

浏览全部资源
扫码关注微信
1.东南大学电气工程学院,江苏省南京市 210096
2.国家电网有限公司,北京市 100031
3.国网江苏省电力有限公司,江苏省南京市 210000
Received:20 July 2023,
Revised:2023-09-13,
Published:10 January 2024
移动端阅览
金旭,张远实,李明等.考虑热舒适度的居民空调负荷调控潜力差异化评估[J].电力系统自动化,2024,48(1):50-58.
JIN Xu,ZHANG Yuanshi,LI Ming,et al.Differentiation Evaluation of Regulation Potential for Residential Air Conditioning Load Considering Thermal Comfort[J].Automation of Electric Power Systems,2024,48(1):50-58.
金旭,张远实,李明等.考虑热舒适度的居民空调负荷调控潜力差异化评估[J].电力系统自动化,2024,48(1):50-58. DOI: 10.7500/AEPS20230720014.
JIN Xu,ZHANG Yuanshi,LI Ming,et al.Differentiation Evaluation of Regulation Potential for Residential Air Conditioning Load Considering Thermal Comfort[J].Automation of Electric Power Systems,2024,48(1):50-58. DOI: 10.7500/AEPS20230720014.
居民空调负荷是电力系统重要的灵活性资源,评估其调控潜力有助于负荷高效管理,促进节能减排。然而,受资金成本、数据存储和隐私保护等因素制约,居民空调状态及参数难以直接测量,差异化评估不同居民空调负荷的调控潜力存在困难。此外,居民使用空调是为了满足自身的热舒适度要求。若评估空调负荷调控潜力时忽视热舒适度限制,将导致评估值过高,降低负荷管理策略的有效性。因此,提出一种考虑热舒适度的居民空调负荷调控潜力差异化评估方法。该方法首先建立居民热舒适度及空调热动力学模型,提取影响居民热舒适度和空调负荷的关键参数;然后,利用隐马尔可夫模型和维特比算法辨识空调状态及参数;在此基础上,计算不同居民热舒适度可接受范围内的空调负荷调控潜力。基于真实居民负荷数据的算例分析表明,所提方法能够准确辨识居民空调状态并有效评估满足居民热舒适度要求的空调负荷调控潜力。
Residential air conditioning load serves as a crucial flexibility resource in the power system
and the evaluation of its regulation potential contributes to efficient load management and promotes energy conservation and emission reduction. However
due to the limits such as financial cost
data storage
and privacy protection
direct measurement of the residential air conditioning states and parameters is difficult
thereby hindering the differentiation evaluation of the air conditioning load regulation potential of different residents. Furthermore
residents use air conditioning to meet their thermal comfort requirements. Neglecting the constraints of thermal comfort when evaluating the air conditioning load regulation potential may result in overestimating evaluation values
thereby diminishing the effectiveness of load management strategies. Hence
this paper proposes a differentiation evaluation method of regulation potential for residential air conditioning load considering the thermal comfort. The method establishes the models for residential thermal comfort and air conditioning thermodynamics
and extracts key parameters that influence the residential thermal comfort and air conditioning load. Subsequently
a hidden Markov model and the Viterbi algorithm are employed to identify the air conditioning states and parameters. On this basis
the air conditioning load regulation potentials within the acceptable ranges of different resident thermal comfort levels are calculated. The case analysis based on real residential load data demonstrates that the proposed method accurately identifies the residential air conditioning states
and effectively evaluates the air conditioning load regulation potential that meets the thermal comfort requirements of residents.
国家发展改革委 , 国家能源局 . 关于印发《“十四五”现代能源体系规划》的通知 [EB/OL].[ 2022-03-22 ]. https://www.gov.cn/zhengce/ https://www.gov.cn/zhengce/ .
National Development and Reform Commission , National Energy Administration . Notice on issuing the “14th Five-Year Plan” for the modern energy system [EB/OL]. [ 2022-03-22 ]. https://www.gov.cn/zhengce/ https://www.gov.cn/zhengce/ .
舒印彪 , 赵勇 , 赵良 , 等 . “双碳”目标下我国能源电力低碳转型路径 [J]. 中国电机工程学报 , 2023 , 43 ( 5 ): 1663 - 1672 .
SHU Yinbiao , ZHAO Yong , ZHAO Liang , et al . Study on low carbon energy transition path toward carbon peak and carbon neutrality [J]. Proceedings of the CSEE , 2023 , 43 ( 5 ): 1663 - 1672 .
胡真 , 汪盼 . 计及可再生能源不确定性的实时需求响应策略研究 [J]. 湖南电力 , 2023 , 43 ( 1 ): 30 - 34 .
HU Zhen , WANG Pan . Research on real-time demand response strategy considering the uncertainty of renewable energy [J]. Hunan Electric Power , 2023 , 43 ( 1 ): 30 - 34 .
孟鑫 , 陈茂林 , 王雄正 , 等 . 可再生能源大规模制氢系统最优效率控制方法 [J]. 湖南电力 , 2023 , 43 ( 3 ): 2 - 8 .
MENG Xin , CHEN Maolin , WANG Xiongzheng , et al . Optimal efficiency control method for large-scale hydrogen production system from renewable energy [J]. Hunan Electric Power , 2023 , 43 ( 3 ): 2 - 8 .
武昭原 , 周明 , 王剑晓 , 等 . 双碳目标下提升电力系统灵活性的市场机制综述 [J]. 中国电机工程学报 , 2022 , 42 ( 21 ): 7746 - 7764 .
WU Zhaoyuan , ZHOU Ming , WANG Jianxiao , et al . Review on market mechanism to enhance the flexibility of power system under the dual-carbon target [J]. Proceedings of the CSEE , 2022 , 42 ( 21 ): 7746 - 7764 .
王彩霞 , 时智勇 , 梁志峰 , 等 . 新能源为主体电力系统的需求侧资源利用关键技术及展望 [J]. 电力系统自动化 , 2021 , 45 ( 16 ): 37 - 48 .
WANG Caixia , SHI Zhiyong , LIANG Zhifeng , et al . Key technologies and prospects of demand-side resource utilization for power systems dominated by renewable energy [J]. Automation of Electric Power Systems , 2021 , 45 ( 16 ): 37 - 48 .
HAN R S , HU Q R , CUI H T , et al . An optimal bidding and scheduling method for load service entities considering demand response uncertainty [J]. Applied Energy , 2022 , 328 : 120167 .
ZHANG Y S , QIAN W Y , YE Y J , et al . A novel non-intrusive load monitoring method based on ResNet-seq2seq networks for energy disaggregation of distributed energy resources integrated with residential houses [J]. Applied Energy , 2023 , 349 : 121703 .
HU Q R , LI F X , FANG X , et al . A framework of residential demand aggregation with financial incentives [J]. IEEE Transactions on Smart Grid , 2018 , 9 ( 1 ): 497 - 505 .
陈璐 , 杨永标 , 徐青山 . 基于时变互补特性的聚合空调调控及恢复策略 [J]. 电力系统自动化 , 2020 , 44 ( 13 ): 39 - 47 .
CHEN Lu , YANG Yongbiao , XU Qingshan . Coordinated control and recovery strategy for aggregated air-conditioner based on time-variant complementary characteristics [J]. Automation of Electric Power Systems , 2020 , 44 ( 13 ): 39 - 47 .
何银国 , 廖菁 , 周红 , 等 . 长沙电网空调负荷与气温相关性分析研究 [J]. 湖南电力 , 2018 , 38 ( 6 ): 28 - 31 .
HE Yinguo , LIAO Jing , ZHOU Hong , et al . Correlation analysis between air conditioning load and temperature in Changsha power grid [J]. Hunan Electric Power , 2018 , 38 ( 6 ): 28 - 31 .
JIA Q G , CHEN S J , YAN Z , et al . Optimal incentive strategy in cloud-edge integrated demand response framework for residential air conditioning loads [J]. IEEE Transactions on Cloud Computing , 2022 , 10 ( 1 ): 31 - 42 .
宋梦 , 高赐威 , 苏卫华 . 面向需求响应应用的空调负荷建模及控制 [J]. 电力系统自动化 , 2016 , 40 ( 14 ): 158 - 167 .
SONG Meng , GAO Ciwei , SU Weihua . Modeling and controlling of air-conditioning load for demand response applications [J]. Automation of Electric Power Systems , 2016 , 40 ( 14 ): 158 - 167 .
WANG J , CHEN X , XIE J , et al . Control strategies of large-scale residential air conditioning loads participating in demand response programs [J]. CSEE Journal of Power and Energy Systems , 2020 , 8 ( 3 ): 880 - 893 .
KWAC J , RAJAGOPAL R . Data-driven targeting of customers for demand response [J]. IEEE Transactions on Smart Grid , 2016 , 7 ( 5 ): 2199 - 2207 .
AFZALAN M , JAZIZADEH F . Residential loads flexibility potential for demand response using energy consumption patterns and user segments [J]. Applied Energy , 2019 , 254 : 113693 .
KOU X , DU Y , LI F X , et al . Model-based and data-driven HVAC control strategies for residential demand response [J]. IEEE Open Access Journal of Power and Energy , 2021 , 8 : 186 - 197 .
LU N . An evaluation of the HVAC load potential for providing load balancing service [J]. IEEE Transactions on Smart Grid , 2012 , 3 ( 3 ): 1263 - 1270 .
CHEN X , WANG J , XIE J , et al . Demand response potential evaluation for residential air conditioning loads [J]. IET Generation , Transmission & Distribution, 2018 , 12 ( 19 ): 4260 - 4268 .
COFFMAN A R , CAMMARDELLA N , BAROOAH P , et al . Aggregate flexibility capacity of TCLs with cycling constraints [J]. IEEE Transactions on Power Systems , 2023 , 38 ( 1 ): 52 - 62 .
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 .
王怡岚 , 童亦斌 , 黄梅 , 等 . 基于需求侧响应的空调负荷虚拟储能模型研究 [J]. 电网技术 , 2017 , 41 ( 2 ): 394 - 401 .
WANG Yilan , TONG Yibin , HUANG Mei , et al . Research on virtual energy storage model of air conditioning loads based on demand response [J]. Power System Technology , 2017 , 41 ( 2 ): 394 - 401 .
包宇庆 , 成丽珉 . 空调负荷二阶等效热参数模型参数辨识方法 [J]. 电力系统自动化 , 2021 , 45 ( 11 ): 37 - 43 .
BAO Yuqing , CHENG Limin . Parameter identification method of second-order equivalent thermal parameter model for air conditioning loads [J]. Automation of Electric Power Systems , 2021 , 45 ( 11 ): 37 - 43 .
吴承鑫 , 沈海军 , 王治华 , 等 . 数据驱动的变频空调负荷模型参数在线辨识方法 [J]. 电力系统自动化 , 2022 , 46 ( 1 ): 120 - 129 .
WU Chengxin , SHEN Haijun , WANG Zhihua , et al . Data-driven online identification method for parameters of inverter air-conditioning load model [J]. Automation of Electric Power Systems , 2022 , 46 ( 1 ): 120 - 129 .
PILLITTERI V , BREWER T . Guidelines for smart grid cybersecurity [R]. Gaithersburg, USA : National Institute of Standards and Technology , 2014 .
ALBERT A , RAJAGOPAL R . Thermal profiling of residential energy use [J]. IEEE Transactions on Power Systems , 2015 , 30 ( 2 ): 602 - 611 .
DYSON M E H , BORGESON S D , TABONE M D , et al . Using smart meter data to estimate demand response potential, with application to solar energy integration [J]. Energy Policy , 2014 , 73 : 607 - 619 .
QI N , CHENG L , XU H L , et al . Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads [J]. Applied Energy , 2020 , 279 : 115708 .
LIANG H S , MA J . Separation of residential space cooling usage from smart meter data [J]. IEEE Transactions on Smart Grid , 2020 , 11 ( 4 ): 3107 - 3118 .
Ergonomics of the thermal environment Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria : ISO 7730 [S]. 2005 .
HAN R S , HU Q R , GUO Z S , et al . Optimal allocation method of residential air-conditioners: trade-off solutions between economic costs and aggregation reliability [J]. IEEE Open Access Journal of Power and Energy , 2022 , 9 : 131 - 142 .
MOR B , GARHWAL S , KUMAR A . A systematic review of hidden Markov models and their applications [J]. Archives of Computational Methods in Engineering , 2021 , 28 ( 3 ): 1429 - 1448 .
BISHOP M C . Pattern recognition and machine learning [M]. New York, USA : Springer Science , 2006 .
Data-driven insights from the nations deepest ever research on customer energy use [EB/OL]. [ 2023-06-25 ]. https://www.pecanstreet.org/ https://www.pecanstreet.org/ .
Global Modeling and Assimilation Office . MERRA-2 tavg 1 _ 2 d_slv_Nx: 2 d, 1 -hourly, time-averaged, single-level, assimilation, diagnosticsVsingle-level 5 .12. 4 [EB/OL]. [ 2023-06-25 ]. https://www.soda-pro.com/web-services/meteo-data/merra https://www.soda-pro.com/web-services/meteo-data/merra .
LIU C , WANG W Y , WANG M , et al . An efficient instance selection algorithm to reconstruct training set for support vector machine [J]. Knowledge-Based Systems , 2017 , 116 : 58 - 73 .
0
Views
0
下载量
4
CSCD
Publicity Resources
Related Articles
Related Author
Related Institution
京公网安备11010802024621