A feasible region evaluation method of renewable energy accommodation capacity

Power systems operate under energy and climate policies with the targets of carbon neutrality and CO2 emissions reduction in recent years. To mitigate environmental problems, it is important to promote the accommodation of renewable energy with strong fluctuations through hosting capacity evaluation...

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Bibliographic Details
Published in:Energy reports Vol. 7; pp. 1513 - 1520
Main Authors: Zhang, Dongdong, Zhao, Jingyi, Dai, Wei, Wang, Cheng, Jian, Jiangyi, Shi, Bochen, Wu, Thomas
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.11.2021
Elsevier
Subjects:
Feasible region
Multi-parametric programming theory
Real-time dispatch
Renewable energy accommodation
Real-time dispatch
Feasible region
Renewable energy accommodation
Multi-parametric programming theory
ISSN:2352-4847, 2352-4847
Online Access:Get full text
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Summary:Power systems operate under energy and climate policies with the targets of carbon neutrality and CO2 emissions reduction in recent years. To mitigate environmental problems, it is important to promote the accommodation of renewable energy with strong fluctuations through hosting capacity evaluation of the distribution network. A feasible region evaluation method is proposed to characterize an accurate region of renewable energy accommodation capacity in the distribution network on short-time scales. The evaluation model based on the optimal power flow model is established with the objective of minimizing the operating cost of the distribution network, satisfying the operational constraints. Through multi-parametric programming, the constraints of the optimal power flow model are projected onto the boundary of the feasible region. Each operation point (the renewable generator output) in the feasible region can be directly obtained, which can serve for real-time dispatch with safety. Simulation results show that the proposed method can accurately obtain the feasible region of renewable energy accommodation capacity and the computational efficiency has been greatly improved compared with the Monte Carlo method.
ISSN:2352-4847
2352-4847
DOI:10.1016/j.egyr.2021.09.091