impact of aerosols in photovoltaic energy production · context introduction results summary and...
TRANSCRIPT
IMPACT OF AEROSOLS INPHOTOVOLTAIC ENERGY
PRODUCTION
C. Gutierrez1, S. Somot2, P. Nabat2, M. Mallet2, M.O. Molina1,M.A. Gaertner1, O. Perpinan3
1Facultad de Ambientales y BioquımicaUniversidad de Castilla-La Mancha, Toledo, Spain
2Centre National de Recherches Meteorologiques (CNRM)Meteo France, Toulouse, France
3Electrical Engineering DepartmentETSIDI-UPM, Madrid, Spain
Climate Change impacts in the Mediterranean Region.16-18 october 2017
INTRODUCTION
SCOPE : Increase of renewable energies installed capacity:
I High penetration challenges: variability.
MOTIVATION : Photovoltaic technology
I 1 variability: CLOUDS + AEROSOLS
Aerosols can affect the photovoltaic energy production in theshort-term and can lead to misleading bankability studies for some
projects in the long-term.
I 2 CGMs and RCMs discrepancy in shortwave solar radiation, SW,scenarios over Europe (Bartok et al.).
OBJECTIVE: Assess the impact of aerosols on PV productionover the Euro-Mediterranean region and its role in projected SWscenarios.
INTRODUCTION
APPROACH: A photovoltaic system transform solar irradiationinto electricity.
2 models are needed for the assessment/forecast of the PV production:
I Climate model for SW + PV production model.
2 different analysis:
I simulations 2003-2009
I simulations RCP 4.5 scenario
INTRODUCTION
APPROACH: A photovoltaic system transform solar irradiationinto electricity.
2 models are needed for the assessment/forecast of the PV production:
I Climate model for SW + PV production model.
2 different analysis:
I simulations 2003-2009
I simulations RCP 4.5 scenario
INTRODUCTION
APPROACH: A photovoltaic system transform solar irradiationinto electricity.
2 models are needed for the assessment/forecast of the PV production:
I Climate model for SW + PV production model.
2 different analysis:
I simulations 2003-2009
I simulations RCP 4.5 scenario
2003-2009
2 simulations AER and NO.CNRM-RCSM4
I AER includes realistic interannual monthly AOD climatology: Nabat et al.2015a, Climate Dynamics
Figura: Nabat et al. 2015a, Climate Dynamics
1. Surface shortwave (SW) comparison with satellite product.I CM-SAF satellite data. SARAH dataset.
2. PVoutput by tracking system
Annual mean of SW. Relative differences
10°W
0°10°E 20°E
30°E
40°E
50°E
25°N
30°N
35°N
40°N
45°N
50°N
55°N CAER.SAT
10°W
0°10°E 20°E
30°E
40°E
50°E
25°N
30°N
35°N
40°N
45°N
50°N
55°N CNO.SAT
−0.1
0.0
0.1
0.2
0.3
0.4
0.5
AER-SAT
NO-SAT
Relative difference in PV productionAER-NO
I Most of the domain around 5 %
I Higher values in northern europe, above 10 %
Seasonal relative differences.
FIXED
TWO
I Differences in the seasonal spatial pattern.I Winter has higher relative values.I Summer has lower relative values but around 5 % for fixed panels in most
of the domain.
PV real data: SEVILLA
AOD
valu
e
0.0
0.5
1.0
0.10 0.15 0.20
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●●
●
caercnosat
●
●
●
I Difference between AER and real data is generally less than 0.5 kWh.
I In some months, AER is closer to real data than CM-SAF dataset.
I AER is better than NO specially in months with high AOD.
SCENARIOS: MED-CORDEX
2 RCMs: CNRM-ALADIN5.2ENEA-PROTHEUS.
I Same GCM: CNRM-CM5
I 50km x 50 km
I RCP 4.5I Diferent aerosol scheme:
ENEA-PROTHEUS:no aerosolCNRM-ALADIN5.2:aerosoldataset (Szopa et al., 2012) 2Dvariability/time-evolving bydecade. 5 different species.
AOD anomaly JJA(2076/2100)-(1981/2005)
JJA anomaly. 1981/2005
SW CLT
I ENEA-PROTHEUS does not reproduce historical brightening.
I Increase in RSDS for both models.
I SW increase not directly linked with CLT decrease forCNRM-ALADIN.
JJA anomaly. 1981/2005
SW vs. CLT SW vs. AOD
I Linear regression between SW and CLT show higher scores forENEA-PROTHEUS ( 0.72 ALADIN5.2, 0.88 PROTHEUS).
JJA anomaly. 1981/2005
SW vs. CLT SW vs. AOD
I Linear regression between SW and CLT show higher scores forENEA-PROTHEUS ( 0.72 ALADIN5.2, 0.88 PROTHEUS).
I The linear model for AOD shows a r2 coefficient of 0.89.
I SW(clt, aod): model r2 is 0.97
JJA anomaly. 1981/2005
SW vs. CLT SW vs. AOD
I Linear regression between SW and CLT show higher scores forENEA-PROTHEUS ( 0.72 ALADIN5.2, 0.88 PROTHEUS).
I The linear model for AOD shows a r2 coefficient of 0.89.
I SW(clt, aod): model r2 is 0.97
JJA mean anomaly (2076/2100)-(1981/2005)
SW
W/m^2
CLT
%
I Different spatial patterns. SW in ENEA-PROTHEUS correlates better with
CLT.
I With some exceptions, the increase of SW is clear over the domain.
JJA mean anomaly (2076/2100)-(1981/2005)
SW
W/m^2
CLT
%
I Spatial correlation values:I CNRM: SW vs. AOD = - 0.55 / SW vs CLT = - 0.70I ENEA: SW vs. CLT= - 0.86
CONTEXTINTRODUCTION
RESULTSSUMMARY AND FUTURE
Summary
I Aerosols have an impact on PV production that is non negligiblein most places of the studied domain for annual and seasonal terms.
I There is a clear improvement on the representation of SW withthe aerosol-included simulations.
I Modelled PV production is close to real data for the periodanalysed.
I Scenarios show the impact of aerosols in SW could be as importantas clouds.
CONTEXTINTRODUCTION
RESULTSSUMMARY AND FUTURE
Next steps
I More real PV data for evaluation ?
I PV production analysis for scenario rcp4.5 with different trackingtypes.
I Use more RCMs ? (FPS MED-CORDEX aerosols)
