An ADMM- based Distributed Algorithm for Economic Dispatch in Multi-energy Systems

被引：2

作者：

Wang, Zhibin ^{[1
]}

Zhu, Shanying ^{[1
]}

Ding, Tie ^{[1
]}

Ding, Tao ^{[2
]}

Li, Fangxing ^{[3
]}

机构：

[1] Shanghai Jiao Tong Univ, Dept Automat, Shanghai, Peoples R China

[2] Xi An Jiao Tong Univ, Sch Elect Engn, Xian, Peoples R China

[3] Univ Tennessee, Dept Elect Engn & Comp Sci, Knoxville, TN USA

来源：

2021 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM) | 2021年

关键词：

Multi-energy systems; economic dispatch; distributed algorithm; ADMM; COMBINED HEAT; OPTIMIZATION; CONSENSUS;

D O I：

10.1109/PESGM46819.2021.9637918

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 systems (MESs) whereby various energy carriers are coordinated at various levels have definite improvements over traditional separate energy systems economically and environmentally. Concentrations on economic dispatch (ED) problem in MESs are increasing to achieve more efficient performance. However, most existing methods use a centralized framework which suffers from large cost of long-distance communications and low robustness in large-scale systems. This paper studies the ED problem in MESs with energy storages in a distributed manner, where local agents cooperate to minimize the overall generation cost based on information from neighboring agents only. By handling the non-convexity incurred by the coupling among different energy carriers and energy storages, a distributed algorithm based on ADMM and dynamic consensus mechanism is proposed. The effectiveness of the proposed algorithm are verified through a set of case studies.

引用

页数：5

共 21 条

[1] Distributed optimization and statistical learning via the alternating direction method of multipliers [J].

Boyd S. ;

Parikh N. ;

Chu E. ;

Peleato B. ;

Eckstein J. .

Foundations and Trends in Machine Learning, 2010, 3 (01) :1-122

[2] Energy hubs for the future [J].

Geidl, Martin ;

Koeppel, Gaudenz ;

Favre-Perrod, Patrick ;

Kloeckl, Bernd ;

Andersson, Goran ;

Froehlich, Klaus .

IEEE POWER & ENERGY MAGAZINE, 2007, 5 (01) :24-30

[3] Integrated energy scheduling under uncertainty in a micro energy grid [J].

Ghasemi, Abolfazl ;

Banejad, Mahdi ;

Rahimiyan, Morteza .

IET GENERATION TRANSMISSION & DISTRIBUTION, 2018, 12 (12) :2887-2896

[4] Multienergy Networks Analytics: Standardized Modeling, Optimization, and Low Carbon Analysis [J].

Huang, Wujing ;

Zhang, Ning ;

Cheng, Yaohua ;

Yang, Jingwei ;

Wang, Yi ;

Kang, Chongqing .

PROCEEDINGS OF THE IEEE, 2020, 108 (09) :1411-1436

[5] Whole-day optimal operation of multiple combined heat and power systems by alternating direction method of multipliers and consensus theory [J].

Huynh Ngoc Tran ;

Narikiyo, Tatsuo ;

Kawanishi, Michihiro ;

Kikuchi, Satoshi ;

Takaba, Shozo .

ENERGY CONVERSION AND MANAGEMENT, 2018, 174 :475-488

[6]

Kar S, 2012, IEEE POW ENER SOC GE

[7] Tutorial on Dynamic Average Consensus THE PROBLEM, ITS APPLICATIONS, AND THE ALGORITHMS [J].

Kia, Solmaz S. ;

Van Scoy, Bryan ;

Cortes, Jorge ;

Freeman, Randy A. ;

Lynch, Kevin M. ;

Martinez, Sonia .

IEEE CONTROL SYSTEMS MAGAZINE, 2019, 39 (03) :40-72

[8] Double-Consensus Based Distributed Optimal Energy Management for Multiple Energy Hubs [J].

Li, Yu-Shuai ;

Li, Tian-Yi ;

Zhou, Jian-Guo ;

Huang, Bo-Nan .

APPLIED SCIENCES-BASEL, 2018, 8 (09)

[9] Decentralized Solution for Combined Heat and Power Dispatch Through Benders Decomposition [J].

Lin, Chenhui ;

Wu, Wenchuan ;

Zhang, Boming ;

Sun, Yong .

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2017, 8 (04) :1361-1372

[10] A holistic review on optimization strategies for combined economic emission dispatch problem [J].

Mahdi, Fahad Parvez ;

Vasant, Pandian ;

Kallimani, Vish ;

Watada, Junzo ;

Fai, Patrick Yeoh Siew ;

Abdullah-Al-Wadud, M. .

RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2018, 81 :3006-3020

← 1 2 3 →