Long-term sustainable operation of hybrid geothermal systems through optimal control

Hybrid geothermal systems such as hybrid GEOTABS typically comprise a geothermal heat pump that supplies the main building thermal energy needs, complemented by a fast-reacting supplementary production and/or emission system for the peak building thermal loads. Optimal predictive controllers such as Model Predictive Control (MPC) are desired for these complex systems due to their optimized and automated energy savings potential (while providing the same or better thermal comfort) thanks to system integration and their anticipative action. However, the predictions of these controllers are typically limited to a few days. Consequently, the controller is unaware whether abusive energy injection/extraction into/from the soil will deplete the source over the years. This paper investigates in which cases the long-term dynamics of the borefield ought to be included in the MPC formulation. A simulation model of a hybrid GEOTABS system is constructed. Different borefield sizes, ground imbalance loads, and electricity/gas ratios are evaluated. The model control inputs are optimized to minimize the energy use in 5 years through (i) a reference Optimal Control Problem (OCP) for the 5 years, solved in hourly timesteps and (ii) an MPC control with a prediction horizon of 1 week. The obtained results reveal that MPC can be up to 20% far from the true optimal, especially in the cases where the borefield is undersized and there is a large cost gap between the different energy systems

A methodology for long-term model predictive control of hybrid geothermal systems: the shadow-cost formulation

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

State Observer for Optimal Control using White-box Building Models

In order to improve the energy efficiency of buildings, optimal control strategies, such as model predictive control (MPC), have proven to be potential techniques for intelligent operation of energy systems in buildings. However, in order to perform well, MPC needs an accurate controller model of the building to make correct predictions of the building thermal needs (feedforward) and the algorithm should ideally use measurement data to update the model to the actual state of the building (feedback). In this paper, a white-box approach is used to develop the controller model for an office building, leading to a model with more than 1000 states. As these states are not directly measurable, a state observer needs to be developed. In this paper, we compare three different state estimation techniques commonly applied to optimal control in buildings by applying them on a simulation model of the office building but fed with real measurement data. The considered observers are stationary Kalman Filter, time-varying Kalman Filter, and Moving Horizon Estimation. Summarizing the results, all estimators can achieve low output estimation error, but on the other hand only Moving Horizon Estimation is capable to keep the state trajectories within the limits thanks to the constraints at expenses of the computational time. As a first step towards real implementation of white-box MPC, in this paper, we have compared different state estimation techniques commonly applied to optimal control in buildings. We selected three different state observers available from the literature and compared their estimation error and robustness against initial conditions and noise in a numerical case study by using a virtual test bed model of a real building

Fluid temperature predictions of geothermal borefields using load estimations via state observers

Fluid temperature predictions of geothermal borefields usually involve temporal superposition of its characteristic g-function, using load aggregation schemes to reduce computational times. Assuming that the ground has linear properties, it can be modelled as a linear state-space system where the states are the aggregated loads. However, the application and accuracy of these models is compromised when the borefield is already operating and its load history is not registered or there are gaps in the data. This paper assesses the performance of state observers to estimate the borefield load history to obtain accurate fluid predictions. Results show that both Time-Varying Kalman Filter (TVKF) and Moving Horizon Estimator (MHE) provide predictions with average and maximum errors below 0.1∘C and 1∘C, respectively. MHE outperforms TVKF in terms of n-step ahead output predictions and load history profile estimates at the expense of about five times more computational time

A Methodology for Long-Term Model Predictive Control of Hybrid Geothermal Systems: The Shadow-Cost Formulation

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

State estimation of control-oriented white-box models for buildings

status: publishe

Development and Impact of a Borefield Controller Model for Model Predictive Control

status: publishe

State estimators applied to a linear white-box geothermal borefield controller model

status: submitte

A Methodology for Long-Term Model Predictive Control of Hybrid Geothermal Systems: The Shadow-Cost Formulation

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.Special issue: Advances in Ground Heat Exchangers and Ground-Coupled Heat Pumpsstatus: publishe

Towards real MPC implementation in an office building using TACO

Model predictive control (MPC) is a promising alternative to rule-based control since it is more suitable to control increasingly complex buildings and thereby realising energy savings and comfort improvement. Practical implementations are however hampered by the complexity of MPC and the expertise required for developing MPC. Therefore, a toolchain for automated control and optimization (TACO) has been developed that automatically translates an object-oriented Modelica model into an efficient MPC code. Since object-oriented models from the Modelica IDEAS library are used, the expertise requirement and development time are reduced significantly. TACO has, however, not yet been applied to a real building and its robustness in real operation still must be demonstrated. The purpose of this paper is to provide a comprehensive overview of the steps that are proposed for implementing an MPC using TACO. We therefore summarise our existing methodology and describe our future extension plans to implement an MPC in the Infrax office building in Brussels by September 2018