Multi-time scale optimal scheduling of electricity-gas hybrid system based on adaptive step size ADMM

被引：0
|
作者：
Zhao B. [1 ]
Ni C. [1 ]
Li Z. [1 ]
Zhang W. [2 ]
Chen J. [2 ]
机构：
[1] Electric Power Research Institute of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou
[2] School of Electrical Engineering, Shandong University, Jinan
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2019年 / 39卷 / 08期
基金：
中国国家自然科学基金;
关键词：
Adaptive step size ADMM; Distributed optimization; Electricity-gas hybrid system; Models; Multi-time scale; Optimal scheduling;
D O I：
10.16081/j.epae.201908017
中图分类号：
学科分类号：
摘要：
With the large-scale configuration of MT(Micro-Turbine) and the CHP(Combined Heat and Po-wer) unit based on MT, the joint optimal scheduling between the electricity system and the natural gas system has attracted more and more attention. Aiming at the optimal scheduling problem of electricity-gas hybrid system, a bi-layer multi-time scale optimal scheduling framework is proposed. Considering the opacity characteristic of information among subsystems, the distributed day-ahead optimal scheduling model is established with the system cost as its objective function in the upper layer, which is based on the day-ahead forecasting data and the adaptive step size ADMM(Alternating Direction Method of Multipliers). In view of the fluctuations of renewable energy and load, a real-time scheduling model is established in the lower la-yer based on the short-term forecasting data, which aims at following the day-ahead scheduling scheme. The effectiveness of the proposed optimal scheduling model and framework of electricity-gas hybrid system is verified by case study. © 2019, Electric Power Automation Equipment Press. All right reserved.
引用
收藏
页码：294 / 299
页数：5
相关论文
共 19 条
  • [1] Jia H., Wang D., Xu X., Et al., Research on some key problems related to integrated energy systems, Automation of Electric Power Systems, 39, 7, pp. 198-207, (2015)
  • [2] Peng K., Zhang C., Xu B., Et al., Status and prospect of pilot projects of integrated energy system with multi-energy collaboration, Electric Power Automation Equipment, 37, 6, pp. 3-10, (2017)
  • [3] Dong S., Wang C., Liang J., Et al., Multi-objective optimal day-ahead dispatch of integrated energy system conside-ring power-to-gas operation cost, Automation of Electric Power Systems, 42, 11, (2018)
  • [4] Hao R., Ai Q., Zhu Y., Et al., Hierarchical optimal dispatch based on energy hub for regional integrated energy system, Electric Power Automation Equipment, 37, 6, pp. 171-178, (2017)
  • [5] Pan Z., Wang K., Qu K., Et al., Coordinated optimal dispatch of electricity-gas-heat multi-energy system considering high penetration of electric vehicles, Automation of Electric Power Systems, 42, 4, pp. 104-112, (2018)
  • [6] Wang J., Gu W., Lu S., Et al., Coordinated planning of multi-district integrated energy system combining heating network model, Automation of Electric Power Systems, 40, 15, pp. 17-24, (2016)
  • [7] He C., Wu L., Liu T.Q., Et al., Robust co-optimization scheduling of electricity and natural gas systems via ADMM, IEEE Transactions on Sustainable Energy, 8, 2, pp. 658-670, (2017)
  • [8] Chen J., Zhang W., Lin D., Et al., Distributed optimal dispatch of integrated electricity-gas-heating system based on improved alternative direction multiplier method, Automation of Electric Power Systems, 43, 7, pp. 50-58, (2019)
  • [9] Mei J., Wei Z., Zhang Y., Et al., Dynamic optimal dispatch with multiple time scale in integrated power and gas energy systems, Automation of Electric Power Systems, 42, 13, pp. 36-42, (2018)
  • [10] Deng J., Hu L., Shao S., Et al., Multi-time scale rol-ling scheduling method for combined heat and power system, Power System Technology, 40, 12, pp. 3796-3802, (2016)
12