考虑温控负荷聚合功率不确定性的楼宇微网鲁棒优化

doi:10.12204/j.issn.1000-7229.2021.04.007

电力建设 ›› 2021, Vol. 42 ›› Issue (4): 59-68.doi: 10.12204/j.issn.1000-7229.2021.04.007

考虑温控负荷聚合功率不确定性的楼宇微网鲁棒优化

华光辉¹, 李晨², 张勇³, 李丹⁴, 刘创⁵, 王程⁵

1.新能源与储能运行控制国家重点实验室(中国电力科学研究院有限公司),南京市 210003
2.国家电力调度控制中心,北京市 100031
3.国网河北省电力有限公司,石家庄市 050024
4.国家电网有限公司直流建设分公司,北京市 100052
5.华北电力大学电气与电子工程学院,北京市 102206

收稿日期:2020-07-29 出版日期:2021-04-01 发布日期:2021-03-30
作者简介:华光辉 (1983),男,硕士,高级工程师,从事新能源发电并网运行控制、分布式发电与储能应用、区域综合能源协调控制技术研究|李晨 (1983),男,硕士,高级工程师,主要研究方向为配电网运行控制管理|张勇 (1976),男,硕士,高级工程师,主要研究方向为电网调度与运行管理|李丹 (1984),女,硕士,工程师,主要研究方向为电力系统的直流输电技术|刘创 (1994),男,硕士研究生,从事电热综合能源系统的优化调度运行研究|王程 (1990),男,博士,副教授,从事综合能源系统、能源互联网、新能源电力系统、鲁棒调度控制、不确定性分析、风险分析等方面的研究
基金资助:
国家电网有限公司科技项目(SGHE0000DKJS1900094)

Robust Optimization of Building Microgrid Considering the Uncertainty of the Aggregated Power of Thermostatically Controlled Loads

HUA Guanghui¹, LI Chen², ZHANG Yong³, LI Dan⁴, LIU Chuang⁵, WANG Cheng⁵

1. State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems(China Electric Power Research Institute),Nanjing 210003, China
2. National Power Dispatching and Control Center, Beijing 100031, China
3. State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050024, China
4. State Grid DC Engineering Construction Company, Beijing 100052, China
5. School of Electrical and Electronic Engineering, North China Electrical Power University, Beijing 102206, China

Received:2020-07-29 Online:2021-04-01 Published:2021-03-30
Supported by:
State Grid Corporation of China Research Program(SGHE0000DKJS1900094)

摘要/Abstract

摘要：

随着集成楼宇微网系统的快速发展,温控负荷(thermostatically controlled loads, TCLs)逐渐成为重要的需求响应资源,然而其分布的分散性和响应的不确定性也对集成楼宇微网的调控提出更高的要求。针对TCLs的分布分散特性,文章提出了一种基于楼宇热阻热容(resistance-capacitance, RC)模型的TCLs聚合方案。该方案在对楼宇RC传热模型进行线性化处理后,依据大数定律实现TCLs聚合,可充分计及楼宇建筑的物理结构参数,不但能适应楼宇建筑的差异化建模需求,且方案的计算效率较高。基于此TCLs聚合方案,文章建立了一种适用于集成楼宇微网系统的两阶段鲁棒优化模型,分别对TCLs聚合功率和分布式可再生能源出力的不确定性进行建模,并采用列和约束生成算法进行求解。算例结果表明,所提TCLs聚合方案在各种室外温度和光照强度下都能满足微网调度决策的精度要求;同时,所建立的两阶段鲁棒优化模型能有效减少实际运行下的联络线功率调整量,增强微网系统运行的稳定性和鲁棒性。

关键词: 智能楼宇微网, 温控负荷聚合, 不确定性, 鲁棒优化

Abstract:

With the rapid development of microgrid systems with multiple intelligent buildings integrated, thermostatically controlled loads (TCLs) have gradually become an important demand-response resource. However, its distribution dispersion and response uncertainty also put forward higher requirements for the regulation. To address the distribution dispersion of TCLs, this paper puts forward a TCLs aggregation scheme based on the building's resistance-capacitance (RC) model. Specifically, the RC heat transfer model is linearized at first, and then the TCLs are aggregated according to the law of large numbers, thence the building's physical structural parameters can be taken into account. This TCLs aggregation scheme not only adapts to the differentiated modeling requirements of buildings but also has preferable calculation efficiency. On the basis of the proposed TCLs aggregation scheme, a two-stage robust optimization model applicable to the integrated building microgrid system is established, which considers the uncertainties of aggregated TCLs and distributed renewable generators, and is solved by the column and constraint generation algorithm. Results show that the proposed TCLs aggregation scheme can reach the accuracy requirements of microgrid scheduling operation under diverse outdoor temperatures and solar radiation. Meanwhile, the established two-stage robust optimization model can effectively reduce the power regulation of tie lines under real circumstances, thence enhancing the stability and robustness of the microgrid system.

Key words: microgrid with buildings, aggregated thermostatically controlled load, uncertainty, robust optimization

中图分类号:

TM73

华光辉, 李晨, 张勇, 李丹, 刘创, 王程. 考虑温控负荷聚合功率不确定性的楼宇微网鲁棒优化[J]. 电力建设, 2021, 42(4): 59-68.

HUA Guanghui, LI Chen, ZHANG Yong, LI Dan, LIU Chuang, WANG Cheng. Robust Optimization of Building Microgrid Considering the Uncertainty of the Aggregated Power of Thermostatically Controlled Loads[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(4): 59-68.

图/表 12

图1 典型楼宇的单区域RC网络模型

Fig.1 Single-user RC network model for building

图2 温控负荷运行状态示意图

Fig.2 Schematic diagram of the operation states of thermostatically controlled loads

图3 鲁棒调度模型两阶段间的交互原理图

Fig.3 Schematic diagram of the interaction between the two stages of the robust scheduling model

图4 集成多楼宇微网系统的运营模式

Fig.4 Integrative operation mode of the microgrid system with multiple smart buildings integrated

表1 温控负荷和智能楼宇的参数范围

Table 1 Ranges of parameters of thermostatically controlled loads and Intelligent building

图5 智能楼宇温控负荷聚合功率估计值及上下界

Fig.5 Estimated aggregated power of TCLs and its upper and lower limits

图6 聚合功率的相对误差分布

Fig.6 Distribution of relative error of integrated power

图7 不同天气条件下的楼宇温控负荷聚合功率

Fig.7 Aggregated power under different weather conditions

图8 微网系统可再生DG出力预测值

Fig.8 Day-ahead forecasting value of the distributed renewable generators in the microgrid

表2 微网可控DG的运行参数

Table 2 Operation parameters of the dispatchable DGs

图9 两种调度运行方案下的微网机组计划对比

Fig.9 Scheduling strategy of microgrid under different methods

表3 两种调度方案在1 000个采样场景下的结果

Table 3 Simulation results with different methods under 1 000 sampled scenarios

参考文献 18

[1]	住房和城乡建筑部科技发展促进中心. 中国建筑节能发展报告(2020年) [M]. 北京: 中国建筑工业出版社, 2020.
	Science and Technology Development Promotion Center of the Ministry of Housing and Urban-Rural Development. China building energy efficiency development report (2020) [M]. Beijing: China Building Industry Press, 2020.
[2]	HANIF S, MASSIER T, GOOI H B, et al. Cost optimal integration of flexible buildings in congested distribution grids[J]. IEEE Transactions on Power Systems, 2017,32(3):2254-2266. doi: 10.1109/TPWRS.2016.2605921 URL
[3]	LIU N, CHEN Q F, LIU J, et al. A heuristic operation strategy for commercial building microgrids containing EVs and PV system[J]. IEEE Transactions on Industrial Electronics, 2015,62(4):2560-2570. doi: 10.1109/TIE.2014.2364553 URL
[4]	王雅丽, 刘洪, 赵娟, 等. 基于网络重构和电热联合需求响应的配电网安全经济调度[J]. 电力建设, 2019,40(4):90-97.
	WANG Yali, LIU Hong, ZHAO Juan, et al. Distribution network economic dispatch based on network reconfiguration and power-heat combined demand response[J]. Electric Power Construction, 2019,40(4):90-97.
[5]	姚垚, 张沛超, 王永权. 温控负荷参与快速频率调整的双层控制方法[J]. 中国电机工程学报, 2018,38(17):4987-4998.
	YAO Yao, ZHANG Peichao, WANG Yongquan. A two-layer control method for thermostatically controlled loads to provide fast frequency regulation[J]. Proceedings of the CSEE, 2018,38(17):4987-4998.
[6]	刘萌, 梁雯, 张晔, 等. 温控负荷群Fokker-Planck方程聚合模型的数值拉普拉斯反变换求解方法[J]. 电力系统保护与控制, 2017,45(23):17-23.
	LIU Meng, LIANG Wen, ZHANG Ye, et al. Numerical inverse Laplace transform solving method of thermostatically controlled load group’s Fokker-Planck equation aggregation model[J]. Power System Protection and Control, 2017,45(23):17-23.
[7]	姜婷玉, 鞠平, 王冲. 考虑用户调节行为随机性的空调负荷聚合功率模型[J]. 电力系统自动化, 2020,44(3):105-113.
	JIANG Tingyu, JU Ping, WANG Chong. Aggregated power model of air-conditioning load considering stochastic adjustment behaviors of consumers[J]. Automation of Electric Power Systems, 2020,44(3):105-113.
[8]	余洋, 卢健斌, 谢仁杰, 等. 基于反推控制的聚合温控负荷与发电机励磁协调控制[J]. 电力系统自动化, 2019,43(16):101-108.
	YU Yang, LU Jianbin, XIE Renjie, et al. Backstepping control based coordinated control of aggregated thermostatically controlled load and generator excitation[J]. Automation of Electric Power Systems, 2019,43(16):101-108.
[9]	LIU M X, SHI Y. Model predictive control of aggregated heterogeneous second-order thermostatically controlled loads for ancillary services[J]. IEEE Transactions on Power Systems, 2016,31(3):1963-1971. doi: 10.1109/TPWRS.2015.2457428 URL
[10]	LI X W, WEN J, BAI E W. Developing a whole building cooling energy forecasting model for on-line operation optimization using proactive system identification[J]. Applied Energy, 2016,164:69-88. doi: 10.1016/j.apenergy.2015.12.002 URL
[11]	王志强, 王珊, 张馨月, 等. 计及用户响应行为差异性的区域电采暖负荷特性建模[J]. 电力系统自动化, 2019,43(7):67-73.
	WANG Zhiqiang, WANG Shan, ZHANG Xinyue, et al. Load characteristics modeling of regional electric heating system considering difference of users response behaviors[J]. Automation of Electric Power Systems, 2019,43(7):67-73.
[12]	沈欣炜, 郭庆来, 许银亮, 等. 考虑多能负荷不确定性的区域综合能源系统鲁棒规划[J]. 电力系统自动化, 2019,43(7):34-41.
	SHEN Xinwei, GUO Qinglai, XU Yinliang, et al. Robust planning method for regional integrated energy system considering multi-energy load uncertainties[J]. Automation of Electric Power Systems, 2019,43(7):34-41.
[13]	李东东, 刘洋, 林顺富, 等. 典型居民温控负荷建模及聚合特性研究[J]. 电测与仪表, 2017,54(16):56-62.
	LI Dongdong, LIU Yang, LIN Shunfu, et al. The study on the modeling and the aggregation characteristics of typical residential thermostatically-controlled load[J]. Electrical Measurement & Instrumentation, 2017,54(16):56-62.
[14]	李亚平, 姚建国, 雍太有, 等. 居民温控负荷聚合功率及响应潜力评估方法研究[J]. 中国电机工程学报, 2017,37(19):5519-5528.
	LI Yaping, YAO Jianguo, YONG Taiyou, et al. Estimation approach to aggregated power and response potential of residential thermostatically controlled loads[J]. Proceedings of the CSEE, 2017,37(19):5519-5528.
[15]	陈厚合, 李泽宁, 靳小龙, 等. 集成智能楼宇的主动配电网建模及优化方法[J]. 中国电机工程学报, 2018,38(22):6550-6563.
	CHEN Houhe, LI Zening, JIN Xiaolong, et al. Modeling and optimization of active distribution network with integrated smart buildings[J]. Proceedings of the CSEE, 2018,38(22):6550-6563.
[16]	WANG C, LIU C, LIN Y Z, et al. Day-ahead dispatch of integrated electric-heat systems considering weather-parameter-driven residential thermal demands[J]. Energy, 2020,203:117847. doi: 10.1016/j.energy.2020.117847 URL
[17]	董军, 聂麟鹏, 聂仕麟, 等. 考虑需求响应与风电不确定性的两阶段鲁棒旋转备用容量优化模型[J]. 电力建设, 2019,40(11):55-64.
	DONG Jun, NIE Linpeng, NIE Shilin, et al. Two-stage robust optimization model for spinning reserve capacity considering demand response and uncertainty of wind power[J]. Electric Power Construction, 2019,40(11):55-64.
[18]	靳小龙, 穆云飞, 贾宏杰, 等. 集成智能楼宇的微网系统多时间尺度模型预测调度方法[J]. 电力系统自动化, 2019,43(16):25-33.
	JIN Xiaolong, MU Yunfei, JIA Hongjie, et al. Model predictive control based multiple-time-scheduling method for microgrid system with smart buildings integrated[J]. Automation of Electric Power Systems, 2019,43(16):25-33.

考虑温控负荷聚合功率不确定性的楼宇微网鲁棒优化

Robust Optimization of Building Microgrid Considering the Uncertainty of the Aggregated Power of Thermostatically Controlled Loads

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 18

相关文章 15

编辑推荐

Metrics

本文评价

[1]	陈涛, 刘洋, 李文峰, 许立雄, 马腾. 基于区块链技术的微网自适应定价策略及经济调度方法[J]. 电力建设, 2021, 42(6): 17-28.
[2]	蔡秀雯, 陈茂新, 陈钢, 方一晨, 张沈习, 程浩忠. 考虑主动管理与需求侧管理的主动配电网分布式光伏最大准入容量计算方法[J]. 电力建设, 2021, 42(5): 38-47.
[3]	韩照洋, 周琳, 刘硕, 李国栋, 付学谦. 考虑不确定性的综合能源系统日前市场申报策略[J]. 电力建设, 2021, 42(4): 121-131.
[4]	陈晓东, 马越, 陈贤邦, 刘洋. 基于数据驱动可调鲁棒的冷-热-电联供综合能源系统日前调度优化[J]. 电力建设, 2021, 42(1): 28-40.
[5]	李红霞, 樊伟, 李楠, 谭彩霞, 许德操, 鞠立伟. 考虑风光不确定性的电气互联虚拟电厂近零碳调度优化模型[J]. 电力建设, 2020, 41(9): 10-19.
[6]	马宇航, 黄媛, 刘俊勇, 路亮. 考虑日内来水不确定和电网断面约束的梯级水电日前调度[J]. 电力建设, 2020, 41(9): 39-49.
[7]	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.
[8]	尚慧玉, 林鸿基, 李忠憓, 赵宏伟, 陈明辉, 阳曾, 文福拴. 计及不确定性的移动应急电源鲁棒优化调度[J]. 电力建设, 2020, 41(8): 111-119.
[9]	陈曦，徐青山，杨永标. 考虑风电不确定性的CCHP型微网日前优化经济调度[J]. 电力建设, 2020, 41(6): 107-113.
[10]	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.
[11]	王李龑，许强，黄开艺，吴健，艾芊，张亚新，贾轩. 计及新能源及负荷不确定性的多能微网系统两阶段随机规划方法[J]. 电力建设, 2020, 41(4): 100-108.
[12]	廖鸿图，黄益宏，施亦治，陈健. 含光储联合体的交直流配电网双层两阶段鲁棒优化运行策略[J]. 电力建设, 2020, 41(3): 110-118.
[13]	潘尔生, 宋毅, 原凯, 郭玥, 程浩忠, 张沈习. 考虑可再生能源接入的综合能源系统规划评述与展望[J]. 电力建设, 2020, 41(12): 3-13.
[14]	吴嘉豪, 曾成碧, 苗虹. 基于机会约束目标规划的综合能源系统经济调度[J]. 电力建设, 2020, 41(12): 49-56.
[15]	王晞, 徐浩, 王海燕, 陈博, 王长浩, 刘洋. 考虑风电和电动汽车不确定性的综合能源系统日前经济调度[J]. 电力建设, 2020, 41(12): 82-91.