Data-driven distributionally robust joint chance-constrained energy management for multi-energy microgrid

被引：21

作者：

Zhai, Junyi ^{[1
,2
]}

Wang, Sheng ^{[3
]}

Guo, Lei ^{[2
,4
]}

Jiang, Yuning ^{[5
]}

Kang, Zhongjian ^{[1
]}

Jones, Colin N. ^{[5
]}

机构：

[1] China Univ Petr East China, Coll New Energy, Qingdao, Peoples R China

[2] State Grid Suzhou City & Energy Res Inst, Suzhou, Peoples R China

[3] Univ Macau, State Key Lab Internet Things Smart City, Macau, Peoples R China

[4] State Grid Energy Res Inst, Beijing, Peoples R China

[5] Ecole Polytech Fed Lausanne, Automat Control Lab, Lausanne, Switzerland

来源：

APPLIED ENERGY | 2022年 / 326卷

基金：

瑞士国家科学基金会;

关键词：

Multi-energy microgrid (MEMG); Distributionally robust joint; chance-constrained (DRJCC); Optimized conditional value-at-risk (CVaR); approximation (OCA); Sequential convex optimization; RESERVE DISPATCH; COORDINATION; ELECTRICITY; SYSTEMS; OPTIMIZATION;

D O I：

10.1016/j.apenergy.2022.119939

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Multi-energy microgrid (MEMG) has the potential to improve the energy utilization efficiency. However, the uncertainty caused by distributed renewable energy resources brings an urgent need for multi-energy co -optimization to ensure secure operation. This paper focuses on the distributionally robust energy management problem for MEMG. Various flexible resources in different energy sectors are utilized for uncertainty mitigation, then, a data-driven Wasserstein distance-based distributionally robust joint chance-constrained (DRJCC) energy management model is proposed. To make the DRJCC model tractable, an optimized conditional value-at-risk (CVaR) approximation (OCA) formulation is proposed to transfer the joint chance-constrained model into a tractable form. Then, an iterative sequential convex optimization algorithm is tailored to reduce the solution conservatism by tuning OCA. Numerical result illustrates the effectiveness of the proposed model.

引用

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