Discussing Different Clustering Methods for the Aggregation of Demand Response and Distributed Generation

被引:0
|
作者
Silva, Catia [1 ]
Faria, Pedro [1 ]
Vale, Zita [1 ]
机构
[1] Polytech Porto, GECAD Res Grp Intelligent Engn & Comp Advancer In, Porto, Portugal
关键词
Aggregation; Demand Response; Distributed Generation; Clustering Methods;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the introduction of the Smart Grid context in the current network, it will be necessary to improve business models to include the use of distributed generation and demand response programs regarding the remuneration of participants as a form of incentive. Throughout this article a methodology is presented which will aggregate generation units and consumers participating in DR programs. A comparison of clustering methods will be carried out in order to understand which one of them will be the most appropriate for the scenario studied. After grouping all the resources, the remuneration of the groups are made considering the maximum rate in each group. The hierarchical clustering proved to be the most appropriate because it grouped the resources so that the total cost for the aggregator was the minimum.
引用
收藏
页码:1645 / 1650
页数:6
相关论文
共 50 条
  • [11] Demand Response Strategy for the Smart Home with the Distributed Photovoltaic Generation
    Miao Guangyao
    Tian Jingchao
    Cao Lingguo
    Zhang Yuyong
    [J]. 2021 5TH INTERNATIONAL CONFERENCE ON GREEN ENERGY AND APPLICATIONS (ICGEA 2021), 2021, : 141 - 146
  • [12] Integration of Distributed Generation in Demand Response Programs: Study Case
    Arias, Luis A.
    Rivas, Edwin
    Santamaria, Francisco
    Quevedo, Andres D.
    [J]. APPLIED COMPUTER SCIENCES IN ENGINEERING, WEA 2018, PT I, 2018, 915 : 561 - 572
  • [13] Distributed Generation and Demand Response Effects on the Distribution Network Planning
    Rey, Marcelo
    Montes de Oca, Sebastian
    Giusto, Alvaro
    Vignolo, Mario
    [J]. PROCEEDINGS OF THE 2018 IEEE PES TRANSMISSION & DISTRIBUTION CONFERENCE AND EXHIBITION - LATIN AMERICA (T&D-LA), 2018,
  • [14] Reconfiguration of Distributed Generation Scheduling to Increase Demand Response Integration
    Spinola, Joao
    Faria, Pedro
    Vale, Zita
    [J]. 2016 13TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET (EEM), 2016,
  • [15] Integrated Planning of Distribution Systems with Distributed Generation and Demand Side Response
    Chen, Haoyong
    Wang, Zengyu
    Yan, Haifeng
    Zou, Haobin
    Luo, Bo
    [J]. CLEAN, EFFICIENT AND AFFORDABLE ENERGY FOR A SUSTAINABLE FUTURE, 2015, 75 : 981 - 986
  • [16] Enabling distributed generation and demand response with enterprise energy management systems
    Jonker, R
    Dijak, P
    [J]. SOLUTIONS FOR ENERGY SECURITY AND FACILITY MANAGEMENT CHALLENGES, 2003, : 393 - 400
  • [17] Stochastic optimization for retailers with distributed wind generation considering demand response
    Golmohamadi, Hessam
    Keypour, Reza
    [J]. JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY, 2018, 6 (04) : 733 - 748
  • [18] Mitigation of Wind Power Fluctuations by Intelligent Response of Demand and Distributed Generation
    MacDougall, Pamela
    Warmer, Cor
    Kok, Koen
    [J]. 2011 2ND IEEE PES INTERNATIONAL CONFERENCE AND EXHIBITION ON INNOVATIVE SMART GRID TECHNOLOGIES (ISGT EUROPE), 2011,
  • [19] Impact of Demand Response on Optimal Sizing of Distributed Generation and Customer Tariff
    Pothireddy, Krishna Mohan Reddy
    Vuddanti, Sandeep
    Salkuti, Surender Reddy
    [J]. ENERGIES, 2022, 15 (01)
  • [20] Optimal Allocation Stochastic Model of Distributed Generation Considering Demand Response
    He, Shuaijia
    Liu, Junyong
    [J]. ENERGIES, 2024, 17 (04)