Automated Scheduling of Household Appliances Using Predictive Mixed Integer Programming

Himanshu Nagpal; Andrea Staino; Biswajit Basu

doi:10.20944/preprints201902.0256.v1

Preprint Article Version 1 This version is not peer-reviewed

Automated Scheduling of Household Appliances Using Predictive Mixed Integer Programming

Himanshu Nagpal ^* , Andrea Staino , Biswajit Basu

Version 1 : Received: 26 February 2019 / Approved: 27 February 2019 / Online: 27 February 2019 (12:10:32 CET)

How to cite: Nagpal, H.; Staino, A.; Basu, B. Automated Scheduling of Household Appliances Using Predictive Mixed Integer Programming. Preprints 2019, 2019020256 (doi: 10.20944/preprints201902.0256.v1). Nagpal, H.; Staino, A.; Basu, B. Automated Scheduling of Household Appliances Using Predictive Mixed Integer Programming. Preprints 2019, 2019020256 (doi: 10.20944/preprints201902.0256.v1).

Abstract

In this work, an algorithm for the scheduling of household appliances to reduce the energy cost and the peak-power consumption is proposed. The system architecture of a home energy management system (HEMS) is presented to operate the appliances. The dynamics of thermal and non-thermal appliances is represented into state-space model to formulate the scheduling task into a mixed-integer-linear-programming (MILP) optimization problem. Model predictive control (MPC) strategy is used to operate the appliances in real-time. The HEMS schedules the appliances in a dynamic manner without any a priori knowledge of the load-consumption pattern. At the same time, HEMS responds to the real-time electricity market and the external environmental conditions (solar radiation, ambient temperature etc). Simulation results exhibit the benefits of proposed HEMS by showing the reduction of up to 47% in electricity cost and up to 48% in peak power consumption.

Subject Areas

Home energy management system, Flexible demand-response, optimal load-scheduling, Mixed Integer Programming, Predictive control, demand-side-management

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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