3.1. Optimising a Zero Carbon Home
To establish if these design criteria could be used to create a more commercially viable zero carbon home, a baseline design was developed using a detached 4 bedroom property. The house type was a compact 3 storey home with a gross internal floor area of 142 m
2 designed to fit into a site masterplan of 50 homes per hectare. The planning portal states that densities of 50 to 100 homes in city centres and 50 to 65 dwellings along transport corridors should be aimed for when designing eco towns [
29]. It is anticipated that at this density all homes could be oriented for maximum solar gain and renewable energy generation.
A detached property was selected as this typology is most effected by heat loss through the building fabric. This makes it harder to achieve zero carbon status then semi-detached or terraced house typologies. The building was oriented to maximise the south facing roof space for renewable energy technologies. The baseline design layout was selected from a site masterplan for an eco-village that had failed to be built on commercial grounds after planning permission was granted.
The first stage in the methodology was to conduct a technical and cost analysis of the best available zero carbon microgeneration technologies. The largest cause of carbon emissions from a home in the UK result from space heating to replace heat lost through the building fabric, heat lost though ventilation, supplying hot water and household electrical demands. Key technologies designed to reduce these loads and supply the residual energy demand via zero carbon energy generation were investigated. Key technological solutions were then combined to create a number of different holistic building fabric, space, hot water and electrical generation systems. The designs were then modelled to establish energy losses, energy usage, energy use reduction and energy production outputs. Costs were obtained from manufacturers and distributors based on a 100 home development. These costs were verified by an independent quantity surveyor based on the drawings submitted. The cost based analysis incorporated both implementation and running costs to observe the effect that cost savings from reduced consumption and income generated.
The initial modelling was conducted using parametric analysis in an equation based model. Parametric analysis was chosen as, whilst not strictly an optimisation method, it can be used to optimise a building if systematically and methodically approached [
30]. Given the combined technical and cost optimisation that this study required, parametric analysis was the best option to use. Parametric analysis allowed the use of spread sheet software to design a model that linked both these elements together so that the effects of changing one parameter could be observed across both the technical and economic outputs.
Within the parametric analysis some of the parameters were fixed and some variable. The fixed parameters were for space heating demands using an 18 °C set point, hot water demand based on 5 person occupancy and 50 L of hot water/person/day, and predicted appliance load electrical consumption including lighting energy use. Other parametric factors were variable and included wall and window u-value, space heating consumption, ventilation rate energy loss with heat recovery, heat recovery efficiency, heating and hot water system efficiency, passive solar gains and internal gains, energy generation by different renewable energy systems, renewable energy system sizing, implementation costs, build costs and running costs. Changes in each of these parameters and the resulting effects on other parameters were then observed to allow for optimisation. U-values were calculated using Build Desk U version 3.4 and Thermal bridging calculations were conducted using THERM [
31]. Equations for thermal loads, heat loss through the fabric, heat loss through ventilation and heat loss through infiltration were taken from Frazer [
32]. The effect of thermal mass, solar gains and internal gains were taken from SAP [
33,
34].
Heat loss was calculated by the equation:
where
H = overall heat loss (W)
Ht = heat loss due to transmission through building envelope (W)
Hv = heat loss caused by ventilation (W)
Hi = heat loss caused by infiltration (W)
The heat loss through the building envelope was calculated by the equation;
where
Ht = transmission heat loss (W)
A = area of exposed surface (m2)
U = overall heat transmission coefficient (W/m2K)
ti = inside air temperature (°C)
to= outside air temperature (°C)
Heat loss due to ventilation was calculated using the following formula;
where
Hv = ventilation heat loss (W)
cp = specific heat capacity of air (J/kg·K)
ρ = density of air (kg/m3)
qv = air volume flow (m3/s)
ti = inside air temperature (°C)
to = outside air temperature (°C)
Mechanical Ventilation with Heat Recovery (MVHR) reduces ventilation heat loss as this was calculated using the using the following:
where β = heat recovery efficiency (%).
The Thermal Mass Parameter (TMP) for a dwelling is required for heating and cooling calculations. Firstly the heat capacity was calculated for the materials. The heat capacity, or kappa value per unit area (k in kJ/m
2K), for the thermal mass elements was calculated as follows:
where
dj is the thickness of layer (mm)
rj is density of layer (kg/m3)
cj is specific heat capacity of layer (J/kg·K)
The calculation was used for all layers in the element, starting at the inside surface and stopped at whichever of the following conditions was encountered first:
The total thickness of the layers exceeds 100 mm
The midpoint of the construction is reached
An insulation layer is reached (defined as thermal conductivity ≤ 0.08 W/mK);
Secondly the above calculations are used in the following formula:
The total TMP includes, walls, ground floor and inter floor materials.The benefit of thermal mass is taken into account in the utilisation factors.
Internal gains arise from lights, appliances, cooking and metabolic gains from the occupants. Useful heat gains and metabolic gains were used in the heating calculations based on their utilisation factors [
33,
34]. SAP standards were used for internal gains and solar gains [
33,
34]. Solar heat gains that arise through glazed elements with a glazing area greater than 60% were included in the calculations [
33,
34]. Solar gains were calculated separately for glazing on different elevation orientations. The equation for calculating solar gains used was:
where
0.9 is the typical average ratio of transmittance at normal incidence
A is the area the opening (m2)
S is the solar flux (sum of direct and diffuse solar radiation) on a surface in W/m2
G is the total solar transmittance factor for the glazed element at normal incidence (from manufacturer)
FF is the frame factor for windows and doors (fraction of opening that is glazed)
Z is the solar access factor due to over shading. This is assumed to be less than 20% due to new build passive solar design and thus an access factor of 1 is used here
S was calculated for the heating season using the formula below for converting horizontal irradiance to vertical:
where
where
Fx (m) is the vertical solar flux for an element in month m with orientation q (W/m2)
(m) is month
Rhtov(θ) is the factor for converting from horizontal to vertical solar flux
θ is the orientation of the opening measured eastwards from North (e.g., East = 90) (°)
ϕ is the latitude of the site (°) = 53.4° N for heating calculations
δ is the solar declination for month m (°)
Sh is the horizontal solar flux (W/m2)
Appliance and electrical loads were established using data from The University of Surrey “Efficient household Appliance Survey” [
35] and manufacturer’s data to calculate loads and run times.
Hot water energy consumption was calculated in kWh per person per annum using the following formula:
Hot water systems also suffer losses due to distribution. Distribution losses of 15% were also added to this figure.
To estimate solar electrical generation and average daily temperatures over the 24 h period the “Joint Research Centre of the European Commission’s” Photovoltaic Geographic Information System (PVGIS) software was used [
36]. PVGIS data is inputted from ground station measurements taken using pyranometer readings and factors beam, diffuse and reflected irradiation into the measurement. The data used in PVGIS is taken over a 10 year data spread [
37]. The PVGIS tool was chosen for both its usability and accuracy. The mean bias error (MBE) was only 0.3% and the root mean square error (RMSE) only 3.7% for the entire dataset within the model [
37]. As such the over-estimation by the model is considered to be as low as 3.2%. London Gatwick was chosen as the weather station.
3.2. Cost Impact Modelling
Each technically viable energy system was analysed to observe both the implementation and running cost impacts. Further design iterations were then conducted to identify additional areas for value engineering by reducing components, substituting materials or improving efficiency versus increasing energy production. By establishing what was technologically possible and then varying the design based on the findings, economic parameters such as implementation costs, build costs, running costs and cash flows were also optimised. Options modelled included different building fabric compositions, varying combinations of PV, air and ground source heat pumps, passive and mechanical heat recovery ventilation systems and thermal stores.
The economic element of the model included a full cost based analysis based on a net benefits-deficits approach to implementation and running costs. The net benefit was calculated using the following formula:
where
NB = Net benefit/deficit
Ti = Total Cash Inflows
Ta= Total Avoided Cost
To = Total Cash Outflows
Cash inflows were determined by capitalising the FITs income, avoided costs were calculated by capitalising energy load reductions and renewable energy consumption, and cash out flows by incorporating residual energy costs for electricity demands at 2014 market rates [
38]. The technical model was used to calculate and compare the energy losses of the zero carbon design with those of a home built to 2013 Part L building regulations. Potential energy savings for the zero carbon design were calculated and then translated into a monetary benefit which could be attributed to elements such as the extra insulation, heat recovery technology and improved air tightness levels. The economic model was projected forward over the 20 year FITs period. The forward projections enabled the viability of the model to be established in the longer term. Fuel price escalation and inflation were also included in the model at 5% and 3% Compound Annual Growth Rates (CAGR) respectively. Fuel price escalation is predictive and subject to significant uncertainty but the mean average of Ofgem’s “Project Discovery” and DUKES was used [
39,
40].
The latest available published data from OFGEM was used for the FITs tariff rate for installation dates up to the end of December 2014 [
41]. Generation rates were used based on the predicted PV system outputs and export tariffs were based on the 50% of the energy being exported [
40]. The net benefit model was also run using FITs estimates for installation dates in 2015. From 2015 onwards price degression rates for install costs were applied which matched degression rates set-out within the FITs scheme. The model was then run without the FITs scheme using only open market power purchase agreement prices for energy exported back to the grid. The rate used was based on FITs export rate at 50% deemed export [
41].
The economic model developed assumed that the extra capital costs for zero carbon design would be passed to the consumer via a higher purchase price. As the initial capital outlay is significant for the combined microgeneration platform, extended mortgage payments were assumed to be the finance method. As such the extra over mortgage costs were incorporated into the calculations for deriving an economic benefit. A mortgage rate of 5% was used over a typical 25 year mortgage period.
The reduced energy demand for both regulated and unregulated energy loads were capitalised and an allowance made for the bought in energy requirement during times of insufficient PV production. A cost saving for the PV produced electricity was also accounted for in the same way. The energy generated was then capitalised using the appropriate FITs rate in the initial case and without the FITs rate in subsequent cases. The totals were then summed and the extra over mortgage costs for the additional insulation and energy system components deducted to give the net benefits-deficit figure for each year.