Open Access

ARTICLE

Research on Evaluation of Multi-Timescale Flexibility and Energy Storage Deployment for the High-Penetration Renewable Energy of Power Systems

Hongliang Wang¹, Jiahua Hu¹, Danhuang Dong¹, Cenfeng Wang¹, Feixia Tang², Yizheng Wang¹, Changsen Feng^2,*

1 Economic Research Institute of State Grid, Hangzhou, 310016, China
2 College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China

* Corresponding Author: Changsen Feng. Email:

(This article belongs to the Special Issue: Artificial Intelligence in Renewable Energy and Storage Systems)

Computer Modeling in Engineering & Sciences 2023, 134(2), 1137-1158. https://doi.org/10.32604/cmes.2022.021965

Received 15 February 2022; Accepted 21 March 2022; Issue published 31 August 2022

Abstract

With the rapid and wide deployment of renewable energy, the operations of the power system are facing greater challenges when dispatching flexible resources to keep power balance. The output power of renewable energy is uncertain, and thus flexible regulation for the power balance is highly demanded. Considering the multi-timescale output characteristics of renewable energy, a flexibility evaluation method based on multi-scale morphological decomposition and a multi-timescale energy storage deployment model based on bi-level decision-making are proposed in this paper. Through the multi-timescale decomposition algorithm on the basis of mathematical morphology, the multi-timescale components are separated to determine the flexibility requirements on different timescales. Based on the obtained flexibility requirements, a multi-timescale energy resources deployment model based on bi-level optimization is established considering the economic performance and the flexibility of system operation. This optimization model can allocate corresponding flexibility resources according to the economy, flexibility and reliability requirements of the power system, and achieve the trade-off between them. Finally, case studies demonstrate the effectiveness of our model and method.

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Keywords

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