[1]季振亚,黄学良,张梓麒,等.基于随机优化的综合能源系统能量管理[J].东南大学学报(自然科学版),2018,48(1):45-53.[doi:10.3969/j.issn.1001-0505.2018.01.008]
 Ji Zhenya,Huang Xueliang,Zhang Ziqi,et al.Energy management for integrated energy systems based on stochastic optimization[J].Journal of Southeast University (Natural Science Edition),2018,48(1):45-53.[doi:10.3969/j.issn.1001-0505.2018.01.008]
点击复制

基于随机优化的综合能源系统能量管理()
分享到:

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
48
期数:
2018年第1期
页码:
45-53
栏目:
环境科学与工程
出版日期:
2018-01-20

文章信息/Info

Title:
Energy management for integrated energy systems based on stochastic optimization
作者:
季振亚12黄学良12张梓麒12孙厚涛12赵家庆3李军4
1东南大学电气工程学院, 南京 210096; 2江苏省智能电网技术与装备重点实验室, 南京 210096; 3国网苏州供电公司, 苏州 215004; 4南京工程学院电力工程学院, 南京 211167
Author(s):
Ji Zhenya12 Huang Xueliang12 Zhang Ziqi12 Sun Houtao12 Zhao Jiaqing3 Li Jun4
1School of Electrical Engineering, Southeast University, Nanjing 210096, China
2Key Laboratory of Jiangsu Province Smart Grid Technology and Equipment, Nanjing 210096, China
3State Grid Suzhou Power Supply Company, Suzhou 215004, China
4School of Electrical Engineering, Nanjing Institute of Technology, Nanjing 211167, China
关键词:
综合能源系统 随机优化 电动汽车有序充电 Benders分解
Keywords:
integrated energy system stochastic optimization electric vehicle coordinated charging Benders decomposition
分类号:
X703.5
DOI:
10.3969/j.issn.1001-0505.2018.01.008
摘要:
为提升综合能源系统经济性,并削减电动汽车广泛应用后增加的用电高峰负荷,提出了一种包含随机优化与并行求解算法的快速能量管理策略.在对多能流子系统耦合与设备运行约束建模后,结合电动汽车有序充电策略,建立包含时域滚动的随机规划下的能量管理模型.为了降低能量管理在线运行的时间成本,一方面采用场景生成与削减技术实现对输入变量预测场景集的合理利用,另一方面求解时应用Benders分解算法实现并行计算.算例结果表明:所提方法与不考虑随机优化的方法相比,系统运行的总用能成本明显降低;与不考虑场景削减及Benders分解的随机优化方法相比,总用能成本稍有提高,但运行时间成本显著下降.
Abstract:
To rise the economical efficiency of integrated energy system and reduce peak electricity loads by the widespread use of electric vehicles, a new energy management strategy is presented based on a stochastic optimization and a parallel solving algorithm to achieve fast solution. The receding horizon based energy management model in a stochastic programming framework is established after the formulation of an integrated energy system with constraints, and is implemented with a coordinating charging strategy for electric vehicles. To reduce the time cost of online operation, a scenario generation and reduction method is used to apply suitable predicted scenario sets, and the Benders decomposition technique is adopted to execute parallel computing. The simulation results show that the proposed method achieves significantly decrease of the operation cost than those without stochastic optimization. Compared with a stochastic method for neither scenario reduction nor Benders decomposition, computing time is notably declined by little operational cost to increase.

参考文献/References:

[1] Pan Z, Guo Q, Sun H. Interactions of district electricity and heating systems considering time-scale characteristics based on quasi-steady multi-energy flow[J]. Applied Energy, 2016, 167: 230-243. DOI:10.1016/j.apenergy.2015.10.095.
[2] 王伟亮, 王丹, 贾宏杰, 等. 能源互联网背景下的典型区域综合能源系统稳态分析研究综述[J]. 中国电机工程学报, 2016, 36(12): 3292-3306. DOI:10.13334/j.0258-8013.pcsee.152858.
Wang Weiliang, Wang Dan, Jia Hongjie, et al. Review of steady-state analysis of typical regional integrated energy system under the background of energy internet[J]. Proceedings of the CSEE, 2016, 36(12): 3292-3306. DOI:10.13334/j.0258-8013.pcsee.152858. (in Chinese)
[3] Mancarella P. MES(multi-energy systems): An overview of concepts and evaluation models[J]. Energy, 2014, 65: 1-17. DOI:10.1016/j.energy.2013.10.041.
[4] 孙宏斌, 潘昭光,郭庆来. 多能流能量管理研究:挑战与展望[J]. 电力系统自动化, 2016, 40(15): 1-8,16. DOI:10.7500/AEPS20160522006.
Sun Hongbin, Pan Zhaoguang, Guo Qinglai. Energy management for multi-energy flow: Challenges and prospect[J]. Automation of Electric Power Systems, 2016, 40(15): 1-8,16. DOI:10.7500/AEPS2016 0522006. (in Chinese)
[5] Liu M, McNamara P, Shorten R, et al. Residential electrical vehicle charging strategies: The good, the bad and the ugly[J]. Journal of Modern Power Systems and Clean Energy, 2015, 3(2): 190-202. DOI:10.1007/s40565-015-0122-2.
[6] Mayne D Q. Model predictive control: Recent developments and future promise[J]. Automatica, 2014, 50(12): 2967-2986. DOI:10.1016/j.automatica.2014.10.128.
[7] 席裕庚, 李德伟, 林姝. 模型预测控制——现状与挑战[J]. 自动化学报, 2013, 39(3): 222-236.
  Xi Yugeng, Li Dewei, Lin Shu. Model predictive control: Status and challenges [J]. Acta Automatica Sinica, 2013, 39(3): 222-236.(in Chinese)
[8] Khan A A, Naeem M, Iqbal M, et al. A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids[J]. Renewable and Sustainable Energy Reviews, 2016, 58: 1664-1683. DOI:10.1016/j.rser.2015.12.259.
[9] 薛禹胜, 郁琛, 赵俊华, 等. 关于短期及超短期风电功率预测的评述[J]. 电力系统自动化, 2015, 39(6): 141-151. DOI:10.7500/AEPS20141218003.
Xue Yusheng, Yu Chen, Zhao Junhua, et al. A review on short-term and ultra-short-term wind power prediction[J]. Automation of Electric Power Systems, 2015, 39(6): 141-151. DOI:10.7500/AEPS2014 1218003. (in Chinese)
[10] 马瑞, 李文晔, 李晅, 等. 分布式冷热电联供系统负荷随机模糊建模[J]. 电力系统自动化, 2016, 40(15): 53-58. DOI:10.7500/AEPS20151021001.
Ma Rui, Li Wenye, Li Xuan, et al. Random fuzzy model for load of distributed combined cooling, heating and power system[J]. Automation of Electric Power Systems, 2016, 40(15): 53-58. DOI:10.7500/AEPS20151021001. (in Chinese)
[11] 张洪财, 胡泽春, 宋永华,等. 考虑时空分布的电动汽车充电负荷预测方法[J]. 电力系统自动化, 2014, 38(1): 13-20. DOI:10.7500/AEPS2013
  0613009.
Zhang Hongcai, Hu Zechun, Song Yonghua, et al. A prediction method for electric vehicle charging load considering spatial and temporal distribution[J]. Automation of Electric Power Systems, 2014, 38(1): 13-20. DOI:10.7500/AEPS20130613009. (in Chinese)
[12] Mesbah A. Stochastic model predictive control: An overview and perspectives for future research[J]. IEEE Control Systems Magazine, 2016, 36(6): 30-44. DOI:10.1109/mcs.2016.2602087.
[13] Patrinos P, Trimboli S, Bemporad A. Stochastic MPC for real-time market-based optimal power dispatch[C]// IEEE Conference on Decision & Control & European Control Conference. Orlando, USA, 2011: 7111-7116.
[14] 张彦, 张涛, 刘亚杰, 等. 基于随机模型预测控制的能源局域网优化调度研究[J]. 中国电机工程学报, 2016, 36(13): 3451-3462,3364. DOI:10.13334/j.0258-8013.pcsee.152491.
Zhang Yan, Zhang Tao, Liu Yajie, et al. Stochastic model predictive control for energy management optimization of an energy local network[J]. Proceedings of the CSEE, 2016, 36(13): 3451-3462,3364. DOI:10.13334/j.0258-8013.pcsee.152491. (in Chinese)
[15] Su W, Wang J, Roh J. Stochastic energy scheduling in microgrids with intermittent renewable energy resources[J]. IEEE Transactions on Smart Grid, 2014, 5(4): 1876-1883. DOI:10.1109/tsg.2013.2280645.
[16] Wang Y, Zhou Z, Botterud A, et al. Stochastic coordinated operation of wind and battery energy storage system considering battery degradation[J]. Journal of Modern Power Systems and Clean Energy, 2016, 4(4): 581-592. DOI:10.1007/s40565-016-0238-z.
[17] Kou P, Liang D, Gao L, et al. Stochastic coordination of plug-in electric vehicles and wind turbines in microgrid: A model predictive control approach[J]. IEEE Transactions on Smart Grid, 2016, 7(3): 1537-1551. DOI:10.1109/tsg.2015.2475316.
[18] Parisio A, Rikos E, Glielmo L. Stochastic model predictive control for economic/environmental operation management of microgrids: An experimental case study[J]. Journal of Process Control, 2016, 43: 24-37. DOI:10.1016/j.jprocont.2016.04.008.
[19] Zheng Q P, Wang J, Liu A L. Stochastic optimization for unit commitment: A review[J]. IEEE Transactions on Power Systems, 2015, 30(4): 1913-1924. DOI:10.1109/tpwrs.2014.2355204.
[20] Growe-Kuska N, Heitsch H, Romisch W. Scenario reduction and scenario tree construction for power management problems[C]// IEEE Power Tech Conference Proceedings. Bologna, Italy,2003: 3-7.
[21] Morales J M, Mínguez R, Conejo A J. A methodology to generate statistically dependent wind speed scenarios[J]. Applied Energy, 2010, 87(3): 843-855. DOI:10.1016/j.apenergy.2009.09.022.
[22] Benders J F. Partitioning procedures for solving mixed-variables programming problems[J]. Numerische Mathematik, 1962, 4(1): 238-252. DOI:10.1007/bf01386316.

备注/Memo

备注/Memo:
收稿日期: 2017-07-15.
作者简介: 季振亚(1988—),女,博士生;黄学良(联系人),男,博士,教授,博士生导师,xlhuang@seu.edu.cn.
基金项目: 科技部重点基础研发计划资助项目(2016YFB0101800)、江苏省科技支撑计划资助项目(BE2014023)、国家电网公司总部科技资助项目(SGTYHT/14-JS-188).
引用本文: 季振亚,黄学良,张梓麒,等.基于随机优化的综合能源系统能量管理[J].东南大学学报(自然科学版),2018,48(1):45-53. DOI:10.3969/j.issn.1001-0505.2018.01.008.
更新日期/Last Update: 2018-01-20