Modeling Energy Demand Aggregators for Residential Consumers

被引:0
|
作者
Di Bella, G. [3 ]
Giarre, L. [3 ]
Ippolito, M. [3 ]
Jean-Marie, A. [1 ,2 ]
Neglia, G. [1 ]
Tinnirello, I. [3 ]
机构
[1] Inria, Maestro Project, Sophia Antipolis, France
[2] Univ Montpellier 2, CNRS, LIRMM, F-34095 Montpellier 5, France
[3] Univ Palermo, Dipartimento Energia Ingn Informaz & Modelli Mate, Palermo, Italy
关键词
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Energy demand aggregators are new actors in the energy scenario: they gather a group of energy consumers and implement a demand-response paradigm. When the energy provider needs to reduce the current energy demand on the grid, it can pay the energy demand aggregator to reduce the load by turning off some of its consumers loads or postponing their activation. Currently this operation involves only greedy energy consumers like industrial plants. In this paper we want to study the potential of aggregating a large number of small energy consumers like home users as it may happen in smart grids. In particular we want to address the feasibility of such approach by considering which scale the aggregator should reach in order to be able to control a significant power load. The challenge of our study derives from residential users' demand being much less predictable than that of industrial plants. For this reason we resort to queuing theory to study analytically the problem and quantify the trade-off between load control and tolerable service delays.
引用
收藏
页码:6286 / 6291
页数:6
相关论文
共 50 条
  • [1] Optimal energy scheduling for residential consumers with demand management strategies
    Dou, Xiaoning
    Bai, Yingmei
    Li, Yan
    Zhu, Lei
    [J]. SMART SCIENCE, 2024, 12 (03) : 474 - 483
  • [2] Demand response for aggregated residential consumers with energy storage sharing
    Paridari, Kaveh
    Parisio, Alessandra
    Sandberg, Henrik
    Johansson, Karl Henrik
    [J]. 2015 54TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2015, : 2024 - 2030
  • [3] Joint game-theoretical optimization for load aggregators in demand response market considering the breach of residential consumers
    Yang, Chen
    Mo, Wen
    Liu, Xiaofeng
    Dong, Xiaofeng
    Wang, Qing
    [J]. FRONTIERS IN ENERGY RESEARCH, 2023, 11
  • [4] Demand Side Management for Residential Consumers
    Kinhekar, Nandkishor
    Padhy, Narayana Prasad
    Gupta, Hari Om
    [J]. 2013 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PES), 2013,
  • [5] Automated Demand Response for Residential Consumers
    Bhosale, Vaibhav
    Hadawale, Prasad
    Borole, Akash
    Kinhekar, Nandkishor
    [J]. 2016 NATIONAL POWER SYSTEMS CONFERENCE (NPSC), 2016,
  • [6] Activity Based Energy Demand Modeling for Residential Buildings
    Subbiah, Rajesh
    Lum, Kristian
    Marathe, Achla
    Marathe, Madhav
    [J]. 2013 IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES (ISGT), 2013,
  • [7] DEMAND RESPONSE AGGREGATORS IN MICROGRID ENERGY TRADING
    Gregori, Maria
    Matamoros, Javier
    Gregoratti, David
    [J]. 2016 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP), 2016, : 921 - 925
  • [8] Score-based incentive demand response for load aggregators considering power-score redeem behavior of residential consumers
    Wang, Yunchu
    Yan, Yong
    Lin, Zhenzhi
    Zhang, Zhi
    Ma, Yuanqian
    Yang, Li
    Chen, Xingying
    Yu, Kun
    [J]. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2024, 162
  • [9] Demand Response Program Integrated With Electrical Energy Storage Systems for Residential Consumers
    Tehrani, Motahar
    Nazar, Mehrdad Setayesh
    Shafie-khah, Miadreza
    Catalao, Joao P. S.
    [J]. IEEE SYSTEMS JOURNAL, 2022, 16 (03): : 4313 - 4324
  • [10] Real Time Demand Response Modeling for Residential Consumers in Smart Grid Considering Renewable Energy With Deep Learning Approach
    Reka, S. Sofana
    Venugopal, Prakash
    Alhelou, Hassan Haes
    Siano, Pierluigi
    Golshan, Mohamad Esmail Hamedani
    [J]. IEEE ACCESS, 2021, 9 : 56551 - 56562