期刊文献+

计及调度优先级的微电网多时间尺度优化调度策略

Multi-time Scale Optimal Scheduling Strategy for Microgrid Consider Scheduling Priority
下载PDF
导出
摘要 随着负荷用户的大量增长,负荷侧可调资源逐渐增多,利用负荷侧需求响应资源参与微电网调度,以提升新能源消纳水平。为充分发挥需求响应资源调度潜力,优化用户侧负荷管理能力,提出一种计及调度优先级的微电网多时间尺度优化调度策略。首先根据不同需求响应资源响应特性进行划分,将价格型需求响应资源与激励型需求响应资源细分为5种类型,构建需求响应模型与调度时段进行匹配;其次,构建“日前-日内1 h-日内15 min”的微电网多时间尺度优化调度模型,对微电网内各类可调资源进行优化调度,并针对直接影响用户用电行为的实时可调资源建立优先级权重;最后,以福建地区某实际微电网为例,仿真验证了该模型的有效性。 With the large increase of load users,the load side adjustable resources gradually increase,and the load side demand response(DR)resources are used to participate in microgrid scheduling to improve the level of new energy consumption.In order to give full play to the scheduling potential of DR resources and optimize the user side load management capability,a multi-time scale optimal scheduling strategy for microgrids that takes into account scheduling priorities was proposed.Firstly,according to the response characteristics of different DR resources,the price-based demand response(PDR)resources and incentive-based demand response(IDR)resources were subdivided into five types,and a DR model was constructed to match the scheduling period.Secondly,the multi-times cale optimal scheduling model of"day-intra-day 1 h-intra-day 15 min"was constructed,optimized scheduling of various adjustable resources in the microgrid,and established priority weights for real-time adjustable resources that have a direct impact on users'electricity consumption behavior.Finally,taking an actual microgrid as an example in Fujian,the simulation verifies the validity of the model.
作者 陈灵 CHEN Ling(State Grid Fujian Electric Power Co.,Ltd.,Fuzhou 350000,Fujian,China)
出处 《电气传动》 2024年第1期61-67,96,共8页 Electric Drive
基金 国网福建省电力有限公司研究项目(52130021004R)。
关键词 多时间尺度调度 调度优先级 用户评价 需求响应资源 微电网 multi-time scale scheduling scheduling priority user evaluation demand response(DR)resources microgrid
  • 相关文献

参考文献9

二级参考文献107

  • 1艾欣,刘晓.基于可信性理论的含风电场电力系统动态经济调度[J].中国电机工程学报,2011,31(S1):12-18. 被引量:55
  • 2娄素华,余欣梅,熊信艮,吴耀武.电力系统机组启停优化问题的改进DPSO算法[J].中国电机工程学报,2005,25(8):30-35. 被引量:35
  • 3Abreu L V L, Khodayar M E, Shahidehpour M, et al. Risk-constrained coordination of cascaded hydro units with variable wind power generation[J]. 1EEE Transactions on Sustainable Energy, 2012, 3(3): 359-368.
  • 4Aalami H A, Moghaddam P M, Yousefi G R. Demand response modeling considering interruptible/curtailable loads and capacity market programs[J]. Applied Energy, 2010, (87): 243-250.
  • 5Ding Huajie, Hu Zechun, Song Yonghua. Stochastic optimization ofthe daily operation of wind farm and pumped-hydro-storage plant[J]. Renewable Energy, 2012(48): 571-578.
  • 6Growe-Kuska N, Heitsch H, Romisch W. Scenario reduction and scenario tree construction for power management problems[C]// Proeeeding oflEEEPowerTechConference. Bologna, Italy: IEEE, 2003: 1-7.
  • 7Kuk-Hyun H, Jong-Hwan K. Genetic quantum algorithm and its application to combinatorial optimization problem[C]//Proceedings of IEEE International Conference on Evolutionary Computation. California, USA: IEEE, 2000: 1354-1360.
  • 8Matthias D. Galus,Stephan Koch,Goeran Andersson.Provision of Load Frequency Control by PHEVs, Controllable Loads, and a Cogeneration Unit. IEEE Transactions on Industrial Electronics . 2011
  • 9Beibei Wang,Dennice F. Gayme,Xiaocong Liu,Chenghao Yuan.Optimal Siting and Sizing of Demand Response in a Transmission Constrained System with High Wind Penetration[J]. International Journal of Electrical Power and Energy Systems . 2014
  • 10Khyati D. Mistry,Ranjit Roy.Impact of demand response program in wind integrated distribution network[J]. Electric Power Systems Research . 2014

共引文献405

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部