REX (V1.01a) David Coley Software Document 35 July 2007

Transcription

1 Centre for Energy and the Environment Tel. (01392) Fax. (01392) Website REX (V1.01a) David Coley Software Document 35 July 2007 Summary REX is a simple to use software tool that allows one to simultaneously size and cost a series of renewable technologies for a building. The user inputs information about the current, or predicted for new-build, energy consumption of the building and the fraction of this they wish to get from renewables. The program then outputs a list of technologies that could meet the defined need, their cost and, where applicable, their size. The tool is approximate and is designed not as a sizing tool for any particular technology but as a way of comparing technologies or getting an idea of the scale and cost of the challenge when requiring that a large fraction of the energy needed by a building is from renewable energy sources. Thus the user will see, for example, that to generate half of the electricity needed by a large secondary school will require a very large PV array or wind turbine. The software has been design to allow one to enter data quickly, obtain a result then edit any of the inputs and obtain a new result, repeatedly. One can therefore rapidly investigate the sensitivity of the result to any of the input parameters. Although designed to size systems for educational buildings, REX can be used on any building and can also be used as part of a classroom exercise. 1. Introduction At the request of Devon County Council, a simple software tool has been developed to help with the sizing of renewable energy (RE) systems on school buildings. The brief was for a tool that Headteachers or architects could use and that allowed them to examine the costs and scale of different technologies simultaneously with the minimum of input data. To accurately size an RE system typically requires a lot of site-specific data and a good working knowledge of the basis of the technology and the availability and details of products. REX does not attempt to provide such accuracy as the level of data required was felt to be off-putting, and in many ways runs counter to what was trying to be achieved. Part of which was to reinforce the message that cheap small RE systems will not provide the majority of the energy needs of a school. REX allows one to quickly see roughly how big a wind turbine, a PV system etc. really is needed to meet a substantial fraction of the energy needs of a school, and the results will often be sobering. In the following the details behind the various algorithms and inputs are described. Most users will not need to read these notes first time around, but should simply use the software and thereby get a reasonable idea of its capabilities and limitations. 2. General Basis and Data Required REX simultaneously considers (other technologies may be added later): PV Wind turbines Wood heat, both pellet and chip Ground source heat pumps Solar thermal

2 The software has been designed to allow one to enter data quickly, obtain a result then edit any of your inputs and obtain a new result, repeatedly. One can therefore easily investigate the sensitivity of the result to any of the input parameters. Use the << and >> buttons to move back and forth between the various screens. The current school, or the planned school for new-build, can be heated by gas, oil, coal or off-peak electricity. The software asks you to enter the annual energy use of the school (for extensions you might like to only consider the amount of energy used by the extension) and the number of pupils. If you don t know the energy consumption estimates are made based on the number of pupils. The number of pupils in the school (or extension) is also used to size each technology. It is therefore important that the energy consumption and the number of pupils refer to the same object don t enter the energy consumption of the whole school and then the pupil numbers for the extension, for example. If in doubt, enter zero in the consumption boxes and just enter the pupil numbers for the school (or extension). REX contains a list of utility prices. These can be altered if needed, as can the default wind speed at the site (a hyperlink is given to a dti website that allows the user to obtain an average wind speed for their site). The user is asked for the fraction of electricity and heat they wish to obtain from renewables and the age of the building (either pre- or post-1980). If the school buildings are of various dates use the average. 3. Outputs REX outputs: the capacity (in kw) of each technology, and for some technologies the physical size; the expected lifetime; the capital cost; the likely savings; the payback time for each technology; the reduction in carbon emissions that might be realised; the cost of each technology per kg of CO 2 saved; and various other outputs based of the social cost of carbon. Although any of these can be used to characterise and compare the technologies, the most useful are likely to be the capital cost and the cost per kg of CO 2 saved. The output can be viewed both graphically and in text form and can be saved to a user-named file. The results screen also has menu tabs to a detailed list of assumptions behind the calculations and more information about each technology. 4. The Form of the Calculations The calculations behind REX are based on the assumptions listed Section 5 and vary for each technology, however they are some generalised points which we will try to describe here. For a deeper analysis please read the assumptions and then request a copy of the code. Boiler Sizing REX is attempting to size and cost RE technologies to meet a user define fraction of the building s current (or expected) energy demand. However, it has been found that it is more accurate to base the size of heating systems on the number of pupils and the age of the building rather than on historic consumption data. REX simply assumes for example that a 1000 m 2 primary school built after 1980 needs an 80 kw boiler, or a 160 kw one if built before Smaller or larger schools are sized proportional. As it was felt that many users would not know the area of the school, or the size of the extension in question, this is estimated from the number of pupils. The amount of fuel (for example) needed to fuel this boiler is than estimated on the basis of the fuel consumption data provided or the number of pupils entered.

3 General Algorithm Although each technology is handled slightly differently the following code (which is for wood chip) demonstrates the general method used and how easy it would be to either change the algorithm or add additional technologies.

4 1. SIZE THE SYSTEM If primary Then If pre1980 Then.capacity = area_of_school * CAPACITY_PER_M2_PRIMARY_PRE1980 Else.capacity = area_of_school * CAPACITY_PER_M2_PRIMARY_POST1980 ElseIf secondary Then '.capacity = heat_use * fraction_heat / HEATED_HOURS_SECONDARY If pre1980 Then.capacity = area_of_school * CAPACITY_PER_M2_SECONDARY_PRE1980 Else.capacity = area_of_school * CAPACITY_PER_M2_SECONDARY_POST1980 Else Stop 2. ESTIMATE THE SIZE OF THE WOOD STORE 'The estimated volume of the chip bunker is twice the weekly requirement. The weekly requirement is 1/20th of the annual heat demand chip_volume = 2 * (heat_use * fraction_heat / 20) / (CHIP_CV * CHIP_DENSITY) 3. ESIMATE THE CAPITIAL COST.capital_cost_no_grant = MARGINAL_CHIP_COST_PER_KW *.capacity 'workout value of grant If.capital_cost_no_grant * 0.4 < # Then.grant =.capital_cost_no_grant * 0.4 Else.grant = #.capital_cost_with_grant =.capital_cost_no_grant -.grant 4. ESTIMATE THE AMOUNT OF RENEWABLE ENERGY CONSUMED PER ANNUM If oil Then.RE_consumed = fraction_heat * oil_use.annual_marginal_saving_no_rocs =.RE_consumed * (oil_price - CHIP_PRICE) -.maintenance_cost *.capital_cost_no_grant / 100.annual_co2_saving =.RE_consumed * (EF_oil - EF_biomass) ElseIf gas Then.RE_consumed = fraction_heat * gas_use.annual_marginal_saving_no_rocs =.RE_consumed * (gas_price - CHIP_PRICE) -.maintenance_cost *.capital_cost_no_grant / 100.annual_co2_saving =.RE_consumed * (EF_gas - EF_biomass) ElseIf coal Then.RE_consumed = fraction_heat * coal_use.annual_marginal_saving_no_rocs =.RE_consumed * (coal_price - CHIP_PRICE) -.maintenance_cost *.capital_cost_no_grant / 100.annual_co2_saving =.RE_consumed * (EF_COAL - EF_biomass) ElseIf elec Then.RE_consumed = fraction_heat * offpeak_elec_use.annual_marginal_saving_no_rocs =.RE_consumed * (offpeak_elec_price - CHIP_PRICE) -.maintenance_cost *

5 .capital_cost_no_grant / 100.annual_co2_saving =.RE_consumed * (EF_elec - EF_biomass) 5. CALCULATE THE VARIOUS OUTPUTS.simple_payback_no_grant_no_ROCs =.capital_cost_no_grant /.annual_marginal_saving_no_rocs.annual_marginal_saving_with_rocs =.annual_marginal_saving_no_rocs 'no ROCs on heat at the moment.simple_payback_with_grant_and_rocs =.capital_cost_with_grant /.annual_marginal_saving_with_rocs.lifetime_co2_saving =.lifetime *.annual_co2_saving.cost_per_kg_co2_saved_with_grant_and_rocs = (.capital_cost_with_grant - (.annual_marginal_saving_with_rocs *.lifetime)) /.lifetime_co2_saving.lifetime_social_cost_of_carbon_saved =.lifetime_co2_saving * SOCIAL_COST_OF_CARBON.simple_payback_with_grant_and_ROC_and_social_costs = (.capital_cost_with_grant -.lifetime_social_cost_of_carbon_saved) /.annual_marginal_saving_with_rocs.social_payback_time =.capital_cost_with_grant / (.lifetime_social_cost_of_carbon_saved /.lifetime).cost_per_kg_co2_saved_with_grant_and_rocs_and_social_costs = (.capital_cost_with_grant - (.annual_marginal_saving_with_rocs *.lifetime) -.lifetime_social_cost_of_carbon_saved) /.lifetime_co2_saving

6 5. List of Assumptions The following is a list of the assumptions behind the calculations. All of the numeric values are constants within REX and can easily be changed. EMISSION FACTORS electricity = kgco2/kwh (the same value is used for marginal and average generation) gas = kgco2/kwh oil = kgco2/kwh biomass = kgco2/kwh coal = kgco2/kwh MISC. electricity sale price = ( /kwh):price renewable elec generated on site is sold to grid. ROC price = ( /kwh): sale price of a ROC. social cost of carbon = 0.07 * (12 / 44) ( /kgco2): number of hours in a year a primary school is occupied (used to estimate what fraction of electricity generated is used by the school)= 1400 number of hours in a year a secondary school is occupied = 1900 days in school year = (52-13) * 5 If the annual consumption of heat energy or electricity is unknown the software assumes the 10th and 50th percentile figures from ENERGY AND WATER BENCHMARKS FOR MAINTAINED SCHOOLS IN ENGLAND: from DfES (i.e. 918kWh/y heat and 219kWh/y elec. for primary; 1297 and 334 for secondary if built before 1980, or (for heat) 290 primary, 409 secondary if after 1980; electricity use is not a function of the age of the building) BOILER SIZING: The mean of the 10th and 50th percentile DfES values are used to find the area of school per pupil, then the boiler is sized on the basis of a 1000 m 2 primary requiring an 80kW boiler and a 17000m 2 secondary needing 1MW - for pre 1980 schools twice these figures is used WIND The turbine diameter is given by: turbine_diameter = 2 * Sqr((fraction_elec * elec_use / n_turbines) / (WINDk * wind_speed ^ 3)) and the capacity by: capacity = (turbine_diameter / WINDa) ^ (1 / WINDb) with a=2.7 and b=0.44. This equation was produced from fitted manufactures' data over the range 600W to 5MW with an R^2 of k = 3.2 and is the factor used by Boyle, OU2004 and other sources. Ideally this should be a function of turbine size. The conversion to cost is via: cost=c*capacity+d with c = , d = This is derived from costing wind power at 3000 per kw at 1kW (various sources) and 1000 per kw at 1MW (dti) but better figures may exist. A minimum turbine cost of 3000 is assumed. Maintenance cost = 0 pa PV PV yield =850 kwh per kwpeak area required to provide 1kWpeak from monocrystalline = 8 m 2. area required to provide 1kWpeak from polycrystalline = 10 m 2. area required to provide 1kWpeak from amorphous = 20 m 2. hours of daylight in year = Used to estimate the fraction of out that is consumed in the school maintenance cost = 0 pa SDHW fossil fuel heat needed to provided DWH per primary pupil per annum = 17.5 kwh. Based on 3kWh/m2 of raw heat per pupil from RETSCREEN, then the 50 percentile figures from ENERGY AND WATER BENCHMARKS FOR MAINTAINED SCHOOLS IN ENGLAND: from DfES to convert to kwh/pupil. This implies only 3.8% of heat is for DHW. fossil fuel heat needed to provided DWH per primary pupil per annum = 24.5 kwh, again this implies only 3.8% of heat is for DHW

7 efficiency of SDHW panels= 170 kwh/m2 per annum. From a RETSCREEN analysis by Sam Hunt. fractional efficiency of an oil or gas boiler when used to provided DHW= 0.5. Used in estimating how much fossil fuel is displaced. _ cost of solar system per m 2 of collector area= Based on a mean of 2,500-4,000 for 4m 2 (London Renewables: Integrating renewable energy into new developments: Toolkit for planners, developers and consultants, September 2004) maximum fraction of the DHW that any solar system can supply= 0.6. maintenance cost = 0 pa BIOMASS marginal cost per kw of a wood pellet boiler over a gas or oil one = 200 marginal cost per kw of a wood chip boiler over a gas or oil one = 200 cost of wood chip fuel = /kwh cost of wood pellet fuel = /kwh calorific value of chips = 10.4 / 3.6 kwh/kg calorific value of pellets =18 / 3.6 kwh/kg storage density of chips = 150kg/m 3 storage density of pellets = 650 kg/m 3 pellet boiler maintenance cost = 2.5 % pa, assumes 2% OM costs, and a ratio of 500 gas, 700 oil, 2000 woodchip for similar sized systems, therefore 2.5% if used over gas, or 2.2% if used over oil chip boiler maintenance cost = 2.5 % pa, assumes 2% OM costs, and a ratio of 500 gas, 2000 woodchip for similar sized systems, therefore 2.5% if used over gas. GSHP coefficient of performance of a GSHP = 4 marginal cost per kwh (heat) for a GSHP and a ground coil over a gas or oil boiler = 900 ( and gas/oil cost in Hunt 81. minimum area need for 1kW for a horizontal coil = 50 m2 maximum area need for 1kW for a horizontal coil = 95 m 2 minimum area need for 1kW for a vertical coil = 1.5 m2 maximum area need for 1kW for a vertical coil = 12 m 2 maintenance cost = 0 pa