Document Name Solar Analytics - Rooftop PV energy analytics - - PowerPoint PPT Presentation

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Document Name Solar Analytics - Rooftop PV energy analytics - - PowerPoint PPT Presentation

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Document Name Solar Analytics - Rooftop PV energy analytics PREPARED BY: Your Name, Your Title and fault diagnosis DATE: 6 TH MARCH 2015 Jonathon Dore Head of Data Analytics Solar Analytics 1 Outline About Solar Analytics Faults

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Document Name

PREPARED BY: Your Name, Your Title DATE: 6TH MARCH 2015

Solar Analytics - Rooftop PV energy analytics and fault diagnosis

Jonathon Dore Head of Data Analytics Solar Analytics

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Outline

  • About Solar Analytics
  • Faults and case studies
  • Research projects
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Outline

  • About Solar Analytics
  • Faults and case studies
  • Research projects
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Our Purpose Our Purpose To maximise the To maximise the value customers value customers receive from their receive from their solar energy system solar energy system

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Coal is not good for humanity Coal is not good for humanity … …

Pollutes atmosphere Degrades land Expensive Doctors report that “coal is a health hazard from start to finish”

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… solar energy is the saviour … … solar energy is the saviour …

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Cheaper Simpler Cleaner

... distributed solar is already ... distributed solar is already colonising our homes … colonising our homes …

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…but there’s a problem …but there’s a problem

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19% CAGR but industry has focussed on large 19% CAGR but industry has focussed on large scale monitoring scale monitoring

Source: GTM research, 2013 80 60 100 20 40

% of sites

Utility Commercial Residential Monitored Not monitored

Leading monitoring providers by revenue Global monitoring penetration

Company Focus Skytron Energy Utility InAccess Utility MeteoControl Utility/Commercial FirstSolar Utility GreenPower SolarLog Utility Utility/Commercial

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Features Residential Commercial Utility

Production Data Performance Analysis Fault Diagnosis

Current residential monitoring products Current residential monitoring products are inadequate are inadequate

Available with Solar Analytics

Readily available Not available

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Solar Analytics is already providing Solar Analytics is already providing the solution the solution

Works with any hardware …. or even none

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Providing useful information Providing useful information

How to fix your system When to install batteries How to save energy

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Providing detailed information Providing detailed information

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Distributed via solar retailers Distributed via solar retailers

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Current Status: 506 Monitored Sites

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People People

  • Stefan Jarnason, Co-founder and Managing Director
  • Valantis Vais, Co-founder and Board Member
  • Dr Renate Egan, Co-founder and Commercial Director
  • Dr John Laird, Co-founder and Software Development Manager
  • Dr Jonathon Dore, Head of Data Strategy & Analytics

+ 10 (software dev, design, sales, service, ops & analysis) + Co-op

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History History

Envais Solar (Valantis)

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Outline

  • About Solar Analytics
  • Faults and case studies
  • Research projects
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Breakdown of PV system failure

  • 26% systems

underperforming

  • 12.1% annual fault rate

Solar Analytics Data

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Breakdown of PV system underperformance

Performance lessons from the real world, SunWiz 2012

  • Earth leakage
  • Soiling
  • DC or AC Isolator
  • Wiring or connectors
  • Intermittent inverter issue
  • Microcracks
  • Hot spot
  • Potential Induced Degradation
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Case Study 1

  • 10kW system in Tasmania
  • 7.5kW @ 32° NW and

2.5 kW @ 122 ° SW Intermittent string loss

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Case Study 2

5 10 15 20 25 30 35 40 Inverter 1 Inverter 2 Energy (kWh)

Daily Energy

Measured Production

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Case Study 2

5 10 15 20 25 30 35 40 Inverter 1 Inverter 2 Energy (kWh)

Daily Energy

Expected Producction Measured Production

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Case Study 2

1 2 3 4 5 6 5:00 AM 6:00 AM 7:00 AM 8:00 AM 9:00 AM 10:00 AM 11:00 AM 12:00 PM 1:00 PM 2:00 PM 3:00 PM 4:00 PM 5:00 PM 6:00 PM 7:00 PM Power (kW)

Average Power (1 hour resolution)

Inverter 1 Inverter 2

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Case Study 2

1 2 3 4 5 6 5:00 AM 6:00 AM 7:00 AM 8:00 AM 9:00 AM 10:00 AM 11:00 AM 12:00 PM 1:00 PM 2:00 PM 3:00 PM 4:00 PM 5:00 PM 6:00 PM 7:00 PM Power (kW)

Average Power (5 min resolution)

Inverter 1 Inverter 2

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Case Study 2

246 250 254 258 262 266 270 1 2 3 4 5 6 10:00:00 AM 10:10:00 AM 10:20:00 AM 10:30:00 AM 10:40:00 AM 10:50:00 AM 11:00:00 AM Voltage (V) Power (kW)

Power and Voltage (5 sec ressolution)

Power (kW) Voltage (V)

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Case Study 3

1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 29 29 29 29 9 10 11 12 2014 Power (W) Sum of Measured Sum of Expected Sum of Shaded

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Case Study 3

1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 6 7 8 9 10 11 12 13 14 15 16 17 18 Energy (Wh) Hour Shaded Hourly Measured

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Case Study 3

1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 29 29 29 29 9 10 11 12 2014 Power (W) Sum of Measured Sum of Expected Sum of Shaded

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Case Study 3

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Outline

  • About Solar Analytics
  • Faults and case studies
  • Research projects
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Research Projects

  • Load disaggregation (Lachlan McDermid, UNSW)
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Research Projects

  • PV generation forecasting

(Johnathan Lee & Rachel Oh, UNSW)

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Research Projects

  • PV generation forecasting (Bibek Joshi, UNSW)
  • Household consumption forecasting

(Baran Yildiz, UNSW)

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Proposed Project

  • Hardware development
  • Home energy management
  • Battery monitoring and optimisation
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Future Projects

  • ARC Linkage?
  • Solar Hot Water?
  • Machine Learning?
  • Grid Management?
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Our Purpose Our Purpose To maximise the To maximise the value customers value customers receive from their receive from their solar energy system solar energy system