Copernicus Institute of Sustainable Development
Smart control and
Big
Data in PV
Wilfried van Sark
Sunday 2015
18 November
2015
Contents
•
Big data
•
PV developments
•
Example projects with Big Data
Advanced Solar Monitoring: GIS
Yield analysis
PV forecasting
Definition Big Data
•
“
Definition of “Big Data” is not always clear and the term is
not always used correctly
”
(Wikipedia)
•
Factors (IF 2 of 3, THEN “Big Data”)
amount of data
speed with which data is acquired or can be accessed
diversity in data
•
unstructured and cannot be stored in traditional
Definition Big Data
Other factors:
•
variation in data
contradicting data may lead to unclear conclusions
•
quality of data:
reliability of data source
•
complexity in data
how to combine unstructured data from different
sources
Why Big Data Approach
•
Big Data creates value in several ways
Creating
transparency
/easy access
Expose variability, improve and manage performance
Replace/support
human
decisions with
automated
algorithms
Innovate
new business models, products and services
PV market development
Data: IEA
-PVPS
Nationaal Actieplan Zonnestroom
update 2016
Electricity usage (25/3/2013)
base load
peak load
21 GW capaciteit8 GWp
2021?
national
…and what if we also add multi GW wind?
Not a problem now, but what if….
Implications on the district level
•
Non-controllable
fluctuations
in grid
•
Network operators will experience large power
fluctuations due to passing clouds
•
Accurate and local
forecasts
needed as well
knowing where the PV systems are
•
TKI projects:
Advanced Solar Monitoring: mapping of PV
installations
TKI-Solar: Advanced Solar Monitoring
(“Big Data”)
Solar Potential Energy usage
GIS data
Energy Management
Solar usage Solar monitoring Meteodata
Data
Solar Potential information created by using a model on
0.5 m resolution Digital Elevation Model from AHN
GIS layers:
•
Building information from cadastre. (BAG
*, Netherlands)
•
Postcode information layer
Present Photovoltaic (PV) installations information and
electricity production data (PIR)
Energy consumption information
*AHN (Actueel Hoogtebestand Nederlands) High resolution LiDAR Data *Basisregistratie Adressen en Gebouwen (BAG)
Data Management
•
Different Data from different sources
•
Common platform
•
Relational database has been created and a spatial entity
has been introduced to manage the data in ArcGIS.
•
PostgreSQL has been used to create this database run
Building Solar Potential Database
Select suitable areas for PV installations (model),
using different classes
Calculate area from the output of the model
Estimate potential capacity based on area with
varying power density depending on class
Estimate annual yield
Method 1
Suitability
Method 2: Classification
•
Areas receiving
> 90%
of solar irradiation. These areas are
optimal:
Class1
•
Areas receiving irradiation between 70%-90%. These areas are
still efficient but less optimal: Class2.
•
Areas receiving about
50%-70%
of irradiation. These areas are
less efficient:
Class3
•
Areas receiving
< 50%
of irradiation. These areas have been
treated as not suitable:
Class4
Potential calculation
CODE Feasibility Legend Potential Yield
Method 1 150 Wp/m2
0
Not Suitable
0
1
Partial (2/3 criteria
satisfied)
750kWh/kWp
2
Suitable
950kWh/kWp
Method 2 100 Wp/m2 flat roofs 150 Wp/m2 sloped roofs0
<50%
0
1
50-70%
600kWh/kWp
2
70-90%
750kWh/kWp
3
>90%
900kWh/kWp
Results
Layered information
on PV potential of
buildings along with
location and
probable yield
estimations
Potential estimations for Apeldoorn using both
methods
Apeldoorn CODE Potential Capacity
(MWp) Potential Yield (GWh) Total Capacity/Yield Method 1 150 Wp/m2
