JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{ghawash:icalepcs2023-mo3ao06,
author = {F. Ghawash and E. Blanco Viñuela and B. Schofield},
title = {{Energy Consumption Optimisation by Using Advanced Control Algorithms}},
% booktitle = {Proc. ICALEPCS'23},
booktitle = {Proc. 19th Int. Conf. Accel. Large Exp. Phys. Control Syst. (ICALEPCS'23)},
eventdate = {2023-10-09/2023-10-13},
pages = {145--152},
paper = {MO3AO06},
language = {english},
keywords = {controls, operation, PLC, MMI, simulation},
venue = {Cape Town, South Africa},
series = {International Conference on Accelerator and Large Experimental Physics Control Systems},
number = {19},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {02},
year = {2024},
issn = {2226-0358},
isbn = {978-3-95450-238-7},
doi = {10.18429/JACoW-ICALEPCS2023-MO3AO06},
url = {https://jacow.org/icalepcs2023/papers/mo3ao06.pdf},
abstract = {{Large industries operate energy-intensive equipment and energy efficiency is an important objective when trying to optimize the final energy consumption. CERN utilizes a large amount of electrical energy to run its accelerators, detectors and test facilities, with a total yearly consumption of 1.3 TWh and peaks of about 200 MW. Final energy consumption reduction can be achieved by dedicated technical solutions and advanced automation technologies, especially those based on optimization algorithms, have revealed a crucial role not only in keeping the processes within required safety and operational conditions but also in incorporating financial factors. MBPC (Model-Based Predictive Control) is a feedback control algorithm which can naturally integrate the capability of achieving reduced energy consumption when including economic factors in the optimization formulation. This paper reports on the experience gathered when applying non-linear MBPC to some of the contributors to the electricity bill at CERN: the cooling and ventilation plants (i.e. cooling towers, chillers, and air handling units). Simulation results with cooling towers showed significant performance improvements and energy savings close to 20\% over conventional heuristic solutions. The control problem formulation, the control strategy validation using a digital twin and the initial results in a real industrial plant are reported together with the experience gained implementing the algorithm in industrial controllers. }},
}