Page:Quantifying a realistic, worldwide wind and solar electricity supply.pdf/5

 Y.Y. Deng et al. / Global Environmental Change 31 (2015) 239–252

For the ten reference countries, we differentiated between building types (multi-family home, single-family home, nonresidential buildings), urbanisation level (rural and urban), period (2010, 2030, 2070) and new or existing buildings and also tried to account for the increasing share of the population living in multifamily homes and urban areas in the future. We ﬁrst estimated base year (2010) values for ﬂoor area per capita in all of these categories based on existing statistics, where available (de la Rue du Can, 2009; DoE, 2009; Diefenbach and Loga, 2011; EIA, 2006; IFO Institute, 1999; IBGE, 2005; Milford, 2009; Ministerio de Fomento, 2010; Ministerio de industria tourismo y comercio, 2007; Schlomann et al., 2009; Statistisches Bundesamt, 2009; Zhou et al., 2007). For future years, we trended historic growth, but moderated this growth where necessary to achieve consistency across countries. This iterative approach yielded values for the ﬂoor area per capita from 10 to 76 m2 in 2010 and 23 to 71 m2 in 2070 for residential buildings and 1–29 m2 in 2010 and 5–37 m2 in 2070 for non-residential buildings based on modelling of building stock development in each of the reference countries. For more detail, see the Supplementary Information online. 2.1.5.2. Roof to ﬂoor ratio/fac¸ade to ﬂoor ratio. We determined roofto-ﬂoor and fac¸ade-to-ﬂoor (North, South, East and West) ratios, differentiating between single-family and multi-family homes in rural and urban areas and non-residential buildings. We established these ratios for three reference countries and mapped all other countries to these reference countries, based on the same characteristics as above. The roof-to-ﬂoor ratios ranged from 0.17 to 1.00. The fac¸ade-to-ﬂoor ratios varied between 0.10 and 0.33 or 0.00 and 0.47 depending on orientation. A zero value represents a wall connected to the wall of an adjacent building. For more detail, see the Supplementary Information online. 2.1.5.3. Suitability. From the total roof and fac¸ade area, the suitable area (synonymous with available area here) was estimated. For this we used a suitability factor of 33% for roofs and 10% for fac¸ades, rising to 30% in 2070. These factors were derived from literature (Bergamasco and Asinari, 2011; Ghosh and Vale, 2006; IEA-PVPS, 2002; Izquierdo et al., 2008; Montavon et al., 2004; NREL, 2008; Pillai and Banerjee, 2007; Scartezzini et al., 2002). 2.2. Potential per area In the ﬁnal step, the conversion of raw resource incident on the available area was converted to usable electricity through an effective conversion efﬁciency. Note that we do not differentiate further between different types of sub-technologies, e.g. mono- vs. polycrystalline solar cells, etc., but characterise each technology by one efﬁciency which is representative of the whole sub-technology spectrum. 2.2.1. Solar electricity production The potential per area for solar electricity production can be calculated from the solar energy incident upon the surface per year and the conversion efﬁciency of the harvesting technology, as shown in Eq. (4). P ð p; tÞ ¼ IðtÞ Á Eð p; tÞ A

(4)

where P/A = potential per area in EJ/km2 per study period and technology (PV vs CSP); p = the study period (2010, 2030, 2070); t = the technology (PV or CSP); I = resource intensity in J/m2 for solar irradiation; E = conversion efﬁciency in % by technology. 2.2.1.1. Resource intensity. For solar photovoltaics (PV) on land or building roofs, the global horizontal irradiation (GHI) per grid cell

243

or country was derived from the solar irradiance data source in kWh/km2/a listed in Table 1. The GHI includes the total gross solar energy from both direct and diffuse radiation incident on a horizontal plane. For buildings, we calculated an average irradiation value per country across all grid cells, weighted by population density. For solar photovoltaics (PV) on building fac¸ades we also calculated the gross vertical irradiation (GVI) in kWh/km2/a by orientation (North, South, East, West). For concentrated solar power (CSP), the direct normal irradiance (DNI) per grid cell in kWh/km2/a was derived from the solar irradiance data source listed in Table 1. The DNI is the total direct gross solar energy incident on an area perpendicular to the incoming radiation. 2.2.1.2. Conversion efﬁciency for PV. The overall conversion efﬁciency for PV is the product of these factors: � The module efﬁciency of the individual solar modules1 (Em) � The performance ratio, capturing the system’s conversion efﬁciency from the module’s output to usable electricity (PR) � (For land-based PV only): A ground coverage value representing the share of land capturing energy, i.e. the share of the total area of a PV plant actually covered with PV cells (GC)2 Table 4 shows the values used for each of these three factors as well as the overall resulting conversion efﬁciency for buildings (EmÁPR) and for land (EmÁPRÁGC). 2.2.1.3. Conversion efﬁciency for CSP. The overall conversion efﬁciency for CSP is the product of two factors: � the net efﬁciency which represents the internal conversion efﬁciency1 of the CSP system and is understood to include the (small) efﬁciency losses in plants with storage capacity. � a space factor which represents the additional factors to take into account when converting from gross resource over the entire plant area to ﬁnal electricity produced from the full plant. The values used for these two factors and the overall resulting conversion efﬁciency are shown in Table 5.Note that assumptions on the development of technologies (in the form of conversion efﬁciencies for PV and CSP and hub heights for wind) were not explicitly linked across technologies in a prior scenario, e.g. on installed capacities, but were deemed consistent as they were derived from similar considerations of continued growth. 2.2.2. Electricity from wind on land and sea The wind power potential per area per year for each grid cell was calculated for each period according to Eq. (5) (based on Held, 2010, p.60) which includes the approximate relationship between the full load hours per year and average annual wind speed, based on a range of turbines, as well as an average power density: P ð p; tÞ ¼ Hð p; t Þ Á D Á Eð p; tÞ A ¼ ða Á y2 À bÞ Á D Á Eð p; tÞ

(5)

where P/A = potential per area in EJ/km2 per period and technology; p = the study period (2010, 2030, 2070); t = the technology (on-shore vs off-shore wind); H = full load hours in a given 1 Note that an average efﬁciency across a range of technologies was used here as the range of module efﬁciencies has a much smaller effect on the overall result than other factors, e.g. availability. 2 The ground coverage factor was derived from typical power densities of solar farms (25–50 MW/km2) in comparison with the raw module power density of a typical solar cell of 125–150 MW/km2.