Hot Water Heating Research Articles

Biomass pellets with organic binders - before and after torrefaction

Given the current energy situation in the world, torrefaction offers a promising technique to produce fuel that can be used as a substitute for coal in combustion. The pelletization process can be simplified by the addition of a binder. The aim of this study was to determine the influence of the binders on the mechanical and energetic properties of the pellets before and after torrefaction. The initial material was meadow hay. The hay was crushed and three binders (lignosulfonate, wheat flour, starch) were added – 2 and 15 wt% The basic powder characterisation (compressibility, internal friction, and angle of repose) were performed on all binders. Lignosulfonate had the best flowability and therefore appeared to be the most suitable from a process point of view. Testing were also carried out on non-torrefied and torrefied pellets. The mechanical-physical properties (hardness, mechanical resistance, wettability) were also determined. Hardness and mechanical resistance for non-torrefied pellets was increased by adding lignosulfonate (15 wt%) and flour (2 wt%). After torrefaction, hardness and pellet durability index of the pellets decreased significantly, but still the best pellets were those with lignosulfonate (15 wt%) and flour (2 wt%). Non-torrefied pellets do not have a good resistance to water absorption, but lignosulfonate in ratio of 15 wt% and as well as flour in ratio of 2 wt% have a positive effect on this feature. Moreover, torrefaction has a positive effect on the pellet's resistance to water absorption. The results were supported by FTIR analysis. Last but not least, a simple calculation of the consumption of pellets for heating and hot water heating for a family home is given.

Assessment of thermally activated inner building components or a high temperature stone storage system to utilize surplus renewable electrical energy

In electrical grids with high renewable percentage, the production is less demand-driven, but more dependent on weather conditions. Usually there are periodical weather patterns in the winter period with substantial amounts of wind energy production. Sometimes this results in a shut-off of the wind turbines due to the inability of the grid to use the produced power (overproduction). This paper assesses two thermal storage systems to service space heating and domestic hot water that exploit the overproduction of electricity and thereby bridge periods of lower electricity production. The first system utilizes inner walls and ceilings with a thermally activated building system (TABS) to store and dissipate heat. The second system is a stand-alone high temperature stone storage system (HTSS). Models to simulate such systems within a whole building simulation framework are introduced and the verification with measured data from field measurements is presented. Both models are used to assess the systems with regard to the speed of loading and the potential to bridge time periods without additional heating in high-performance buildings. It is shown that both systems can provide heating and domestic hot water (DHW) up to 13 days without substantial additional electricity.

Artificial neural network model for predicting CO2 heat pump behaviour in domestic hot water and space heating systems

The natural refrigerant, CO2, possesses thermophysical properties that make it highly suitable for domestic hot water (DHW) production using heat pump technology. In this study, the development and validation of an artificial neural network (ANN) model that enables efficient design and control of a CO2 heat pump is presented. The study employs experimental data from a CO2 heat pump with a nominal heat capacity of 8 kW. The fully instrumented rig includes the heat pump and a pump rig designed to generate system temperatures representative of various space heat and DHW demands. A comprehensive dataset was generated through systematic variation of inlet temperatures and setpoints. The ANN provides predictions for outlet temperatures, heat production, and electricity consumption utilizing inlet flow rates, temperatures, and setpoints as inputs. These predictions are important for condition monitoring or in a smart operation management framework that determines optimal schedules for the machine.

Over- and underconsumption of residential heating: Analyzing occupant impacts on performance gaps between calculated and actual heating demand

Previous research estimates that building physics and occupant practices equally contribute to the explanation of variations in residential heating consumption. However, the so-called performance gap, where calculated heating demand diverge from actual heating consumption, indicates that the relation between occupant practices and building physics vary across energy efficiency of the building. In this paper, using data from 2019 to 2021, we investigate such interactions 1) by comparing the calculated heating demand with actual energy consumption for residential space heating and domestic hot water (DHW) across energy performance certificates (A to G), and 2) by investigating variations in over- and underconsumption (deviance from calculated to actual) across socio-economic characteristics of households. In line with previous studies, we find that households living in energy inefficient houses tend to have lower heating demand than expected. Moreover, we find that lower-income households and households living in rural area has less overconsumption, indicating that they have more frugal heating practices.

Analysis of the performance and operation of a photovoltaic-battery heat pump system based on field measurement data

Photovoltaic-heat pump (PV-HP) combinations with battery and energy management systems are becoming increasingly popular due to their ability to increase the autarchy and utilization of self-generated PV electricity. This trend is driven by the ongoing electrification of the heating sector and the growing disparity between growing electricity costs and reducing feed-in tariffs in Germany. Smart control strategies can be employed to control and optimize the heat pump operation to achieve higher self-consumption of PV electricity. This work presents the evaluation results of a smart-grid ready controlled PV-HP-battery system in a single-family household in Germany, using 1-minute-high-resolution field measurement data. Within 12 months evaluation period, a self-consumption of 43 % was determined. The solar fraction of the HP amounts to 36 %, enabled also due to higher set temperatures for space heating and domestic hot water production. Accordingly, the SPF decreases by 4.0 % the space heating and by 5.7 % in the domestic hot water mode. The combined seasonal performance factor for the heat pump system increases from 4.2 to 6.7, when only considering the electricity taken from the grid and disregarding the locally generated electricity supplied from photovoltaic and battery units.

The race between hydrogen and heat pumps for space and water heating: A model-based scenario analysis

This paper analyses different levels and means of the electrification of space and hot water heating using an explorative modelling approach. The analysis provides guidance to the ongoing discussion on favourable pathways for heating buildings and the role of secondary energy carriers such as hydrogen or synthetic fuels. In total, 12 different scenarios were modelled with decarbonisation pathways until 2050, which cover all 27 member states of the European Union. Two highly detailed optimisation models were combined to cover the building stock and the upstream energy supply sector. The analysis shows that decarbonisation pathways for space and water heating based on large shares of heat pumps have at least 11% lower system costs in 2050 than pathways with large shares of hydrogen or synthetic fuels. This translates into system cost savings of around €70 bn. Heat pumps are cost-efficient in decentralised systems and in centralised district heating systems. Hence, heat pumps should be the favoured option to achieve a cost-optimal solution for heating buildings. Accordingly, the paper makes a novel and significant contribution to understanding suitable and cost-efficient decarbonisation pathways for space and hot water heating via electrification. The results of the paper can provide robust guidance for policymakers.

Comparative analysis of a multi-generation system using different conventional & nano based working fuels

The present study focuses on the energy and exergy analyses of a multi-generation system that uses both non-renewable and renewable energy sources, fulfilling the energy requirements of a community in a sustainable manner. The operational comparison of the proposed system with both conventional and non-conventional nano-based working fuels is also studied. The proposed system provides a total of six outputs which include electricity, desalinated water, space heating, space cooling, hot water, and waste heat for industrial purposes. The effects of variation in geothermal fluid’s mass flow rate, pressure, and quality on system outputs are evaluated. System performance with different fuels having varied calorific values is evaluated and it is found that the fuels with the highest calorific values provide the best possible system outputs. The outputs of the system at a specific working condition are 6.7 MW of electricity, 650 kW of waste heat, 10.54 kg/s of desalinated water, 41 kg/s of hot water, 331.7 kW of heating and 314.1 kW of cooling.

TRNSYS MODEL OF THE COMBI BOILER DOMESTIC HOT WATER CIRCUIT WITH A FOCUS ON THE PARAMETER DEFINITION OF THE PLATE HEAT EXCHANGER

Combi boilers used for both space and domestic hot water heating are one of the common household appliances. Modelling the domestic hot water circuit of a combi boiler for the preliminary evaluation of the laboratory testing is of crucial importance since it decreases the time, cost, and energy spent on the trials. There are various modelling approaches established by the authors of this paper. Domestic hot water circuit of the appliance is modelled previously making use of the Transient System Simulation Tool (TRNSYS 18) and a good agreement is achieved with the experimental and numerical data for the economic mode simulations. The drawback of the current TRNSYS model is the dependence on the experimental data for some of the parameter definitions of the components selected from the TRNSYS library, i.e. UA (multiplication of the overall heat transfer coefficient and the heat transfer area) input of the plate heat exchanger and heat retention effect of the heat cell block. In this paper, a constant value is assigned to the parameter definition of UA instead of a time dependent varied profile. Mean absolute errors covering the steady-state and transient operating regions for the domestic hot water inlet and outlet temperature difference in economic mode simulations stay nearly the same around 0,46°C, 0,82°C, and 0,53°C for 5 l/min, 7 l/min, and 8,7 l/min, respectively, under constant and variable UA approaches. Comfort operating scheme model is established with a couple of experimental data as of the principal application of the constant UA approach. Mean absolute error of the overall domestic hot water inlet and outlet temperature difference profile decreases to 0,5°C in the comfort mode simulation.

Optimal control of a solar-driven seasonal sorption storage system through deep reinforcement learning

Deep reinforcement learning (DRL) has demonstrated its effectiveness in the control of energy systems, although it has not yet been applied to sorption thermal energy storage (TES) systems. The operation of sorption TES systems is notably more complicated compared to other TES variants. The discharge of a sorption TES occurs at a particular desorption and evaporation temperature. Achieving a continuous and efficient discharge of a sorption TES is a challenging control task if heat required at the evaporator is obtained from the sun or the environment. Its operation is especially complicated during winter, because of the limited availability of solar irradiation and low ambient temperatures. Thus, this study analyzes for first time in the literature the competitiveness of deep reinforcement learning to control a solar-driven seasonal sorption TES system and compares it against traditional optimized rule-based control strategy. The system, located in Central Europe, supplied domestic hot water and space heating to a single-family house. Two DRL models were developed and trained to operate the system under two different sets of data: 120 winter consecutive days and 60 winter non-consecutive days. The results showed that the DRL control strategy reduced the system operational costs by 28% in a 60 winter days scenario. For a 120 winter days scenario, the operational cost savings decreased to 13% because the smart control performed worst once the sorption TES was fully discharged. These results were derived from a four-year validation data set, bolstering their robustness. The study demonstrates the successful application of DRL in controlling a solar-driven seasonal sorption TES system, yielding considerable economic savings compared to an RBC strategy. Subsequent work will consist of implementing the smart control strategy at prototype level to assess its performance.

Biofuel, solar-biofuel or geothermal energy; which resource can better contribute to an integrated energy system for residential energy demands

In this study, an integrated energy system is deployed to generate electricity, heating, cooling, and domestic hot water for a community considering three fully renewable energy source scenarios: biofuel (Case I), solar-biofuel (Case II), and geothermal energy (Case III). The proposed systems are modeled, assessed, optimized, and compared thermodynamically, economically, and environmentally; in addition, a proton exchange membrane electrolyzer (PEME) is utilized to convert the surplus electricity to stored hydrogen to raise the exergy efficiency and package value-added. Mathematical models are fully developed from thermodynamic and economic perspectives, taking a cumulative inflation rate into account via the Consumer Price Index (CPI). The novelty of the study is to focus on the comparison of the proposed renewable resources as energy inputs for the same condition (community loads), as there is a gap in this regard in the literature. Presented systems are optimized using Non-dominated Sorting Genetic Algorithm-II (NSGA-II) for minimum net costs as well as maximum exergy efficiency to reach the optimum operating conditions for each case during the peak cooling and heating loads. Moreover, for a more precise comparison of the proposed cases from an exergetic, economic, and environmental point of view, dynamic (hourly) behaviors of systems are analyzed over a year. A comprehensive parametric study is carried out to reckon the influence of borehole drilling depth, geofluid mass flow rate, and soil temperature gradient on case III to appraise its competitiveness in different locations, as well as the effects of the number of collectors on total cost rate and exergy efficiency for case II. Results indicate the annual net costs of $107.1k, $99.5k, and -$505.1k (where the negative sign stands for "income" instead of "cost") and average exergy efficiencies of 19.1 %, 26.3 %, and 41.3 % for cases I, II and III respectively.

Estimating residential space heating and domestic hot water from truncated smart heat data

The EU aims to digitize the building stock across all member states to better understand energy use and achieve energy efficiency goals to address climate change. Smart heat meters are currently used for billing purposes in district heating (DH) grids. Their data is recorded as integer kWh values, which restricts usability for the modeling and analysis of DH networks. Previous research devised a methodology to estimate space heating (SH) and domestic hot water (DHW) energy from total heating data, but the data truncation process reduced accuracy. This study integrates the SPMS (Smooth–Pointwise Move–Scale) algorithm, which estimates decimal values from DH truncated measurements, to improve the accuracy of the DHW and SH disaggregation methods. The study applies these two methodologies to a dataset of 28 Danish apartments and compares the results against full-resolution and truncated data to evaluate performance. Another dataset, named “optimal dataset” is also assessed to determine overall estimation accuracy. Results show that SPMS reduces the disaggregation methodology error of SH and DHW compared to the truncated data. The optimal dataset outperforms the current methodology, indicating a potential for improving and scaling the methodology for larger datasets.

Heat pumps on exhaust air for space heating and domestic hot water, in very high energy performance multifamily building (Geneva, Switzerland): feedback in actual condition of use

This paper, which is based on a monitored case study, analyses the issues and potential of heat recovery on exhaust air in new multi-family buildings, for combined DHW and SH production by way of heat pumps, in combination with a gas boiler for peak loads. Analysis of the hydraulic diagram, as well as readings of heat and energy meters, allows to point out the causes of an actual HP share (38%) much lower than the projected value (61%), while bringing up the necessity to elaborate standard schemes and sizing rules for such systems.

Optihood – a multi-objective analysis and optimization framework for building energy systems at neighborhood scale

Strategic planning of future energy systems in a district is essential to fully exploit the benefits from sector coupling. In particular, new solutions may arise when a group of buildings is considered rather than individual buildings. Therefore, in order to identify optimal production and storage technologies within a district, new tools are needed. In this paper we present the open-source software framework optihood, which allows for multi-objective optimization and analysis of energy systems for neighborhoods. The tool assists researchers and, in a future stage, energy planners in the design of energy systems as well as in optimizing their operation strategy. After introducing the tool, its applicability is demonstrated with a case study, wherein we investigated the supply of a small neighborhood of four residential buildings with space heat, domestic hot water and electricity demands. The optimizer was allowed to consider heat pumps (geothermal/air-source), PV, batteries and hot water storage tanks as available technologies. We show comparisons between optimal solutions in terms of total annualized cost for the buildings considered (a) together as a group and (b) individually.

Energy assessment of a district by integrating solar thermal in district heating network: a dynamic analysis approach

This work focuses on the energy upgrade of a neighbourhood located in Yverdon-les-Bains, Switzerland. Starting from a potential low-temperature district heating network, the possible integration of new buildings equipped with solar thermal collectors capable of interacting with the network is explored. The methodology proposed in this work focuses on the study of the energy balance of the district at reduced time intervals. Bringing the level of detail to every single hour of the year, the concept of energy balance is superseded by the hourly averaged power balance as a specific tool for a detailed exploration of the energy flows exchanged within the district. Self-sufficiency has also been identified as key performance indicators to better understand the ability of the entire district to satisfy its heating and domestic hot water needs, modelled in CitySim’s urban energy environment. The outcomes have shown that an annual energy balance is ineffective and unrealistic for describing performances of the district. On the other hand, the hourly averaged power balance proves to be a powerful tool for understanding the dynamics of the neighbourhood. The district’s energy flexibility, which relies on energy sharing, is a characteristic that cannot be assessed annually. Using the new proposed methodology to evaluate the district’s thermal energy sharing, it was discovered that the district functions as a Zero Energy District for 28.9% of the year. With the outcomes presented in this study, it is now possible to comprehend how a dynamic performance assessment positively impacts a district’s redevelopment strategies.

Field study of energy and environmental performance of ground source heat pumps retrofitted in a cluster of UK social housing dwellings

This paper uses an empirical field based approach to examine seasonal variation in heat pump electricity use and indoor temperatures across a cluster of 50 social housing dwellings retrofitted with ground source heat pumps (GSHP). The case study dwellings were co-located in a socially deprived housing estate in Oxford (UK). Time series monitoring data was collected on indoor, outdoor and target temperatures, heat pump electricity use (kWh), space heating and hot water operation status (on/off) in non-heating and heating seasons. Contextual data about the dwellings were gathered through heat pump surveys and energy performance certificates. Results showed 60% rise in electricity use during the heating season with statistically significant correlation observed between indoor and target temperatures, energy efficiency rating and monthly electricity use. When electricity use was normalised for outdoor temperature, lower electricity use per heating degree day (HDD) was observed in the heating season in the majority of dwellings, confirming the efficient running of the heat pumps. Mean daily indoor temperatures were found to be stable across majority of dwellings, with peak electricity use largely happening outside of the national evening peak period of 4pm-7pm. Despite there being a ‘Timer’ mode in relation to outside weather, most of the residents used the ‘Manual’ mode of operation to adjust target temperatures, possibly to overcome the low output temperatures of heat pumps. While resident training will help to enhance understanding of heating controls, smarter operation of heat pumps through automation based on optimisation of indoor and outdoor temperature, occupancy patterns, heating preferences and electricity price signals could result in further energy savings.

Discrete optimal designs for distributed energy systems with nonconvex multiphase optimal power flow

The optimal selection, sizing, and location of small-scale technologies within a grid-connected distributed energy system (DES) can contribute to reducing carbon emissions, consumer costs, and network imbalances. There is a significant lack of studies on how DES designs, especially those with electrified heating systems, impact unbalanced low-voltage distribution networks to which most DES are connected. This is the first study to present an optimisation framework for obtaining discrete technology sizing and selection for grid-connected DES design, while simultaneously considering multiphase optimal power flow (MOPF) constraints to accurately represent unbalanced low-voltage distribution networks. An algorithm is developed to solve the resulting Mixed-Integer Nonlinear Programming (MINLP) formulation. It employs a decomposition based on Mixed-Integer Linear Programming (MILP) and Nonlinear Programming (NLP) and uses integer cuts and complementarity reformulations to obtain discrete designs that are also feasible with respect to the network constraints. A heuristic modification to the original algorithm is also proposed to improve computational speed. Improved formulations for selecting feasible combinations of air source heat pumps (ASHPs) and hot water storage tanks are also presented. Two networks of varying size are used to test the optimisation methods. Designs with electrified heating (ASHPs and tanks) are compared to those with conventional gas boilers. The algorithms outperform one of the existing state-of-the-art commercial deterministic MINLP solvers, which fails to find any solutions in two instances within specified time limits. While feasible solutions were obtained for all cases, convergence was not achieved for all, especially for those involving the larger network. Where converged, the algorithm with the heuristic modification has achieved results up to 70% faster than the original algorithm. Results for case studies suggest that including ASHPs can support up to 16% higher renewable generation capacity compared to gas boilers, albeit with higher ASHP investment costs, as local generation and consumption minimises network violations associated with excess power export. The results also show the importance of including nonlinear power flow constraints in DES design problems. The optimisation framework and results can be used to inform stakeholders such as policymakers and network operators, to increase renewable energy capacity and aid the decarbonisation of domestic heating systems.

Performance analysis and multi-objective optimization of a combined system of Brayton cycle and compression energy storage based on supercritical carbon dioxide

The energy storage system plays a pivotal role in optimizing the power grid’s peak mobilization. In this study, we propose a combined cycle of supercritical carbon dioxide (sCO2) recompression cycle (sCO2-RC) coupled with compressed sCO2 energy storage (S-CCES) system. Two distinct layouts are thoroughly investigated, each corresponding to different auxiliary heat source locations: utilizing waste heat to heat hot water of S-CCES (SCW-CCES) and heat CO2 of S-CCES (SCC-CCES). Through comprehensive thermodynamic modeling and analysis, we evaluate the performance of both layouts and conduct multi-objective optimization using a genetic algorithm. The results indicate that, under identical design conditions, the heat input leads to a respective increase of 4.25 MW and 7.02 MW in the output power of S-CCES for the SCW-CCES and SCC-CCES layouts. Furthermore, parametric analysis reveals that the performance of SCC-CCES surpasses that of SCW-CCES when considering performance indicators other than round-trip efficiency (RTE). The results obtained from multi-objective optimization demonstrate that the optimal solution for SCW-CCES achieves a higher RTE of 25.94 %, while the optimal solution for SCC-CCES exhibits a superior levelized cost of electricity and exergy efficiency, amounting to 68.94 $/MWh and 58.76 %, respectively.

A study on how efficient measures for secondary district heating system performance can be encouraged by motivational tariffs

BackgroundDistrict Heating (DH) is a technology that provides heating and domestic hot water to buildings and is an important technology for supporting the European energy transition. As such the heating systems increasingly resort to renewable heat sources and waste heat, it is even more important that they operate in the most efficient way possible. DH companies have access to the primary network of which they can impact system performance. To maximize the efficiency of the system, however, it is important that the system at the building level, known as the secondary system, is also efficient; otherwise, overall system efficiency is reduced. To increase system efficiency, return temperatures from the secondary system into the primary system have been targeted through motivational tariffs. There is limited information on how to establish a motivational tariff that motivates the customer to improve both the primary and secondary systems, which is a gap that this paper aims to fill.ResultsIn this paper, the impacts of retrofit actions in secondary systems are assessed through simulations. The identified relevant refurbishment measures to lower the return temperature to the primary system are variable flow pumps, low-temperature radiators, parallel heat exchangers, and a pass-through DHW system. Apart from simulated refurbishments, we also identify that the secondary system sometimes generates excess heat, which is valuable to recover, especially during peak load periods for the primary system. Hence, motivational tariffs targeting secondary system efficiency should also encompass an incentive for the customer to make use of waste heat in the secondary circuit to lower peak demand for the DH system.ConclusionsTo date, the most commonly used parameters introduced to customers are linked to the flow of water through the customer’s asset and the bonus malus principle. The results from simulations show that DH companies can introduce additional parameters to support customers in guiding their secondary system to perform more efficiently. Increased overall system efficiency has a positive impact on both costs and emissions.

Hot water immersion is associated with higher thermal comfort than dry passive heating for a similar rise in rectal temperature and plasma interleukin-6 concentration.

To compare the perceptual responses and interleukin-6 (IL-6) concentration following rectal temperature-matched dry heat exposure (DH) and hot water immersion (HWI). Twelve healthy young adults (BMI 23.5 ± 3.6kg/m2; age: 25.8 ± 5.7years) underwent 3 trials in randomised order: DH (air temperature 68.9°C), HWI (water temperature 37.5°C), and thermoneutral dry exposure (CON, air temperature 27.3°C). Blood samples to determine IL-6 plasma concentration were collected; basic affect and thermal comfort, rectal and skin temperature (Tskin) were assessed throughout the intervention. Rectal temperature (Trec) did not differ between DH (end temperature 38.0 ± 0.4°C) and HWI (37.9 ± 0.2°C, P = 0.16), but was higher compared with CON (37.0 ± 0.3°C; P ≤ 0.004). Plasma IL-6 concentration was similar after DH (pre to post: 0.8 ± 0.5 to 1.4 ± 1.5pg·ml-1) and HWI (0.5 ± 0.2 to 0.9 ± 0.6pg·ml-1; P = 0.46), but higher compared with CON (0.6 ± 0.5 to 0.6 ± 0.4pg·ml-1; P = 0.01). At the end of the intervention, basic affect and thermal comfort were most unfavourable during DH (Basic affect; DH: - 0.7 ± 2.9, HWI: 0.8 ± 1.9, CON 1.9 ± 1.9, P ≤ 0.004; Thermal comfort; 2.6 ± 0.8, HWI: 1.4 ± 0.9 and CON: 0.2 ± 0.4; P ≤ 0.004). Mean Tskin was highest for DH, followed by HWI, and lowest for CON (DH: 38.5 ± 1.3°C, HWI: 36.2 ± 0.5°C, CON: 31.6 ± 0.7°C, P < 0.001). The IL-6 response did not differ between DH and HWI when matched for the elevation in Trec. However, thermal comfort was lower during DH compared to HWI, which may be related to the higher Tskin during DH.

Experimental analysis of the air-to-water ejector-based R290 heat pump system for domestic application

The aim of this study was to analyze and compare the performance of an 8 kW ejector-based R290 heat pump system for hot water heating applications that operates with two different expansion devices: a throttling valve and a two-phase ejector. The vapor compression system was designed to compare the unit operating in direct expansion and ejector-based working modes under similar operating conditions. The test results were assessed for overall system performance measured by COP and heating capacity and for single-component operation. The ejector mode allowed operation at the same range of ambient temperatures as direct expansion mode with the potential to completely replace the expansion valve. The COP of ejector system was up to 2.6, which was 38% higher than direct expansion when working under similar conditions for an ambient temperature of −12.8°C. Moreover, the COP in direct expansion mode decreased significantly along the decrease of ambient temperature, whereas for the ejector mode this decrease was considerably smaller.