A Data-driven Distributionally Robust Operational Model for Urban Integrated Energy Systems

Hongjun Gao; Zhenyu Liu; Youbo Liu; Lingfeng Wang; Junyong Liu

doi:10.17775/CSEEJPES.2019.03240

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A Data-driven Distributionally Robust Operational Model for Urban Integrated Energy Systems

Regular Papers | 更新时间：2026-10-15

- A Data-driven Distributionally Robust Operational Model for Urban Integrated Energy Systems
- A Data-driven Distributionally Robust Operational Model for Urban Integrated Energy Systems
- 中国电机工程学会电力与能源系统学报(英文) 2022年8卷第3期页码：789-800
- 作者机构：
  
  1. College of Electrical Engineering, Sichuan University,Chengdu,China
  2. Department of Electrical Engineering and Computer Science, University of Wisconsin-, Milwaukee,WI,Milwaukee,USA
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2019.03240
  中图分类号：
- 纸质出版：2022-05-01
- 稿件说明：
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Hongjun Gao, Zhenyu Liu, Youbo Liu, 等. A Data-driven Distributionally Robust Operational Model for Urban Integrated Energy Systems[J]. 中国电机工程学会电力与能源系统学报(英文), 2022,8(3):789-800.

Hongjun Gao, Zhenyu Liu, Youbo Liu, et al. A Data-driven Distributionally Robust Operational Model for Urban Integrated Energy Systems[J]. CSEE Journal of Power and Energy Systems, 2022, 8(3): 789-800.
Hongjun Gao, Zhenyu Liu, Youbo Liu, 等. A Data-driven Distributionally Robust Operational Model for Urban Integrated Energy Systems[J]. 中国电机工程学会电力与能源系统学报(英文), 2022,8(3):789-800. DOI： 10.17775/CSEEJPES.2019.03240.

Hongjun Gao, Zhenyu Liu, Youbo Liu, et al. A Data-driven Distributionally Robust Operational Model for Urban Integrated Energy Systems[J]. CSEE Journal of Power and Energy Systems, 2022, 8(3): 789-800. DOI： 10.17775/CSEEJPES.2019.03240.

摘要

Abstract

A multi-energy conversion can effectively increase the utilization of renewable energy in the urban integrated energy system (UIES). Meanwhile

the uncertainties of renewable energy resources (e.g.

wind energy) also bring increased challenges to the operation of UIES. In this study

a typical two-stage data-driven distributionally robust operation (DDRO) model based on finite scenarios is proposed for UIES including power

gas and heat networks to obtain a salient strategy from both an economic and robustness perspective. In the first stage

the forecasted information for wind power is especially included to improve the economic aspect of robust decisions. The worst probability distribution for the selected known real-time wind power scenarios can be identified in the second stage where the power differences caused by the real-time uncertainties of wind power can be mitigated by flexible regulation of energy purchasing and coupling units (such as gas turbine

power to gas equipment

electric boiler and gas boiler). Moreover

norm-1 and norm-inf co-constraints are utilized to construct a confidence set for the probability distributions of uncertain wind power. The whole two-stage model is solved by the column-and-constraint generation (CCG) algorithm. Finally

case studies are conducted to show the performance of the proposed model and various approaches.

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