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

Figueroa, Iago Cupeiro; Cimmino, Massimo; Drgoňa, Ján; Helsen, Lieve

doi:10.1080/19401493.2020.1838612

Cited by 8 publications

(5 citation statements)

References 28 publications

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“…Therefore a larger minimum number of short-term states or a larger short-term radius r ST should be enforced. The error obtained with the analytical formulation from Cupeiro Figueroa et al ( , 2021 is always minimal, in the order of 10 −2 K. However, the number of states of the analytical model for 25 years rises to 96 states in both cases, while the proposed RC models have only 15 states in the 4 boreholes field and 12 in the 37 boreholes field. An increase of the error at the end of the considered 25 years period is observed for both controller models.…”

Section: Model Validation and Comparisonmentioning

confidence: 89%

“…The deviations between the proposed RC model vs the IBPSA borefield model and the analytical controller model of Cupeiro Figueroa et al ( , 2021 vs the IBPSA borefield model are shown in Figure 5 for the different validation datasets. The proposed RC model error depends on the fit obtained.…”

Section: Model Validation and Comparisonmentioning

confidence: 99%

“…However, the implementation of the borefield load shifting algorithm in optimal control can be cumbersome depending on the optimization framework and solver continuity requirements. A continuous formulation of the load shifting algorithm can be found in Cupeiro Figueroa et al ( , 2021.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 4: Cyclic load profiles used for the controloriented borefield models validation model of CupeiroFigueroa et al ( , 2021, which is based on the IBPSA borefield model but describing the load shifting algorithm using a continuous formulation, is also included for comparison.…”

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confidence: 99%

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A low-order semi-physical borefield model for optimal control applications

Figueroa¹,

Helsen²

2021

Building Simulation Conference Proceedings

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Accounting for the borefield long-term dynamic behavior in optimal control applications can lead to substantial savings. To that end, a borefield controller model that covers the long-term time scale (i.e., interactions between boreholes) is necessary. However, the direct use of an analytical model may result in a computational burden increase due to the high number of required states. This paper presents a novel loworder resistance-capacity (RC) network model that accounts for the borefield long-term dynamic behavior based on the physical information provided by its characteristic thermal response. It is shown that the mean square error (MSE) of the borefield outlet fluid temperature remains below 0.015 K 2 in two example borefields using a total number of states below 15. The validation experiment revealed that attention should be paid to the short-term dynamics since the MSE indicator captures the error of the g-function over the full time horizon and as a consequence large short-term dynamic errors in the fluid temperatures are not detected. Key Innovations• A borefield RC network model that accounts for the long-term effects is developed.• The model has a low number of states compared to the analytical model counterpart. • The model is suited for (long-term) optimal control applications. Practical ImplicationsThis model facilitates the incorporation of the borefield long-term dynamic behavior in model-based optimal control applications (such as Model Predictive Control), especially in cases where the optimization framework requires a low number of states.

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Section: Model Validation and Comparisonmentioning

confidence: 89%

Section: Model Validation and Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

A low-order semi-physical borefield model for optimal control applications

Figueroa¹,

Helsen²

2021

Building Simulation Conference Proceedings

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“…Although state estimation can reduce the short-term error [24,25] that appears due to not capturing the thermal interactions between boreholes, the RC network is not suitable for long-term predictions.…”

Section: Mpc In (Hybrid) Geothermal Systemsmentioning

confidence: 99%

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

2020

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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.

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Development of a Long-Term Operational Optimization Model for a Building Energy System Supplied by a Geothermal Field

2022

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In order to reduce energy consumption and CO2 emissions in the building sector, more and more renewable energy sources are integrated into energy systems. Especially geothermal fields combined with heat pumps are able to supply buildings with heat and cold at low carbon emissions. However, using geothermal fields as heat and cold source influences the ground temperature. Consequently, the ground temperature can change dramatically over a building’s lifetime, leading to less efficient operation of the energy system. Therefore, a sustainable operation is required to ensure the long-term efficiency of geothermal fields. In this paper, we develop an optimization model to derive operating strategies for an efficient long-term operation of a building energy system coupled to a geothermal field. The investigated energy system is the main building of the E.ON Energy Research Center in Aachen, Germany, which includes a heat pump, two boilers, a combined heat, and power unit, a glycol cooler, and a geothermal field with 41 probes. For each component, we develop energy-based sub-models, which are connected to form the overall system. The geothermal field is modeled by using a g-functions approach as well as a simplified resistance-capacitance approach. To achieve short computing times and realize an optimization horizon of several years, the optimization problem is formulated as mixed-integer linear programming (MILP). The developed model is optimized regarding two different objectives: the minimization of energy costs and the minimization of long-term temperature changes in the ground. Conclusions for an efficient and sustainable operation of the field, especially for the cooling supply, can be derived from the optimization results. It is shown that a state of equilibrium should be aimed to achieve an energy-efficient operation, in which the temperature of the field is close to the initial ground temperature.

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

Cited by 8 publications

References 28 publications

A low-order semi-physical borefield model for optimal control applications

A low-order semi-physical borefield model for optimal control applications

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

Development of a Long-Term Operational Optimization Model for a Building Energy System Supplied by a Geothermal Field

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