Energy and reserve dispatch with distributionally robust joint chance constraints

被引：67

作者：

Ordoudis, Christos ^{[1
]}

Nguyen, Viet Anh ^{[2
]}

Kuhn, Daniel ^{[3
]}

Pinson, Pierre ^{[1
]}

机构：

[1] Tech Univ Denmark DTU, Lyngby, Denmark

[2] Stanford Univ, Stanford, CA 94305 USA

[3] Ecole Polytech Fed Lausanne EPFL, Lausanne, Switzerland

来源：

OPERATIONS RESEARCH LETTERS | 2021年 / 49卷 / 03期

基金：

瑞士国家科学基金会;

关键词：

Distributionally robust optimization; Energy and reserve dispatch; Joint chance constraints; Wasserstein metric; OPTIMAL POWER-FLOW; OPTIMIZATION; ELECTRICITY; UNCERTAINTY;

D O I：

10.1016/j.orl.2021.01.012

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

We develop a two-stage stochastic program for energy and reserve dispatch of a joint power and gas system with a high penetration of renewables. Data-driven distributionally robust chance constraints ensure that there is no load shedding and renewable spillage with high probability. We solve this problem efficiently using conditional value-at-risk approximations and linear decision rules. Out-of-sample experiments show that this model dominates the corresponding stochastic program without chance constraints that models the effects of load shedding and renewable spillage explicitly. (C) 2021 Elsevier B.V. All rights reserved.

引用

页码：291 / 299

页数：9

共 36 条

[1] Optimal Wind Farm Allocation in Multi-Area Power Systems Using Distributionally Robust Optimization Approach [J].