Economic versus energetic model predictive control of a cold production plant with thermal energy storage

TL;DR

This paper compares economic and energetic model predictive control strategies for a cold storage plant, revealing trade-offs between energy consumption and cost savings across different seasonal scenarios.

Contribution

It is the first study to compare energetic and economic MPC approaches in a cold storage plant with detailed modeling and non-convex optimization.

Findings

Economic MPC reduces costs by 2.94% compared to energetic MPC.

Energetic MPC consumes about 2.15% more energy during high electric seasons.

Results vary depending on seasonal tariffs and electric seasons.

Abstract

Economic model predictive control has been proposed as a means for solving the unit loading and unit allocation problem in multi-chiller cooling plants. The adjective economic stems from the use of financial cost due to electricity consumption in a time horizon, such is the loss function minimized at each sampling period. The energetic approach is rarely encountered. This article presents for the first time a comparison between the energetic optimization objective and the economic one. The comparison is made on a cooling plant using air-cooled water chillers and a cold storage system. Models developed have been integrated into Simscape, and non-convex mixed optimization methods used to achieve optimal control trajectories for both energetic and economic goals considered separately. The results over several scenarios, and in different seasons, support the consideration of the energetic approach despite the current prevalence of the economic one. The results are dependent on the electric season and the available tariffs. In particular, for the high electric season and considering a representative tariff, the results show that an increment of about 2.15% in energy consumption takes place when using the economic approach instead of the energetic one. On the other hand, a reduction in cost of 2.94% is achieved.