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Adaptive clustering-based hierarchical layout optimization for large-scale integrated energy systems

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journal contribution
posted on 01.03.2021, 09:27 by Hui Guo, Tianling Shi, Fei Wang, Lijun Zhang, Zhengyu LinZhengyu Lin
Different energy systems are generally planned and operated independently, which result in the low energy utilization, weak self-healing ability, and low system reliability. Therefore, an adaptive clustering-based hierarchical layout optimization method is proposed for a large-scale integrated energy system, considering energy balance, transmission losses and construction costs. First, an adaptive clustering partition method based on energy balance and load moments is proposed to determine the optimal location of energy hubs and to allocate each distributed generation and load to different energy hubs, forming multiple regional integrated energy systems adaptively. Then, the proposed hierarchical layout optimization model is formulated as to find the modified minimum spanning tree of regional integrated energy system and multi-regional integrated energy systems respectively, to construct an economical and reliable interconnection network. Finally, the effectiveness of the optimization model and strategy is verified by simulations.

Funding

National Key R&D Program of China (Project no.2017YFE0112400)

European Union’s Horizon 2020 research and innovation programme (Project no.734796)

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IET Renewable Power Generation

Volume

14

Issue

17

Pages

3336-3345

Publisher

Institution of Engineering and Technology

Version

AM (Accepted Manuscript)

Rights holder

© IET

Publisher statement

This paper is a postprint of a paper submitted to and accepted for publication in IET Renewable Power Generation and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library

Acceptance date

27/08/2020

Publication date

2021-02-16

Copyright date

2020

ISSN

1752-1416

Language

en

Depositor

Dr Zhengyu Lin. Deposit date: 16 February 2021