Smart control and Big Data in PV

Transcription

1 Copernicus Institute of Sustainable Development Smart control and Big Data in PV Wilfried van Sark Sunday November /36

2 Contents Big data PV developments Example projects with Big Data Advanced Solar Monitoring: GIS Yield analysis PV forecasting Conclusions 2/36 2

3 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 database 3/36

4 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 4/36

5 5/36 5

6 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 McKinsey & Company /36

7 7/36

8 PV market development Data: IEA-PVPS, CBS 8/36

9 PV (future) growth in NL 9/36

10 Nationaal Actieplan Zonnestroom update /36

11 Not a problem now, but what if. Electricity usage (25/3/2013) national 21 GW capaciteit peak load 8 GWp base load 2021? 11/36 and what if we also add multi GW wind?

12 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 Solar Forecasting & Smart Grids 12/36

13 TKI-Solar: Advanced Solar Monitoring ( Big Data ) Solar Potential Energy Management Energy usage focus Meteodata GIS data Solar usage Solar monitoring 13/36

14 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) 14/36

15 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 queries and create files for use in ArcGIS. 15/36

16 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 Pilot area: Apeldoorn and surroundings 16/36

17 Method 1 Suitability 12 17/36

18 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 18/36

19 Potential calculation CODE Feasibility Legend Potential Yield Method 1 0 Not Suitable Wp/m 2 1 Partial (2/3 criteria satisfied) 750kWh/kWp 2 Suitable 950kWh/kWp Method Wp/m 2 flat roofs 150 Wp/m 2 sloped roofs 0 <50% % 600kWh/kWp % 750kWh/kWp 3 >90% 900kWh/kWp 19/36

20 Method 1 results 15 20/36

21 Method 2 21/36

22 Results Layered information on PV potential of buildings along with location and probable yield estimations 22/36

23 Potential estimations for Apeldoorn using both methods Apeldoorn CODE Potential Capacity (MWp) Potential Yield (GWh) Total Capacity/Yield Method Wp/m 2 Method Wp/m 2 flat roofs 150 Wp/m 2 sloping roofs 0 Not Suitable Not Suitable MWp GWh MWp GWh 23/36

24 PIR PV data Combine model results with PIR to find potential additional locations 24/36

25 Example: Postcode rose policy (Apeldoorn) Dolla, Kausika, /36

26 Next phase: will allow for control, business models!? Grid information (EAN) Electricity demand Electricity production Real time meteo data (KNMI) Utility data layers Phase 2 Elevation (3D) PV Potential Postal Code Terrain information Building Layer Existing PV installations Phase 1 26/36

27 Big Data PV Monitoring 250,000 PV systems (PIR) Power data 5 minute time resolution Production OUTPUT Register POR GIS maps 27/36

28 [IEA-PVPS-Task 13] GIS mapping of PV yield 2014: kwh/kwp Moraitis, Kausika, /36

29 ongoing Moraitis, Kausika, /36

30 PV peer-to-peer forecasting Short-term, high resolution Global Horizontal Irradiance (GHI) forecasting based on cross correlation time lag Solar Forecasting as input for optimizing local use/storage of Photovoltaic (PV) power and reducing variability (see poster Boudewijn Elsinga) 202 Rooftop PV-systems (< 5 kw p ) in the Province of Utrecht (NL), covering approximately 1400 km 2 AC Power Output measurements of 0.7 W and 2 sec. raw data resolution; interpolation used for GHI and Clearness Index, reconstruction with Perez model 30/36

31 Two PV systems some distance apart hit by the same cloud Elsinga, 2015 relative shift 31/36

32 Shift many systems upwind and average Elsinga, /36

33 Forecast quality metrics Relative root mean square error Forecast skill Shows in how far forecast is better than persistence Positive: better than persistence (cloudy day) Negative: worse than persistence (clear day) 33/36

34 Example Elsinga, 2015 rrmse Forecast skill 34/36

35 Conclusions ASM1 project Different big data sets onto the same GIS platform for the pilot area creates added value Working model for the estimation of solar PV potential using high-resolution LiDAR data and GIS techniques GIS based visualization of yield Peer-to-peer forecasting Quality depends on weather type Next steps: control, markets, business Based on big data sets and data analysis and manipulation 35/36

36 Acknowledgements ASM1: Bhavya Kausika, Wiep Folkerts, Bouke Siebenga, Paul Hermans, City of Apeldoorn Yield visualization Panos Moraitis, IEA-PVPS-Task13 Forecasting Boudewijn Elsinga, Lou Ramaekers, Bas Vet, Paul Raats, Santiago Penate Vera, and 202 PV system owners 36/36