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Abstract
Model Predictive Control (MPC) predictive’s nature makes it attractive for controlling high-capacity structures such as thermally activated building systems (TABS). Using weather predictions in the order of days, the system is able to react in advance to changes in the building heating and cooling needs. However, this prediction horizon window may be sub-optimal when hybrid geothermal systems are used, since the ground dynamics are in the order of months and even years. This paper proposes a methodology that includes a shadow-cost in the objective function to take into account the long-term effects that appear in the borefield. The shadow-cost is computed for a given long-term horizon that is discretized over time using predictions of the building heating and cooling needs. The methodology is applied to a case with only heating and active regeneration of the ground thermal balance. Results show that the formulation with the shadow cost is able to optimally use the active regeneration, reducing the overall operational costs at the expenses of an increased computational time. The effects of the shadow cost long-term horizon and the predictions accuracy are also investigated.
Highlights
Model Predictive Control (MPC) is an optimal control methodology that has shown a significant potential for energy and cost savings in building energy systems operation
Strategy 2 is able to maximize the use of the ground-source heat pumps (GSHPs) and minimize the use of the auxiliary system by injecting the highest amount of energy into the ground
This paper presents a novel methodology to account for the long-term effects of the geothermal borefield in hybrid geothermal installations that use MPC
Summary
Model Predictive Control (MPC) is an optimal control methodology that has shown a significant potential for energy and cost savings in building energy systems operation. The MPC principle is based on using a model of the system and disturbances predictions to forecast its future behavior over a prediction horizon. Using this information, an optimization problem is solved to find the optimal control sequence that minimizes a given cost or objective function under a specific set of constraints. MPC relies on a ‘receding horizon’ strategy, i.e., at each control time-step only the first control signal of the sequence is applied and the prediction horizon is shifted towards the future step, where the optimization problem is solved again. Typical objective functions in Energies 2020, 13, 6203; doi:10.3390/en13236203 www.mdpi.com/journal/energies
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