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Dynamic Energy Management of a Residential Energy Eco-System

[+] Author Affiliations
Matteo Muratori, Chin-Yao Chang, Giorgio Rizzoni, Wei Zhang

The Ohio State University, Columbus, OH

Paper No. DSCC2013-3828, pp. V001T13A002; 10 pages
  • ASME 2013 Dynamic Systems and Control Conference
  • Volume 1: Aerial Vehicles; Aerospace Control; Alternative Energy; Automotive Control Systems; Battery Systems; Beams and Flexible Structures; Biologically-Inspired Control and its Applications; Bio-Medical and Bio-Mechanical Systems; Biomedical Robots and Rehab; Bipeds and Locomotion; Control Design Methods for Adv. Powertrain Systems and Components; Control of Adv. Combustion Engines, Building Energy Systems, Mechanical Systems; Control, Monitoring, and Energy Harvesting of Vibratory Systems
  • Palo Alto, California, USA, October 21–23, 2013
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5612-3
  • Copyright © 2013 by ASME


In this paper, an automated dynamic energy management framework for a generic residential eco-system is presented. The proposed distributed, non-disruptive management framework is based on highly-resolved personal energy consumption models developed using a novel bottom-up approach that quantifies consumer energy use behavior. The incorporation of stochastic consumers behavior provides more accurate estimation of the actual amount of available controllable resources, hence enabling better interactions with the grid. The energy management problem is solved by use of a dynamic programming algorithm that considers household members’ behavior, as predicted by the highly-resolved stochastic personal energy consumption model, and finds the optimal schedule for all the controllable appliances and plug-in electric vehicles charging. The algorithm is flexible enough to accommodate diverse costs functions, and simulate different scenarios.

Copyright © 2013 by ASME



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