Since inception in 2013 our platform has been constantly evolving to improve the management of rooftop Solar PV installations. Available as a stand alone license or as part of our Operations and Maintenance and/or Asset Management service package(s). Our unique cloud- based platform, Ecovision Asset Management System (EAMS) continuously monitors your PV assets, providing key management data for your portfolio. We can monitor sites every 30 minutes and raise alarms for generation, communication and underperformance faults.
Notable EAMS functionality includes:
- Full meter reading service, from daily reads to half- hourly data capture and associated analytics
- Performance compared to all installations on our platform (on a normalised basis)
- Automated meter submissions to your FIT provider
- Fault alarms for generation, communication and underperforming with intelligent queue ordering and rapid response fault diagnosis
- Unique performance monitoring functionality (full details in the following slides)
- Complete workflow management and reporting from technical alarms to legal queries
- All generation, fault, submission and associated site documentation securely backed up
- Calculation of accrued and realised revenue
- Maintenance of site fixed asset registers
Irradiation Performance Monitoring
We have been working with a leading data science consultancy whose products and support are used by the National grid. The objective to integrate observed irradiation data into our monitoring platform in a way that is meaningful and reflects a sites installed components and attributes.
Domestic rooftop and C&I sites rarely have pyranometers installed. As such it is difficult to know what sites in any given location should have produced daily with any degree of accuracy.
Many portfolios are managed against Industry datasets based on long term average irradiation. This approach makes it impossible to manage O&M against accurate irradiation for performance guarantees. Or if adjustment factors are applied for month or quarter end reporting any underperforming issues will be in the past meaning the generation and associated revenue in the period are lost.
- Site historic generation, availability and irradiation data creates a site specific digital twin that reflects installed components, capacity, orientation, pitch etc Also serves to check the accuracy of asset registers
- Measured irradiation from official world meteorological sites is integrated into our platform and translated by the digital twin for each site providing accurate daily generation numbers that reference actual irradiation
- As this is calculated daily as a minimum (can be up to half hourly depending on system size and preference) specific underperformance alerts are triggered, reasons identified meaning generation and associated revenues can be maximised quicker and not lost
- Weather observation data from official World Meteorological Organisation (WMO) is used – approximately 250 sites across the UK with good spatial coverage.
- Simulated observations from an extended set of approximately 1000 virtual sites across the UK used to fill in any spatial gaps in WMO network.
- Data produced by blending outputs from the 2 main UK weather models – UK Met office and European Centre for Medium Range Weather Forecasting ECMWF.
- Using complex algorithms expected site generation is split into statistically probable categories including P50, P90 and a blended model that draws data from sites in close proximity if there is insufficient site specific data for the model to work
- 12 month site specific rolling forecast based on updated long term average information from measured irradiation and site performance
- The table below outlines all model outputs that are generated daily for each site.
Daily Generated Model Outputs
|metered_generation||Actual metered generation from the site|
|model_estimate||Modelled estimate of what is expected from the site based on historic behaviour and actual weather conditions|
|p95_low||95% confidence lower limit, low level of the confidence band that with 95% probability should be the generation figure|
|p50_low||50% confidence lower limit, low level of the confidence band that with 50% probability should be the generation figure|
|p50_high||50% confidence upper limit, upper level of the confidence band that with 50% probability should be the generation figure|
95% confidence upper limit, upper level of the confidence band that with 95% probability should be the generation figure
|95% confidence upper limit, upper level of the confidence band that with 95% probability should be the generation figure|
|clear_sky_limit||Upper limit of generation based on clear sky weather conditions; note this is a modelled value so can be exceeded in extreme conditions|
|normal_weather_estimate||Estimate of generation that would be expected based on seasonal normal weather conditions. Rolling long term (10 yr) seasonal average, mapped to weather locations.|
Peer Review Methodology
Objective: To optimise generation OK and revenue by identifying component failures
- Daily generation meter reading taken for the ‘test’ site
- Daily generation reading taken for all other sites in the same and 8 adjacent grid squares
- All readings ‘Normalised’ to 1kw and adjusted for site specific attributes (e.g. pitch, orientation etc.)
- ‘Normalised’ readings for each site compared to all peers daily
Fault: <75% mean generation of peers over 5 days (normalised generation per kWp of capacity)