supply risk of power system.

Shanghai University of Electric Power (CN)
Openalex Percentile: Top 80%
Power Systems and Technologies
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