Reduced-order Decomposition and Coordination approach for Markov-based Stochastic UC with Distributed Wind Farms and BESS

To achieve carbon neutrality, US states are enhancing renewable energy use and encouraging battery integration. This paper addresses the challenges posed by renewable energy uncertainties in resource operation. We formulate unit commitment (UC) with distributed wind generation and grid-scale batteri...

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Bibliographic Details
Published in2024 IEEE Power & Energy Society General Meeting (PESGM) pp. 1 - 5
Main Authors Raghunathan, Niranjan, Wang, Zongjie, Yan, Bing, Zhao, Tianqiao, Yue, Meng
Format Conference Proceeding
LanguageEnglish
Published IEEE 21.07.2024
Subjects
Analytical models
Batteries
Context modeling
Distributed wind
Markov processes
Principal component analysis
Renewable energy sources
Renewable integration
SAVLR
Scalability
Stochastic processes
Stochastic UC
Wind
Wind energy
Wind farms
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Summary:To achieve carbon neutrality, US states are enhancing renewable energy use and encouraging battery integration. This paper addresses the challenges posed by renewable energy uncertainties in resource operation. We formulate unit commitment (UC) with distributed wind generation and grid-scale batteries as a Markov-based stochastic problem. Due to scalability issues with increasing wind farms, we decompose the model into approximate area subproblems using reduced-order models and Principal Component Analysis (PCA). These subproblems are efficiently resolved and coordinated through a Surrogate Absolute-Value Lagrangian Relaxation-based framework. The simulation results on the IEEE 118-bus system with 75% wind penetration level have demonstrated the effectiveness and efficiency of the proposed method at managing these complexities and highlight the potential benefits of integrating batteries.
ISSN:1944-9933
DOI:10.1109/PESGM51994.2024.10689245