Model-predictive energy management system for thermal batch production processes using online load prediction

Predictive energy management systems (EMS) enable industrial plants to participate in the modern power market and reduce energy cost. In this paper, a novel modular model predictive EMS specifically designed for industrial thermal batch processes is presented. The EMS consists of a two-layer mixed-integer model predictive controller and an online load predictor, and thus solves the main challenges of EMS in industry - high implementation costs and the possible reduction of production reliability. The modular formulation of the optimization problem enables system integrators to implement the EMS without time-consuming modelling tasks and elaborate parameter tuning. The online load predictor estimates the typical pulse-like heat loads of batch processes ensuring both - reliable production and maximal flexibility of the power demand. The utilization of real-time data provides additional robustness against uncertainties caused by human operators. The performance of the EMS is evaluated in a case study of an existing food plant. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Automated summary

This paper presents a novel modular model predictive energy management system (EMS) specifically designed for industrial thermal batch processes. The EMS, consisting of a two-layer mixed-integer model predictive controller and an online load predictor, addresses the main challenges in industry regarding high implementation costs and potential reduction in production reliability. The modular formulation of the optimization problem allows for easy implementation of the EMS without the need for time-consuming modeling tasks and parameter tuning. The online load predictor estimates pulse-like heat loads in batch processes, ensuring reliable production and maximum flexibility in power demand. The utilization of real-time data enhances robustness against uncertainties caused by human operators. The performance of the EMS is evaluated through a case study of an existing food plant.