Page:Quantifying a realistic, worldwide wind and solar electricity supply.pdf/6

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

244

Table 4 Conversion efﬁciency values used for building- and land-based PV. Year

2010 2030 2070

Module efﬁciency (Em)

16% 24% 35%

Performance ratio (PR)

75% 80% 80%

Gross conversion efﬁciency (buildings) (EmÁPR) 12% 19% 28%

Ground coverage (land) (GC)

20% 23% 30%

Net conversion efﬁciency (land) (EmÁPRÁGC) 2.4% 4.5% 8.4%

Table 6 Efﬁciency values used for wind power (Ind. = industrialised, Dev. = developing country). Category

Land class or distance from shore class

Land

Forest

Ind.

Agriculture

timeframe; D = power density, here 7 MW/km ; E = conversion efﬁciency in % by technology; n2 = the average wind speed at hub height in that timeframe; a = 728; b = 2368.

0.98

Dev. 0.90

Grassland/Barren land Sea

2

Operational efﬁciency

0–10 km

0.925

0.95

10–50 km

0.925

0.95

50–200 km

0.9

0.925

Array efﬁciency Ind.

Dev.

[1.0]

[1.0]

[1.0]

[1.0]

0.925

0.90

0.9

0.8

2.4. Classiﬁcation of grid cells with infrastructure as a marker of grids 2.2.2.1. Conversion efﬁciency. There are also small efﬁciency losses in wind power production which lower the overall resource intensity. The intensity is therefore moderated (multiplied) by the following additional efﬁciency factors, which range from 90% to 100%. � Operational efﬁciency: The share of total possible operation time that the wind turbine is actually producing electricity, i.e. not stopped for maintenance. We have varied this by country (industrialised vs. developing) and by distance from shore for offshore wind. � Array efﬁciency: Multiple turbines can cause interference. The amount of interference depends on land use for onshore wind (not applicable to solitary turbines, as assumed here for agriculture and forest).

To assess whether the potential would be able to supply power into an electricity grid, we used a combination of data sets to estimate the distance from ‘‘infrastructure’’ which we use as a marker for the distance from electricity grids. The presence of infrastructure was based on a combination of: � land cover being of ‘built-up’ type or being within 100 km of a cell of such land cover � population density being above 500/km2 or within 100 km of a cell of such density � presence of rail network lines in the cell

The values used for efﬁciencies for wind power are shown in Table 6.

Note that road networks were deemed too pervasive to be a good indicator of electricity connections. The exact approach to the infrastructure classiﬁcation is depicted in Fig. 1. We have attempted to depict the expansion of settlements through time by combining these datasets differently for 2070 vs 2010/2030.

2.3. Distance from infrastructure

2.5. Distance classiﬁcation

With the exception of offshore wind, renewable electricity installations today do not usually require additional electricity transmission capacity to be connected to existing transmission and distribution grids. When assessing long-term potentials across larger areas, however, the distance from existing electricity lines may be important, depending on the share of the cost of connection in the overall cost for the installation. To get a ﬁrst impression of the distribution of our calculated potentials across differing distances from existing electricity grids we have tried to assess the distance of each grid cell from an electricity grid that connects to a signiﬁcant demand for electricity. Since a global data set for electricity lines was not readily available, we have estimated a ‘distance from infrastructure’ as a marker for electricity grids based on the approach described in the following. Each grid cell was either classed as ‘with infrastructure’ or ‘without infrastructure’, based on land cover, population density and railways as detailed below. For each grid cell without infrastructure we then calculated the distance to the nearest grid cell with infrastructure and then grouped grid cells into distance classes based on the results.

Based on the previous step we then decided to group the calculated potentials into the following groups:

Table 5 Conversion efﬁciency values used for CSP. Year 2010 2030 2070

Net efﬁciency

Space factor

Overall conversion efﬁciency

15% 18% 20%

20% 20% 20%

3.0% 3.6% 4.0%

� For offshore resources: Above or below 70 km from infrastructure � For land-based resources: Above 500 km, below 500 km or below 100 km from infrastructure 2.6. Hydro- and geothermal electricity 2.6.1. Hydroelectricity We differentiate between ‘small’ and ‘large’ hydroelectricity potentials. ‘Small’ hydroelectricity tends to denote run-of-the-river type installations, using newer technologies and resulting in smaller projects of around <10 MW in size although no ofﬁcial deﬁnition exists. ‘Large’ hydroelectricity is commonly used to denote traditional reservoir projects of larger capacity, up to several tens of gigawatts. We used a range of existing data sources on hydroelectricity potentials to get an estimate of the potentials split into these two categories (DLR, 2006; IJHD, 2008; IEA, 2012b, 2013). � The International Hydropower & Dams World Atlas (IHDWA) contains estimates for total hydroelectricity potential (IJHD, 2008) � The IEA’s Small-Hydro Atlas which was designed to report potential for small hydroelectricity projects, but still lacks data for most countries (IEA, 2013) � The IEA’s ofﬁcial statistics on current (2007/2009) production of hydroelectricity were used to ﬁll gaps in the reported historic data (IEA, 2012b)