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A fuzzy clustering approach to a demand response model
Affiliation:1. Departamento de Física, Escola de Ciências e Tecnologia, Universidade de Évora, Évora, Portugal;2. IDMEC/LAETA, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal;3. Department of Electrical Engineering and Automation, Instituto Superior de Engenharia de Lisboa, Lisbon, Portugal;1. Department of Computer Science, University of Massachusetts, Lowell, MA 01854, USA;2. Department of Computer and Information Science, Fordham University, Bronx, NY 10458, USA;3. Department of Computer Science and Technology, Xi’an Jiaotong University, PR China;1. Department of Mechanical Engineering, School of Engineering, Aalto University, Otakaari 4, 02150 Espoo, Finland;2. Department of Mathematics and Systems Analysis, School of Science, Aalto University, P.O. Box 11100, 00076 Aalto, Finland;1. Department of Electrical Engineering, DHA Suffa University, Karachi, Pakistan;2. Department of Telecommunication, Dawood University, Karachi, Pakistan;3. Department of Electrical Engineering, CUI Abbottabad Campus, Abbottabad, Pakistan;1. School of Information Science and Engineering, University of Jinan, China;2. Faculty of Science and Technology, University of Macau, Macau, China;3. Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong, China;1. Science & Technology on Integrated Information System Laboratory Institute of Software, Chinese Academy of Sciences, Beijing 100190, China;2. Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA;3. Invenia Labs, 27 Parkside Place, Parkside, Cambridge CB1 1HQ, UK
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.
Keywords:Demand response  Smart grid  Fuzzy clustering  Load management
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