基于需求响应技术的主动配电网优化调度

doi:10.3969/j.issn.1000－7229.2017.03.013

摘要/Abstract

摘要： 针对可再生能源大量接入配电网后,由于其自身出力的随机性和波动性给配网的调控所带来的困难，引入需求响应技术，充分利用用户受电价影响改变用电模式的能力，提出了一种主动配电网(active distribution network，ADN)鲁棒优化调度方法，在保证系统运行安全可靠的基础上优化配网运行。该方法分为三个阶段：第一阶段，通过不确定集的形式表征可再生能源出力的随机性，并通过极端场景法对集合进行削减，最终将不确定性参数转化为确定性参数；第二阶段，运用需求响应技术，通过电价激励达到调控负荷的目的，从而实现对负荷削峰填谷的作用；第三阶段，运用两层规划模型将可再生能源的无功调节能力与配电网传统调控手段相配合，统筹调控，从而实现降低主动配电网的网损和电压波动，减少传统设备的调节次数，优化配网运行。最后基于美国PG&E 69节点系统对所提方法的有效性进行了验证。

关键词: , 主动配电网(ADN), 可再生能源, 可调鲁棒优化, 需求响应, 两层规划模型

Abstract: With the increasing penetration of renewable power source into the distribution network, distribution network control has been a challenging issue due to the uncertainty and volatility nature of renewable power source. This paper

proposes the robust optimization scheduling for active distribution network (ADN) through introducing demand response technology and the full use of elastic load adjustment ability of users affected by electricity prices, which can optimize the operation of distribution network on the basis of ensuring the safe and reliable operation of the system. The proposed method is divided into three stages. In the first stage, in order to transform the uncertain parameters into deterministic parameters, renewable energy output uncertainty is described by uncertain set and the extreme scenario method is adopted to cut down the set. In the second stage, the proposed method uses demand response technology and electricity price incentive to regulate the load, so as to realize the function of power peak load shifting. In the third stage, based on the bi-level planning model, the reactive power adjustment ability of renewable energy can cooperate with the traditional control methods of distribution network as a whole regulation, for the purpose of reducing the network loss and voltage fluctuation of ADN, and the regulation number of traditional equipment, as well as the optimization of distribution network operation. Finally, the effectiveness of the proposed method is validated based on the American PG&E 69-bus system.

Key words: active distribution network (ADN), renewable sources, adjustable robust optimization, demand respond, bi-level planning model

中图分类号:

TM 711

金炜，骆晨，徐斌，王刘芳，李伟. 基于需求响应技术的主动配电网优化调度[J]. 电力建设, 2017, 38(3): 93-.

JIN Wei, LUO Chen, XU Bin, WANG Liufang, LI Wei. Optimal Scheduling of Active Distribution Network Based on Demand Respond Technology

[J]. Electric Power Construction, 2017, 38(3): 93-.

参考文献

［1］赵波, 王财胜, 周金辉, 等. 主动配电网现状与未来发展［J］. 电力系统自动化, 2014, 38(18):125-135．

ZHAO Bo, WANG Caisheng, ZHOU Jinhui, et al. Present andfuture development trend of active distribution network［J］. Automation of

Electric Power Systems, 2014, 38(18):125-135．

［2］王建, 李兴源, 邱晓燕. 含有分布式发电装置的电力系统研究综述［J］．电力系统自动化, 2005, 29(24): 90-97．

WANG Jian, LiXingyuan, QIU Xiaoyan, Power system research on distributed generation penetration［J］. Automation of Electric Power Systems, 2005, 29(24): 90-97.

［3］杨新法, 苏剑, 吕志鹏, 等．微电网技术综述［J］．中国电机工程学报, 2014, 34(1): 57-70．

YANG Xinfa, SU Jian, L Zhipeng, et al. Overview on micro-grid technology［J］. Proceedings of the CSEE, 2014, 34(1): 57-70．

［4］ZHOU Q, BIALEK J W．Generation curtailment to manage voltage constraints in distribution networks［J］. IET Generation, Transmission & Distribution, 2007, 1(3):492-498．

［5］陶顺, 骆晨, 薛佳佳, 等. 基于两阶段规划法的有源配电网综合电压优化控制［J］. 电力系统自动化, 2015, 39(20):54-59．

TAO Shun, LUO Chen, XUE Jiajia , et al. Comprehensive voltage control optimization in active distribution based on two-stage planning［J］. Automation of Electric Power Systems, 2015, 39(20):54-59．

［6］GABASH A, LI P．Active-reactive optimal power flow in distribution networks with embedded generation and battery storage［J］．IEEE Transactions on Power Systems, 2012, 27(4): 2026-2035．

［7］彭春华，谢鹏, 陈臣.大规模光伏电站接入电网可调节鲁棒优化调度［J］.中国电机工程学报, 2014, 34(25): 4324-4332.

PENG Chunhua, XIE Peng, CHEN Chen. Adjustable robust optimal dispatch of power system with large-scale photovoltaic power stations［J］. Proceedings of the CSEE, 2014, 34(25): 4324-4332.

［8］WANG Q, GUAN Y, WANG J. A chance-constrained two-stage stochastic program for unit commitment with uncertain wind power output ［J］. IEEE Transactions on Power System, 2012, 27(1): 206-215．

［9］JIANG R, WANG J, GUAN Y. Robust unit commitment with wind power and pumped storage hydro［J］. IEEE Transactions on Power System, 2012, 27(2): 800-810．

［10］THIMMAPURAMP R, KIM J, BOTTERUD A, et al. Modeling and simulation of price elasticity of demand using an agent-based model ［C］//2010 Innovative Smart Grid Technologies．Gaithersburg：IEEE，2010:1-8.

［11］SU C, KIRSCHEN D. Quantifying the effect of demand response on electricity markets［J］. IEEE Transactions on Power System, 2009, 24(3):1199-1207.

［12］KIRSCHEN D. Demand-side view of electricity markets［J］. IEEE Transactions on Power System, 2003, 18(2):520-527.

［13］李生虎, 丁明．电力市场中基于电价弹性的电力系统运行可靠性的协调控制［J］. 中国电机工程学报, 2005,25(24):34-40.

LI Shenghu, DING Ming. Coordination to power system operational reliability in power market based on price elasticity［J］. Proceedings of the CSEE，2005,25(24):34-40．

［14］LI Y P, YU D, XU D D, et al. An electricity charge strategy based on dynamic rewards and punishment［C］// Proceedings of Chinese Control and Decision Conference (CCDC). Changsha, China， 2014.

［15］徐秋实，邓长虹, 赵维兴, 等. 含风电电力系统的多场景鲁棒调度方法［J］.电网技术, 2014, 38(3): 653-661.

XU Qiushi, DENG Changhong, ZHAO Weixing, et al. A multi-scenario robust dispatch method for power grid integrated with wind farms［J］. Power System Technology, 2014, 38(3): 653-661.

［16］GUANG Y Y, ZHAO Y D, KIT P W. A modified differential evolution algorithm with fitness sharing for power system planning［J］．IEEE Transactions on Power Systems, 2012, 27(4): 2026-2035．

［17］刘柏私, 谢开贵, 周家启．配电网重构的动态规划算法［J］, 中国电机工程学报, 2005,25(9):29-34.

LIU Bosi, XIE Kaigui, ZHOU Jiaqi. Electrical distribution networks reconfiguration using dynamic programming［J］. Proceedings of the CSEE, 2005, 25(9): 29-34．

［18］BARAN M E, WU F F. Optimal capacitor placement on radial distribution system ［J］. IEEE Transactions on Power Delivery, 1989, 4(1): 725-734．

［19］国家电网公司.电力系统电压质量和无功电力管理规定：DL/T 1198-2013［S］．北京:中国标准出版社, 2013.

［20］WANG Qianfan, WANG Jianhui, GUAN Yongpei. Stochastic unit commitment with uncertain demand response［J］. IEEE Transactions on Power Delivery, 2013, 28(1): 562-563．

[1]	汤吉鸿, 章德, 丛凡超, 田国梁, 吴贵烘, 李宇骏, 禹海峰. 基于线性二次型最优控制的多端直流最优功率调制方案[J]. 电力建设, 2020, 41(8): 32-39.
[2]	赵洱岽, 王浩, 林弘杨. 现货市场中基于演化博弈的火电企业阶梯报价策略[J]. 电力建设, 2020, 41(8): 68-71.
[3]	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.
[4]	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.
[5]	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.
[6]	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.
[7]	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.
[8]	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.
[9]	宋建，束洪春，董俊，梁雨婷，李雨龙，杨博. 基于GM(1，1)与BP神经网络的综合负荷预测[J]. 电力建设, 2020, 41(5): 75-80.
[10]	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.
[11]	董凌1， 2，年珩3，范越2，赵建勇3，张智3，林振智3. 能源互联网背景下共享储能的商业模式探索与实践[J]. 电力建设, 2020, 41(4): 38-44.
[12]	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.
[13]	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.
[14]	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.
[15]	郭莉，吴晨，陈立，丛小涵，苗曦云，王蓓蓓. 可再生能源跨区消纳的受端电网阻塞机理分析[J]. 电力建设, 2020, 41(2): 21-29.