Distributed Chance-Constrained Optimal Dispatch for Integrated Energy System With Electro-Thermal Couple and Wind-Storage Coordination

Cross-regional long-distance transmission is a promising way for utilizing renewable energy sources (RESs) with geographically imbalanced distribution. However, the stochastic fluctuations of RESs will be transferred to the receiving energy networks, and the complex operating conditions of cross-regional transmission lines further affect energy delivery. In this paper, we propose a coordinated optimization model for an integrated energy system (IES) that considers the uncertain injection from long-distance wind farms. First, a dynamic capacity calculation model of overhead transmission lines (OTLs) is established, taking into account the electro-thermal couple characteristics and real-time ambient conditions. Second, an IES model integrating electricity and natural gas distribution networks is built and converted into second-order conic programming using the McCormick approach. Third, a distributed chance-constrained optimization framework is proposed to coordinate the wind-storage system and IES. The probabilistic constraints are converted into certain constraints based on stochastic scenario sampling. Besides, to maintain the variable consistency of the connection lines between cross-regional systems, we employ the virtual node approach to achieve a fast-distributed iterative solution. Finally, simulation results on a modified IEEE33-Belgium20 test system demonstrate the superiority of the proposed method in operational economy and RESs utilization.

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Publication Details

Journal
IEEE Transactions on Industry Applications
Published
2024-10-03
DOI
https://doi.org/10.1109/tia.2024.3472653
Citations
21
Primary Topic
Power Systems and Renewable Energy
Type
article
Field-Weighted Citation Impact
3.56

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article

Distributed Chance-Constrained Optimal Dispatch for Integrated Energy System With Electro-Thermal Couple and Wind-Storage Coordination

Can Cui, Yaxin Su, Yong Dai, Chengfu Wang et al.
21 citations
IEEE Transactions on Industry Applications
Power Systems and Renewable Energy
3.56
article

Distributed Chance-Constrained Optimal Dispatch for Integrated Energy System With Electro-Thermal Couple and Wind-Storage Coordination

Can Cui, Yaxin Su, Yong Dai, Chengfu Wang, Wei Zhang, Zhen-Wei Zhang, Yong Wang, Yan Wang
article en
21 citations

Abstract

Cross-regional long-distance transmission is a promising way for utilizing renewable energy sources (RESs) with geographically imbalanced distribution. However, the stochastic fluctuations of RESs will be transferred to the receiving energy networks, and the complex operating conditions of cross-regional transmission lines further affect energy delivery. In this paper, we propose a coordinated optimization model for an integrated energy system (IES) that considers the uncertain injection from long-distance wind farms. First, a dynamic capacity calculation model of overhead transmission lines (OTLs) is established, taking into account the electro-thermal couple characteristics and real-time ambient conditions. Second, an IES model integrating electricity and natural gas distribution networks is built and converted into second-order conic programming using the McCormick approach. Third, a distributed chance-constrained optimization framework is proposed to coordinate the wind-storage system and IES. The probabilistic constraints are converted into certain constraints based on stochastic scenario sampling. Besides, to maintain the variable consistency of the connection lines between cross-regional systems, we employ the virtual node approach to achieve a fast-distributed iterative solution. Finally, simulation results on a modified IEEE33-Belgium20 test system demonstrate the superiority of the proposed method in operational economy and RESs utilization.

IEEE Transactions on Industry ApplicationsVol. 61(1)
Shandong University (CN), Shanghai Electric (China) (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 8%
Power Systems and Renewable Energy
3.56
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