A fuzzy clustering approach to a demand response model

Abstract This paper proposes a novel demand response model using a fuzzy subtractive cluster approach. The model development provides support to domestic consumer decisions on controllable loads management, considering consumers’ consumption needs and the appropriate load shape or rescheduling in order to achieve possible economic benefits. The model based on fuzzy subtractive clustering method considers clusters of domestic consumption covering an adequate consumption range. Analysis of different scenarios is presented considering available electric power and electric energy prices. Simulation results are presented and conclusions of the proposed demand response model are discussed.

[1]  S. Powers,et al.  Residential demand response reduces air pollutant emissions on peak electricity demand days in New York City , 2013 .

[2]  Candan Gokceoglu,et al.  Application of fuzzy inference system and nonlinear regression models for predicting rock brittleness , 2010, Expert Syst. Appl..

[3]  Farrokh Aminifar,et al.  Load commitment in a smart home , 2012 .

[4]  G.R. Yousefi,et al.  Demand Response model considering EDRP and TOU programs , 2008, 2008 IEEE/PES Transmission and Distribution Conference and Exposition.

[5]  Mahesh Sooriyabandara,et al.  System behaviour modelling for demand response provision in a smart grid , 2013 .

[6]  S. Martin,et al.  Demand-side management in smart grid operation considering electric vehicles load shifting and vehicle-to-grid support , 2015 .

[7]  S. D. Naik,et al.  Effect of line contingency on static voltage stability and maximum loadability in large multi bus power system , 2015 .

[8]  R. Pestana,et al.  Voltage Collapse: Real Time and Preventive Analysis in the Portuguese Transmission System , 2007, 2007 IEEE Lausanne Power Tech.

[9]  Taghi M. Khoshgoftaar,et al.  An application of fuzzy clustering to software quality prediction , 2000, Proceedings 3rd IEEE Symposium on Application-Specific Systems and Software Engineering Technology.

[10]  Na Li,et al.  Optimal demand response: Problem formulation and deterministic case , 2012 .

[11]  S. Kennedy,et al.  Optimization of Time-Based Rates in forward energy markets , 2010, 2010 7th International Conference on the European Energy Market.

[12]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[13]  Saifur Rahman,et al.  An efficient approach to identify and integrate demand-side management on electric utility generation planning , 1996 .

[14]  Stephen L. Chiu,et al.  Extracting Fuzzy Rules from Data for Function Approximation and Pattern Classification , 2000 .

[15]  Haibo He,et al.  Multi-Contingency Cascading Analysis of Smart Grid Based on Self-Organizing Map , 2013, IEEE Transactions on Information Forensics and Security.

[16]  Mohammed H. Albadi,et al.  A summary of demand response in electricity markets , 2008 .

[17]  Susan Krumdieck,et al.  Scenario analysis of residential demand response at network peak periods , 2012 .

[18]  Ijeoma Onyeji,et al.  Consumer engagement: An insight from smart grid projects in Europe , 2013 .

[19]  Kudret Demirli,et al.  Subtractive clustering based modeling of job sequencing with parametric search , 2003, Fuzzy Sets Syst..

[20]  D Venu Madhava Chary Contingency analysis in power systems transfer capability computation and enhancement using facts devices in deregulated power system , 2011 .

[21]  Melody Y. Kiang,et al.  A comparative assessment of classification methods , 2003, Decis. Support Syst..

[22]  B. Simhachalam,et al.  Performance comparison of fuzzy and non-fuzzy classification methods , 2016 .

[23]  Holmes Finch,et al.  Comparison of Distance Measures in Cluster Analysis with Dichotomous Data , 2021, Journal of Data Science.

[24]  M. P. Moghaddam,et al.  Flexible demand response programs modeling in competitive electricity markets , 2011 .

[25]  Alessandra Parisio,et al.  Estimating the impacts of demand response by simulating household behaviours under price and CO2 signals , 2014 .

[26]  Peter Palensky,et al.  Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads , 2011, IEEE Transactions on Industrial Informatics.

[27]  Hamidreza Zareipour,et al.  Electricity Price and Demand Forecasting in Smart Grids , 2012, IEEE Transactions on Smart Grid.

[28]  Shahram Jadid,et al.  Economic-environmental energy and reserve scheduling of smart distribution systems: A multiobjective mathematical programming approach , 2014 .

[29]  Jiangfeng Zhang,et al.  Optimal scheduling of household appliances for demand response , 2014 .

[30]  Nishu Sharma,et al.  A Comparative Study Of Data Clustering Techniques , 2013 .

[31]  Daphne Ngar-yin Mah,et al.  The role of the state in sustainable energy transitions: A case study of large smart grid demonstration projects in Japan , 2013 .

[32]  Bernard De Baets,et al.  Comparison of clustering algorithms in the identification of Takagi-Sugeno models: A hydrological case study , 2006, Fuzzy Sets Syst..

[33]  Pierluigi Siano,et al.  Demand response and smart grids—A survey , 2014 .

[34]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[35]  Zengfeng Wang Comparison of Four Kinds of Fuzzy C-Means Clustering Methods , 2010, 2010 Third International Symposium on Information Processing.

[36]  S. Vadhva,et al.  Smart grid, Distributed Generation, and standards , 2011, 2011 IEEE Power and Energy Society General Meeting.

[37]  Ahmad Faruqui,et al.  Unlocking the €53 Billion Savings from Smart Meters in the EU - How Increasing the Adoption of Dynamic Tariffs Could Make or Break the EU’s Smart Grid Investment , 2009 .

[38]  Raymond E. Bonner,et al.  On Some Clustering Techniques , 1964, IBM J. Res. Dev..

[39]  Robert Babuška,et al.  Fuzzy model for the prediction of unconfined compressive strength of rock samples , 1999 .

[40]  Goran Strbac,et al.  Demand side management: Benefits and challenges ☆ , 2008 .

[41]  A. Torres,et al.  A multi-agent system providing demand response services from residential consumers , 2015 .

[42]  Mary Ann Piette,et al.  Solutions for Summer Electric Power Shortages: Demand Response andits Applications in Air Conditioning and Refrigerating Systems , 2007 .

[43]  Sandy Kerr,et al.  Classifying carbon credit buyers according to their attitudes towards and involvement in CDM sustainability labels , 2011 .

[44]  Tormod Næs,et al.  The flexibility of fuzzy clustering illustrated by examples , 1999 .

[45]  Jianxun Liu,et al.  A mountain means clustering algorithm , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[46]  Mohsen Kalantar,et al.  Stochastic frequency-security constrained energy and reserve management of an inverter interfaced islanded microgrid considering demand response programs , 2015 .

[47]  Anil Kumar Gupta,et al.  Effect of Different Distance Measures on the Performance of K-Means Algorithm: An Experimental Study in Matlab , 2014, ArXiv.

[48]  V. M. F. Mendes,et al.  Fuzzy clustering applied to a demand response model in a smart grid contingency scenario , 2014, 2014 International Symposium on Power Electronics, Electrical Drives, Automation and Motion.

[49]  José Luis Díez,et al.  Dynamic clustering segmentation applied to load profiles of energy consumption from Spanish customers , 2014 .

[50]  M. Parsa Moghaddam,et al.  Modeling and prioritizing demand response programs in power markets , 2010 .

[51]  R. A. Rahmat,et al.  GENERATION OF FUZZY RULES WITH SUBTRACTIVE CLUSTERING , 2005 .

[52]  Sarah Busche,et al.  Power systems balancing with high penetration renewables: The potential of demand response in Hawaii , 2013 .

[53]  Pak Chung Wong,et al.  A novel application of parallel betweenness centrality to power grid contingency analysis , 2010, 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS).