A Big Data approach to the solar PV market: design and results of a pilot in The Netherlands
Bhavya Kausika
1,*, Wiep Folkerts
2, Wilfried van Sark
1, Bouke Siebenga
3, Paul Hermans
41 Copernicus Institute of Sustainable Development, Utrecht University, Heidelberglaan 2, 3584 CS Utrecht, The Netherlands 2 Solar Energy Application Centre (SEAC), High Tech Campus 21, Eindhoven, The Netherlands
3 I-Real, Stationsweg 30, Terborg, the Netherlands
4 Aurum Europe, Zandsteen 6, Hoofddorp, The Netherlands Email: B.B.Kausika@uu.nl
* Corresponding author
INTRODUCTION
In this study, we investigate how a Big Data approach of large connected data sets can contribute to addressing supply-demand balancing. The objective of the study was to answer the following research questions
1. How is the local generation associated with local demand over time?
2. What are the possible strategies to address the cumulative supply-demand mismatch?
3. What are the appropriate strategies for time resolved balancing?
RESULTS
Bala Bhavya Kausika
Van Unnikgebouw 913| Heidelberglaan 2 | 3584 CS Utrecht | t. 030 253 4921 | B.B.Kausika@uu.nl |
METHODOLOGY DESCRIPTION
1. Collect data
2. Create clean and useable datasets
3. Add spatial entity for visualization and analysis in GIS.
4. Make meaningful conclusions
The pilot area we chose is defined by the Dutch postal codes 73 and 81. This area covers the city of Apeldoorn and rural villages like Vaassen, Epe and Heerde. The time resolution we used is one hour, and the spatial resolution varying between household level to postal code. The analysis started with the mapping of installed systems, capacity and production, building information, digital elevation models, solar potential and electricity consumption.
No of buildings
Buildings with
installations
Present Installed
capacity
Potential capacity
Present Electricity consumption
Potential PV production Test Run on
1.25 sq km 4000 21 0.06 MWp 3.6 MWp 14,000 MWh 3,150 MWh
Total Pilot Area
29,2764 3,828* 17.55 MWp 430 MWp 950,848.5
MWh 376,250
MWh
Suitable aspect
High radiation Suitable
elevation Suitable
slope
Desirable location
Criteria
met? Not suitable
Suitable sites
Faculty of Geosciences
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Electricity produced kWh
Month
Comparison of 3 different capacity systems in the pilot area for the year 2013
31.28 kWp installation 10.5 kWp installation 8 kWp installation
0 100 200 300 400 500 600
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Engergy kWh
Month
Scenario : If the household had a solar panel
Electricity consumption of a household (4216 kWh) Electricity production from a 2.25 kWp installation in the same area (1767.6 kWh )
Interim Conclusions
We were able to bring for the pilot area data sets from different sources onto the same GIS base.It was found that about 1.61% of the total electricity consumption is covered by PV in the pilot area for the domestic sector. Therefore, there is scope for filling this gap. Grid operators would have to keep track of all the ongoing installations and be able to manage the feed–in and the consumption and consumers will be able to understand and forecast the generation of solar power as a function of place and time which will enable smooth management of supply and demand. We performed a new analysis of PV potential based on the AHN elevation data. The roof potential is not large enough for 100% coverage of the electricity demand. About 39% coverage can be achieved by rooftop PV. Time resolved balancing will require a combination of demand steering (variable tariff setting) and storage.
A method to find the PV potential from elevation data. This method has been used to find the potential capacity and would be implemented for future calculations.
* data acquired from PIR (Rijkswaterstraat, Ministry of Infrastructure and the Environment, Netherlands), AURUM and SOLAR CARE .
YES
NO
Installed capacity per post code in the pilot area and locations of installed
panels
0,00 0,50 1,00 1,50 2,00 2,50
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Energy kWh
Time in hours
Energy consumption and production in hourly resolution on 12th December, 2013 for a household in Post Code 8131
Electricity Production Electricity consumption
