Data-Driven Joint Distributionally Robust Chance-Constrained Operation for Multiple Integrated Electricity and Heating Systems

Integrating heating and electricity networks offers extra flexibility to the energy system operation while improving energy utilization efficiency. This paper proposes a data-driven joint distributionally robust chance-constrained (DRCC) operation model for multiple integrated electricity and heatin...

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Vydáno v:IEEE transactions on sustainable energy Ročník 15; číslo 3; s. 1782 - 1798
Hlavní autoři: Zhai, Junyi, Jiang, Yuning, Zhou, Ming, Shi, Yuanming, Chen, Wei, Jones, Colin N.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Alternating minimization algorithm
Cogeneration
Constraints
data-driven
Decision making
distributed optimization
Electricity
Electricity supply industry
Energy utilization
Gas industry
Heating systems
integrated electricity and heating systems (IEHSs)
Integrated energy systems
joint distributionally robust chance-constrained
optimized CVaR approximation (OCA)
Pipelines
Power system modeling
Renewable energy
Renewable energy sources
Robustness
Tightness
Uncertainty
ISSN:1949-3029, 1949-3037
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Shrnutí:Integrating heating and electricity networks offers extra flexibility to the energy system operation while improving energy utilization efficiency. This paper proposes a data-driven joint distributionally robust chance-constrained (DRCC) operation model for multiple integrated electricity and heating systems (IEHSs). Flexible reserve resources in IEHS are exploited to mitigate the uncertainty of renewable energy. A distributed and parallel joint DRCC operation framework is developed to preserve the decision-making independence of multiple IEHSs, where the optimized CVaR approximation (OCA) approach is developed to transform the local joint DRCC model into a tractable model. An alternating minimization algorithm is presented to improve the tightness of OCA for joint chance constraints by iteratively tuning the OCA. Case studies on the IEEE 33-bus system with four IEHSs and the IEEE 141-bus system with eight IEHSs demonstrate the effectiveness of the proposed approach.
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ISSN:1949-3029
1949-3037
DOI:10.1109/TSTE.2024.3379162