需求侧资源参与分布式电源并网的协同调控

潘超; 范宫博; 包钰婷; 李润宇; 于凤娇; 鲍峰

doi:10.19912/j.0254-0096.tynxb.2021-0170

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太阳能学报 ›› 2023, Vol. 44 ›› Issue (4) : 306-315. DOI: 10.19912/j.0254-0096.tynxb.2021-0170

需求侧资源参与分布式电源并网的协同调控

潘超¹, 范宫博¹, 包钰婷¹, 李润宇¹, 于凤娇¹, 鲍峰²

作者信息 +

COLLABORATIVE REGULATION OF DISTRIBUTED POWER GRID CONNECTION PARTICJPATED BY DEMAND SIDE RESOURCE

Pan Chao¹, Fan Gongbo¹, Bao Yuting¹, Li Runyu¹, Yu Fengjiao¹, Bao Feng²

Author information +

文章历史 +

摘要

风光电源渗透率的提高增大了电网消纳新能源的难度,针对该问题引入需求侧资源响应模型,采用阶段式优化方法对含分布式电源的电网协同调控进行研究。在第一阶段依据网损灵敏度确定分布式电源与抽蓄接入位置,第二阶段考虑经济成本确定受控资源容量,第三阶段按照各类资源响应容量划分不同场景,考虑主/被动资源协同作用,分析电网在经济运行、风光使用、电压稳定性及削峰填谷等方面的效果。为提高阶段优化的求解效率并保持解集多样性,采用改进粒子群算法进行计算。最后结合东北地区某实际电网进行仿真分析,验证了所提方法的合理性和有效性。

Abstract

As the penetration rate of wind and solar power in network rises, it is more difficult for power grid to absorb new energy of the distributed generation. To solve this problem, a demand-side resource response model is stabished. Staged optimization method is used to study coordinated regulation of power grids with distributed generation. In the first stage, the grid-connected position of distributed generation and pumped storage is optimized by the sensitivity of network loss. In the second stage, the controlled resource capacity is determined according to the economic cost. On this basis, different scenarios are divided with the response capacity in the third stage. Considering the synergy of active and passive resources, the effects of different optimal cases on economic operation, new energy utilization, voltage stability and peak cut of power grid are analyzed. In order to enhance the solution efficiency of staged optimization and the diversity of solution sets, an improved particle swarm algorithm is used. Finally, simulation analysis of a practical power grid in Northeast China validates the correctness and effectiveness of this method.

导出引用

潘超, 范宫博, 包钰婷, 李润宇, 于凤娇, 鲍峰. 需求侧资源参与分布式电源并网的协同调控[J]. 太阳能学报. 2023, 44(4): 306-315 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0170

Pan Chao, Fan Gongbo, Bao Yuting, Li Runyu, Yu Fengjiao, Bao Feng. COLLABORATIVE REGULATION OF DISTRIBUTED POWER GRID CONNECTION PARTICJPATED BY DEMAND SIDE RESOURCE[J]. Acta Energiae Solaris Sinica. 2023, 44(4): 306-315 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0170

中图分类号： TM732

参考文献

[1] 孙毅, 李泽坤, 黄绍模, 等. 基于分布式需求侧资源备调池的低频减载优化策略研究[J]. 电网技术, 2020, 44(3): 1016-1026.
SUN Y, LI Z K, HUANG S M, et al.An improved UFLS strategy based on distributed demand side resource pools[J]. Power system technology, 2020, 44(3): 1016-1026.
[2] 丁明, 胡迪, 毕锐, 等. 含高渗透率可再生能源的配电网可靠性分析[J]. 太阳能学报, 2020, 41(2): 194-202.
DING M, HU D, BI R, et al.Reliability analysis of distribution system containing high penetration renewable energy[J]. Acta energiae solaris sinica, 2020, 41(2): 194-202.
[3] ANWAR M B, QAZI H W, BURKE D J, et al.Harnessing the flexibility of demand-side resources[J]. IEEE transactions on smart grid, 2019, 10(4): 4151-4163.
[4] 禹威威, 刘世林, 陈其工, 等. 考虑需求侧管理的光伏微电网多目标优化调度方法[J]. 太阳能学报, 2017, 38(11): 2972-2981.
YU W W, LIU S L, CHEN Q G, et al.Multi-objective optimization scheduling method for photovoltaic based microgrid considering demand side management[J]. Acta energiae solaris sinica, 2017, 38(11): 2972-2981.
[5] MUHANJI S O, MUZKIKYAN A, FARID A M.Distributed control for distributed energy resources: long-term challenges and lessons learned[J]. IEEE access, 2018, 6: 32737-32753.
[6] 吴红斌, 董淑涛, 丁明. 考虑需求侧响应的风电功率分层平滑方法[J]. 太阳能学报, 2018, 39(9): 2418-2424.
WU H B, DONG S T, DING M.A layered method for smoothing fluctuation of wind power based on demand response[J]. Acta energiae solaris sinica, 2018, 39(9): 2418-2424.
[7] 沈瑜, 岳园园, 闫华光, 等. 地区电网需求响应资源聚合与调控策略研究[J]. 电网技术, 2017, 41(10): 3341-3347.
SHEN Y, YUE Y Y, YAN H G, et al.Research on aggregation and optimization strategies of demand response resources for district power grid[J]. Power system technology, 2017, 41(10): 3341-3347.
[8] ARUN S L, SELVAN M P.Intelligent residential energy management system for dynamic demand response in smart buildings[J]. IEEE systems journal, 2018, 12(2): 1329-1340.
[9] MURATORI M, RIZZONI G.Residential demand response: dynamic energy management and time-varying electricity pricing[J]. IEEE transactions on power systems, 2016, 31(2): 1108-1117.
[10] 张宁, 胡兆光, 周渝慧, 等. 考虑需求侧低碳资源的新型模糊双目标机组组合模型[J]. 电力系统自动化, 2014, 38(17): 25-30.
ZHANG N, HU Z G, ZHOU Y H, et al.A novel fuzzy bi-objective unit commitment model considering demand side low-carbon resources[J]. Automation of electric power systems, 2014, 38(17): 25-30.
[11] 曾鸣, 彭丽霖, 孙静惠, 等. 兼容需求侧可调控资源的分布式能源系统经济优化运行及其求解算法[J]. 电网技术, 2016, 40(6): 1650-1656.
ZENG M, PENG L L, SUN J H, et al.Economic optimization and corresponding algorithm for distributed energy system compatible with demand-side resources[J]. Power system technology, 2016, 40(6): 1650-1656.
[12] 杨丽君, 杨博, 安立明, 等. 考虑电动汽车响应的光储微电网储能优化配置[J]. 太阳能学报, 2020, 41(4): 340-347.
YANG L J, YANG B, AN L M, et al.OPTIMAL configuration of grid-connected PV-and-storage microgrid considering evs' demand response[J]. Acta energiae solaris sinica, 2020, 41(4): 340-347.
[13] 卢恩, 王一, 陈雨果, 等. 含复杂电源结构的电网调峰优化模型[J]. 电力系统自动化, 2013, 37(20): 119-126.
LU E, WANG Y, CHEN Y G, et al.Optimization model for peak load regulation of power system with complex generation structure[J]. Automation of electric power systems, 2013, 37(20): 119-126.
[14] 许周, 孙永辉, 谢东亮, 等. 计及电/热柔性负荷的区域综合能源系统储能优化配置[J]. 电力系统自动化, 2020, 44(2): 53-59.
XU Z, SUN Y H, XIE D L, et al.Optimal configuration of energy storage for integrated region energy system considering power/thermal flexible load[J]. Automation of electric power systems, 2020, 44(2): 53-59.
[15] 庄园, 王磊. 分布式电源在配电网络中优化选址与定容的研究[J]. 电力系统保护与控制, 2012, 40(20): 73-78.
ZHUANG Y, WANG L.Research of distributed generation optimal layout and capacity confirmation in distribution network[J]. Power system protection and control, 2012, 40(20): 73-78.
[16] 丁明, 吴杰, 张晶晶. 面向风电平抑的混合储能系统容量配置方法[J]. 太阳能学报, 2019, 40(3): 593-599.
DING M, WU J, ZHANG J J.Capacity optimization method of hybrid energy storage system for wind power smoothing[J]. Acta energiae solaris sinica, 2019, 40(3): 593-599.
[17] 王立乔, 鲍利斌, 孙孝峰. 基于变异粒子群算法的光伏系统最大功率点跟踪研究[J]. 太阳能学报, 2016, 37(3): 743-751.
WANG L Q, BAO L B, SUN X F.Study of photovoltaic system maximum power point tracking based on mutation particle swarm optimization[J]. Acta energiae solaris sinica, 2016, 37(3): 743-751.
[18] 高红均, 刘俊勇. 考虑不同类型DG和负荷建模的主动配电网协同规划[J]. 中国电机工程学报, 2016, 36(18): 4911-4922, 5115.
GAO H J, LIU J Y.Coordinated planning considering different types of DG and load in active distribution network[J]. Proceedings of the CSEE, 2016, 36(18): 4911-4922, 5115.
[19] 李振坤, 岳美, 胡荣, 等. 计及分布式电源与可平移负荷的变电站优化规划[J]. 中国电机工程学报, 2016, 36(18): 4883-4893, 5112.
LI Z K, YUE M, HU R, et al.Optimal planning of substation considering distributed generation and shiftable loads[J]. Proceedings of the CSEE, 2016, 36(18): 4883-4893, 5112.
[20] 王典, 潘超, 鹿丽, 等. 计及风-光时序相关特性的源-储并网阶段式规划策略[J]. 东北电力大学学报, 2020, 40(4): 1-10.
WANG D, PAN C, LU L, et al.Source-storage staged planning strategy considering wind-photovoltaic timing related characteristics[J]. Journal of Northeast Electric Power University, 2020, 40(4): 1-10.
[21] GILS H C.Assessment of the theoretical demand response potential in Europe[J]. Energy, 2014, 67: 1-18.
[22] 王蓓蓓. 面向智能电网的用户需求响应特性和能力研究综化[J]. 中国电机工程学报, 2014, 34(22): 3654-3663.
WANG B B.Research on consumers'response characterics and ability under smart grid: a literatures survey[J]. Proceedings of the CSEE, 2014, 34(22): 3654-3663.
[23] ESMAILNAJAD S, SUNDQUIST J.Demand side management in Swedish industry-an investigation of load management in major Swedish industries[D]. Goteborg: Chalmers University of Technology, 2014.