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Regular Paper | Open Access

Distributed Optimization for Frequency-constrained Coordinated Stochastic Economic Dispatch in Integrated Transmission and Distribution System

Ye TianZhengshuo Li( )
School of Electrical Engineering, Shandong University, Jinan 250061, China
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Abstract

When large-scale, uncertain, centralized, and distributed renewable energy sources are connected to a power system, separate dispatch of the transmission power system (TPS)and the active distribution network (ADN) will compromise the network and frequency security of the system. To address these problems, this paper proposes a frequency-constrained coordinated stochastic economic dispatch (FC-CSED) model for an integrated transmission and distribution (ITD) system. In this model, the dynamic frequency and network security constraints of the ITD system are constructed, and joint chance constraints are adopted to handle the uncertainty. Then, the control parameters of inverter-based resources, the base point power, and the regulation reserve of all dispatchable resources in the ITD system are jointly optimized to minimize operating cost. TPS and ADNs can deliver base point power bidirectionally and provide frequency regulation support bidirectionally, which extends the existing reserve assumption in ITD dispatch and enhances the operational security of the ITD system. Moreover, based on the alternating direction of the multiplier algorithm, a two-layer distributed optimization framework is proposed to solve the FCCSED model. Case studies show the FC-CSED model can fully utilize the potential of multiple regulation resources to improve the security performance of the ITD system, and TPS and ADNs can be coordinated efficiently through the proposed distributed optimization framework.

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CSEE Journal of Power and Energy Systems
Pages 1348-1363

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Cite this article:
Tian Y, Li Z. Distributed Optimization for Frequency-constrained Coordinated Stochastic Economic Dispatch in Integrated Transmission and Distribution System. CSEE Journal of Power and Energy Systems, 2026, 12(3): 1348-1363. https://doi.org/10.17775/CSEEJPES.2023.04100

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Received: 19 May 2023
Revised: 04 August 2023
Accepted: 29 August 2023
Published: 14 February 2024
© 2023 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).