• Energy Research

Model predictive controllers (MPC) have shown great potential for activating the energy flexibility of thermal loads, especially in buildings equipped with heat pump systems. In this work, an MPC controller is developed and tested within a co-simulation framework which couples an optimization software with a dynamic building simulation tool. The development phase is described in detail, in particular the methods to obtain simplified models to be used by the controller. The building envelope and the heat pump performance (based on experimental data) were thus modelled, both in heating and cooling seasons. Three different objective functions of the MPC are tested on a study case consisting of a Spanish residential building: promising results are obtained when the controller aims at minimizing operational costs (savings of 13–29%) or CO2 marginal emissions (savings of 19–29%). The development efforts, the required tuning and sensitivity of the MPC algorithm parameters, the adaptations needed between the cooling and heating operations are also discussed and put into perspective with the obtained benefits in terms of savings, comfort and load-shifting Peer Reviewed

Abstract Energy flexibility is a cost-effective solution to facilitate secure operation of the energy system while integrating large share of renewables. Thermal energy infrastructure is a great asset for flexibility in systems with widely developed district heating networks. The aim of the present work is to investigate the potential for low-energy residential buildings to be operated flexibly, according to the needs of district heating system. An apartment block is studied, utilizing the storage capacity of thermal mass as storage medium. Two sets of data are utilized: heat load of Greater Copenhagen and dynamic heat production cost which is used as a price signal for the scheduling of the heating use in the building. Scenarios with different control signals are determined in order to achieve load shifting. The findings show that pre-heating is highly effective for load shifting and peak load reduction. During morning peak load hours, energy use is reduced in all scenarios between 40% and 87%. Although with load shifting higher energy use may occur, it occurs mostly at times when the city heat load is lower and heat production is less expensive and less carbon-intensive. Indoor temperature has a wider range and/or more fluctuations, yet remains within acceptable limits.

Thermal mass of buildings and domestic hot water tanks represent interesting sources ofthermal energy storage readily available in the existing building stock. To exploit them to their full potential,advanced control strategies and a coupling to the power grid with heat pump systems represent the mostpromising combination. In this paper, model predictive control (MPC) strategies are developed and tested ina semi-virtual environment laboratory setup: a real heat pump is operated from within a controlled climatechamber and coupled with loads of a virtual building, i.e., a detailed dynamic building simulation tool.Different MPC strategies are tested in this laboratory setup, with the goals to minimize either the deliveredthermal energy to the building, the operational costs of the heat pump, or the CO2emissions related to theheat pump use. The results highlight the ability of the MPC controller to perform load-shifting by chargingthe thermal energy storages at favorable times, and the satisfactory performance of the control strategies isanalyzed in terms of different indicators, such as costs, comfort, carbon footprint, and energy flexibility. Thepractical challenges encountered during the implementation with a real heat pump are also discussed andprovide additional valuable insights Peer Reviewed

This study presents an optimization algorithm for Model Predictive Control (MPC) of the HVAC systems in multi-family residential buildings assessing the performance of four objective functions. Implemented in C++, using the free OR-Tools optimization library, the model is formulated a Mixed Integer-Linear Programming (MILP) problem. The study analyses the results of tests conducted on a 20-dwelling block in Switzerland across various weather and occupancy conditions, resulting in a parametric study of 64 cases. The models developed for the MPC are Grey-box type for the interconnected energy systems: the building, thermal storage tanks, a heat pump, the ventilation system and PV collectors, highlighting a radiant wall heating system integrated into the building facade. The tanks and the heat pump models were informed with manufacturer data, while for the building a R3C3 thermal-electrical equivalent model was developed, calibrated using TRNSYS simulations with a root mean square error of 1.7%. Findings demonstrate how the algorithm optimizes the operation according to the desired criteria, while ensuring indoor comfort with a 15-minute time resolution. The time execution of the majority of cases is under 3 min in a low-specs computer, affirming its practical viability for real-world implementation.

Constant emission factors to assess the carbon footprint of buildings energy use, as usually included in national Building Technical Codes, show their limitations since the electrical grid mix changes constantly. For this reason, hourly-based methods using time-varying penalty signals to calculate carbon emissions and primary energy use in buildings constitute more effective assessment methods, especially with the aim to activate energy flexibility in buildings based on those inputs. Such signals have been developed and tested in the present work. The robustness and effectiveness of the methods is tested throughout two study cases. The first case compares the impact of using hourly signals over constant factors from the standards. For that purpose, a measured aggregated consumption profile corresponding to 226 real households is analyzed. In the second study case, demand response is implemented through control strategies reacting to the hourly penalty signals, aiming to decrease the emissions, primary energy use and cost. Results for the first case reveal that hourly rates better capture the variability of the electric grid compared to constant yearly factors from national standards, with a 50% difference in carbon emissions and a 20% overestimation with primary energy. Results from the second study case show how the implemented modulation strategies offer benefits in the flexible scenarios compared to the base scenarios, in terms of accumulated emissions or primary energy. Improvements are especially perceived when splitting data seasonally and considering periods with higher demand. Furthermore, this study provides insights for developing energy flexibility inputs when assessing the building performance during critical events such as the COVID19 pandemic or extreme weather conditions, where hourly and seasonal variation might have greater impact. Demand response mechanisms as energy flexibility strategies studied through this work might help in the reduction of total emissions and primary energy. Depending if the goal is to shift the demand due to environmental or economical reasons, different modulation strategies can be implemented to reach greater benefits.

In this study, simulation work has been carried out to investigate the impact of a demand-side management control strategy in a residential nZEB. A refurbished apartment within a multi-family dwelling representative of Mediterranean building habits was chosen as a study case and modelled within a simulation framework. A flexibility strategy based on set-point modulation depending on the energy price was applied to the building. The impact of the control strategy on thermal comfort was studied in detail with several methods retrieved from the standards or other literature, differentiating the effects on day and night living zones. It revealed a slight decrease of comfort when implementing flexibility, although this was not prejudicial. In addition, the applied strategy caused a simultaneous increase of the electricity used for heating by up to 7% and a reduction of the corresponding energy costs by up to around 20%. The proposed control thereby constitutes a promising solution for shifting heating loads towards periods of lower prices and is able to provide benefits for both the user and the grid sides. Beyond that, the activation of energy flexibility in buildings (nZEB in the present case) will participate in a more successful integration of renewable energy sources (RES) in the energy mix.

The Energy Performance of Buildings Directive (EPBD) has set a target to achieve carbon-neutral building stock and generate 80% of its electricity from renewable sources by 2050. While Model Predictive Control (MPC) can contribute significantly to energy flexibility in buildings, its remote implementation remains relatively unexplored, especially in the residential sector. The purpose of this research is to demonstrate the reliability, robustness, and computational efficiency of a cloud-based application of an MPC called Smart Energy Management (SEM) on a multi-family residential building. The SEM was tested on a virtual building model in TRNSYS using an open-source distributed event streaming platform for data exchange and synchronization. Simplified models for thermal behavior prediction, including an R3C3 model of the building, were developed in C++. The SEM was evaluated in eight scenarios with varying weather conditions, optimization criteria, and runtime periods. The results demonstrate that the SEM maintains stability and robustness over a 2-week period with a 15-minute planning resolution while ensuring thermal comfort. The C++ implementation of the optimization algorithm enables SEM deployment on low-spec servers, supporting cost-effective applications in real buildings with minimal intervention.