assessing the value of improved variable renewable energy
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Assessing the value of improved variable renewable energy forecasting accuracy in the South African power system
WindAc ConferenceCape Town. 5-6 November 2018
Jarrad Wright Greg Landwehr Erol Chartan
- jwright@csir.co.za- CSIR (ZA)
- glandwehr@csir.co.za- CSIR (ZA)
- ErolKevin.Chartan@nrel.gov- NREL (USA)
2
Conclusions7
Outcomes6
Some selected scenarios5
System value of VRE forecasting4
Impacts of weather systems on VRE forecasts in South Africa3
Future plans for VRE deployment2
The South African power system and recent VRE deployment1
Overview
3
Conclusions7
Outcomes6
Some selected scenarios5
System value of VRE forecasting4
Impacts of weather systems on VRE forecasts in South Africa3
Future plans for VRE deployment2
The South African power system and recent VRE deployment1
Overview
4
South African power system is coal dominated but has recently begun supplementing this with variable renewables (wind and solar PV)
Sources: CSIR analyses (energy estimated based on production-cost modelling outcomes, 2017)
5
South Africa has world class wind & solar resource and can leverage these for future electricity requirementsRenewable energy resource maps for South Africa
Interim (5 km) High-Resolution Wind Resource Map for South Africa, Metadata and further information, SANEDI, Oct 2017; Global Horizontal Irradiation Map, Solargis,
http://www.sapvia.co.za/sa-solar-irradiation-maps/, accessed May 2018;
Wind Speed (m/s) Irradiance (kWh/m2)
7
257560
1 075
300 300210
960
965
1 460
2 078 2 078
1 474
1 474 1 474
2017 2020
3 852
20162013 20192014 H1 2018
3 134
2015
200
467
1 520
2 040
3 852
+1 053
+520
+1 094
+718 +0
Solar PV
Wind
CSP
Supply Sources
Notes: RSA = Republic of South Africa. Solar PV capacity = capacity at point of common coupling. Wind includes Eskom’s Sere wind farm (100 MW). Sources: Eskom; DoE IPP Office
From 1 November 2013 to 30 Jun 2018, 2 078 MW of wind, 1 474 MW of large-scale solar PV and 300 MW of CSP became operational in RSA
Capacityoperational in MW
(end of year)
8
1.1
0.01 0.05
20142013
2.2
2.6
2.5
2015
1.6
3.7
0.5
3.1
2016
3.3
2018
5.0
1.1 0.7
2017 2019
9.0
2020
0.1
2.2
4.7
6.9
9.0
0.5
3.9
Wind
Solar PV
CSP
Estimated2018 H2production
Notes: Wind includes Eskom’s Sere wind farm (100 MW). CSP energy measured from date when more than two CSP plant were commissioned. Wind and solar PV energy excludes curtailment and is thus lower than actual wind and solar PV generation
Sources: Eskom; DoE IPP Office
Annual energy produced in TWh
In 2017, 9.0 TWh from wind, PV and CSP whilst in H1 2018, 5.2 TWh of wind, PV and CSP energy produced in RSA
Supply Sources
10
Conclusions7
Outcomes6
Some selected scenarios5
System value of VRE forecasting4
Impacts of weather systems on VRE forecasts in South Africa3
Future plans for VRE deployment2
The South African power system and recent VRE deployment1
Overview
11
Energy mix is planned to be 17-21% variable renewable energy across all scenarios in Draft IRP 2018 by 2030 and up to >60% by 2050
Energy production [TWh]
22 (7%)
44 (14%)
25 (8%)
42 (13%)
22 (7%)
IRP1
IRP3
14 (4%)
22 (7%)
44 (14%)
IRP5
44 (14%)
IRP6
23 (7%)
35 (11%)
44 (14%)
Rec.
IRP7
Demand: 313 TWh
OtherWind Solar PV
2030
Sources: DoE Draft IRP 2018; CSIR analysis
Energy production [TWh]
78 (20%)
IRP5
164 (42%)
IRP6
110 (28%)
IRP1
63 (16%)IRP3
64 (16%)
107 (27%)
67 (17%)
IRP7
106 (27%)
64 (16%)
106 (27%)
Rec.
2040 2050
Demand: 392 TWhDemand: 353 TWh
Energy production [TWh]
54 (15%)
47 (13%)
Rec.
IRP5
129 (36%)
90 (25%)
IRP1
IRP3
47 (13%)
47 (13%)
89 (25%)
47 (13%)
90 (25%)
IRP6
IRP7
90 (25%)
13
Conclusions7
Outcomes6
Some selected scenarios5
System value of VRE forecasting4
Impacts of weather systems on VRE forecasts in South Africa3
Future plans for VRE deployment2
The South African power system and recent VRE deployment1
Overview
14 16 July 2017
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Several Major Climate InfluencersSouth African Climate
Warren, M., Climatology – a South African Perspective, http://slideplayer.com/slide/10180352/, accessed May 2018
Summer – October to MarchWinter – April to September
South Africa terrain elevation (SRTM+, NASA version 3)
Dynamics of late Cenozoic aeolian deposition along the South African coast: a record of evolving climate and ecosystems, http://sp.lyellcollection.org/content/388/1/353
17
Several Major Climate InfluencersSouth African Climate
Warren, M., Climatology – a South African Perspective, http://slideplayer.com/slide/10180352/, accessed May 2018
1.
Escarpment height
2000 m to 3482 m
Escarpment
South Africa terrain elevation (SRTM+, NASA version 3)
Dynamics of late Cenozoic aeolian deposition along the South African coast: a record of evolving climate and ecosystems, http://sp.lyellcollection.org/content/388/1/353
Ocean Currents
18
Mid Latitude Cyclones (MLC’s)Challenging Weather Systems to Forecast
SA Weather and Disaster Observation Service, http://sawdis1.blogspot.co.za/2012/10/images-mid-latitude-cyclone-offshore.htmlvan Wyk, E., van Tonder, GJ., Vermeulen, D., Characteristics of local groundwater recharge cycles in South African semi-arid hard rock terrains -rainwater input, Water SA vol. 37 n.2 Pretoria Apr 2011
Mid Latitude Cyclone
19de Villiers, M., Roll cloud on the South African east coast, Weather vol. 66, Issue 2, Jan 2011Lange, M., Evaluation of forecasts, meteorological characteristics in South Africa and lessons learned, Workshop presentation, Eskom, Nov 2017
Berg WindsChallenging Weather Systems to Forecast
Change in wind directionSynoptic ‘Berg Wind’
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Challenging Weather Systems to ForecastCloud build up on the West coast and interior and Low Level Jet formation (LLJ)
Lange, M., Evaluation of forecasts, meteorological characteristics in South Africa and lessons learned, Workshop presentation, Eskom, Nov 2017
21
16.815.9
16.8
15.1
12.511.5
12.3
14.6
16.9
19.0 19.318.1
15.7
Febru
ary
Janu
ary
Jun
e
Decem
ber
Ap
ril
March
May
July
Au
gust
Septem
ber
Octo
ber
No
vemb
er
Average
Uncertainty with VRE forecasts in South Africa highly dependent on localised weather systems – we have started to understand thisSemi-Operational VRE forecasting model
Sources: CSIR Short Term VRE forecasting model initial results; Energy and Meteo Systems intra-day and day-ahead high level forecasts for existing wind and solar PV generators
(LANGE, 2018)
13.1 13.4 13.7 13.6
12.311.4
13.3 13.013.7 14.0
13.1 12.8 13.1
Ap
ril
Janu
ary
March
Febru
ary
Au
gust
May
July
Jun
e
Decem
ber
Septem
ber
Octo
ber
No
vemb
er
Average
Wind(MAE forecast, %)
Solar PV(MAE forecast, %)
22
Conclusions7
Outcomes6
Some selected scenarios5
System value of VRE forecasting4
Impacts of weather systems on VRE forecasts in South Africa3
Future plans for VRE deployment2
The South African power system and recent VRE deployment1
Overview
25
15
0
10
5
20
25
30
35
VRE Uncertainty results in different residual demand – worsened at high penetration levels
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Sources: CSIR analysis
Demand
GW e.g. 10 GW installed wind and solar PV capacity
26
20
15
0
5
10
25
30
35
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Demand
Residual demand (DA)
GW
Subtracting forecasted wind & solar PV = residual demand
VRE Uncertainty results in different residual demand – worsened at high penetration levels
e.g. 10 GW installed wind and solar PV capacity
Sources: CSIR analysis
27
20
0
5
30
15
10
25
35
0
5
25
15
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10
20
35
2.7 GW(+7%)
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Demand
Residual demand (DA)
Residual demand (RT)
VRE Uncertainty results in different residual demand – worsened at high penetration levels
Solar PV (DA)
Wind (DA)
GW e.g. 10 GW installed wind and solar PV capacity
Sources: CSIR analysis
28
Day 0
Day 0
DAUCED
(1 step of 1 day)
RTUpdated UCED
(24 steps of 1 hour)
Improved forecastUnit commitmentDispatchReserves
Day 1
DAUCED
(1 step of 1 day)
Day 1
RTUpdated UCED
(24 steps of 1 hour)
Note: UCED = Unit Commitment and Economic Dispatch; DA = Day-ahead, RT = Real-time
Day 2
DAUCED
(1 step of 1 day)
Day 2
RTUpdated UCED
(24 steps of 1 hour)
. . .
. . .
Improved forecastUnit commitmentDispatchReserves
Using a production cost model of a representative South African system – establish the change in system costs as the forecast improves
29
Conclusions7
Outcomes6
Some selected scenarios5
System value of VRE forecasting4
Impacts of weather systems on VRE forecasts in South Africa3
Future plans for VRE deployment2
The South African power system and recent VRE deployment1
Overview
31
Conclusions7
Two US jurisdictions6.2
South Africa6.1
Outcomes6
Some selected scenarios5
System value of VRE forecasting4
Impacts of weather systems on VRE forecasts in South Africa3
Future plans for VRE deployment2
The South African power system and recent VRE deployment1
Overview
32
Conclusions7
Two US jurisdictions6.2
South Africa6.1
Outcomes6
Some selected scenarios5
System value of VRE forecasting4
Impacts of weather systems on VRE forecasts in South Africa3
Future plans for VRE deployment2
The South African power system and recent VRE deployment1
Overview
33
0.0% 10.0%5.0% 15.0% 20.0%
0.00%
0.10%
0.20%
0.30%
0.40%
VRE penetration level[% of annual energy demand]
Production cost saving[%]
Current RSApower system
Value of an improved VRE forecast is already notable at low VRE penetration – increases with increasing VRE penetrationRelative cost difference with perfect foresight and forecast uncertainty
Relative to the state-of-the-artforecast available in South Africafor VRE (solar PV and wind), animproved forecast could result innotables savings
20% improvement:
≈ 0.02 - 0.12% of production costs≈ 1.4 – 5.8 USD-million/yr1
40% improvement:
≈ 0.04 - 0.21% of production costs≈ 2.3 – 10.1 USD-million/yr1
1 USD:ZAR = 13.32 (2017-average)
Sources: CSIR analysis
20% improvement
40% improvement
34
Conclusions7
Two US jurisdictions6.2
South Africa6.1
Outcomes6
Some selected scenarios5
System value of VRE forecasting4
Impacts of weather systems on VRE forecasts in South Africa3
Future plans for VRE deployment2
The South African power system and recent VRE deployment1
Overview
35
Two very different jurisdictions in the U.S. – previous studies quantified the value of VRE forecast
T e c h n o lo g y I n s t a l le d
C a p a c it y [M W ]
E n e r g y s h a r e
e s t . [ % ]
C o a l 5 7 5 0 0 4 8
N u c le a r 1 3 0 0 0 1 6
G a s 5 9 9 0 0 2 4
O i l 4 1 5 0 < 0 .1
H y d r o 3 3 0 0 2
W in d 1 4 7 0 0 9
S o la r P V 4 0 0 < 1
B io m a s s 2 2 4 < 1
P u m p e d S to r a g e 2 5 1 8 -
T e c h n o lo g y I n s t a l le d
C a p a c it y [M W ]
E n e r g y s h a r e
e s t . [ % ]
C o a l 5 2 0 .2
N u c le a r 2 6 9 4 9
G a s 4 2 2 2 7 4 3
O i l 3 5 2 < 0 .1
H y d r o 1 4 0 0 2 2 1
W in d 5 6 3 2 6
S o la r P V 9 5 8 8 1 1
B io m a s s 1 3 1 4 2
P u m p e d S to r a g e - -
G e o th e r m a l 2 6 9 4 6
MISO CAISO
Sources: NREL; U.S. DoE; CAISO; MISO
Approach for both jurisdictions
Varied VRE penetration: 12-56%VRE forecast improvement: 0%, 20%, 40%
36
System costs savings range as a result of the differing energy mixes –MISO quite similar to South Africa
≈ 0.20% of production costs
≈ 0.25-0.35% of production costs
≈ 0.10-0.20% of production costs
≈ 0.30-0.40% of production costs
T e c h n o lo g y I n s t a l le d
C a p a c it y [M W ]
E n e r g y s h a r e
e s t . [ % ]
C o a l 5 7 5 0 0 4 8
N u c le a r 1 3 0 0 0 1 6
G a s 5 9 9 0 0 2 4
O i l 4 1 5 0 < 0 .1
H y d r o 3 3 0 0 2
W in d 1 4 7 0 0 9
S o la r P V 4 0 0 < 1
B io m a s s 2 2 4 < 1
P u m p e d S to r a g e 2 5 1 8 -
T e c h n o lo g y I n s t a l le d
C a p a c it y [M W ]
E n e r g y s h a r e
e s t . [ % ]
C o a l 5 2 0 .2
N u c le a r 2 6 9 4 9
G a s 4 2 2 2 7 4 3
O i l 3 5 2 < 0 .1
H y d r o 1 4 0 0 2 2 1
W in d 5 6 3 2 6
S o la r P V 9 5 8 8 1 1
B io m a s s 1 3 1 4 2
P u m p e d S to r a g e - -
G e o th e r m a l 2 6 9 4 6
MISO CAISO
20% improvement (10-20% VRE penetration)
40% improvement (10-20% VRE penetration)
Sources: NREL; U.S. DoE; CAISO; MISO
37
Conclusions7
Outcomes6
Some selected scenarios5
System value of VRE forecasting4
Impacts of weather systems on VRE forecasts in South Africa3
Future plans for VRE deployment2
The South African power system and recent VRE deployment1
Overview
38
Conclusions
South Africa has started deploying VRE (solar PV and wind) with plans for significant further expansion
Value of improved VRE forecast in representative South African system at a national scale has been quantified
Value of improved VRE forecast in South Africa increases with increasing VRE penetration levels (as expected)
Value is ≈0.02-0.12%1 and ≈0.04-0.21%2 of production costs for VRE ranges considered (0-20% - by energy)
When comparing to representative U.S power systems (MISO, CAISO) - outcomes show more correlation andalignment with the coal-dominated MISO region than that of gas-dominated CAISO region (as expected)
Future research is necessary for higher variable renewable energy penetration levels
A number of unique weather systems exist in South Africa – a better understanding these can facilitate thepresented improvements to VRE forecasts
1 For 20% VRE forecast improvement; 2 For 40% VRE forecast improvement
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