基于典型运行场景聚类的电力系统灵活性评估方法
Flexibility Evaluation Method for Power System Based on Clustering of Typical Operating Scenarios
查看参考文献18篇
文摘
|
以风光水为代表的可再生能源电源会增加电力系统的不确定性.为了保证高比例可再生能源电力系统的灵活运行,提出一种典型运行场景电力系统灵活性评估方法.利用改进的K-means算法,将新能源和负荷的运行场景进行聚类组合得到典型运行场景.从区域内供需平衡、区域内潮流分布和区域间输电能力3个角度提出灵活性评估指标;计算每种典型场景的灵活性评估指标,并根据每种场景的出现概率计算得到综合评估指标以评估系统的整体灵活性.最后,基于南方某地区实际新能源和负荷历史数据在改进的IEEE 39节点系统上进行电力系统灵活性评估.结果表明,该聚类方法和灵活性指标可以有效反映电力系统的灵活性. |
其他语种文摘
|
The development of renewable energy represented by wind, photovoltaic, and hydropower can increase the uncertainty of power systems.In order to ensure the flexible operation of power systems with a high proportion of renewable energy, a power system flexibility evaluation method based on typical operating scenarios was proposed. Through a modified K-means algorithm, the operating scenarios of renewable energy and load were clustered to obtain typical scenarios. The flexibility evaluation indexes were proposed from three perspectives including regional supply and demand balance, regional power flow distribution, and inter-regional transmission capacity.The flexibility evaluation index of each scenario, and the comprehensive evaluation index based on the appearance probability of each scenario were calculated to evaluate the flexibility of the system.Based on the actual historical output data of new energy and the load of a certain region in the south, the flexibility evaluation was performed on a modified IEEE 39 system.The results show that the proposed clustering method and flexibility index can effectively reflect the flexibility of the system. |
来源
|
上海交通大学学报
,2021,55(7):802-813 【核心库】
|
DOI
|
10.16183/j.cnki.jsjtu.2020.012
|
关键词
|
场景聚类
;
电力系统
;
灵活性
;
评估方法
;
新能源
|
地址
|
1.
云南电网有限责任公司, 昆明, 650011
2.
上海交通大学电子信息与电气工程学院, 上海, 200240
|
语种
|
中文 |
文献类型
|
研究性论文 |
ISSN
|
1006-2467 |
学科
|
电工技术 |
基金
|
上海市教委科研创新重大项目
;
南方电网公司重点科技项目
|
文献收藏号
|
CSCD:7046860
|
参考文献 共
18
共1页
|
1.
中国可再生能源电力并网研究协作组国家可再生能源中心组.
高比例可再生能源并网与电力转型:释放电力系统灵活性,2017:80-81
|
CSCD被引
1
次
|
|
|
|
2.
鲁宗相. 含高比例可再生能源电力系统灵活性规划及挑战.
电力系统自动化,2016,40(13):147-158
|
CSCD被引
176
次
|
|
|
|
3.
肖定垚. 电力系统灵活性及其评价综述.
电网技术,2014,38(6):1569-1576
|
CSCD被引
62
次
|
|
|
|
4.
施涛. 电力系统灵活性评价研究综述.
电力系统保护与控制,2016,44(5):146-154
|
CSCD被引
23
次
|
|
|
|
5.
Agency I E.
Empowering variable renewables-options for flexible electricity systems,2009
|
CSCD被引
3
次
|
|
|
|
6.
North American Electric Reliability Corporation.
Special Report: Potential reliability impacts of emerging flexible resources,2010:2-6
|
CSCD被引
4
次
|
|
|
|
7.
Lannoye E. Evaluation of power system flexibility.
IEEE Transactions on Power Systems,2012,27(2):922-931
|
CSCD被引
61
次
|
|
|
|
8.
鲁宗相. 高比例可再生能源并网的电力系统灵活性评价与平衡机理.
中国电机工程学报,2017,37(1):9-19
|
CSCD被引
187
次
|
|
|
|
9.
周光东. 含波动性电源的电力系统运行灵活性评价方法研究.
电网技术,2019,43(6):2139-2146
|
CSCD被引
22
次
|
|
|
|
10.
Wang Q. Enhancing power system operational flexibility with flexible ramping products: A review.
IEEE Transactions on Industrial Informatics,2017,13(4):1652-1664
|
CSCD被引
32
次
|
|
|
|
11.
Tian X. A comprehensive flexibility optimization strategy on power system with high-percentage renewable energy.
2017 2nd International Conference on Power and Renewable Energy (ICPRE),2017:553-558
|
CSCD被引
1
次
|
|
|
|
12.
李海波. 大规模风电并网的电力系统运行灵活性评估.
电网技术,2015,39(6):1672-1678
|
CSCD被引
91
次
|
|
|
|
13.
詹勋淞. 基于形态学分解的大规模风光并网电力系统多时间尺度灵活性评估.
电网技术,2019,43(11):3890-3898
|
CSCD被引
18
次
|
|
|
|
14.
Alvarez R. Novel methodology for selecting representative operating points for the TNEP.
IEEE Transactions on Power Systems,2017,32(3):2234-2242
|
CSCD被引
3
次
|
|
|
|
15.
Acosta J S. Optimal multi-scenario, multi-objective allocation of fault indicators in electrical distribution systems using a mixed-integer linear programming model.
IEEE Transactions on Smart Grid,2019,10(4):4508-4519
|
CSCD被引
5
次
|
|
|
|
16.
黄越辉. 基于K-means MCMC 算法的中长期风电时间序列建模方法研究.
电网技术,2019,43(7):2469-2476
|
CSCD被引
24
次
|
|
|
|
17.
Zhang H. A novel clustering algorithm combining niche genetic algorithm with canopy and K-means.
International Conference on Artificial Intelligence & Big Data,2018:26-32
|
CSCD被引
1
次
|
|
|
|
18.
周伟. 基于Canopy聚类的谱聚类算法.
计算机工程与科学,2019,41(6):1095-1100
|
CSCD被引
2
次
|
|
|
|
|