|本期目录/Table of Contents|

[1]张明光,赵文渊,李鹏程.多能源互补的虚拟电厂低碳调度研究[J].工业仪表与自动化装置,2022,(05):76-83.[doi:10.19950/j.cnki.cn61-1121/th.2022.05.015]
　ZHANG Mingguang,ZHAO Wenyuan,LI Pengcheng.Research on low-carbon scheduling of virtual power plants with complementary multi-energy sources[J].Industrial Instrumentation & Automation,2022,(05):76-83.[doi:10.19950/j.cnki.cn61-1121/th.2022.05.015]
点击复制

多能源互补的虚拟电厂低碳调度研究

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2022年05期
页码:
76-83
栏目:
出版日期:
2022-10-15

文章信息/Info

Title:
Research on low-carbon scheduling of virtual power plants with complementary multi-energy sources
文章编号:
1000-0682（2022）05-0000-00
作者:
张明光1; 2; 3; 赵文渊1; 2; 3; 李鹏程1; 2; 3
1.兰州理工大学 电气工程与信息工程学院;
2.甘肃省工业过程先进控制重点实验室;
3.兰州理工大学 电气与控制工程国家级实验教学示范中心，甘肃 兰州730050
Author(s):
ZHANG Mingguang1; 2; 3; ZHAO Wenyuan1; 2; 3; LI Pengcheng1; 2; 3
1 School of Electrical and Information Engineering/Lanzhou University of Technology;
2 Gansu Provincial Key Laboratory of Advanced Control of Industrial Processes;
3. Lanzhou University of Technology Electrical and Control Engineering National Experimental Teaching Demonstration Center, Gansu Lanzhou 730050, China
关键词:
碳捕集机组; 可再生能源; 虚拟电厂; 优化调度方法
Keywords:
carbon capture unit; renewable energy; virtual power plant; optimal dispatch method
分类号:
TM731
DOI:
10.19950/j.cnki.cn61-1121/th.2022.05.015
文献标志码:
A
摘要:
为了有效降低电力系统的运行成本及碳排放量，该文在分析各机组运行特点的基础上，充分考虑火电机组发电成本、可再生能源运行维护成本、碳排放权收益及各项约束条件，建立了多目标函数。通过合理调度风能、光能和水能等可再生能源发电使综合成本最优，提出多能源互补的虚拟电厂低碳调度模型。算例采用北方某风光发电基地实测数据，并基于粒子群算法进行分析获得多个场景下风光预测功率。算例仿真结果表明所提模型能够降低系统运行成本、减少CO2排放量。
Abstract:
In this paper, a multi-energy complementary low-carbon dispatching model is proposed for virtual power plants, which can effectively reduce the operating cost and carbon emission of the power system. On the basis of analyzing the operation characteristics of each unit, the power generation cost of thermal power units, the operation and maintenance cost of renewable energy, the income of carbon emission rights and various constraints are fully considered, and a multi-objective function is established. The overall cost is optimized by rationally dispatching power generation from renewable energy sources such as wind energy, solar energy and water energy. The calculation example uses the measured data of a wind and solar power generation base in the north, and analyzes it based on the particle swarm algorithm to obtain the forecast power of wind and wind in multiple scenarios. The simulation results of an example show that the proposed model can reduce the operating cost of the system and reduce the carbon dioxide emission.

参考文献/References:

[1]ZHANG N , KANG C , Kirschen D S , et al. Planning Pumped Storage Capacity for Wind Power Integration[J]. IEEE Transactions on Sustainable Energy, 2013, 4(2):393-401.

[2]MEYER L . Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change[M].
[3]GRUBB M , JAMASB T , POLLITT M G . Delivering a Low Carbon Electricity System[J]. Michael Gerald Pollitt, 2008.
[4]常金旺,刘波,薛建明,等.风光储供给火电厂厂用电的消纳技术及其可靠性研究[J].供用电,2020,37(07):81-87.
[5]李江,马昊天,宋田宇.风-储联合系统爬坡事件的日前能量最优平抑方法[J/OL].中国电机工程学报.
[6]宋汶秦,赵玲霞,徐建委,等光热参与电网调峰的风-光-火优化调度策略[J].计算机仿真,2020,37(11):97-103.
[7]李树林,戴嘉彤,董海鹰,等.考虑相关性的风光抽蓄互补发电系统优化运行[J].电力系统及其自动化学报,2019,31(11):92-102.
[8]WU M , WANG W , DENG W , et al. Back-to-back PV generation system for electrified railway and its control strategy[C]// 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific). IEEE, 2017.
[9]韩学义.电力行业二氧化碳捕集、利用与封存现状与展望[J].中国资源综合利用,2020,38(02):110-117.
[10]Santos-Alamillos F J , David Pozo-Vázquez, José A. Ruiz-Arias, et al. Combining wind farms with concentrating solar plants to provide stable renewable power[J]. Renewable Energy, 2015, 76:539-550.
[11]魏向向,杨德昌,叶斌.能源互联网中虚拟电厂的运行模式及启示[J].电力建设,2016,37(04):1-9.
[12]肖钧文,黄辉先,刘晓舟.大规模风光储并网碳经济性评估[J].太阳能学报,2020,41(06):171-177.
[13]梅光银,龚锦霞,郑元黎.考虑风光出力相关性和碳排放限额的多能互补虚拟电厂的调度策略[J/OL].电力系统及其自动化学报.
[14]刘永前,梁超,阎洁,等.风-光电站中储能系统混合最优配置及其经济性研究[J].中国电力,2020,53(12):143-150.
[15]王明松.风-光-蓄-火联合发电系统的两阶段优化调度策略[J].电网与清洁能源,2020,36(05):75-82.
[16]周任军,孙洪,唐夏菲,等.双碳量约束下风电–碳捕集虚拟电厂低碳经济调度[J].中国电机工程学报,2018,38(06):1675-1683+1904.
[17]印月,刘天琪,何川,等.风-光-水-火多能互补系统随机优化调度[J].电测与仪表,2020,57(16):51-58.
[18]XU T , NING Z . Coordinated Operation of Concentrated Solar Power and Wind Resources for the Provision of Energy and Reserve Services[J]. IEEE Transactions on Power Systems, 2016(2):1.
[19]MOUTIS P, HATZIARGYRIOU N D. Decision Trees-Aided Active Power Reduction of a Virtual Power Plant for Power System Over-Frequency Mitigation[J]. IEEE transactions on industrial informatics, 2015.
[20]PANDZIC H , KUZLE I , CAPUDER T . Virtual power plant mid-term dispatch optimization[J]. Applied Energy, 2013, 101(JAN.):134-141.
[21]PEIK-Herfeh M , SEIFI H , SHEIKH-El-Eslami M K . Decision making of a virtual power plant under uncertainties for bidding in a day-ahead market using point estimate method[J]. International Journal of Electrical Power & Energy Systems, 2013, 44(1):88-98.
[22]陟晶,张高航,邵冲,等.含大规模风电及光热电站的电力系统优化调度方法[J].电力工程技术,2021,40(01):79-85.
[23]SOARES J , FOTOUHI GMA , VALE Z , et al. A multi-objective model for the day-ahead energy resource scheduling of a smart grid with high penetration of sensitive loads[J]. Applied Energy, 2016, 162(JAN.15):1074-1088.
[24]仲悟之,黄思宇,崔杨,等.考虑源荷不确定性的风电–光热–碳捕集虚拟电厂协调优化调度[J].电网技术,2020,44(09):3424-3432.
[25]吴雄,王秀丽,李骏,等.风电储能混合系统的联合调度模型及求解[J].中国电机工程学报,2013,33(13):10-17.

相似文献/References:

[1]冯 涛,肖亚苏,王可宁,等.海岛海水淡化可再生能源管理系统的开发与应用[J].工业仪表与自动化装置,2018,(04):70.[doi:1000-0682（2018）04-0000-00]
　FENG Tao,XIAO Yasu,WANG Kening,et al.Development and application of island seawater desalination renewable energy management system[J].Industrial Instrumentation & Automation,2018,(05):70.[doi:1000-0682（2018）04-0000-00]

备注/Memo

备注/Memo:
收稿日期：2022-06-14

基金项目：
国家自然基金（71963024）

作者简介：
张明光（1971），男，甘肃省武威人，硕士，教授，研究方向为电力系统自动化、综合能源系统。
更新日期/Last Update: 1900-01-01