Non-biomass renewables - MESSAGE-GLOBIOM

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Model Documentation - MESSAGE-GLOBIOM

Corresponding documentation
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Model information
Model link
Institution International Institute for Applied Systems Analysis (IIASA), Austria, http://data.ene.iiasa.ac.at/message-globiom/.
Solution concept General equilibrium (closed economy)
Solution method Optimization
Anticipation

Table 1 shows the assumed total potentials of non-biomass renewable energy deployment (by resource type) in the MESSAGE model. In addition, the assumptions are compared with technical potential estimates from the Global Energy Assessment (Rogner et al., 2012 1). In this context, it is important to note that typical MESSAGE scenarios do not consider the full technical potential of renewable energy resources, but rather only a subset of those potentials, owing to additional constraints (e.g., sustainability criteria, technology diffusion and systems integration issues, and other economic considerations) that may not be fully captured within the model. These constraints may lead to a significant reduction of the technical potential.

Table 1: Assumed global non-biomass renewable energy deployment potentials in the MESSAGE-GLOBIOM model. Estimates from the Global Energy Assessment (Rogner et al., 2012 1) also added for comparison
Source MESSAGE Rogner et al., 2012 1
Deployment Potential [EJ/yr] Technical Potential [EJ/yr]
Hydro 38 50 - 60
Wind (on-/offshore) 689/287 1250 - 2250
Solar PV 6064 62,000 - 280,000
CSP 2132 same as Solar PV above
Geothermal 23 810 - 1400

Notes: MESSAGE-GLOBIOM renewable energy potentials are based on Pietzcker et al. (2014) 2, Eurek et al. (in review) 3, Christiansson (1995) 4, and Rogner et al (2012) 1. The potentials for non-combustible renewable energy sources are specified in terms of the electricity or heat that can be produced by specific technologies (i.e., from a secondary energy perspective). By contrast, the technical potentials from Rogner et al (2012) 1 refer to the flows of energy that could become available as inputs for technology conversion. So for example, the technical potential for wind is given as the kinetic energy available for wind power generation, whereas the deployment potential would only be the electricity that could be generated by the wind turbines.

Regional resource potentials for solar and wind are classified according to resource quality (annual capacity factor) based on Pietzcker et al. (2014 2) and Eurek et al. (in review 3). Regional resource potentials as implemented into MESSAGE-GLOBIOM are provided by region and capacity factor for solar PV, concentrating solar power (CSP), and onshore/offshore wind in Johnson et al. (in review 5). The physical potential of these sources is assumed to be the same across all SSPs. Table 2, Table 3, Table 4, Table 6 show the resource potential for solar PV, CSP, on- and offshore wind respectively. For wind, Table 5 and Table 7 list the capacity factors corresponding to the wind classes used in the resource tables. It is important to note that part of the resource that is useable at economically competitive costs is assumed to differ widely (see Section Energy Conversion of MESSAGE-GLOBIOM).

Table 2: Resource potential (EJ) by region and capacity factor for solar photovoltaic (PV) technology (Johnson et al. in review 1). For a description of each of the regions represented in the table, see Spatial dimension of MESSAGE-GLOBIOM
Capacity Factor (fraction per year)
0.28 0.21 0.20 0.19 0.18 0.17 0.15 0.14
AFR 0.0 1.1 46.5 176.6 233.4 218.2 169.9 61.9
CPA 0.0 0.0 0.0 10.3 194.3 315.5 159.4 41.9
EEU 0.0 0.0 0.0 0.0 0.0 0.0 0.1 1.0
FSU 0.0 0.0 0.0 0.2 2.8 23.6 94.9 116.6
LAM 0.1 4.9 49.4 165.6 157.5 167.4 81.4 48.5
MEA 0.2 3.1 100.8 533.6 621.8 310.1 75.3 14.5
NAM 0.0 0.3 24.3 140.4 131.0 116.3 155.7 106.4
PAO 0.0 0.0 0.1 2.2 53.1 226.4 311.2 158.9
PAS 0.0 0.0 0.0 0.2 0.8 17.0 31.2 12.8
SAS 0.0 0.0 6.1 42.7 67.2 82.3 23.7 4.1
WEU 0.0 0.1 0.2 3.0 12.8 39.4 58.3 33.3
Global 0.3 9.6 227.4 1074.7 1474.6 1516.3 1160.9 600.0

Table 3: Resource potential (EJ) by region and capacity factor for concentrating solar power (CSP) technologies with solar multiples (SM) of 1 and 3 (Johnson et al. in review 1)
Capacity Factor (fraction of year)
SM1 0.27 0.25 0.23 0.22 0.20 0.18 0.17 0.15
SM3 0.75 0.68 0.64 0.59 0.55 0.50 0.46 0.41
AFR 0.0 3.6 19.0 81.6 106.7 62.8 59.6 37.8
CPA 0.0 0.0 0.0 0.0 0.0 0.3 11.5 53.0
EEU 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
FSU 0.0 0.0 0.0 0.0 0.0 0.1 0.4 6.1
LAM 0.0 2.0 7.0 11.8 29.3 57.1 56.8 53.5
MEA 0.1 3.7 24.8 122.4 155.3 144.5 68.4 34.0
NAM 0.0 0.0 0.0 6.3 19.7 20.2 29.6 43.2
PAO 0.0 3.0 75.1 326.9 158.3 140.4 40.2 10.2
PAS 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.6
SAS 0.0 0.0 0.0 0.1 3.9 8.7 16.1 9.8
WEU 0.0 0.0 0.0 0.0 0.2 0.7 2.4 3.0
Global 0.1 12.3 126.0 549.2 473.3 434.8 285.0 251.3

Table 4: Resource potential (EJ) by region and wind class for onshore wind (Johnson et al. in review 1)
Wind Class
3 4 5 6 7 8+
AFR 38.2 21.3 13.4 6.8 2.6 2.1
CPA 24.7 11.4 5.4 2.6 0.3 0.0
EEU 6.1 5.7 0.3 0.0 0.0 0.0
FSU 52.3 83.8 5.8 0.8 0.0 0.0
LAM 33.5 15.9 9.6 5.7 3.9 3.7
MEA 56.1 22.2 6.0 2.1 0.9 0.3
NAM 28.6 66.4 23.7 1.5 0.4 0.0
PAO 18.9 18.8 3.6 1.4 1.8 0.5
PAS 5.2 2.9 0.8 0.2 0.0 0.0
SAS 12.3 7.9 2.4 1.6 0.9 0.3
WEU 16.1 10.5 6.6 8.2 3.7 0.6
World 292.1 266.8 77.5 30.9 14.3 7.5

Table 5: Capacity factor by region and wind class for onshore wind (Johnson et al. in review 1)
Wind Class
3 4 5 6 7 8+
AFR 0.24 0.28 0.32 0.36 0.40 0.45
CPA 0.24 0.28 0.32 0.36 0.38 0.45
EEU 0.24 0.27 0.31 0.36 0.38 0.45
FSU 0.24 0.28 0.31 0.35 0.38 0.45
LAM 0.24 0.28 0.32 0.36 0.39 0.46
MEA 0.24 0.27 0.32 0.35 0.39 0.45
NAM 0.24 0.28 0.31 0.36 0.39 0.45
PAO 0.24 0.28 0.32 0.36 0.40 0.43
PAS 0.24 0.27 0.32 0.35 0.40 0.45
SAS 0.24 0.27 0.32 0.36 0.39 0.42
WEU 0.24 0.28 0.32 0.36 0.39 0.43

Table 6: Resource potential (EJ) by region and wind class for offshore wind (Johnson et al. in review 1)
Wind Class
3 4 5 6 7 8+
AFR 3.1 2.4 2.0 2.0 1.1 1.7
CPA 3.5 4.3 2.6 0.9 1.3 0.1
EEU 0.7 0.6 1.0 0.0 0.0 0.0
FSU 1.8 4.6 14.2 13.3 4.3 0.7
LAM 7.1 7.3 5.3 2.7 2.6 5.9
MEA 3.2 0.9 0.8 0.9 0.6 0.9
NAM 4.5 18.2 24.0 16.0 7.3 2.1
PAO 5.8 11.2 15.3 9.8 2.6 2.5
PAS 5.3 6.6 4.7 1.5 0.1 0.0
SAS 1.9 0.9 0.6 0.5 0.0 0.0
WEU 3.5 4.7 8.8 12.9 10.3 0.9
World 40.4 61.5 79.4 60.5 30.3 14.8

Table 7: Capacity factor by region and wind class for offshore wind (Johnson et al. in review 1)
Wind Class
3 4 5 6 7 8+
AFR 0.24 0.28 0.32 0.36 0.41 0.47
CPA 0.24 0.28 0.32 0.36 0.40 0.42
EEU 0.24 0.29 0.32 0.34 0.40 0.42
FSU 0.25 0.28 0.32 0.35 0.39 0.43
LAM 0.24 0.28 0.32 0.36 0.40 0.49
MEA 0.24 0.28 0.32 0.36 0.40 0.45
NAM 0.25 0.28 0.32 0.36 0.40 0.43
PAO 0.24 0.28 0.32 0.36 0.40 0.47
PAS 0.24 0.28 0.32 0.35 0.39 0.42
SAS 0.24 0.27 0.32 0.36 0.40 0.42
WEU 0.24 0.28 0.32 0.36 0.40 0.42

References

  1. a b c  | |  H Rogner, Roberto F Aguilera, Christina Archer, Ruggero Bertani, S Bhattacharya, M Dusseault, Luc Gagnon, H Harbel, Monique Hoogwijk, Arthur Johnson (2012). Chapter 7 - Energy resources and potentials. In Global Energy Assessment - Toward a Sustainable Future(pp. 423--512). Cambridge University Press, Cambridge, UK and New York, NY, USA and the International Institute for Applied Systems Analysis, Laxenburg, Austria.
  2. a b  | |  R C Pietzcker, D Stetter, S Manger, G Luderer (2014). Using the sun to decarbonize the power sector: The economic potential of photovoltaics and concentrating solar power. Applied Energy, 135 (), 704-720.
  3. a b  | |  K Eurek, P Sullivan, M Gleason, D Hettinger, D M Heimiller, A Lopez (2016). An improved global wind resource estimate for integrated assessment models. Energy Economics, In Review ().
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  5. ^  | |  Nils Johnson, Manfred Strubegger, Madleine McPherson, Simon Parkinson, Volker Krey, Patrick Sullivan (2016). A reduced-form approach for representing the impacts of wind and solar PV deployment on the structure and operation of the electricity system. Energy Economics, In Review ().