The Sustainable Energy for All initiative (SE4ALL), launched by the UN in 2012, seeks to achieve a) Universal access to modern energy services b) Improve energy efficiency and c) Increase renewable energy contribution to the global energy mix by 2030. BNEF (Bloomberg New Energy Finance) reports that 70% of the new power capacity built between 2013 and 2030 will be in renewable sectors; 75% of this renewable capacity is expected to be from Solar & Wind Energy projects.
Both Wind & Solar project power outputs have a direct correlation to weather. Variability in weather translates to volatility in the volume of power generated i.e. a source of economic and financial risk for project managers.
These sources of risks coupled with the need for high levels market funding to realize the goals of SE4ALL have spawned an opportunity for innovative financial debt instruments in the market place. To enable institutional investors and insurance companies to measure risk to ROI owing to weather, we discuss weather conditions and their afore-mentioned impact on solar-PV performance. We will also briefly touch upon POUNDRA’s weather station product and our ability to offer a customized solution.
Effect of Weather on PV System Output: The electrical output of Solar-PV modules is dependent on two key factors or variables: 1) Incident Sunlight and 2) Module Temperature. All other environmental & weather factors (cloud, soiling, rain, snow, hail, wind etc…), can be considering second-order factors to the extent that they directly impact incident sunlight and module temperature. While output current of Solar-PV modules has a positive correlation with sunlight, output voltage has a negative correlation with module temperature. Hence, PV systems work optimally under high sun and low module temperature. While winter power efficiencies of PV modules are higher than summer efficiencies, summer energy is typically greater due to higher sunlight.
Sunlight and Clouds: Solar radiation varies with time of day, geographic location, seasonal weather and contextual environment factors specific to the installation site. The National Renewable Energy Laboratory (NREL) has deployed a network of high accuracy and high precision weather stations around the globe. Long term (> 50 years) solar irradiance and other weather measurements performed at these deployment sites led to determination of what is termed a Typical Meteorological Year (TMY3) for several locations. This TMY3 data is used to both predict future PV system performance and run comparative analysis against current year performance. Although, TMY3 is widely considered an adequate measure for predicting PV performance, the 20-year (PV system lifetime) average weather, at any given site, can vary ±10% from TMY3 for that site. In addition, any one year weather may vary by as much as ±30% from TMY3. Tracking the positon of the sun and orienting the Solar-PV panel to be perpendicular to the rays of the sun will help ensure maximizing harvesting of solar energy. Obstructions, natural & man made, surrounding the installation can impose a shadow on the Solar PV panel at various times of the day.
Cloud cover affects incident sunlight and power output of solar PV installations. When Renewable Energy becomes a larger part of the energy mix, such variations owing to cloud cover, can contribute to grid instability. Larger PV installation can however smooth out fluctuations caused by cloud shadows to a greater degree than is the case with single panels of small rooftop arrays (or smaller installation i.e. less than <1MW). Statistical analysis on cloud patterns and impact on PV systems can help us design systems to mitigate the effect of passing cloud on Solar-PV performance. Electricity Storage and/or infrastructure improvements and software aimed at stabilizing power fluctuations are all part of the solution portfolio to mitigate the effects of cloud cover.
Module Cell Temperature: Module cell temperature is affected by ambient temperature. Since it is difficult to measure cell temperature, back of module temperatures are typically measured. Back of module temperature can be used to estimate cell temperature by applying a positive bias, typically 2°C. Ambient temperature has a direct correlation to back-of-module temperature. The cell temperature also varies with the amount of air cooling available to the module. Wind plays an important role in cooling modules and power output depending on speed, direction, module installation & orientation, and possible wind “shadows”. Hence, if structural constraints and wind loading allows, it serves well to tilt the modules away from the ground or roof or install trackers to allow some degree of air cooling. The amount of electrical energy extracted from the PV module also affects cell temperature. Electrically loaded PV modules at maximum power point, operate at lower temperatures than open-circuited or short-circuited modules.
Soiling and Rain: Soiling of PV modules reduces the amount of incident sunlight and hence output of PV. PV module soiling is a localized phenomenon and is dependent on nearby sources of dirt. Relative humidity, ambient temperature, wind speed, fog, and other environmental conditions affect soiling. The increase in energy (not power) due to cleaning soiled modules should be evaluated against the labor, water, and other resources required to clean modules. Rain plays an important role in cleaning solar modules. The amount of rainfall in a year should also be considered to determine if modules should be cleaned. For example, in the metro-Phoenix (AZ, US) area, most flat plate installations can be allowed to be naturally cleaned by rain, with only about a 2% reduction in annual energy versus monthly cleaning. Cleaning after certain events like excessive bird droppings or dust storms may be justified.
POUNDRA – Weather Station Product: POUNDRA has built and commissioned weather stations for solar installations to evaluate system performance. POUNDRA’ s weather station products are built on Campbell Scientific’s Data Acquisition System along with high accuracy, high precision sensors for harnessing data on incident irradiation, ambient temperature, back of module temperature, wind speed and direction, rain and snow, relative humidity, and atmospheric pressure. The system is programmed to collect and store data at fast intervals (1s – 5s). DNP3, Modbus, or PakBus interfaces are supported to interface with utility SCADA. The weather station product from POUNDRA offer multiple levels of customization including choice of sensors, features supported, data transparency, flexibility, distributed analytics and add-on services support.