Scenarios for Carbon Abatement in Dwellings by Implementation of Stirling Engine Micro-CHP Systems David Kane, Marcus Newborough Energy Academy, Heriot-Watt University, Edinburgh Abstract The Building Integrated Micro-Generation Model, a transient thermal and electrical demand estimation tool, has been developed to predict the performance of four micro-generation systems within a dwelling of specified construction during four simulation days corresponding to different climatic conditions. The thermal and electrical demands were estimated for a specified pattern of occupancy, appliance and domestic hot water usage relating to one of the domestic building variants from the Carbon Vision Buildings TARBASE programme. Consideration was given to a base case condensing boiler and three micro-CHP systems of varying electrical capacities (i.e. 0.5kWe, 1.1kWe and 1.8kWe) and electrical efficiencies (i.e. 18%, 28% and 38% respectively). The results of these simulation scenarios were quantified. For example, on the overcast winter day, the relative carbon saving (versus the base case) of each of the respective micro-CHP implementations was 6.4%, 11.7% and 17.7%. The primary aim of this research is to investigate the factors that affect the simulated and actual performance of micro-CHP systems in terms of carbon emissions. The main factors identified were transient thermal and electrical demand, micro-CHP system efficiencies and capacities, thermal and electrical storage capabilities and system control regime. The importance of carbon credit for the export of electrical output to the national grid to the carbon saving figures was highlighted, as was the major contribution to carbon saving from electrical import avoidance. Introduction In the UK, the domestic sector accounts for 30% of total energy demand, with an average 83% of this demand for the provision of space heating and domestic hot water, and the remainder for electrical lights and appliances . Of the approximate 25 million dwellings in the UK, around 17 million are fitted with domestic gas central-heating (DCH) boilers . The maturity of gas DCH boiler technology has resulted in efficiencies of up to 93% for gas condensing DCH boilers, leaving minimal possible carbon savings through further improvements of DCH technology. Therefore, research into carbon abatement has pursued other avenues, one of which is co-generation of heat and electricity, using a micro-CHP system. This generates electricity whilst recovering the majority of otherwise wasted heat, and can reach overall efficiencies in excess of 90% . The carbon saving is primarily attributable to the reduction in use of centrally-generated network electricity, which has a carbon intensity (kgCO2/kWh) that is more than twice that of natural gas. Several candidate technologies for micro-CHP are under development and the ultimate market potential for countries with extensive natural gas networks is large. One recent study  estimated that the UK market potential for micro-CHP systems based on Stirling Engine prime-mover technology was 13.5 million units, or around 40% of the housing stock. Hence it is prudent to investigate and identify the factors affecting the carbon savings achieved by dwelling-integrated micro-CHP systems. This work has ramifications for the estimation of possible carbon abatement, and the specification of future micro-CHP systems, as it seeks to illustrate the variation in carbon savings between micro-CHP system specifications, and target dwelling construction and occupancy. In this paper, the major factors which influence the effectiveness of the micro-CHP approach are highlighted. Using the specifically designed, in-development Building Integrated Micro-Generation (BIM-G) Model, the predicted performance of three generic micro-CHP systems within a specified dwelling is analysed. The temporal variation of the electrical and thermal demand profiles generated for the dwelling corresponds to a specified construction, occupancy pattern and appliance utilization schedule. By evaluation of “real-time” thermal and electrical demand and transient generation response, on a 5 second basis, dwelling carbon emissions can be calculated with more accuracy, a hypothesis supported by a recent paper . BIM-G Model Overview The BIM-G Model is under development as a research tool to produce synthetic thermal and electrical demand profiles for domestic buildings, at high temporal precision, in order to permit the investigation of detailed changes in end use and micro-CHP system design. The BIM-G model can be used to quantify the performance of the system using pre-defined performance metrics. The model utilizes a bottom-up approach to domestic energy modelling through the use of scripts which specify the nature and timing of appliance, lighting and DHW usage, dwelling occupancy and thermal comfort requirements. The model accounts for the transient nature of thermal energy demand in the building by considering the effects of thermal mass in both the building fabric and space heating distribution system. The model itself simplifies the geometry of the dwelling into two 1-dimensional boundaries (separating the total volume of internal air from the external environment) representing the wall and roof; and a 1- dimensional boundary separating the internal air from the ground. Heat is exchanged between the external surface of each element and the environment through convective and radiative processes. Heat exchange between the internal surface of each element and the internal air is via conductive and estimated radiative processes. Energy is conducted between surfaces of each element, which acts as a thermal storage mechanism, governed by the specific heat and volume of each element. Simulation Scenario Definition The initial step in this research was to define a set of scenarios, in terms of BIM-G input scripts and physical dwelling characteristics. The work utilises the domestic variant definitions emerging from the Carbon Vision TARBASE programme , which aims to deliver technological interventions to appliances, building fabric and energy generation to reduce the carbon footprint of existing buildings by 50% within a 2030 timeframe. Physical Dwelling Characteristics 2 The selected building variant is a detached dwelling, of 180m floor area, constructed between 1988 and 1994, conforming to the building regulations of that time. The dwelling is of timber frame construction, with a 25% glazing/gross external wall ratio and a total ventilation rate (comprising infiltration and manual ventilation) of 0.76 air changes per hour. The dwelling is assumed to reside in the area of Oban, on the west coast of Scotland, and all thermal calculations are performed using corresponding climate data (i.e. external air temperature, and diffuse and direct solar irradiation measurements). Household Characteristics and Occupancy Pattern The household selected to reside in the dwelling is relatively financially prosperous (the relevance of which is discussed later), and comprises two working parents, one working offspring and a school- attending child. The scenario defined for this round of simulations represents a typical working day, characterised by “active occupancy” (i.e. household occupied and occupiers arisen from sleep) for several hours in the morning, followed by a long period of vacancy, and finally a long period of “active occupancy” starting from late afternoon, stretching until all occupants retire to bed. Such an occupancy pattern is utilised by a popular daily steady-state domestic energy estimation model developed by the Building Research Establishment . The timing of appliance, DHW and manual ventilation events correspond to times of household occupancy, as do the corresponding metabolic and appliance casual thermal gains. Appliance Ownership A range of possibilities exists with respect to the number, variety and age of appliances fitted in a home. Load signatures vary with appliance type and in some cases depend on age and usage technique. The aggregate electrical load profile is determined by the transient nature of appliance (including illumination) usage. For this initial analysis, the dwelling was assigned a set of appliances, both electrical and DHW related, from which the appliance and DHW usage scripts were composed. The selection of appliance set was made using ownership data referenced by the assumed socio- economic status of the household. The appliance set for this scenario includes an electric oven, gas hob and DHW mixer shower. In comparison to other possible scenarios, the electrical appliance usage on the simulation day can be assumed to be low, in comparison to other possible simulation scenarios, as supported by data from a previous TARBASE study . Climate Data The climatic data set used with the BIM-G Model was converted from International Weather for Energy Calculations measurements for Oban, Scotland . For the purposes of this scenario, four climate day varieties were specified; extreme summer day, extreme winter day with clear skies, extreme overcast winter day and shoulder day. The spread of climate days allow the micro-CHP systems to be analysed over a range of space heating requirements, due to variations in external air temperature, incident solar radiation and thermal energy stored in the building fabric. Space Heating & DHW Distribution System As discussed previously, a 1-dimensional space heating distribution system was specified for the dwelling, in order to estimate the transient nature of thermal supply and demand. This distribution system entails a heat emitter and pipework, each with a surface area equal to that estimated using an industry design guide  and the Energy Saving Trust’s “Whole House Boiler Sizing Method” . The volume of space heating water, also estimated by the same method, is heated by the heat-generating device, i.e. DCH boiler or micro-CHP system. The transient temperature profile of this space heating water dictates the required heat generator output and space heating input to the dwelling from the radiator and exposed pipework. The DHW system comprises a 180-litre DHW tank, insulated appropriately to achieve an average heat loss of 76 Watts. In specification of the DHW usage profiles for the mixer showers, it is assumed that the DHW undergoes a 10 degrees Celsius drop between tank and shower water mixer, which requires a higher draw-off rate than a zero heat loss assumption. For all DHW events, a dead-leg period of 30 seconds is assumed, to account for sub-requirement temperature water in the DHW piping which is discarded. All micro-generation systems defined in this scenario utilise the existing distribution systems and DHW storage tank. Micro-Generation System Specifications Base Case – 20kW Condensing Boiler In order to quantify carbon emission reduction due to the implementation of micro-CHP to the dwelling, a reference case must be considered, representative of the dwelling before intervention. In this case, a standard DCH condensing boiler was specified, with a constant generation efficiency of 88.0%. The maximum thermal output of the boiler is 20kW, and the minimum operating output is 4kW. Stirling Engine Micro-CHP Systems The prime mover incorporated in each of the generic micro-CHP systems is a Stirling external combustion engine, with varying electrical and thermal capacities as detailed in Table 1. As the heat recovery efficiency remains constant across each system, both the electrical and overall efficiencies vary, as detailed in Table 1. An auxiliary burner can supply a maximum and minimum of 15kW and 3kW respectively, at a constant generation efficiency of 88.0%. The prime mover is controlled in a manner where it operates only at full load, i.e. no part load operation, whereas the auxiliary burner, if called upon, can freely modulate between minimum and full load operation. Table 1: Electrical and Thermal Capacities and Efficiencies of Generic Micro-CHP Systems Generation System Electrical Thermal Electrical Electrical Capacity (kW) Capacity (kW) Efficiency (%) Efficiency (%) 0.5kWe Micro-CHP 0.5 1.97 18.0 88.7 1.1kWe Micro-CHP 1.1 2.44 28.0 90.1 1.8kWe Micro-CHP 1.8 2.53 38..0 91.5 Results & Discussion Reference Case The following section displays and discusses the results for the base case scenario, where thermal energy is supplied from the condensing boiler, and electrical demand solely from the national electrical grid. In Table 2, details of the estimated energy demand of the dwelling for each climate day are given, where the appliance usage and occupancy patterns remain unchanged. Variations in DHW demand between days are attributable to the proportional assignment of boiler thermal output between space heating and DHW circuits. As space heating demand, and hence boiler output, increases, small amounts of thermal surplus (i.e. when demand is below minimum boiler operating output) may be transferred to the DHW circuit, overshooting the DHW storage tank temperature by an allowable margin. The increases in electrical demand with total thermal demand are due to increased boiler firing and circulation pump usage. Table 2: Estimated Energy Demand of each Climate Day disaggregated by Type Climate Day Space Heating DHW Demand Total Thermal Electrical Demand (kWh) (kWh) Demand (kWh) Demand (kWh) Overcast Winter Day 72.1 15.1 87.2 14.0 Clear Winter Day 62.6 13.9 76.5 13.9 Shoulder Day 37.7 14.0 51.6 13.8 Summer Day 0 14.0 14.0 13.7 NB: Energy Demand estimated as the energy delivered to Space Heating and DHW heat exchangers The graphs below (Figures 1 and 2) illustrate the transient space heating, DHW and electrical demand, resultant internal air temperature and external air temperature on the overcast winter day and shoulder day respectively. Figure 1: Transient Demand and Internal & External Air Temperatures for Overcast Winter Day Figure 2: Transient Demand and Internal & External Air Temperatures for Shoulder Day The primary performance metric of any thermal generation device supplying a space heating system is the internal air temperature during thermal comfort demand periods, and the difference from thermal comfort target temperature. The thermal comfort target temperature used during these simulations is 21°C, where occupants regard any temperature within a +/- 1.5°C band of this temperature to be acceptable. Therefore, any internal air temperature between 19.5°C and 22.5°C would fulfil thermal comfort requirements. Furthermore, previous research  has identified a band of temperatures outside this range (16-23°C during winter, 18-25°C during shoulder months) where the internal air temperature is acceptable to 80% of possible occupants. The thermal comfort demand periods during this simulation are between 07:00 and 08:30, and 16:30 and 23:00. Micro-CHP Implementation Cases The BIM-G Model was used to estimate the transient performance of three micro-CHP systems, with a spread of electrical output and efficiencies. The system specifications of these units where chosen for several reasons. The system with 0.5kWe output was chosen to represent a unit with output less than the average electrical demand (approximately 580 Watts, as calculated on a daily basis). A 1.1kWe unit was designed to match performance information found on a micro-CHP system in current development; and the 1.8kWe unit was selected to investigate the effects of larger electrical output systems. The tables below (Tables 3 – 6) depict the thermal and electrical output of each micro-CHP system on each climate day type. Additionally, the electrical import and export to the grid is quantified. Table 3: Estimated System Generation Levels – Shoulder Day Generation SE Thermal Aux Burner SE Electrical Electrical Electrical System Generation Thermal Generation Import Export (kWh) (kWh) Generation (kWh) (kWh) (kWh) Condensing Boiler 0 51.6 0 13.8 0 0.5kWe SE CHP 7.4 44.0 2.1 12.0 0.3 1.1kWe SE CHP 10.4 41.0 4.6 10.6 1.4 1.8kWe SE CHP 12.6 38.9 7.6 10.2 4.0 Table 4: Estimated System Generation Levels – Summer Day Generation SE Thermal Aux Burner SE Electrical Electrical Electrical System Generation Thermal Generation Import Export (kWh) (kWh) Generation (kWh) (kWh) (kWh) Condensing Boiler 0 14.0 0 13.7 0 0.5kWe SE CHP 1.8 12.2 0.6 13.3 0.0 1.1kWe SE CHP 2.5 11.5 1.0 12.9 0.2 1.8kWe SE CHP 2.9 11.1 1.7 12.7 0.7 Table 5: Estimated System Generation Levels – Overcast Winter Day Generation SE Thermal Aux Burner SE Electrical Electrical Electrical System Generation Thermal Generation Import Export (kWh) (kWh) Generation (kWh) (kWh) (kWh) Condensing Boiler 0 87.2 0 14.0 0 0.5kWe SE CHP 16.4 68.9 4.7 9.7 0.4 1.1kWe SE CHP 23.2 62.3 8.9 6.1 2.5 1.8kWe SE CHP 27.9 57.5 17.0 4.9 7.9 Table 6: Estimated System Generation Levels – Clear Winter Day Generation SE Thermal Aux Burner SE Electrical Electrical Electrical System Generation Thermal Generation Import Export (kWh) (kWh) Generation (kWh) (kWh) (kWh) Condensing Boiler 0 76.5 0 13.8 0 0.5kWe SE CHP 14.1 62.1 2.1 12.0 0.3 1.1kWe SE CHP 19.9 56.4 4.6 10.6 1.4 1.8kWe SE CHP 24.0 52.3 14.6 6.4 7.0 As expected, Stirling Engine thermal and electrical output increases with Stirling Engine electrical capacity, as does electrical export, whilst electrical import and auxiliary burner thermal output decreases. The variation in total thermal output is attributable to the maximum and minimum operating thermal output of each system, and the resulting effects on transient generation. In the graph below (Figure 3), the carbon emissions from each generation system, on each climate day, are districtised by source, i.e. total system gas consumption (at an intensity of 0.19kgCO2/kWh) and electrical import and electrical export (at an intensity of 0.43kgCO2/kWh). In this research, it has been assumed that full “carbon credit” is given to exported electricity, as it is assumed that it displaces electrical generation required for other users, at full grid electrical intensity of 0.43kgCO2/kWh. In Figure 4, there are several trends apparent in the carbon saving results, if the summer climate day results are discarded. The summer results, in percentage carbon saving terms, appear disproportionally high in relation to the remaining climate days. This is a consequence of fuel wastage experience during the boiler “cold start” periods, which is not as apparent during other days with much higher thermal demand. Ongoing development of the BIM-G model will clarify this effect and it’s applicability to real-life situations. The carbon saving for any climate day increases with electrical output of the Stirling Engine, within the size range investigated in this research. This appears to be a consequence of avoided electrical imports and credited electrical exports, both of which have a carbon intensity around 2.3 that of the natural gas consumed by the generation systems. The carbon saving for any generation system increases with the daily thermal demand of the simulation day, a consequence of longer total prime mover operation time, which in turn increases avoided electrical imports and credited electrical exports. Figure 3: Carbon Emissions by source from each Generation System on each Climate Day Figure 4: % Carbon Savings from Base Case from each Generation System and Climate Day Conclusions A transient thermal and electrical demand estimation tool was used to model the performance of four building integrated micro-generation systems within a specific dwelling during four simulation days corresponding to different climates. These systems include a base case condensing boiler and three micro-CHP systems of varying electrical and thermal capacities and efficiencies. The results of these simulation scenarios were quantified, and carbon emission figures calculated pertaining to selected operational measurements. The relative carbon savings (versus the base case) of each micro-CHP implementation scenario were presented for each simulation day to give a first order estimate of their carbon abatement potential. Although several interesting trends can be identified in these results, the primary aim of this research was to highlight the factors that affect the simulated and actual performance of micro-CHP systems in terms of carbon emissions. Further research is required to quantify the relative effect of each factor on carbon savings, and the cumulative effects on micro-CHP system sizing and design. In brief, these factors are: • Magnitude and transient nature of thermal demand, i.e. Space Heating and DHW, including those factors that directly determine thermal demand, namely: o Casual gains from appliances and occupants o Magnitude and timing of thermal comfort requirements o Exterior climate o Dwelling construction • Magnitude and transient nature of electrical demand, including those factors that directly determine electrical demand, namely: o Occupancy and appliance use patterns o Ownership and transient electrical load of appliances o Exterior climate • Co-incidence of thermal and electrical demand, and the ratio and transient nature of such co- incidence • Ability and efficiency to store excess electrical and/or thermal energy generated on-site • Ability and/or desire to export electrical generation from the dwelling, and the magnitude of “carbon credit” assignable to such an export • Thermal and electrical efficiencies of prime-mover and auxiliary generators, in steady state and start-up conditions • Thermal and electrical capacities of prime-mover and auxiliary generators, including minimum operating power outputs and modulating ability • Control regime of micro-CHP system, including start-up sequence, electrical and/or thermal load following ability and technology dependent on/off switching event limitations Further research is planned to consider each factor in detail, as discussed above, in tandem with continuing development of the Building Integrated Micro-Generation model. 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