Probabilistic Assessment and Interaction for Adjustable Potential of Thermostatically Controlled Loads Considering User Uncertainty

ZHAO Long; SUN Yi; WANG Xian; LIU Xiaoming; SUN Donglei

doi:10.19725/j.cnki.1007-2322.2023.0221

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

Probabilistic Assessment and Interaction for Adjustable Potential of Thermostatically Controlled Loads Considering User Uncertainty

更新时间：2025-10-22

- Probabilistic Assessment and Interaction for Adjustable Potential of Thermostatically Controlled Loads Considering User Uncertainty
- Vol. 42, Issue 5, Pages: 978-986(2025)
- 作者机构：
  
  国网山东省电力公司经济技术研究院,山东省,济南市,250021
- 作者简介：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2023.0221
  CLC： 5
- Published：2025
- 稿件说明：
移动端阅览
ZHAO Long, SUN Yi, WANG Xian, et al. Probabilistic Assessment and Interaction for Adjustable Potential of Thermostatically Controlled Loads Considering User Uncertainty[J]. 2025, 42(5): 978-986.
DOI：

ZHAO Long, SUN Yi, WANG Xian, et al. Probabilistic Assessment and Interaction for Adjustable Potential of Thermostatically Controlled Loads Considering User Uncertainty[J]. 2025, 42(5): 978-986. DOI： 10.19725/j.cnki.1007-2322.2023.0221.

摘要

准确评估空调温控负荷集群的可调潜力并加以利用，可实现光伏等新能源波动平抑，维持电网安全稳定运行。针对目前研究存在的模型驱动方法的模型参数泛化、数据驱动方法的物理概念弱化和鲁棒性较差等问题，提出一种计及不确定性的温控负荷集群可调节潜力概率评估方法。首先，开展基于模型与数据混合驱动的温控负荷集群建模，实现日内温控集群实际负荷曲线的跟踪。然后，在考虑实际空调所处建筑物参数不可测和用户用电行为不确定两方面，基于KL散度验证所提方法的鲁棒性。最后，基于混合驱动模型建立空调负荷集群响应能力的概率评估与互动机制，以山东某地区10 kV馈线为实际算例，结果表明所提方法能够有效抑制潮流返送现象，实现分布式光伏的完全就地消纳。

Abstract

Accurate evaluation on the adjustable potential of air conditioner thermostatically controlled load cluster and its utilization can mitigate fluctuations in new energy such as photovoltaics

and it is of great significance in maintaining the safe and stable operation of power grids. Aiming to address the challenges related to model parameter generalization

physical concept weakening and poor robustness of data-driven methods

a probabilistic assessment method is proposed for the adjustable potential of thermostatically controlled load cluster

taking uncertainties into account. Firstly

the thermostatically controlled load cluster modeling based on hybrid model and data is utilized to track the actual load curve of the thermostatically controlled cluster in a day. Then

the robustness of the proposed method is validated based on KL divergence

considering the unmeasured building parameters and the uncertain user consumption behavior. Finally

the probabilistic assessment and interaction mechanism of the response capacity of the air conditioner load cluster are established based on the hybrid-driven model

taking a 10kV feeder line in Shandong Province as a practical instance. The results demonstrate the effectiveness of the proposed method in mitigating the power flow backflow and realizing the complete local consumption of distributed PV.

关键词

Keywords

references

ZHUO Zhenyu, ZHANG Ning, XIE Xiaorong, et al. Key technologies and developing challenges of power system with high proportion of renewable energy[J]. Automation of Electric Power Systems, 2021, 45(09): 171−191(in Chinese).

卓振宇, 张宁, 谢小荣, 等. 高比例可再生能源电力系统关键技术及发展挑战[J]. 电力系统自动化, 2021, 45(09): 171−191.

LI Mingjie, CHEN Guoping, DONG Cun, et al. Research on power balance of high proportion renewable energy system[J]. Power System Technology, 2019, 43(11): 3979−3986(in Chinese).

李明节, 陈国平, 董存, 等. 新能源电力系统电力电量平衡问题研究[J]. 电网技术, 2019, 43(11): 3979−3986.

HE Jinsong, YE Peng, ZHANG Tao, et al. Research overview of air conditioning load for power system application[J]. Journal of Shenyang Institute of Engineering(Natural Science), 2019, 15(04): 343−349(in Chinese).

何金松, 叶鹏, 张涛, 等. 面向电力系统应用的空调负荷研究综述[J]. 沈阳工程学院学报(自然科学版), 2019, 15(04): 343−349.

SHI Qingxin, CHEN C, ANDREA M, et al. Estimating the profile of incentive-based demand response (IBDR) by integrating technical models and social-behavioral factors[J]. IEEE Transactions on Smart Grid, 2020, 11(1): 171−183.

HASSAN F. The path of the smart grid[J]. IEEE Power and Energy Magazine, 2010, 8(1): 18−28.

ZIADI Z, TAIRA S, OSHIRO, et al. Optimal power scheduling for smart grids considering controllable loads and high penetration of photovoltaic generation[J]. IEEE Transactions on Smart Grid, 2014, 5(5): 2350−2359.

HUI H X, DING Y, SHI O X, et al. 5G network-based internet of things for demand response in smart grid: A survey on application potential[J]. Applied Energy, 2020, 257: 113972−113986.

XIE K, HUI H X, DING Y. Review of modeling and control strategy of thermostatically controlled loads for virtual energy storage system[J]. Protection and Control of Modern Power Systems, 2019, 4(1): 1−13.

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(03): 105−113(in Chinese).

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(03): 105−113.

YAO Yao, ZHANG Peichao, WANG Yongquan, et al. A two-layer control method for thermostatically controlled loads to provide fast frequency regulation[J]. Proceedings of the CSEE, 2018, 38(17): 4987−4998+5296(in Chinese).

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+5296.

CHENG Limin, BAO Yuqing. A day-ahead scheduling of large-scale thermostatically controlled loads model considering second-order equivalent thermal parameters model[J]. IEEE Access, 2020, 8(8): 102321−102334.

YANG Xiu, FU Guangnu, LIU Fang, et al. Potential evaluation and control strategy of air conditioning load aggregation response considering multiple factors[J]. Power System Technology, 2022, 46(02): 699−714(in Chinese).

杨秀, 傅广努, 刘方, 等. 考虑多重因素的空调负荷聚合响应潜力评估及控制策略研究[J]. 电网技术, 2022, 46(02): 699−714.

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+5829(in Chinese).

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+5829.

WANG Hongtao, ZHANG Liwei, MU Gang, et al. A parameter optimization method and evaluation of aggregation ability of thermostatically-controlled load model[J]. Journal of Harbin University of Science and Technology, 2020, 25(05): 23−31(in Chinese).

王宏涛, 张利伟, 穆钢. 一种温控负荷模型参数优化方法及聚合能力评估[J]. 哈尔滨理工大学学报, 2020, 25(05): 23−31.

Al Xin, ZHAO Yuequn, ZHOU Shupeng, et al. Study on virtual energy storage features of air conditioning load direct load control[J]. Proceedings of the CSEE, 2016, 36(06): 1596−1603(in Chinese).

艾欣, 赵阅群, 周树鹏. 空调负荷直接负荷控制虚拟储能特性研究[J]. 中国电机工程学报, 2016, 36(06): 1596−1603.

HAO He, WU Di, LIAN Jianming, et al. Optimal coordination of building loads and energy storage for power grid and end user services[J]. IEEE Transactions on Smart Grid, 2018, 9(5): 4335−4345.

CHEN Can, DU Weizhu, BAI Kai, et al. Potential assessment and interaction framework of air conditioning thermostatically controlled load cluster participating in photovoltaic consumption[J]. Modern Electric Power, 2024, 41(3): 479-489(in Chinese).

陈璨, 杜维柱, 白恺, 等. 空调温控负荷集群参与光伏消纳的潜力评估与互动框架[J]. 现代电力, 2024, 41(3): 479-489.

ABIRI-JAHROMI, AMIR, BOUFFARD, et al. Contingency-type reserve leveraged through aggregated thermostatically-controlled loads-part I: characterization and control[J]. IEEE Transactions on Power Systems, 2016, 31(3): 1972−1980.

WALTER B, ANATOLY Z, LI J. A phase model approach for thermostatically controlled load demand response[J]. Applied Energy, 2018, 228: 667−680.

MACKAY D. Information theory, inference, and learning algorithms[M].England: Cambridge University Press, 2003.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Potential Assessment and Interaction Framework of Air Conditioning Thermostatically Controlled Load Cluster Participating in Photovoltaic Consumption

Load Control Scheme of Inverter Air Conditioner Under The Influence of Multiple Response Potential Factors

Probabilistic Assessment on the Output Power of Wind Turbine

Related Author

Can CHEN

Weizhu DU

Kai BAI

Beibei SUN

Liang SUN

Xinyuan FU

Junyong WU

Jun HE

Related Institution

State Grid Jibei Electric Power Co., Ltd. Research Institute, Xicheng District

State Grid Jibei Electric Power Company Limited, Xicheng District

School of Electrical Engineering, Beijing Jiaotong University, Haidian District

College of Electric Power Engineering, Shanghai University of Electric Power, Yangpu District

North China Electric Power University

AI问答

⁰