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

TL;DR

The paper benchmarks energetic versus economic model predictive control for a cooling plant with thermal energy storage using non-convex mixed-integer optimization solved by a genetic algorithm. It employs Simscape-based models and real data to compare two objective functions across multiple electric seasons and tariffs, highlighting how TES operation mediates the trade-off between energy use and electricity cost. Results show that energetic MPC achieves lower energy consumption, while economic MPC reduces cost, with the balance between them strongly influenced by tariff structure and season. The study provides guidance for selecting MPC objectives in TES-enabled cooling plants and suggests avenues for further tuning and parametric analysis to optimize performance under varying price signals.

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.

Figures (12)

• Figure 1: Diagram of the cold generation plant. The two possible configurable water circuits using the two three-way valves, which are related to the working modes of the TES, are represented in green color for the charging of the TES and in purple color for the discharging case.
• Figure 2: Block diagram of the system in Simscape.
• Figure 3: Block diagrams of the Economic and the Energetic optimizers.
• Figure 4: Simulation input profiles. Real and forecasted cooling load ($\dot{Q}_L$ and $\dot{Q}^{*}_{L}$ respectively) and real and forecasted environment temperature ($T_{env}$ and $T^{*}_{env}$ respectively). Detail views for one day are shown in red subgraphs.
• Figure 5: Cooling power of chillers, load and TES resulting of Energetic MPC optimization.