Heat Demand in Non-Residential Buildings: Renovation and Subsidy Effects

Heat savings in buildings in a 100% renewable heat and power system in Denmark with different shares of district heating

Heat demand of buildings and related CO2 emissions caused by energy supply contribute to global climate change. Spatial data-based heat planning enables municipalities to reorganize local heating sectors towards efficient use of regional renewable energy resources. Here, annual heat demand of residential buildings is modeled and mapped for a German federal state to provide regional basic data. Using a 3D building stock model and standard values of building-type-specific heat demand from a regional building typology in a Geographic Information Systems (GIS)-based bottom-up approach, a first base reference is modeled. Two spatial data sets with information on the construction period of residential buildings, aggregated on municipality sections and hectare grid cells, are used to show how census-based spatial data sets can enhance the approach. Partial results from all three models are validated against reported regional data on heat demand as well as against gas consumption of a municipality. All three models overestimate reported heat demand on regional levels by 16% to 19%, but underestimate demand by up to 8% on city levels. Using the hectare grid cells data set leads to best prediction accuracy values at municipality section level, showing the benefit of integrating this high detailed spatial data set on building age.

Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms

Until today the space heat demand of residential buildings in northern and middle European countries is still mainly supplied by the combustion of fossil fuels (mostly gas and oil). The sector therefore contributes a major share of the yearly energy related CO2 emissions of these countries. One reason for the low renewable penetration in the heating sector is, that the largest heat demand occurs during the winter period whereas in contrast high production rates of renewables prevalently occur during the summer period. To overcome this seasonal discrepancy this paper proposes a novel long term storage system based on the thermochemical reaction of calcium hydroxide to calcium oxide and water. Basic idea of the concept is to use excess electricity, for example from roof top photovoltaic systems, during the summer time to drive the endothermal charging reaction. The charged material can then be stored in simple containers at ambient temperature and the chemical potential is preserved without energy losses for an unlimited period of time. During the winter the thermal energy, which is released by performing the exothermal back reaction, provides the heat demand of the building. In contrast to so far analysed reaction systems for seasonal storage, the system is discharged with liquid water instead of water vapour, which enhances the discharging process, technically and energetically. Moreover using electrical energy for charging, instead of solar thermal energy, allows a flexible adaption of the storage operational times. This way, the system can be operated so, that the waste heat, which necessarily occurs during the charging process, can completely be used to satisfy the domestic hot water production during the summer. This newly identified operation principle enables a significant increase of the systems storage efficiency. A detailed analysis of the energy balance combined with a first case study of the integration into the building revealed that a potential storage efficiency of up to 96 % can be reached. In brief, this paper presents a completely new technological concept which couples the power and heat sector by cost efficient long term energy storage and evaluates the potential for the application in residential buildings.

New concepts and technologies are needed to upgrade conventional energy systems and cope with environmental challenges. However, while emerging new technologies may serve to improve energy efficiency at the local level, they might also have disruptive effects at the system level. This paper investigates the potentially disruptive impacts of upscaling local wastewater heat recovery at the building level on the performance of the wastewater treatment and district heating systems in Stockholm. A hybrid model based on data-driven and process-driven mathematical models was developed to simulate the performance of the wastewater system and interlinkages among different actors. Two types of technologies (heat exchanger and heat pump) and different technology penetration scenarios (10%, 20%, and 40%) are considered for heat recovery in buildings. If 20% of the buildings install heat exchangers, the amount of heat demand in buildings decreases by 3% and total heat losses in the sewerage network decreases by 2%. In the case of local heat recovery using heat pumps in 20% of the buildings, there is a 4% reduction in the heat demand in buildings and 3% decrease in total heat losses in the network. Meanwhile, the heat demand in the wastewater treatment plant increases by 2% (with heat exchangers) or 4% (with heat pumps). Moreover, the district heating company recovers 5% and 9% less heat from the wastewater treatment plant, respectively, as compared to current conditions. These findings indicate that enhanced heat recovery in buildings could have disruptive impacts on currently centralized energy and water service provision over time. This justifies closer consideration of the balance between local and system-level solutions as policymakers define goals for energy efficiency, and evaluate potential societal and economic implications of different alternatives.

This analysis elaborates further the concept of physical and economic suitability for district heating in EU28 by an aggregation regarding key dimensions such as land areas, populations, heat demands, and investment volumes. This aggregation is based on a resolution on hectare level by slicing the total land area into 437 million pieces. Results show that heat demands in buildings are present in 9% of the land area. Because of high concentrations in towns and cities, 78% of the total heat demand in buildings originate from dense urban areas that constitute 1.4% of the total land area and 70% of the population. Due to these high heat densities above 50 MJ/m2 per year, the paper evaluates a setting where district heating is individually expanded in each member state for reaching a common 50% heat market proportion in EU28 at lowest cost. At this saturation rate, the aggregated EU28 district heat deliveries would increase to 5.4 EJ/a at current heat demands and represents an expansion investment volume, starting from current level of 1.3 EJ, of approximately 270 billion euro for heat distribution pipes. Given the current high heat densities in European urban areas, this study principally confirms earlier expectations by quantitative estimations.

A novel spatial–temporal space heating and hot water demand method for expansion analysis of district heating systems

This paper focuses on evaluating the technical, economic and ecological effects of the transition from the current high temperature charts to the lower temperature charts in the Moscow DHS by means of a developed spreadsheet-based model. A methodology suitable for assessing results of potential transition to lower temperatures in DHS of cities in Russia and worldwide is proposed and implemented in the model. The reference case of 2016 and three cases with decreased heat demand in buildings by 5, 10, and 20% were considered. The results show that fuel savings of 678–872 ktce a can be achieved with respect to the current temperature charts in the Moscow DHS. The 110/50°С temperature chart is the most profitable option, with net present values varying from 4.64 to 10.74 bn RUB depending on the case. The 95/50°С chart, which leads to a reduction of 1.325–1.387 MtCO2/a, has the least impact on the environment. A more significant CO2 emissions reduction can be achieved by strong energy-saving measures and broad utilization of renewable and waste energy. The essential prerequisite for the transition is a reduction of the heat demand in buildings by at least 20%.

In this paper, we present a multi-variant analysis of the determination of the accuracy of the seasonal heat demand in buildings. The research was based on the linear regression method for data obtained during short periods of measurement. The analyses were carried out using computer simulation, and the numerical models of the multifamily building and school building were used for the simulation. The simulations were performed using the TRNSYS, ESP-r, and CONTAM programs. The multi-zone models of the buildings were validated based on the measurement data. The impact of the building’s parameters (airtightness, insulation, and occupancy schedule) on the determination of the accuracy of the seasonal heat demand was analyzed. The analyses allowed guidelines to be developed for determining the seasonal energy consumption for heating and ventilation based on short periods of heat demand measurements and to determine the optimal duration of the measurement period.

Modelling of buildings' heat demand provides decision makers information on the required energy on a house level. This is important, among other, to orient supply and subsidy policies. In residential buildings of the Netherlands around 70% of the gas is consumed on space heating, which is therefore the dominant share of the total heat demand. Different parameters have influence on space heat demand of a residential building: such as building architecture, material, age and inhabitants behavior. Collecting such information manually on a building level is a laborious process which can in many cases be substituted by automated extraction from geospatial information. This study proposes an automated workflow to extract the key parameters on heat demand of residential buildings. We have implemented the workflow in Amsterdam using geospatial technology and geospatial data. The case study revealed that the automatic extraction of data increased the efficiency of the whole process and the scalability of the analysis. Subsequently, we focused on renewable energy as heat supply source. We model the heat energy gain of a solar air collector, as a renewable energy source, using geospatial information for each building. Heat demand and solar air collector heat supply results for each building are crucial elements for decision making, both at policy level and at individual citizens to decide on investing in renewable energy. In order to make such information available to the largest possible audience, we implemented this information flow in an interactive web application. In the Netherlands 35% of the total energy is consumed in the built area. This amount equals to 30% of the total CO2 emissions in this country. Energy consumption ratio between residential and utility buildings is almost 50/50. Considering the total amount, we come to the conclusion that reducing energy consumption in residential sector will lead to considerable reduction in total consumed energy of the Netherlands. Currently there are policies on European and Netherlands level for the energy reduction in built areas. Based on EPBD directives, from 2020 new buildings should be energy neutral. Reducing energy consumption in buildings and using renewable energy sources are the important goals in EPBD directives for European Union. In the Netherlands more than 55% of the total energy is being consumed for space heating which is large share of the total used energy. Decreasing this energy amount and using renewable energy sources for space heating will lead to considerable energy savings in building areas. To move towards this goal, the first step will be having an overview on the current status of space heating demand in building and neighbourhood level. This will help decision makers and energy sector by proving them the heat demand information. However, estimating the heat demand for each building manually is an expensive and time consuming task, especially when performed for the whole country or even a whole city. Therefore automated procedures for estimating the heat demand is greatly appreciated.

Techno-economic analysis of a solar district heating system with seasonal thermal storage in the UK

Sustainable urban heat strategies: Perspectives from integrated district energy choices and energy conservation in buildings. Case studies in Torino and Stockholm

Easily adaptable indoor temperature and heat demand models were applied in the predictive optimization of the heat demand at the city level to improve energy efficiency in heating. Real measured district heating data from 201 large buildings, including apartment buildings, schools and commercial, public, and office buildings, was utilized. Indoor temperature and heat demand of all 201 individual buildings were modelled and the models were applied in the optimization utilizing two different optimization strategies. Results demonstrate that the applied modelling approach enables the utilization of buildings as short-term heat storages in the optimization of the heat demand leading to significant improvements in energy efficiency both at the city level and in individual buildings.

The widespread use of combined heat and power (CHP) distributed generation (DG) for buildings could significantly increase energy efficiency and reduce greenhouse gas and air pollution emissions. By displacing both electricity from conventional centralized power plants and heat from decentralized boilers, CHP DG could reduce primary feedstock fuel consumption in the U.S. by approximately 20%, or 6,000 terawatt hours. However, optimally integrating CHP DG within buildings is challenging. This work aims to elucidate optimal system sizing and design of micro-CHP fuel cell systems (FCSs) integrated with commercial buildings. This modeling effort compares and contrasts the performance of high temperature polymer electrolyte membrane (PEM) fuel cell systems (HTPEM FCSs) and solid oxide fuel cell (SOFC) systems for commercial buildings. A parallel research effort is independently analyzing measured data from HTPEM FCSs installed in commercial buildings. Measured data from that effort is integrated into this modeling work. In certain regions, there has been a research and development and commercialization trend moving from using low temperature PEM FCSs (e.g. with a stack temperature of around 80°C) to using HTPEM FCSs (e.g. with a stack temperature of around 160°C) and to using SOFC systems (e.g. with a stack temperature of around 700°C) for CHP building applications, given the higher temperature of the available waste heat from these systems. In this work FCS performance data is coupled with building energy system models from the U.S. Department of Energy (DOE) using EnergyPlus™ whole-building energy simulation software. Using these baseline reference commercial building model data, parameters are examined including heat demand for space heating and for domestic hot water heating over time, temperatures and water flow rates associated with this heat demand, and building electrical demand over time, to evaluate FCS integration within the building. Examining the data obtained through the simulation exercise in this work, it is found that in a large office building, with heat demand temperatures in the range of 82°C for space heating and 60°C for hot water heating, an HTPEM FCS with an exhaust temperature of 47°C can potentially access, at a maximum, 19% of the total building heating demand. By contrast, in a small office building, with heat demand temperatures in the range of 23°C (supply air temperature) for space heating and 60°C for hot water heating, it is found that this HTPEM FCS can potentially access, at a maximum, 90% of the total building heating demand. Examining the temporal characteristics of the building heat demand to determine FCS sizing, it is found that a maximum of 50% of the time, the heat demand can be served with an HTPEM FCS with a thermal capacity of 8 kilowatts (kW) (0.05 kW for small office) and an electrical capacity of approximately 4.5 kilowatts-electric (kWe) (0.45 kWe for small office). A maximum of 80% of the time, the heat demand can be served with an HTPEM FCS with a thermal capacity of 85 kW (0.16 kW for small office) and an electrical capacity of approximately 73 kWe (0.14 kWe for small office). The simulation results further indicate that an SOFC has advantages over an HTPEM FCS that originate from its higher exhaust temperature (between 25°C and 315°C), which allows it to meet a greater percentage of the building heating demand (up to 100%). This enables an SOFC to serve a larger percentage of the building stock and a wider variety of building heating systems. Furthermore, if the CHP FCSs are grid independent (i.e., it is not possible to supply electrical power back to the grid), then the heat-to-power ratio of an FCS can be an important parameter. In such a scenario, the heat-to-power ratio of an SOFC (approximately 0.33) is closer to the heat-to-power ratio of a building (approximately 0.081, averaged over an entire year). In a stand-alone configuration, when the CHP DG has a heat-to-power ratio that more closely matches that of the buildings, the utilization of the DG system is likely to be higher and its economics and environmental impacts more favorable.

Smart electric thermal storage heating devices can be used for demand response, congestion management and incorporated within unit commitment and dispatch of generation resources for more efficient control of power systems. This paper presents an experiment-based thermal modelling approach of residential buildings which will be scaled to a national aggregate level to be used in overall power system modelling involving smart electric thermal storages. A simplified thermal network based on electrical RC-circuit analogy was developed to replicate building's thermal dynamics and model residential heat demand at national scale. To obtain the equivalent parameters for the RC model, physical experiments were conducted during which buildings were let to cool down and then heated for several times and indoor and outdoor temperature, heat consumption and solar radiation was recorded. The identified model exhibits a good performance which improves when solar gains are considered within it. Different control strategies of the heating equipment were examined and the hourly heat demand over the year was estimated.