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Solar irradiation 6-h horizon forecasts (left) for the 21st of June 2017 (updated at 2 a.m., 8 a.m., 2 p.m. and 8 p.m.). Model of the real-time irradiation derived from a kernel smoothing approach on the updated forecasts (right).
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![Varying temperature constraints–night [11pm:5am]; 18 ∘C. Note the CO2...](publication/341879052/figure/fig5/AS:11431281397602365@1745509567728/Varying-temperature-constraints-night-11pm5am-18-C-Note-the-CO2-emission-intensity_Q320.jpg)
An optimized heat pump control for building heating was developed for minimizing CO2 emissions from related electrical power generation. The control is using weather and CO2 emission forecasts as inputs to a Model Predictive Control (MPC)—a multivariate control algorithm using a dynamic process model, constraints and a cost function to be minimized...
Citations
... The parameter estimation process is carried out in R [25], employing the CTSM package [26], a tool designed for estimating embedded parameters in continuous-time stochastic state-space models. This package has been developed over many years, with its accuracy validated in various fields including building systems, refrigeration, and heat pumps [14,27,28]. ...
... The parameter estimation process is carried out in R [25] , employing the CTSM package [26], a tool designed for estimating embedded parameters in continuous-time stochastic state-space models. This package has been developed over many years, with its accuracy validated in various fields including building systems, refrigeration, and heat pumps [14,27,28]. ...
Optimizing energy efficiency in existing buildings can yield substantial savings, though collecting the necessary data for energy modelling often poses challenges. This study developed a flexible, room-level framework for evaluating retrofit strategies using simplified energy models. The approach, based on the RC model, estimated parameters from readily available data such as solar radiation, indoor and outdoor temperatures, and heating system characteristics. The model was validated through case studies of an office and a daycare room in Denmark, guiding energy retrofit decisions. Results showed that adding roof insulation provided greater energy savings compared to wall insulation. A multi-objective optimization was employed to balance energy efficiency and thermal comfort, achieving a 6.58% reduction in energy demand during January while maintaining occupant comfort for 744 h. This framework not only facilitates building–energy retrofitting but also supports the development of digital twins and operational optimization, improving both energy performance and indoor environmental quality.
... Here, an overview of solutions for users' thermal comfort [8]- [15] and thermal resource [16]- [20] management is given. ...
... In this section of the paper, all the control strategies proposed for thermal resource management [16]- [20] in public buildings equipped with multi-energy MGs are described in detail. The existing PID and RB controllers as well as the optimization-based/free model predictive controllers are presented. ...
... The strategy aims at taking advantage of the PV power generation surplus and periods of low electricity tariffs or periods of low CO 2 emissions. Taking advantage of periods of low CO 2 emissions in the control of a heat pump has already been put forward by Dahl Knudsen and Pettersen [19] and Leerbeck et al. [20]. The objective function J HP/TES is defined as follows (5): ...
Multi-energy microgrids (MGs) are emerging as an efficient way to address the challenge of integrating distributed energy resources (DERs) into buildings. Building-integrated MGs offer flexibility and reliability through system optimization. In this context, the Interreg SUDOE project IMPROVEMENT (In-tegration of Combined Cooling, Heating and Power Microgrids in Zero Energy Public Buildings with High Power Quality and Continuity Requirements) was launched at the end of the year 2019 with the aim of developing efficient solutions allowing public buildings with critical loads to be turned into net-zero-energy buildings (nZEBs). The work presented in this paper deals with the development of an energy management system (EMS) for the management of thermal resources and users' thermal comfort in public buildings [1]. Optimization-based/free model predictive control (MPC) algorithms are presented and validated using simulations and data collected in a public building equipped with a multi-energy MG. Models of the thermal MG components were developed. The strategy currently used in the building relies on proportional-integral-derivative (PID) and rule-based (RB) controllers. The interconnection between the thermal part and the electrical part of the building-integrated MG is managed by taking advantage of the solar photovoltaic (PV) power generation surplus. The optimization-based MPC EMS has the best performance but is rather computationally expensive. The optimization-free MPC EMS is slightly less efficient but has a significantly reduced computational cost, making it the best solution for in situ implementation.
... In addition, a shift in consumption from off-peak to on-peak periods can be observed. In [57], an optimized heat pump control for building heating is developed with the aim of minimizing power generation-related CO 2 emissions. Weather and CO 2 emission forecasts are used as inputs to an MPC controller. ...
... The strategy aims to take advantage of the PV power generation surplus and periods of low electricity tariffs or periods of low CO 2 emissions. Taking advantage of periods of low CO 2 emissions in the control of a heat pump has already been put forward by Dahl Knudsen and Pettersen [56] and Leerbeck et al. [57]. The objective function J HP/TES is dened in the following way (26): 26) where N p is the number of time steps per hour (the time step is 10 min), H p is the prediction horizon of the MPC controller (i.e., 24 h), C r is the normalized economic cost, G r is the normalized CO 2 emissions, and φ a , φ b , and φ c are coefcients (empirically determined). ...
The efficient integration of distributed energy resources (DERs) in buildings is a challenge that can be addressed through the deployment of multienergy microgrids (MGs). In this context, the Interreg SUDOE project IMPROVEMENT was launched at the end of the year 2019 with the aim of developing efficient solutions allowing public buildings with critical loads to be turned into net-zero-energy buildings (nZEBs). The work presented in this paper deals with the development of a predictive energy management system (PEMS) for the management of thermal resources and users’ thermal comfort in public buildings. Optimization-based/optimization-free model predictive control (MPC) algorithms are presented and validated in simulations using data collected in a public building equipped with a multienergy MG. Models of the thermal MG components were developed. The strategy currently used in the building relies on proportional–integral–derivative (PID) and rule-based (RB) controllers. The interconnection between the thermal part and the electrical part of the building-integrated MG is managed by taking advantage of the solar photovoltaic (PV) power generation surplus. The optimization-based MPC EMS has the best performance but is rather computationally expensive. The optimization-free MPC EMS is slightly less efficient but has a significantly reduced computational cost, making it the best solution for in situ implementation.
... Several studies show that this leads to substantial rebound effects and does not represent reality. These studies ask for an hourly-based, dynamic emission value to address the fluctuation in the emissions in the electrical energy grid in a correct manner and to open up the potential for intelligent control strategies [3,[9][10][11]. As this phenomenon depends on the electrical energy grid and the local weather conditions of a building, this is not only a problem for Germany but demands international application, as presented in this contextual parametric study. ...
... As this paper outlines, weather and emission data hold great potential to optimize building operations. Previous studies also mention the possibility and the lack of application of such concepts that, in the authors' opinions, are based on the complexity and inertness of data-intense control algorithms [9][10][11]. Building operation managers and even more building users are not experts in building data science and do not accept complex and expensive control algorithms. ...
Building operational energy alone accounts for 28% of global carbon emissions. A sustainable building operation promises enormous savings, especially under the increasing concern of climate change and the rising trends of the digitalization and electrification of buildings. Intelligent control strategies play a crucial role in building systems and electrical energy grids to reach the EU goal of carbon neutrality in 2050 and to manage the rising availability of regenerative energy. This study aims to prove that one can create energy and emission savings with simple weather and emission predictive control (WEPC). Furthermore, this should prove that the simplicity of this approach is key for the applicability of this concept in the built world. A thermodynamic simulation (TRNSYS) evaluates the performance of different variants. The parametrical study varies building construction, location, weather, and emission data and gives an outlook for 2050. The study showcases five different climate locations and reveals heating and cooling energy savings of up to 50 kWh/(m 2 a) and emission savings between 5 and 25% for various building types without harming thermal comfort. This endorses the initial statement to simplify building energy concepts. Furthermore, it proposes preventing energy designers from overoptimizing buildings with technology as the solution to a climate-responsible energy concept.
... Pedersen et al. [99] studied retrofitted MFHs using BTM and found that the costs were reduced by up to 6 % and CO 2 emissions by up to 3 %, depending on the energy efficiency of the retrofit scenarios. Leerbeck et al. [93] investigated the possibilities of controlling HPs in SFHs towards minimized CO 2 emissions from related electric power generation and found that the emission savings potential reached up to 17 % for well-insulated buildings but only 3 % for poorly insulated. Vigna et al. [48] studied smart operation of low-energy SFHs on cluster level and found that energy-related CO 2 emissions could be reduced by up to 18 %. ...
... Johra et al. [5] studied SFHs and found that the use of FH improved the flexibility performance by up to 44 % (low-insulation dwellings) and 8 % (high-insulation dwellings) compared to radiators. Leerbeck et al. [93] evaluated the environmental benefits of controlling HPs in SFHs and found that the CO 2 -emission savings for well-insulated buildings reached 9-17 % in case of FH and 10-12 % in case of radiators, depending on the thickness of the concrete slab. Arteconi et al. [94] investigated the load-shifting potential of HP-heated SFHs by switching of the HP during peak hours. ...
... Firstly, a strong correlation might exist between the electricity demand of HPs and real-time carbon intensities of the power generation system. This could notably deviate from the average carbon intensity [4]. Second, the surge in electricity demand due to the widespread use ing and load shifting, thereby enhancing the reliability of the grid [10]. ...
... Péan et al. [23] developed an electricity mix-responsive MPC controller for reducing carbon emissions from the space heating and cooling systems of a residential building in Tarragona, Spain; compared to a conventional thermostat controller that did not consider grid electricity mix dynamics, the proposed controller achieved 29% and 19% carbon emission reductions for the space heating in a 3-day-long simulation in winter and cooling systems in a 3-day-long simulation in summer, respectively. Leerbeck et al. [24] developed an electricity mix-responsive MPC controller for reducing carbon emissions from the space heating system of a family house and an office building in East Denmark; compared to a baseline controller that did not consider grid electricity mix dynamics, the proposed MPC controller achieved carbon emission reductions of 0.5% -12.4% ...
Electrification and distributed energy resources (DERs) are vital for reducing the building sector's carbon footprint. However, conventional reactive control is insufficient in addressing many current building-operation-related challenges, impeding building decarbonization. To reduce building carbon emissions, it is essential to consider dynamic grid electricity mix and incorporate the coordination between DERs and building energy systems in building control. This study develops a novel model predictive control (MPC)-based integrated energy management framework for buildings with multiple DERs considering dynamic grid electricity mix and pricing. A linear, integrated high-fidelity model encompassing adaptive thermal comfort, building thermodynamics, humidity, space conditioning, water heating, renewable energy, electric energy storage, and electric vehicle, is developed. An MPC controller is developed based on this model. To demonstrate the applicability, the developed framework is applied to a single-family home with an energy management system through whole-year simulations considering three climate zones: warm, mixed, and cold. In the simulations, the framework reduces the whole-building electricity costs and carbon emissions by 11.9% - 38.3% and 7.2% - 25.1%, respectively, compared to conventional control. Furthermore, the framework can reduce percent discomfort time from 25.7% - 47.4% to nearly 0%, compared to conventional control. The framework also can shift 86.4% - 100% of peak loads to off-peak periods, while conventional control cannot achieve such performance. The case study results also suggest that pursuing cost savings is possible in tandem with carbon emission reduction to achieve co-benefits (e.g., simultaneous 37.7% and 21.9% reductions in electricity costs and carbon emissions, respectively) with the proposed framework.
... Leerbeck et al. [25] assessed the CO 2 -saving effect of heat pump heating based on the detailed hourly heat demand of reference buildings in Denmark. The model calculated expected emission reductions between 0 and 20%. ...
Utilizing heat pumps has varied benefits, including decreasing the proportion of fossil fuels in the energy mix and reducing CO2 emissions compared with other heating modes. However, this effect greatly depends on the type of external energy and the type of the applied heat pump system. In our study, two different types of heat pumps, three different modes of operation, three different types of auxiliary energy, and three different CO2 emission values from electricity generation were selected to calculate the CO2 emissions related to heating a theoretical house and calculate the CO2 emissions reduction compared with gas firing. According to the calculations, a wide range of CO2 emission reductions can be achieved, from scenarios where there is no reduction to scenarios where the reduction is 94.7% in monovalent mode. When operating in a bivalent mode, the values are less favorable, and several systems show no reduction, particularly when operating in an alternate mode at a bivalent temperature of 2 °C. However, the reduction in fossil CO2 emissions can be kept at a high value (up to 56.7% with Hungary’s electricity mix) in a bivalent system by using biomass as a resource of auxiliary energy and geothermal heat pumps, which is very similar to the CO2 emission reduction in monovalent systems (54.1%).
... Existing research on emission reduction-driven control for buildings or EVs is primarily focused on optimization-based methods, where optimization techniques are used to design control strategies that achieve desired system behavior while minimizing selected performance metrics. Leerbeck et al. [4] developed a model predictive control (MPC) based heat pump controller for building space heating. The control inputs include weather and CO 2 emission forecasts. ...
... The literature shows a lack of rule-based coordination at the community-scale level for buildings and EVs. Reference Objective Scale Control method Controlled system DERs [4] Emission reduction Building (house, office) MPC HVAC None [5] Emission and cost reduction, flexibility Building (multifamily) MPC HVAC, DHW, TES, EV PV, TES [ 12 ] Emission and cost reduction, thermal energy Building (apartment) MPC HVAC, DHW TES [ 13 ] Emission reduction Grid Optimization EV N/A [6] Emission reduction, less wind curtailment Grid Optimization EV Wind [ 14 ] Emission reduction Traffic analysis zone Optimization EV N/A [7] Emission reduction Building (mixed-use, office, seminar center) RL HVAC None [ 9 ] Emission, cost, and peak load reduction Building (residential) Rule-based HVAC, DHW TES [ 10 ] Emission reduction Community (residential) Rule-based HVAC, DHW, battery PV, battery, TES [ 11 ] Emission reduction Community (mixed-use) Rule-based EV None Proposed work Emission reduction Community (mixed-use) Rule-based HVAC, EV, battery PV, battery, TES Despite the advancement of optimization-based building and EV controllers in research, rule-based controllers are still the dominant controllers used in most real-world applications in buildings and EV chargers. This type of control strategy design typically involves defining a set of rules or logical statements to determine the appropriate control actions for a given system state. ...
... Similar to the coordinated control, the battery will only be charged when the net-load is negative and the battery is not full. A mathematical description of this logic is shown in Eq. (4) . Given that the utility rate structure will have peak demand charges, which typically make up a significant portion of energy bills, the battery discharging should help mitigate the peak building loads and thus reduce total energy bills. ...
The progression of electrification in the building and transportation sectors brings new opportunities for energy decarbonization. With higher dependence on the grid power supply, the variation of the grid carbon emission intensity can be utilized to reduce the carbon emissions from the two sectors. Existing coordinated control methods for buildings with distributed energy resources (DERs) either consider electricity price or renewable energy generation as the input signal, or adopt optimization in the decision-making, which is difficult to implement in the real-world environment. This paper aims to propose and validate an easy-to-deploy rule-based carbon responsive control framework that facilitates coordination between all-electric buildings and electric vehicles (EVs). The signals of the grid carbon emission intensity and the local photovoltaics (PV) generation are used for shifting the controllable loads. Extensive simulations were conducted using a model of an all-electric mixed-use community in a cold climate to validate the control performance with metrics such as emissions, energy consumption, peak demand, and EV end-of-day state-of-charge (SOC). Our study identifies that 4.5% to 27.1% of annual emission reduction can be achieved with limited impact on energy costs, peak demand, and thermal comfort. Additionally, up to 32.7% of EV emission reduction can be obtained if the EV owners reduce the target SOC by less than 21.2%.
... Fischer et al. [17] analyzed different operational strategies for capacity-controlled HPs connected to thermal storage in German multifamily houses to maximize energy performance and utilisation of on-site PV production, while minimizing energy costs. Leerbeck et al. [18] developed an optimal controller using model predictive control and external parameters such as weather and CO 2 emission forecasts, to minimize CO 2 emissions of buildings equipped with a HP unit. Rominger et al. [19] experimentally investigated a new system architecture to provide frequency containment reserve through aggregation of heating, ventilation, and air conditioning systems. ...
... The results showed the effectiveness of the proposed coordinated control method in mitigating voltage violations without compromising end-user comfort, namely no shortage of domestic hot water during voltage control periods. Our extensive literature review shows that we can categorize these papers in two main groups, 1) studies that focused on exploiting the implicit flexibility of HPs, i.e., [7]- [18], and 2) research works that developed algorithms to procure flexibility of HPs explicitly, i.e., [19]- [22]. In implicit flexibility procurement, the flexibility is obtained by offering time-varying electricity prices to the end-users. ...
Swimming pool heating systems are known as one of the best flexible resources in buildings. However, they can be flexible only for a certain number of hours throughout a day due to the comfort constraints of the users. In this study, a new approach is proposed to determine a group of contract hour sets to procure maximum flexibility of swimming pool heating systems supplied by heat pumps for trading in the regulation market while respecting the comfort of users. The main advantage of the contract hour sets is the certainty in response to flexibility requests. The proposed approach consists of three main steps. First, a stochastic mixed-integer linear program is proposed to find the optimal operation of a swimming pool heating system that has agreed to provide flexibility in a contract hours set. Then, a metric is proposed to evaluate the effectiveness of contract hour sets using the results obtained in the first step. Finally, an algorithm is proposed to identify a group of the most efficient contract hour sets using the calculated metric. The proposed approach is validated through comprehensive simulation studies for a summerhouse with an indoor pool heated by a heat pump. Also, a cost–benefit analysis is performed to examine the feasibility of these contract hour sets from financial viewpoint. Simulation results show that the maximum contract hours can vary from 2 to 12 h depending on the building occupation pattern and the minimum payment to owners is between 0.03 to 0.06 (Euro/kW).
