HVAC Demand Response in Commercial Buildings: A Review

NAVIGATE

Table of Contents

STATISTICS

Viewed2308

Downloads1103

HVAC Demand Response in Commercial Buildings: A Review

[HTML] PDF下载 (1103)

[1]Meng Qinglong,Wang Wenqiang,Li Weilin,et al.HVAC Demand Response in Commercial Buildings: A Review[J].Journal of Zhengzhou University (Engineering Science),2021,42(05):92-99.[doi:10.13705/j.issn.1671-6833.2021.05.012]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 42卷 Number of periods: 2021 05 Page number: 92-99 Column: Public date: 2021-09-10

Title:: HVAC Demand Response in Commercial Buildings: A Review

Author(s):: Meng Qinglong; Wang Wenqiang; Li Weilin; Xiong Chengyan; Li Yang; Responsibility;; School of Construction Engineering, Chang’an University; China Qiyuan Engineering Design and Research Institute Co., Ltd.; School of Civil Engineering, Zhengzhou University;

Keywords:: HVAC; demand response; building to grid; potential prediction; response strategy

CLC:: -

DOI:: 10.13705/j.issn.1671-6833.2021.05.012

Abstract:: Aiming at the problems of air conditioning system participating in power grid demand response, the demand response (DR) of multi energy interaction in building power grid is comprehensively studied and analyzed from the perspective of HVAC system characteristics. The definition and classification of HVAC demand response are summarized, and the methods of using model predictive control (MPC) algorithm, genetic algorithm (GA) and other algorithms to predict the potential of HVAC demand response are discussed. The principles and applicability of DR strategies such as resetting regional temperature, increasing air supply temperature, resetting chilled water temperature and so on are summarized and analyzed. The analysis shows that for DR projects where the user′s thermal comfort is improved after the implementation of DR， this strategy can be considered to reduce energy consumption during daily system operation, and the combination of active energy storage strategy and conventional DR strategy can effectively solve the load rebound problem of DR events. Therefore auxiliary services should be considered for those users with large adjustable air-conditioning load.

References:: [1] 中国电力企业联合会. 2018—2019年度全国电力供需形势分析预测报告[R].北京：中国电力企业联合会, 2019.
[2] U.S. Department of Energy. Benefits of demand response in electricity markets and recommendations for achieving them[R]. Washington D C: U.S. Department of Energy, 2006.
[3] 章健, 张玉晓, 熊壮壮, 等. 计及DR的新能源配电网电压无功协调优化[J]. 郑州大学学报(工学版), 2020, 41(2): 61-66.
[4] 王蓓蓓, 朱峰, 嵇文路, 等. 中央空调降负荷潜力建模及影响因素分析[J].电力系统自动化,2016,40(19):44-52.
[5] SHAN K, WANG S W, YAN C C, et al. Building demand response and control methods for smart grids: a review[J]. Science and technology for the built environment, 2016, 22(6): 692-704.
[6] HAN J Q, PIETTE M A. Solutions for summer electric power shortages: demand response and its applications in air conditioning and refrigerating systems[J]. Refrigeration air conditioning & electric power machi-nery, 2008, 29(1): 1-4.
[7] MA K, YUAN C L, YANG J, et al. Switched control strategies of aggregated commercial HVAC systems for demand response in smart grids[J]. Energies, 2017, 10(7): 953-959.
[8] 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.
[9] TANG R, WANG S W. Model predictive control for thermal energy storage and thermal comfort optimization of building demand response in smart grids[J]. Applied energy, 2019, 242: 873-882.
[10] BIANCHINI G, CASINI M, PEPE D, et al. An integrated model predictive control approach for optimal HVAC and energy storage operation in large-scale buildings[J]. Applied energy, 2019, 240: 327-340.
[11] SHAN K, WANG S W, TANG R. Direct chiller power limiting for peak demand limiting control in buildings: methodology and on-site validation[J]. Automation in construction, 2018, 85: 333-343.
[12] GAO D C, SUN Y J. A GA-based coordinated demand response control for building group level peak demand limiting with benefits to grid power balance[J]. Energy and buildings, 2016, 110: 31-40.
[13] YIN R X, KARA E C, LI Y P, et al. Quantifying flexibility of commercial and residential loads for demand response using setpoint changes[J]. Applied energy, 2016, 177: 149-164.
[14] NARAMURA T, MORIKAWA J, NINAGAWA C. Prediction model on room temperature side effect due to FastADR aggregation for a cluster of building air-conditioning facilities[J]. Electrical engineering in Japan, 2017, 199(3): 17-25.
[15] POMBEIRO H, MACHADO M J, SILVA C. Dynamic programming and genetic algorithms to control a HVAC system: maximizing thermal comfort and minimizing cost with PV production and storage[J]. Sustainable cities and society, 2017, 34: 228-238.
[16] JINDAL A, KUMAR N, RODRIGUES J J P C. A heuristic-based smart HVAC energy management scheme for university buildings [J]. IEEE transactions on industrial informatics, 2018, 14(11): 5074-5086.
[17] WANG Z Y, WANG Y R, ZENG R C, et al. Random forest based hourly building energy prediction[J]. Energy and buildings, 2018, 171: 11-25.
[18] SMARRA F, JAIN A, DE RUBEIS T, et al. Data-driven model predictive control using random forests for building energy optimization and climate control[J]. Applied energy, 2018, 226: 1252-1272.
[19] WANG Z, PARKINSON T, LI P X, et al. The squeaky wheel: machine learning for anomaly detection in subjective thermal comfort votes[J]. Building and environment, 2019, 151: 219-227
[20] VAZQUEZ-CANTELI J R, NAGY Z. Reinforcement learning for demand response: a review of algorithms and modeling techniques[J]. Applied energy, 2019, 235: 1072-1089.
[21] LU R Z, HONG S H. Incentive-based demand response for smart grid with reinforcement learning and deep neural network[J]. Applied energy, 2019, 236: 937-949.
[22] 戚野白, 王丹, 贾宏杰, 等. 基于局部终端温度调节的中央空调需求响应控制策略[J]. 电力系统自动化, 2015, 39(17): 82-88.
[23] LI W L, CHU Y Y, XU P, et al. A transient model for the thermal inertia of chilled-water systems during demand response[J]. Energy and buildings, 2017, 150: 383-395.
[24] CUI B R, GAO D C, XIAO F, et al. Model-based optimal design of active cool thermal energy storage for maximal life-cycle cost saving from demand management in commercial buildings[J]. Applied energy, 2017, 201: 382-396.
[25] JONES C B, CARTER C. Trusted interconnections between a centralized controller and commercial building HVAC systems for reliable demand response[J]. IEEE access, 2017, 5: 11063-11073.
[26] VERBEKE S, AUDENAERT A. Thermal inertia in buildings: a review of impacts across climate and building use[J]. Renewable and sustainable energy reviews, 2018, 82: 2300-2318.
[27] ZHU N, WANG S W, XU X H, et al. A simplified dynamic model of building structures integrated with shaped-stabilized phase change materials[J]. International journal of thermal sciences, 2010, 49(9): 1722-1731.
[28] SHAN K, WANG J Y, HU M, et al. A model-based control strategy to recover cooling energy from thermal mass in commercial buildings[J]. Energy, 2019, 172: 958-967.
[29] MOTEGI N,PIETTE M A,WATSON D S, et al. Introduction to commercial building control strategies and techniques for demand response-appendices[R].Oak Ridge, TN:Office of Scientific and Technical Information (OSTI),2007.
[30] AGHNIAEY S, LAWRENCE T M. The impact of increased cooling setpoint temperature during demand response events on occupant thermal comfort in commercial buildings: a review[J]. Energy and buildings, 2018, 173: 19-27.

Similar References:

Memo

Last Update: 2021-10-11