计及需求响应的主动配电网双层优化调度模型

doi:10.3969/j.issn.1000－7229.2018.09.014

电力建设 ›› 2018, Vol. 39 ›› Issue (9): 112-119.doi: 10.3969/j.issn.1000－7229.2018.09.014

计及需求响应的主动配电网双层优化调度模型

华亮亮1,2 ，黄伟1，葛良军1，刘立夫1

1.华北电力大学电气与电子工程学院，北京市，102206；2.国网蒙东电力通辽供电公司，内蒙古自治区通辽市，028000

出版日期:2018-09-01
作者简介:华亮亮（1980），女，高级工程师，工程硕士，研究方向为电力系统运行与规划、微网控制及新能源并网；黄伟（1962），男，教授，研究方向为电力系统运行与规划、微网控制及新能源并网；葛良军（1992），男，硕士研究生，通信作者，研究方向为主动配电网运行与控制；刘立夫（1992），男，硕士研究生，研究方向为智能电网与新能源利用技术。
基金资助:
国网蒙东公司科技项目（分布式新能源与配网协同发展体系建设及规划技术研究）

Bi-level Optimal Dispatch Model of Active Distribution Network with Demand Response

HUA Liangliang1,2，HUANG Wei1, GE Liangjun1, LIU Lifu1

1. School of Electrical and Electronic Engineering，North China Electric Power University, Beijing 102206, China；2.State Grid Tongliao Power Supply Company, Tongliao 028000，Inner Mongolia Autonomous Region, China

Online:2018-09-01
Supported by:
This work is supported by State Grid Tongliao Power Supply Company Research Program"Research on Distributed New Energy and Distribution Network Cooperative Development System Construction and Planning Technology".

摘要/Abstract

摘要： 主动配电网（active distribution network，ADN）能够综合运用需求响应和协调优化管理两大手段，通过源荷协调互动，最大限度上实现分布式电源的高效消纳和系统运行的安全经济。文章提出了计及需求响应的主动配电网双层优化调度模型。其中电价协调层以价格型需求响应为核心，通过调整日前实时电价优化各时段负荷需求；可调度单元控制层基于负荷需求，在计及ADN内部不确定性因素对系统安全影响的前提下，以运行成本最低为目标制定系统运行计划。调度模型双层之间基于信息双向流动实现协调互动，为提高求解效率，提出了将智能优化算法和传统优化算法相结合的求解策略。算例结果表明，所建模型能够保障系统安全裕度、降低配网运行成本、提高用户满意度和减小负荷峰谷差。

关键词: 主动配电网（ADN）, 双层互动优化调度, 需求响应, 灵敏度分析

Abstract: Active distribution network can promote effective consumption of distributed generation and ensure safe and economic operation of the system by taking advantage of demand response and coordinated optimization to guide source-load coordination. Considering the uncertainties of system, a bi-level optimal dispatch model of active distribution network is proposed. The price coordination layer, taking the price-type demand response as the core, optimizes the load demand by adjusting day-ahead electricity price. The schedulable unit control layer makes the operational plan of system according to load demand to minimize the operating cost considering the effect of uncertainties within active distribution network on the safety of system. The two layers exchange information with each other and achieve the coordination and interaction. A solution strategy combining intelligent optimization algorithm and traditional optimization algorithm is proposed to improve the solution efficiency. The results of the example show that the proposed model can ensure the safety of system, improve customer satisfaction, as well as reduce the system operating cost and peak-valley difference.

Key words: active distribution network, bi-level interactive dispatch optimization, demand response, sensitivity analysis

中图分类号:

TM 732

华亮亮,黄伟，葛良军，刘立夫. 计及需求响应的主动配电网双层优化调度模型[J]. 电力建设, 2018, 39(9): 112-119.

HUA Liangliang，HUANG Wei, GE Liangjun, LIU Lifu. Bi-level Optimal Dispatch Model of Active Distribution Network with Demand Response[J]. Electric Power Construction, 2018, 39(9): 112-119.

参考文献

［1］杨汾艳, 张跃, 曾杰,等. 计及分布式光伏配电网供电能力计算方法［J］. 中国电力, 2016, 49（12）:150-155.

YANG Fenyan, ZHANG Yue, ZENG Jie, et al. Calculation method of power supply capacity of distribution network considering distributed PV［J］. Electric Power, 2016, 49（12）: 150-155.

［2］张振高, 王学军, 李慧,等. 需求响应对电力需求影响的概率模型［J］. 电力系统及其自动化学报, 2017, 29（4）:115-121.

ZHANG Zhengao, WANG Xuejun, LI Hui, et al. Probability model of the influence of demand response on electricity demand［J］. Proceedings of the CSU-EPSA, 2017, 29（4）: 115-121.

［3］李浩闪, 李燕青. 分时电价下考虑用户侧响应的日前调度计划建模与分析［J］. 华东电力, 2014, 42（2）:314-318.

LI Haoshan, LI Yanqing. Day-ahead generation dispatch modeling and analysis considering demand response under time-of-use price［J］. East China Electirc Power, 2014, 42（2）:314-318.

［4］Mozafari B, Bashirvand M, Nikzad M, et al. A SCUC-based approach to determine time-of-use tariffs［C］// International Conference on Environment and Electrical Engineering. IEEE, 2012:429-433.

［5］别朝红, 胡国伟, 谢海鹏,等. 考虑需求响应的含风电电力系统的优化调度［J］. 电力系统自动化, 2014, 38（13）:115-120.

BIE Chaohong, HU Guowei, XIE Haipeng, et al. Optimal dispatch for wind power integrated systems considering demand response［J］. Automation of Electric Power Systems, 2014, 38（13）:115-120.

［6］刘小聪, 王蓓蓓, 李扬,等. 基于实时电价的大规模风电消纳机组组合和经济调度模型［J］. 电网技术, 2014, 38（11）:2955-2963.

LIU Xiaocong, WANG Beibei, LI Yang, et al. Unit commitment model and economic dispatch model based on real time ricing for large-sacle wind power accommodation［J］. Power System Technology, 2014, 38（11）:2955-2963.

［7］王珂, 姚建国, 姚良忠,等. 电力柔性负荷调度研究综述［J］. 电力系统自动化, 2014, 38（20）:127-135.

WANG Ke, YAO Jiaoguo, YAO Liangzhong, et al. Survey of research on flexible loads scheduling technologies［J］. Automation of Electric Power Systems, 2014, 38（20）:127-135.

［8］秦祯芳, 岳顺民, 余贻鑫,等. 零售端电力市场中的电量电价弹性矩阵［J］. 电力系统自动化, 2004, 28（5）:16-19.

QIN Zhenfang, YUE Shunmin, YU Yinxin, et al. Price elasticity matrix of demand in current retail power market［J］. Automation of Electric Power Systems, 2004, 28（5）:16-19.

［9］张粒子, 王超, 程瑜. 系统时段性电力供不应求形势下的峰谷分时电价优化调整模型［C］// 中国高等学校电力系统及其自动化专业第二十五届学术年会论文集. 长沙，2009：1-4.

ZHANG Lizi, WANG Chao, CHEN Yu. Peak-valley time-of-use price adjustment model under the condition of system power supply shortage［C］// China University of Power Systems and Automation Professional Academic Annual Meeting. Changsha，2009：1-4.

［10］Aalami H A, Moghaddam M P, Yousefi G R. Demand response modeling considering Interruptible/Curtailable loads and capacity market programs［J］. Applied Energy, 2010, 87（1）:243-250.

［11］丁伟, 袁家海, 胡兆光. 基于用户价格响应和满意度的峰谷分时电价决策模型［J］. 电力系统自动化, 2005, 29（20）:10-14.

DING Wei, YUAN Jiahai, HU Zhaoguang. Time-of-use decision model considering user reaction and satisfaction index［J］. Automation of Electric Power Systems, 2005, 29（20）:10-14.

［12］胡国强, 贺仁睦. 基于模糊满意度的水火电力系统多目标短期优化调度［J］. 华北电力大学学报（自然科学版）, 2007, 34（3）:1-5.

HU Guoqiang, HE Renmu. Multi-objective short-term optimization dispatching for hydrothermal power systems using interactive fuzzy satisfying method［J］. Journal of North China Electirc Power University, 2007, 34（3）:1-5.

［13］Venkatesan N. Modeling and integration of demand response and demand side resources for smart grid application in distribution systems［D］. Morgantown: West Virginia Universitys, 2011.

[1]	王瀚琳, 刘洋, 许立雄, 陈贤邦, 谭思维. 考虑风电消纳的区域多微网分层协调优化模型[J]. 电力建设, 2020, 41(8): 87-98.
[2]	李恒强，王骏，蒋星燃，匡仲琴，徐石明，杨永标. 面向电网调峰辅助服务的空调负荷调控策略及其虚拟机组建模[J]. 电力建设, 2020, 41(7): 49-57.
[3]	赵晨，何宇俊，罗钢，龚超，赵越，张轩，陈启鑫. 基于泛在互联的虚拟电厂参与实时市场模型[J]. 电力建设, 2020, 41(6): 36-43.
[4]	苏俊妮，张鑫，赵俊炜，张锐，曲明辉，杨艳红. 计及需求响应和储能的电-气综合能源系统优化调度[J]. 电力建设, 2020, 41(5): 1-8.
[5]	都成，魏震波. 基于模糊激励型需求响应的微电网两阶段优化调度模型[J]. 电力建设, 2020, 41(5): 37-44.
[6]	李鹏，杨莘博，李慧璇，田春筝，李锰，谭忠富. 计及多能源多需求响应手段的园区综合能源系统优化调度模型[J]. 电力建设, 2020, 41(5): 45-57.
[7]	祁兵，叶欣，李彬，陈宋宋，李源非，石坤. 考虑IEC标准的用户侧电力物联网体系架构研究[J]. 电力建设, 2020, 41(5): 92-99.
[8]	许志恒，张勇军，余兆荣，王玕. 考虑激励型需求侧响应的电-气互联系统两阶段随机优化调度[J]. 电力建设, 2020, 41(4): 81-89.
[9]	张军六，樊伟，谭忠富，鞠立伟，德格吉日夫，杨莘博，孙婧霞. 计及需求响应的气电互联虚拟电厂多目标调度优化模型[J]. 电力建设, 2020, 41(2): 1-10.
[10]	周洪益，钱苇航，柏晶晶，卫志农，孙国强，臧海祥. 能源区块链的典型应用场景分析及项目实践[J]. 电力建设, 2020, 41(2): 11-20.
[11]	张涌新，沈弘，文俊. 面向综合需求响应的综合能源系统储能控制面向综合需求响应的综合能源系统储能控制[J]. 电力建设, 2019, 40(9): 43-51.
[12]	王莉，吴国沛，曾顺奇，何仲潇，董树锋. 考虑上级电网与工业园区互动的综合需求响应[J]. 电力建设, 2019, 40(9): 52-63.
[13]	袁桂丽，董金凤，魏更，贾新潮. 基于需求响应和多能互补的冷热电联产微网优化调度[J]. 电力建设, 2019, 40(9): 64-72.
[14]	刘拥军，曹晓峻，胡朝阳，王珂，徐立中，高赐威. 数据网络参与多调峰辅助服务市场组合优化调度策略[J]. 电力建设, 2019, 40(9): 82-90.
[15]	王星华，刘升伟，陈豪君，彭显刚，周亚武. 考虑用户差异性的售电公司需求响应电价模型[J]. 电力建设, 2019, 40(9): 116-123.

计及需求响应的主动配电网双层优化调度模型

Bi-level Optimal Dispatch Model of Active Distribution Network with Demand Response

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价