A distributed computing framework for multi-stage stochastic planning of renewable power systems with energy storage as flexibility option

被引:46
作者
Flores-Quiroz, Angela [1 ,2 ]
Strunz, Kai [1 ]
机构
[1] Tech Univ Berlin, Chair Sustainable Elect Networks & Sources Energy, Berlin, Germany
[2] Univ Chile, Dept Elect Engn, Santiago, Chile
关键词
Power system planning; Stochastic optimization; Renewable energy; Energy storage; Operational flexibility; Distributed computing; OPERATIONAL FLEXIBILITY; GENERATION; IMPACT; STABILIZATION; TRANSMISSION; RESERVES; MODELS;
D O I
10.1016/j.apenergy.2021.116736
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
An integrated generation, transmission, and energy storage planning model accounting for short-term con-straints and long-term uncertainty is proposed. The model allows to accurately quantify the value of flexibility options in renewable power systems by representing short-term operation through the unit commitment constraints. Long-term uncertainty is represented through a scenario tree. The resulting model is a large-scale multi-stage stochastic mixed-integer programming problem. To overcome the computational burden, a distributed computing framework based on the novel Column Generation and Sharing algorithm is proposed. The performance improvement of the proposed approach is demonstrated through study cases applied to the NREL 118-bus power system. The results confirm the added value of modeling short-term constraints and long-term uncertainty simultaneously. The computational case studies show that the proposed solution approach clearly outperforms the state of the art in terms of computational performance and accuracy. The proposed planning framework is used to assess the value of energy storage systems in the transition to a low-carbon power system.
引用
收藏
页数:15
相关论文
共 52 条
[1]  
[Anonymous], 2012, THESIS
[2]  
[Anonymous], 2013, 68 DIW
[3]  
[Anonymous], 2005, Column Gener, DOI [DOI 10.1007/0-387-25486-21/COVER, 10.1007/0-387-25486-2]
[4]   COMBINING PROGRESSIVE HEDGING WITH A FRANK-WOLFE METHOD TO COMPUTE LAGRANGIAN DUAL BOUNDS IN STOCHASTIC MIXED-INTEGER PROGRAMMING [J].
Boland, Natashia ;
Christiansen, Jeffrey ;
Dandurand, Brian ;
Eberhard, Andrew ;
Linderoth, Jeff ;
Luedtke, James ;
Oliveira, Fabricio .
SIAM JOURNAL ON OPTIMIZATION, 2018, 28 (02) :1312-1336
[5]   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
[6]   The impact of short-term variability and uncertainty on long-term power planning [J].
Bylling, Henrik C. ;
Pineda, Salvador ;
Boomsma, Trine K. .
ANNALS OF OPERATIONS RESEARCH, 2020, 284 (01) :199-223
[7]  
Capros P., 2016, EU Reference Scenario 2016-Energy, transport and GHG emissions Trends to 2050
[8]   Assessing the Economic Benefits of Compressed Air Energy Storage for Mitigating Wind Curtailment [J].
Cleary, Brendan ;
Duffy, Aidan ;
O'Connor, Alan ;
Conlon, Michael ;
Fthenakis, Vasilis .
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2015, 6 (03) :1021-1028
[9]   The value of energy storage in decarbonizing the electricity sector [J].
de Sisternes, Fernando J. ;
Jenkins, Jesse D. ;
Botterud, Audun .
APPLIED ENERGY, 2016, 175 :368-379
[10]   Duality gaps in nonconvex stochastic optimization [J].
Dentcheva, D ;
Römisch, W .
MATHEMATICAL PROGRAMMING, 2004, 101 (03) :515-535