Clustering-Based Penalty Signal Design for Flexibility Utilization

被引:10
|
作者
Rosin, Argo [1 ,2 ]
Ahmadiahangar, Roya [1 ,2 ]
Azizi, Elnaz [1 ,3 ]
Sahoo, Subham [4 ]
Vinnikov, Dmitri [1 ,2 ]
Blaabjerg, Frede [4 ]
Dragicevic, Tomislav [5 ]
Bolouki, Sadegh [3 ]
机构
[1] Tallinn Univ Technol, Dept Elect Power Engn & Mechatron, EE-12616 Tallinn, Estonia
[2] Tallinn Univ Technol, Smart City Ctr Excellence Finest Twins, EE-12616 Tallinn, Estonia
[3] Tarbiat Modares Univ, Dept Elect & Comp Engn, Tehran 1411713116, Iran
[4] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark
[5] Tech Univ Denmark, Dept Elect Engn, DK-2800 Lyngby, Denmark
基金
欧盟地平线“2020”;
关键词
Simulation; Microgrids; Time measurement; Batteries; Complexity theory; State of charge; Signal design; Demand-side flexibility; microgrid clusters; individual penalty signal; clustering; ENERGY MANAGEMENT; COSIMULATION; SYSTEMS; STORAGE;
D O I
10.1109/ACCESS.2020.3038822
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
As the penetration level of renewable energy sources (RES) increases, the associated technical challenges in the power systems rise. Enhancing the utilization of energy flexibility is known to be the main key to overcome the load-supply balance challenge caused by RES. In this regard, the trend is toward the utilization of demand-side flexibility. Meanwhile, individual penalty signals positively affect the utilization of available flexibility from the demand-side. Previous studies in this field are based on designing penalty signals according to electricity price and regardless of the demand situation. However, designing and implementing a proper penalty signal with finite amplitude requires analyzing large datasets of load, storage and generation. Therefore, to fill this gap in designing a proper penalty signal we have proposed a novel approach in which, clustering is used to overcome the complexity of analyzing large datasets. The main goal of the proposed method is to utilize energy flexibility from responsive batteries according to a request from the aggregator without violating the consumers' privacy and comfort level. Therefore, aggregator's attainable load and generation datasets are used in the case studies to maintain the practicality of the proposed method. Simulation results show the proposed penalty signal designing method effectively increases the available flexibility of microgrids.
引用
收藏
页码:208850 / 208860
页数:11
相关论文
共 50 条
  • [21] Clustering-Based Predictive Process Monitoring
    Di Francescomarino, Chiara
    Dumas, Marlon
    Maggi, Fabrizio Maria
    Teinemaa, Irene
    IEEE TRANSACTIONS ON SERVICES COMPUTING, 2019, 12 (06) : 896 - 909
  • [22] A clustering-based system to automate transfer function design for medical image visualization
    Binh P. Nguyen
    Wei-Liang Tay
    Chee-Kong Chui
    Sim-Heng Ong
    The Visual Computer, 2012, 28 : 181 - 191
  • [23] Metric learning with clustering-based constraints
    Xinyao Guo
    Chuangyin Dang
    Jianqing Liang
    Wei Wei
    Jiye Liang
    International Journal of Machine Learning and Cybernetics, 2021, 12 : 3597 - 3605
  • [24] Clustering-based preconditioning for stochastic programs
    Yankai Cao
    Carl D. Laird
    Victor M. Zavala
    Computational Optimization and Applications, 2016, 64 : 379 - 406
  • [25] Novel clustering-based pruning algorithms
    Paweł Zyblewski
    Michał Woźniak
    Pattern Analysis and Applications, 2020, 23 : 1049 - 1058
  • [26] A clustering-based system to automate transfer function design for medical image visualization
    Nguyen, Binh P.
    Tay, Wei-Liang
    Chui, Chee-Kong
    Ong, Sim-Heng
    VISUAL COMPUTER, 2012, 28 (02) : 181 - 191
  • [27] ICN clustering-based approach for VANETs
    Lamia Chaari Fourati
    Samiha Ayed
    Mohamed Ali Ben Rejeb
    Annals of Telecommunications, 2021, 76 : 745 - 757
  • [28] Clustering-based diversity improvement in top-N recommendation
    Aytekin, Tevfik
    Karakaya, Mahmut Ozge
    JOURNAL OF INTELLIGENT INFORMATION SYSTEMS, 2014, 42 (01) : 1 - 18
  • [29] A Distributed and Clustering-Based Algorithm for the Enumeration Problem in Abstract Argumentation
    Doutre, Sylvie
    Lafages, Mickael
    Lagasquie-Schiex, Marie-Christine
    PRINCIPLES AND PRACTICE OF MULTI-AGENT SYSTEMS (PRIMA 2019), 2019, 11873 : 87 - 105
  • [30] Convolutional neural network and clustering-based codebook design method for massive MIMO systems
    Jing Xing
    Die Hu
    EURASIP Journal on Advances in Signal Processing, 2022