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HomeTechnology

1. Modeling site conditions | 2. Simulating PV plant response | 3. Analyzing solar power metrics

Modeling site conditions#

At Solargis, we provide extensive and accurate weather information, with a specific focus on those developing or operating PV power plants.

The site's solar and weather conditions have a direct impact on the performance throughout the entire lifecycle of a PV project—from site selection to design, financing, and power plant operations and maintenance.

The following parameters describe these conditions: solar irradiance, air temperature, wind, humidity, ground albedo, and others.

What for

  • Provides irradiance values reaching ground with the assumption of the absence of clouds and attenuation from aerosols (due to desert dust, smoke from wildfires, volcanoes, etc.).
  • Calculates the attenuation effect of clouds on solar irradiance and provides a continuous stream of long periods of data in sub-hourly resolution.
  • Allows having values from very recent periods in near real-time.

How it works

  • Using an accurate sun position model, it calculates extraterrestrial irradiance and its variation throughout time, taking into account key factors like altitude, atmospheric optical depth, water vapor, and ozone concentrations.
  • Quantifies the presence of clouds from satellite data acquired by geostationary satellites on several spectral channels. 
  • Combining the previous steps, Global Horizontal Irradiance (GHI) values are obtained as an outcome.

Main outputs

  • Global Horizontal Irradiance, GHI
  • Global Horizontal Irradiance for clear-sky, GHIc
  • Spatial resolution: 250 m
  • Time granularity: Sub-hourly
  • Period: since the satellite launch date (oldest satellite available since 1994)

What for

  • Global irradiance is decomposed into two main components: direct and diffuse.
  • Required for obtaining global tilted irradiance (GTI) and expected power output (PVOUT) at a later stage.

How it works

  • Relationships between components are derived from radiative transfer or empirically from observations.
  • Based on the well-known relationship between the Global Horizontal, Direct Normal, and Diffuse clearness indices.

Main outputs

  • Direct Normal Irradiance, DNI
  • Diffuse Horizontal Irradiance, DIF

What for

  • Provides meteo parameters like air temperature, wind, humidity, etc.
  • Used to know conditions required by PV simulation models to calculate energy conversion efficiency and estimation of system losses.

How it works

  • Ingestion of data from numerical weather models (NWP) received by weather data centers, e.g. ECMWF.
  • Enhancement of original spatial resolution of the NWP inputs to a higher resolution by spatial disaggregation and use of Digital Elevation Models (DEM).

Main outputs

  • Temperature, TEMP
  • Wind Speed, WS
  • Wind Direction, WD
  • Wind Gust, WG
  • Relative Humidity, RH
  • Precipitation, PREC
  • Snow water equivalent, SWE
  • Precipitable water, PWAT
  • Spatial resolution:  1km - 25km
  • Time granularity: hourly

What for

  • Provides values of ground albedo for any site.
  • Albedo values are essential to calculate reflected irradiance accurately.

How it works

  • Data from satellite instrument MODIS is used as the primary data source.
  • It is combined with data from NWP (Numeric Weather Prediction) models to prepare a gap-free for all kinds of land surfaces (including ephemeral snow).

Main outputs

  • Ground albedo, ALB
  • Spatial resolution: 500 m
  • Time granularity: Daily
  • Period: since the year 2000

What for

  • Identifies non-valid data records using quality control procedures.
  • Detects possible issues with the sensors.

How it works

  • Identification and correction of time shifts, time drifts, and other time-related issues based on testing diurnal symmetry, critical for all subsequent quality tests.
  • Detection of issues on solar irradiance data and flagging invalid values related to nighttime/daytime, artificial static values, breaking physical limits, and consistency of irradiance components.
  • Detection of issues on meteo data.
  • Detection of other specific data problems, such as instrument shading or tracker malfunction detection.

 

Main outputs

  • Filtered dataset with flagged values.
  • Related recommendations for measuring instruments.

What for

  • If ground measurements are available on the project site and comply with the required length and quality requirements, the irradiance model can be adapted to achieve higher accuracy.
  • Information from other measured meteorological parameters can also be used to obtain a new long-term data stream that is more representative of the site.

How it works

  • Adaptation of satellite-based GHI and DNI values. This method corrects the bias (systematic deviation) and fits the cumulative distribution functions.
  • Adaptation of the input parameters and data used in the solar radiation model. More complex parameters, such as Aerosol Optical Depth and/or Cloud Index, are adjusted using this approach.

 

Main outputs

  • New multi-year time series with lower uncertainty.

What for

  • Collects all parameters affecting PV performance in one dataset for the entire available period. 
  • Generates a summarized dataset of 8760 hourly values representing a "typical year".
  • Provides other summarized datasets according to P90, or any other PXX scenario.

How it works

  • Generates TMY by selecting representative months based on statistical alignment and similarity of cumulative distribution functions (CDFs).
  • Parameters are weighted according to the application.

Main outputs

  • Time series dataset.
  • Typical Meterological Year (TMY) datasets
  • Spatial resolution: 250 m
  • Time granularity: Sub-hourly, hourly

What for

  • Able to estimate higher-resolution solar irradiance data of up to 1-minute granularity.
  • It helps understand full resource variability and achieve optimum PV plant design.

How it works

  • Collects a database of high-resolution ground measurements of similar characteristics of the site of interest.
  • By applying Markov process methods, 1-minute stochastic profiles are generated.

Main outputs

  • Stochastic 1-minute  Time Series dataset of solar irradiance.

What for

  • Generates site-specific topography.

  • Provide insights to the PV simulator for the calculation of incident irradiance and energy yield.

How it works

  • Processing of digital terrain models, a comprehensive terrain characterization is conducted for the site
  • Calculation of surface slope, and surface azimuth. Horizon data is generated by aggregating points from the surrounding terrain into a 360º orientation series around the site.

Main outputs

  • Elevation (ELE)
  • Slope (SLO)
  • Surface azimuth (AZI)
  • Horizon (HOR)

Simulating PV plant response#

It's not just about accurately knowing the site conditions. You must also understand how the PV power plant will react under specific conditions.

This means understanding and developing research on every energy conversion step from solar photons to electricity.

How will outside conditions affect cell temperature and conversion efficiency? How does shading impact my specific field of modules? How much soiling shall I expect? How much loss is on the DC and AC sides?

What for

  • Calculates the energy collected by solar modules of a PV plant.
  • It is able to obtain incident irradiance for fixed and tracking systems of all kinds, azimuth, and orientation.
  • It has the capability to calculate bifacial module gains.

How it works

  • Uses a high-resolution terrain model for calculation of the far shading effect.
  • Near shading effect can be applied through 3D modeling of nearby objects and buildings. PV plant self-shading is also calculated.
  • For the calculation of diffuse tilted irradiance, it combines isotropic and anisotropic sky models.
  • Takes into account the ground albedo to obtain gains from reflected irradiance at the front and rear side of the modules (raytracing model).
  • Applies the effect of soling and snow on the incident energy calculation.

Main outputs

  • Global Tilted Irradiance, GTI.

What for

  • Provides an estimate of the expected soiling loss on the PV plant due to dust deposition.

 

How it works

  • Collects data on expected particles that are likely to lay on PV modules.
  • Add the natural effect of precipitation on the modules.
  • Calculates the expected soiling loss.

Main outputs

  • Monthly values of soiling loss for the PV plant.

What for

  • Estimates snow accumulation on PV panels
  • Helps with the calculation of related energy production losses with higher accuracy

How it works

  • Collects meteorological data from global reanalysis models
  • Estimates snow coverage on PV panels, accounting for accumulation, melting, and sliding

Main outputs

  • Snow loss factor
  • Time granularity: monthly

What for

  • Gives the energy generated by the PV modules for each instant of time.
  • Able to run simulations for all types of PV modules, including all manufacturers and technologies.
  • It can provide estimates of system losses on the DC side components.

How it works

  • Takes into account non-linear, voltage-current dependency or I-V curve (single diode model).
  • Applies the effect of cell temperature on the conversion efficiency.
  • Considers the PV plant string configuration to calculate the estimated output and takes into account mismatch due to different MPP operating points of modules connected.
  • Calculates expected heat losses in the combiner boxes,  interconnections, and cables on the DC side.

Main outputs

  • PV output, PVOUT (after PV conversion in DC)
  • Module temperature, TMOD.

What for

  • Calculates the losses on the inverter when transforming DC into AC.

How it works

  • Application of each type of inverter efficiency function (dependence of the inverter efficiency on the inverter load and inverter input voltage).
  • Use of high-granularity input data to provide more accurate results.

Main outputs

  • PV output, PVOUT (after conversion from DC  to AC).

What for

  • Calculates the system losses on the AC side components.

How it works

  • Calculates expected heat losses in the combiner boxes,  interconnections, and cables on the AC side.
  • Takes into account additional losses due to transformers and grid availability.

Main outputs

  • PV output, PVOUT (after losses on the AC side).

Analyzing solar power metrics#

To unlock the value of data, we need to understand the challenges that solar developers, asset managers, and operators face every day.

This means running geographical and temporal analyses to obtain valuable information for PV plant stakeholders across all project stages: long-term yield analysis to unlock finance opportunities, short-term forecasting to operate in energy markets, performance assessment for O&M planning, and many other use cases.

What for

  • Characterizes variability of energy generation and site meteorology.
  • Helps understand extreme operating conditions for the PV plant.

How it works

  • Statistical analysis covering interannual variability, seasonal, intraday, and sub-hourly variations.
  • Identification of extreme situations.

Main outputs

  • Maximum and minimum values.
  • Key variability statistics.

What for

  • Enables calculation of expected deviation ranges of solar resource.
  • Helps make robust financial plans for PV projects.

How it works

  • Collects extensive validation using reference measurements and models.
  • Analysis of contributing factors that lead to deviations, combining regional and site-specific analyses.

 

Main outputs

  • Estimated uncertainty for solar irradiance inputs
  • Estimated uncertainty for PV simulation

 

What for

  • Estimates expected energy during the PV plant lifetime.
  • Able to provide estimates for most expected and least expected scenarios.

How it works

  • Analyzes full-time series of data available.
  • Based on extensive data validation, accounts for uncertainties across the yield calculation chain.

Main outputs

  • Values for P50, P90, or any scenario.
  • Data uncertainty for specific sites.

What for

  • Allows regular reporting of theoretical achievable production for existing PV plants.
  • Identifies underperforming situations on the PV plant.

How it works

  • Calculation and analysis of key indicators (after filtering non-valid measured data records).
  • Characterization of differences between the measured and modeled data.

Main outputs

  • Real vs. expected energy data comparison.
  • Performance ratio (PR).
  • Actual vs. average solar irradiation values.

What for

  • Provides energy estimations for the next hours and days.
  • Used for energy management purposes at the grid level.
  • Allows a better plan of O&M activities of solar power plants.
  • Energy systems designers use historical forecasts for battery sizing and optimization.

How it works

  • Receives inputs from several Numerical Weather Prediction models(NPW) and selects the best stream of data based on historical comparisons.
  •  Cloud Motion Vector models based on satellite imagery for predictions for the next hours’ horizon.

Main outputs

  • 14-days energy predictions.
  • Subhourly updates.
  • Historical forecasts.

Useful resources#

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