Stochastic Real-Time Economic Dispatch for Integrated Electric and Gas Systems Considering Uncertainty Propagation and Pipeline Leakage
<|reference_start|>Stochastic Real-Time Economic Dispatch for Integrated Electric and Gas Systems Considering Uncertainty Propagation and Pipeline Leakage: Gas-fired units (GFUs) with rapid regulation capabilities are considered an effective tool to mitigate fluctuations in the generation of renewable energy sources and have coupled electricity power systems (EPSs) and natural gas systems (NGSs) more tightly. However, this tight coupling leads to uncertainty propagation, a challenge for the real-time dispatch of such integrated electric and gas systems (IEGSs). Moreover, pipeline leakage failures in the NGS may threaten the electricity supply reliability of the EPS through GFUs. To address these problems, this paper first establishes an operational model considering gas pipeline dynamic characteristics under uncertain leakage failures for the NGS and then presents a stochastic IEGS real-time economic dispatch (RTED) model considering both uncertainty propagation and pipeline leakage uncertainty. To quickly solve this complicated large-scale stochastic optimization problem, a novel notion of the coupling boundary dynamic adjustment region considering pipeline leakage failure (LCBDAR) is proposed to characterize the dynamic characteristics of the NGS boundary connecting GFUs. Based on the LCBDAR, a noniterative decentralized solution is proposed to decompose the original stochastic RTED model into two subproblems that are solved separately by the EPS and NGS operators, thus preserving their data privacy. In particular, only one-time data interaction from the NGS to the EPS is required. Case studies on several IEGSs at different scales demonstrate the effectiveness of the proposed method.<|reference_end|>
@article{zhao2024stochastic,
    title={Stochastic Real-Time Economic Dispatch for Integrated Electric and Gas
  Systems Considering Uncertainty Propagation and Pipeline Leakage},
    author={eiyao Zhao, Zhengshuo Li, Jiahui Zhang, Xiang Bai, Jia Su},
    journal={arXiv preprint arXiv:2408.08101},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08101},
    primaryClass={eess.SY cs.SY}
}