Premium
Stochastic modeling and scalable predictive control for automated demand response
Author(s) -
Kobayashi Koichi,
Hiraishi Kunihiko
Publication year - 2021
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5313
Subject(s) - demand response , scalability , computer science , mathematical optimization , model predictive control , set (abstract data type) , linear programming , integer programming , consumption (sociology) , markov chain , control (management) , electricity , algorithm , machine learning , artificial intelligence , engineering , mathematics , social science , database , sociology , electrical engineering , programming language
Automated demand response (ADR) is a utility program that is designed to achieve electricity conservation. An ADR program is regarded as the problem of controlling the power consumption of a set of consumers. In this article, we propose a control‐theoretic approach for an ADR program. First, a mathematical model of the power consumption is proposed. This model can express complex behavior by switching a Markov chain. Its effectiveness is illustrated by modeling the power consumption of an air‐conditioner. Next, a new method of model predictive control for a set of consumers is developed using the proposed model. The control strategy at each time is chosen from a given finite set by solving a mixed integer linear programming (MILP) problem. The advantage of the proposed method is that the MILP problem is scalable with respect to the number of consumers. To show its effectiveness, we present a numerical example.