using TES
The results relating to this research task is presented in Chapter 6 3.8.1 Overview
This research task involved simulating the impacts of the TES capacity, building insulation, occupancy and the operation conditions changing from the TES Reference Case. Four building archetype models are used which correspond to the mean floor area of the UK housing stock, of approximately 90m2, and the four most common built forms which are detached, semi-detached, mid terrace and purpose built flats. The TES capacity variation included changing the physical size and the water storage temperature. The values selected are: 1) Three TES tank sizes (0.25m3, 0.5m3 and 0.75m3) and 2) Two hot water storage temperatures (75℃, 95℃). Five parameters are varied in this exercise to account for building insulation, occupancy and operational condition differences commonly found in the UK. These are: 1) Thermal insulation (1980, 1990, 2002s and 2010 levels); 2) Location (London Gatwick and Aberdeen); 3) Heating duration (6 hours, 9 hours, 12 hours and 16 hours); 4) Thermostat set point (19℃, 21℃ and 23℃), and 5) Number of occupiers (one person and three persons).
As in tasks 2 and 3, predictions were generated indicating the effects of these parameter changes on the overall energy consumption, heating power demand and the thermal condition of the occupied space.
3.8.2 Model configuration and simulation arrangement
The heat demand shift achievable in buildings and its impact on thermal comfort would depend on the heat energy storage capacity of the TES and the heat loss or gain resulting from the operational conditions and the building fabric. The parameters
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changed to account for these are: TES capacity, building insulation, size, location, heating duration, thermostat set point and number of occupants as discussed below. Two groups of simulations are performed which are:
1. TES capacity impacts – The objective here was to explore and predict the effects of varying TES physical size and hot water storage temperatures on the HDS achievable, and its impacts on the thermal comfort of the occupied space, power and energy demand in comparison with the TES reference case simulations. Simulations were performed with three physical TES sizes and two water storage temperatures. The building thermal insulation, occupancy and operational parameters as per the Base Case were used. The differences in the parameters are illustrated by the underlined bold text in Table 3-13.
2. Impacts of thermal insulation, thermostat setting, location, heating duration and occupancy - The objective here was to explore the effects of varying building archetypes, occupancy and operational options on the thermal conditions in the occupied space and the energy consumption of the dwellings in comparison with the Base Case HDS effects. Predictions are generated with varying building thermal insulation, size, location, heating duration, thermostat settings, and occupancy in comparison with the TES Reference Case values as shown in Table 3-13.
Table 3-13. Summary of the model variable configuration used to simulate the impact of TES capacity and building thermal performance and operational conditions on heating energy demand shifting capabilities and the thermal comfort impacts in common UK buildings.
Parametric analysis - Simulations scenario options
TES Reference Case TES capacity impacts Impacts of thermal insulation, thermostat setting, location, heating
duration and occupancy
Building Archetype Used 12 4 (Det90, SDet90, MTer90, Flat90) 4 (Det90, SDet90, MTer90, Flat90)
1. Building regulation 1990 1990 1980, 1990, 2002, 2010
2. Thermostat setting Gatwick Gatwick Gatwick, Aberdeen
3. Heating duration 9 hours 9 hours 6, 9, 12, 16 hours
4. Location 21℃ 21℃ 19℃, 21℃, 23℃
5. Occupancy 2 adults & 1 child (Type B) 2 adults & 1 child (Type B) 2 adults & 1 child (Type B) , 1 adult (Type A),
6. DHW consumption 53lt/person/day Spread equally during daytime occupied hours 53lt/person/day Spread equally during daytime occupied hours 53lt/person/day Spread equally during daytime occupied hours
7. Internal gain TV, Cooker, Lighting, Occupant TV, Cooker, Lighting, Occupant TV, Cooker, Lighting, Occupant
8. HVAC System As per Figure 3-13 As per Figure 3-13 As per Figure 3-13
9. Simulation period 60 days from 2nd January 60 days from 2nd January 60 days from 2nd January
10. TES Intervention 0.25m3 0.25m3, 0.5m3, 0.75m3 0.25m3
11. TES water temperature 75℃ 75℃, 95℃ 75℃
12. Demand Shift Period (DSP) 2 hours: 17:00-19:00 3-hours: 17:00-20:00 4-hours: 17:00-21:00 2 hours: 17:00-19:00 3-hours: 17:00-20:00 4-hours: 17:00-21:00 4-hours: 17:00 - 21:00
13. TES Top-up Period (TTP) 00:00 – 07:00 00:00 – 07:00 00:00 – 07:00
Figure 3-21 illustrate the configuration and arrangement of the various parts of the model including buildings archetypes, heating system and the operational parameters. The buildings, occupancy and operational conditions used are summarised in Table 3-13. The HVAC and the TES systems are configured as described previously in Section 3.5.7 and Section 3.7.2. During the TES charging,
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the water is heated to the required temperature during 00.00 and 07.00 hours each day, and no further heating of the water outside this period is allowed. The discharging of the TES occurs during the DSP when the hot water in the TES system circulates through the heating system servicing the DHW and space heating needs. The heating system switches to the mains grid at the end of the DSP, and restore the space temperature to the desired set point.
Dynamic inputs (time varying) Occupancy profile
Heating profile
Lighting profile
Appliance use profile Dynamic inputs
(time varying) Occupancy profile
Heating profile
Lighting profile
Appliance use profile Dynamic inputs
(time varying) Occupancy profile
Heating profile
Lighting profile
Appliance use profile Dynamic inputs
(time varying) Occupancy profile
Heating profile
Lighting profile
Appliance use profile
Dynamic inputs (time varying)
Occupancy profile
Heating profile
Lighting profile
Appliance use profile
Dynamic inputs (time varying)
Occupancy profile
Heating profile
Lighting profile
Appliance use profile
Dynamic inputs (time varying)
Occupancy profile
Heating profile
Lighting profile
Appliance use profile Dynamic inputs (time varying) Occupancy profile (2) Heating duration (4) Weather/External gains (1) Lnternal Gain (1) HVAC Model (1) Building Archetype (4) Output Thermal condition Power demand Energy demand Data Analysis Ln Microsoft Excel Variables Location (2) Thermal insulation (4) DSP (3) Simulation control Time resolution Output data format Thermostat setting (3) Time of day (00:00-24:00) Time of day (00:00-24:00) Day (1 – 60 from 2nd Janauary ) TES Model (6)
Figure 3-21. Illustration of model configuration used for carrying out parametric analysis of the impacts of varying TES storage capacity and the building thermal insulation, location, heating duration, thermostat set point and occupancy.
As described in Section 3.7, the HDS is achieved by serving both the DHW and the space heating needs during the DSP using heat energy stored in the TES. The real- time occupied space heating period does not change from the original setting. The rational for selecting 17:00 as the start of the DSP is that the national grid load starts to rise at around this time and peaks shortly after, at around 18:00.
To ensure that the impacts of these changes can be compared, only one variable was changed during each simulation whilst the rest remained as per the TES Reference Case setting as explained previously in Section 3.7.2. Predictions were generated for the output variables previously described in Sections 3.5.9, and as also discussed later in Chapter 6.2. As before, simulations are performed for 60 winter days from the 2nd of January and with a time resolution of 1 minute. Also as before, the output data was recorded in text and graphical formats and Microsoft Excel was used to extract information relating to the three areas of model performance and the seven performance metrics as described in Section 3.5.9.
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3.9 Summary
This research work is based on three research questions: 1) What are the dynamics of heat demand in buildings and how do they vary with building typologies, fabric thermal insulation, occupancy and operational conditions?; 2) What is the scope of heat demand shifting in time by using residential building scale TES, and what are the key parameters which impact on their effectiveness?, and 3) What are the potential benefits and impacts of using residential building scale TES?
This chapter outlined the methodology used in carrying out four research activities to explore the answers to these questions, as summarised below:
1) Task 1: model development and validation – consisting of producing twelve Base Case building archetype models incorporating wet central heating systems powered by resistance element heating and sensible TES systems in TRNSYS. To ensure validity of the models and plausibility of the results, validation exercises were carried out through inter model comparison, and by comparing the results generated in this work with published measured data for similar buildings and operational conditions;
2) Task 2: simulations performed to analyse the energy consumption, heating energy cost and the thermal performance characteristic of most common building, occupancy and operational options found in the UK housing sector, without Thermal Energy Storage (TES) and heat demand shifting.
3) Task 3: simulations performed to generate predictions of heat energy demand shift using active sensible TES, from the grid peak times to the grid off-peak times by three fixed durations (2 hours, 3 hours and 4 hours), and the related impacts and benefits.
4) Task 4: simulations performed to simulate how the effectiveness of TES and its impacts changes with varying parameters such as TES capacity, building thermal insulation, physical, occupancy and operational conditions.
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4 C
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HERMAL PERFORMANCE CHARACTERISTICS OFDOMESTIC BUILDINGS
4.1 Introduction
T
his chapter presents the results of the simulations carried out to gain anunderstanding of the thermal and energy performance of domestic buildings, and how these are affected by changes in the building thermal insulation, physical, occupancy and operational conditions. No TES was applied during this activity.
This chapter is arranged as described below:
Section 4.2: Output variables and the performance analysis metrics: Provides an overview of the
simulation output variables for which results are generated, and the metrics used to compare the energy and thermal performance of the different building, occupancy and operational scenarios.
Section 4.3: Demonstration of the simulated input and output variables: Provides an overview of the
model performance, demonstrating and discussing: the input variables, function of the heating system, indoor temperature profiles, heating load profiles and the energy demand predictions for several sample days.
Section 4.4: Performance analysis of the Base Case buildings: Contains the results of the occupied
space thermal condition, heating power and energy demand characteristic predictions for the 12 Base Case scenarios. The heating energy cost predictions based on currently available standard electricity price tariff is presented.
Section 4.5: Performance impacts of thermal insulation, thermostat setting, location, heating duration and
occupancy: Contains the results of the impacts on the thermal condition, heating power and energy demand characteristic predictions due to changes in the thermal insulation level, thermostat set point, heating duration, location and occupancy variables.
Section 4.6: Provides a summary of this chapter.