| Issue |
E3S Web Conf.
Volume 385, 2023
2023 8th International Symposium on Energy Science and Chemical Engineering (ISESCE 2023)
|
|
|---|---|---|
| Article Number | 01017 | |
| Number of page(s) | 5 | |
| Section | Energy Development and Utilization and Energy Storage Technology | |
| DOI | https://doi.org/10.1051/e3sconf/202338501017 | |
| Published online | 04 May 2023 | |
Integrated energy operation considering the dependence of multiple wind turbines
1 School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, Shandong, 255000, China
2 Shandong Deyou Electric Co., Ltd, Zibo, Shandong, 255000, China
3 School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200340, China
a jiaopihua2013@163.com
b* 20604010003@sdut.edu.cn
c sunguoqi09@163.com
d scaixu412@163.com
e wanglelepj@163.com
f sungpeibott@163.com
A key pathway to deep decarbonization of the energy sector is the increased use of wind power in integrated energy systems in the situation of the demand for low-carbon development. However, existing work on wind power in the integrated energy system does not adequately take into account the low carbon emissions in the context of multiple wind turbine dependency. In this respect, it is essential to examine a new type of framework for coordinated operations for meeting operational, low carbon and dependence requirements of multiple wind turbines. Here, a low-carbon oriented electrical-gas-heat energy system including carbon emission, carbon capture and carbon trading is proposed. In particular, the multivariate Gaussian copula in the region is used to investigate the dependence of power output from multiple wind turbines. Results from case studies demonstrate the feasibility of the proposed model in achieving a balanced weigh up between economics and low carbon emissions by the dependence on multiple wind turbines.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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