Residential end-uses disaggregation and demand response evaluation using integral transforms

被引:0
|
作者
Antonio GABALDóN [1 ]
Roque MOLINA [1 ]
Alejandro MARíN-PARRA [1 ]
Sergio VALERO-VERDú [2 ]
Carlos áLVAREZ [3 ]
机构
[1] ETS de Ingeniería Industrial, Universidad Politécnica de Cartagena
[2] EPS de Elche, Universidad Miguel Hernández
[3] Institute for Energy Engineering, Universidad Politécnica de Valencia
关键词
Demand response; Hilbert transform; Load monitoring; Instantaneous frequency; Aggregation; Smart meters;
D O I
暂无
中图分类号
TM73 [电力系统的调度、管理、通信];
学科分类号
摘要
Demand response is a basic tool used to develop modern power systems and electricity markets. Residential and commercial segments account for 40%–50% of the overall electricity demand. These segments need to overcome major obstacles before they can be included in a demand response portfolio. The objective of this paper is to tackle some of the technical barriers and explain how the potential of enabling technology(smart meters) can be harnessed, to evaluate the potential of customers for demand response(end-uses and their behaviors) and,moreover, to validate customers’ effective response to market prices or system events by means of non-intrusive methods. A tool based on the Hilbert transform is improved herein to identify and characterize the most suitable loads for the aforesaid purpose, whereby important characteristics such as cycling frequency, power level and pulse width are identified. The proposed methodology allows the filtering of aggregated load according to the amplitudes of elemental loads, independently of the frequency of their behaviors that could be altered by internal or external inputs such as weather or demand response. In this way, the assessment and verification of customer response can be improved by solving the problem of load aggregation with the help of integral transforms.
引用
收藏
页码:91 / 104
页数:14
相关论文
共 50 条
  • [21] Evaluation of Voluntary Residential Demand Response in Smart Grids
    Barijough, Sanam Mirzazad
    Chen, Wei-Peng
    2014 INTERNATIONAL CONFERENCE ON INTELLIGENT GREEN BUILDING AND SMART GRID (IGBSG), 2014,
  • [22] Hydrogen production and End-Uses from combined heat, hydrogen and power system by using local resources
    Hamad, Tarek A.
    Agll, Abdulhakim A.
    Hamad, Yousif M.
    Bapat, Sushrut
    Thomas, Mathew
    Martin, Kevin B.
    Sheffield, John W.
    RENEWABLE ENERGY, 2014, 71 : 381 - 386
  • [23] Effects of changes in residential end-uses and behavior on aggregate carbon intensity: comparison of 10 OECD countries for the period 1970 through 1993
    Greening, LA
    Ting, M
    Krackler, TJ
    ENERGY ECONOMICS, 2001, 23 (02) : 153 - 178
  • [24] Demand, End-Uses, and Conservation of Alpine Medicinal Plant Neopicrorhiza scrophulariiflora (Pennell) D. Y. Hong in Central Himalaya
    Kafle, Gandhiv
    Bhattarai , Indira
    Siwakoti, Mohan
    Shrestha, Arjun Kumar
    EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE, 2018, 2018
  • [25] Demand response potential evaluation for residential air conditioning loads
    Chen, Xingying
    Wang, Jixiang
    Xie, Jun
    Xu, Shuyang
    Yu, Kun
    Gan, Lei
    IET GENERATION TRANSMISSION & DISTRIBUTION, 2018, 12 (19) : 4260 - 4268
  • [26] Evaluation of residential HVAC control strategies for demand response programs
    Katipamula, Srinivas
    Lu, Ning
    ASHRAE TRANSACTIONS 2006, VOL 112, PT 1, 2006, 112 : 535 - +
  • [27] Evaluation of potential for peak demand reduction of residential buildings by household appliances with demand response
    Ono, Tetsushi
    Hagishima, Aya
    Tanimoto, Jun
    ELECTRONICS AND COMMUNICATIONS IN JAPAN, 2022, 105 (04)
  • [28] Evaluation of Potential for Peak Demand Reduction of Residential Buildings by Household Appliances with Demand Response
    Ono T.
    Hagishima A.
    Tanimoto J.
    IEEJ Transactions on Electronics, Information and Systems, 2022, 142 (08) : 909 - 918
  • [29] Emerging investigator series: disaggregating residential sector high-resolution smart water meter data into appliance end-uses with unsupervised machine learning
    Bethke, Gabrielle M.
    Cohen, Abigail R.
    Stillwell, Ashlynn S.
    ENVIRONMENTAL SCIENCE-WATER RESEARCH & TECHNOLOGY, 2021, 7 (03) : 487 - 503
  • [30] Evaluation of integral transforms using special functions with applications to biological tissues
    A. Belafhal
    S. Chib
    F. Khannous
    T. Usman
    Computational and Applied Mathematics, 2021, 40