Fully Parallel Optimization of Coordinated Electricity and Natural Gas Systems on High-Performance Computing

被引:1
|
作者
Gong, Lin [1 ]
Peng, Yehong [2 ]
Zhang, Chenxu [2 ]
Fu, Yong [2 ]
机构
[1] Jackson State Univ, ECE & CS Dept, Jackson, MS 39217 USA
[2] Mississippi State Univ, ECE Dept, Starkville, MS 39759 USA
基金
美国国家科学基金会;
关键词
Natural gas; Power systems; Computational modeling; Generators; Systems operation; Meteorology; Parallel processing; Electric power and natural gas systems; high-performance computing; parallel optimization; INTEGRATED ELECTRICITY; RELIABILITY EVALUATION; ENERGY-FLOW; POWER; DECOMPOSITION; RESTORATION; FRAMEWORK; DISPATCH; MODEL;
D O I
10.1109/TSG.2023.3235247
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Intensified interactions between electric power and natural gas infrastructures raise significant demands to coordinate their system operations. This paper proposes a fully parallel optimization method that can achieve a rapid decision-making on the day-ahead coordinated operation of electricity and natural gas systems on the high-performance computing (HPC) platform. The proposed method can flexibly tailor decomposition strategies to solve the optimization problem according to unique features of problem models in both electric power and natural gas systems. Particularly, the operation problem of power system is split by function and time into numbers of singe-unit subproblems and single-period network subproblems, while the operation problem of natural gas system is decomposed into multiple area subproblems. All the scalable subproblems are solved and coordinated quickly in a fully parallel manner on HPC for improving computational efficiency and tractability of complex electricity-gas co-optimization problem. By optimally coordinate electricity and natural gas systems under different operating conditions, the proposed method could improve the energy economics as well as the system resilience to various outages due to extreme events. Numerical results demonstrate the effectiveness and efficiency of our proposed co-optimization method and its HPC implementation.
引用
收藏
页码:3499 / 3511
页数:13
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