© ABB PP&PS FES Italia October 20, 2015 | Slide 1 Advanced solutions for solar plants Sergio Asenjo, Head of Solar Center of Competence, June 10th 2010

© ABB Solar COC Spain October 20, 2015 | Slide 2 Photovoltaic plant automation Architecture The system will manage, among traditional automation functions/features:  Solar tracking system, when available, for production maximization  Performance calculation of the different stages  ABB patented Switching System for optimizing inverter efficiency  Troubleshooting management of strings  Integration of plant security and surveillance system  Production automatic reporting system

© ABB Solar COC Spain October 20, 2015 | Slide 3 Solar standard solution Technology highlights  High precision shadowing control algorithm for solar tracking  Extensible and scalable solution for any plant size  Switching system for optimizing inverter efficiency  Performance/efficiency oriented supervision system

© ABB Solar COC Spain October 20, 2015 | Slide 4 Solar standard solution Technology highlights High precision shadowing control algorithm for solar tracking  Shadowing prevention according to tracker dimensions and plant layout  Other systems use “backtracking correction”, thus preventing unnecessary movements and efficiency losses

© ABB Solar COC Spain October 20, 2015 | Slide 5 Solar standard solution Technology highlights  High precision shadowing control algorithm for solar tracking  ABB algorithm calculates the optimal position modeling panels and tracker structure geometry

© ABB Solar COC Spain October 20, 2015 | Slide 6 Photovoltaic plant automation Architecture LAN 2 Local Automation Solar Tracker Inverters MV an LV Swicthgears DCS Transformers OPERATOR WORKPLACE Remote Office Internet Remote Access LAN 1

© ABB Solar COC Spain October 20, 2015 | Slide 7 Photovoltaic plant automation Function allocation  At the DCS level is controlled  Solar plant power electronics device controls  Optimization - switching  Neural networks - intelligent forecast and approximation  Alarms and events handling  At local automation is performed  Trackers  Accurate solar tracking algorithm  One and two axis movement control implementation  Power connection box  Power connection box management  Current per line current control to detect strings failures

© ABB Solar COC Spain October 20, 2015 | Slide 8 Supervision & control systems Photovoltaic plant automation Local automation architecture

© ABB Solar COC Spain October 20, 2015 | Slide 9 Photovoltaic plant automation Operator mimics

© ABB Solar COC Spain October 20, 2015 | Slide 10 Photovoltaic plant automation Operator mimics

© ABB Solar COC Spain October 20, 2015 | Slide 11 Solar standard solution Technology highlights Switching System for optimizing inverter efficiency  Input power distribution for optimizing inverter efficiency  Switching principles:  Inverter low performance at low loads  Inverter high performance at medium-high loads  One inverter working at medium load, better than two inverters working at low load  Load balancing among inverters

© ABB Solar COC Spain October 20, 2015 | Slide 12 Solar standard solution Technology highlights Switching System for optimizing inverter efficiency  Low performance  High performance

© ABB Solar COC Spain October 20, 2015 | Slide 13 Photovoltaic plant automation Advanced optimization  DCS advanced control functions  Operation of the switch over cabinet  Optimization based theoretical calculations  Neural networks analysis

© ABB Solar COC Spain October 20, 2015 | Slide 14 Photovoltaic plant automation Advanced optimization Over the Maximum Power Point Tracking algorithm (MPPT) to increase performance in operational points like low sun conditions it has been developed a set of algorithms based on Artificial Neural Networks (ANN) and designed to adapt themselves to the particular conditions of every PV plant

© ABB Solar COC Spain October 20, 2015 | Slide 15 Solar standard solution Technology highlights Switching system for optimizing inverter efficiency  Neuronal Network is an adaptive approximation method to achieve a more accurate calculation of output power in case of switching  Working Principle:  Two inverters: PI1=I1*V1 ; PI2=I2*V2  Switching all strings to Inverter 1  One inverter; PI=PI1+PI2 (Ideal)  One inverter; PI’=PI1’+PI2’ (real)

© ABB Solar COC Spain October 20, 2015 | Slide 16 Solar standard solution Technology highlights Switching System for optimizing inverter efficiency  The difference is in the PV turbine equivalent I-V curve (affected by panel degradation, dirtiness, etc..)  Neuronal network learns from real values to get progressively a better PI’

© ABB Solar COC Spain October 20, 2015 | Slide 17 Solar standard solution Technology highlights Performance/efficiency oriented supervision system  Real time plant performance ratio calculation based on:  Irradiation  Panels strings  Inverters  Transformers

© ABB Solar COC Spain October 20, 2015 | Slide 18 New advanced features Oriented to performance  Efficiency calculation:  For individual elements (strings, trackers, inverters…)  For stages  For the whole plant  To allocate malfunctions in the shortest time  Alarms for deviation in real time (alarms)  Reports

© ABB Solar COC Spain October 20, 2015 | Slide 19 Stages for performance Calculations Modules Efficiency Tracking Efficiency Cabling efficiency Inverters and Swicthing Efficiency Trasnformers efficiency Irradiation Temperature Strings Inverters Inverters output Modules Characteristics Tracking - Perfect - Optimal distribution Inverter characteristics Swicthing scheme Transformers characteristics Real Position String Tracker Inverters Transformer Trafo Counter DC cable Design charactericits DC field A V A V A V A V

© ABB Solar COC Spain October 20, 2015 | Slide 20 Real performance Devices for measuring  Measurements devices:  Weather station  Pyranometers  Reference cells  Inclinometers  Strings measurements  Inverters measurement  Input DC  Output ac  Transformers  Electrical metering

© ABB Solar COC Spain October 20, 2015 | Slide 21 Theoretical performance Calculation methods  Equipment characteristics  Modules behavior  Tracking models  Perfect  Optimal  Cabling design  Switching, inverter curves  Transformers performance curves  Control system strategy and features  PLCs, SCADA, Databases

© ABB Solar COC Spain October 20, 2015 | Slide 22 Energy balance reports 18/12/2009 Modules PlantLíneStringRadiation Output Measured Output CalculatedEff. MeasuredEff. CalculatedRatio P1P1-L1P1-L1-S18 KWh1,2 KWh1,22 KWh14%14,5%96,6% P1-L1-S28 KWh1,2 KWh1,22 KWh14%14,5%96,6 % P1-L1-S38 KWh1,2 KWh1,22 KWh14%14,5%96,6 % P1-L124 KWh3,6 KWh3,66 Kwh14%14,5%96,6 % P1-L2P1-L2-S18 KWh1,2 KWh1,22 KWh14%14,5%96,6 % P1-L2-S28 KWh0,9 KWh1,22 KWh11,25%14,5%77,58% P1-L2-S38 KWh1,2 KWh1,22 KWh14%14,5%96,6% P1-L224 KWh3,3 KWh3,66 Kwh12,5%14,5%90,26% P1--48 KWh6,9 KWh7,32 Kwh13,78%14,5%93,52% P2P2-L1P2-L1-S18 KWh1,2 KWh1,22 KWh14%14,5%96,6 % P2-L1-S28 KWh1,2 KWh1,22 KWh14%14,5%96,6 % P2-L1-S38 KWh1,1 KWh1,22 KWh13%14,5%90,11 % P2-L124 KWh3,5 KWh3,66 Kwh13,64%14,5%94,35% P2--24 KWh3,5 KWh3,66 Kwh13,64%14,5%94,35% Summary-- 72 KWh10,4 KWh10,98 Kwh13,71%14,5%93,80%

© ABB Solar COC Spain October 20, 2015 | Slide 23  Production increase. Nubosidad Wind position.  Production in normal conditions  Production during high wind Hail Position  Production in normal conditions  Production during hail situation. ABB system optimization Automatic Switching system during hail and high wind

© ABB Solar COC Spain October 20, 2015 | Slide 24 Dawn Cloudiness Dawn - nightfallr Red color area  production increase ABB system optimization Automatic Switching system in dawn, nightfall and clouds

© ABB Solar COC Spain October 20, 2015 | Slide 25 Solar standard solution Technology improvements Performance/efficiency increased by 0,8% to 2,5% Production increased during the whole day, starting earlier and shutting off later.

© ABB Solar COC Spain October 20, 2015 | Slide 26 Photovoltaical power plant (PV) Reference plant

© ABB Solar COC Spain October 20, 2015 | Slide 27