Adaptive Robust Day-Ahead Dispatch for Urban Energy Systems

Increasing interactions between urban electrical and gas systems have the potential to transform smart grids into smart cities, where coordinated dispatch for interdependent urban infrastructure is desirable. This paper proposes an adaptive robust day-ahead energy-reserve co-optimization approach fo...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2019-02, Vol.66 (2), p.1379-1390
Main Authors: Sheng Chen, Zhinong Wei, Guoqiang Sun, Cheung, Kwok W., Wang, Dan, Haixiang Zang
Format: Article
Language:English
Subjects:
Adaptation models
Adaptive robust optimization
Adaptive systems
Binding
binding scenario subset
Computational modeling
day-ahead energy-reserve dispatch
Energy
Iterative algorithms
Iterative methods
Optimization
Resistance heating
Robustness
Smart grid
Uncertainty
urban energy system
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Summary:Increasing interactions between urban electrical and gas systems have the potential to transform smart grids into smart cities, where coordinated dispatch for interdependent urban infrastructure is desirable. This paper proposes an adaptive robust day-ahead energy-reserve co-optimization approach for urban energy systems, in which day-ahead energy-reserve dispatch and real-time energy-balancing regulation are employed as first- and second-stage decisions, respectively. By introducing an auxiliary variable to denote the worst-case balancing cost, we transform the trilevel optimization problem into a single-level model considering generated representative scenarios. Moreover, we propose an iterative algorithm composed of nested problems, where the master problem solves the single-level robust model considering the binding scenario subset identified by the subproblem. In this way, a more computationally tractable model can be obtained, owing to the greatly reduced number of scenarios. Finally, test results on an integrated urban energy network verify the computational superiority of the proposed solution methodology afforded by identifying the binding scenario subset. The economic benefits of the proposed adaptive robust framework are also discussed.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2017.2787605