Dust, leaves and bird droppings
Even if the weather cooperates and there are no buildings in the way, solar panels can be obscured with dust, leaves or bird droppings. These can further reduce the energy collected by the solar panel.
Unexpected Events
While the sources above are the main sources that lower the amount of solar energy available, there are other conditions that can occur from time to time that are even harder to predict. As an example, smoke from forest fires hung over our headquarters this past year. When we measured on otherwise sunny days, the smoke was reducing the usual amount of available energy by about 30%.
Dealing with Variability – Traditional Solar Modelling
The usual way of tackling variability has been to design solutions tailored to each project based on location and an estimate of a worst-case scenario for available sunlight. This includes accounting for the shortest day (and longest night) of the year and the number of consecutive days of rain that can be expected in a location.
Calculation 1: Load Estimation
The first step is to determine the total energy the system will need to maintain light over the year to meet specified lighting requirements. From this, the amount of watt hours per day needed, including any energy losses by the system are calculated.
Calculation 2: Solar Panel Size
You then need to determine how much energy the solar module will capture. To get the right sized solar modules, you then need to take the total energy required and divide it by the amount of sun available. It is imperative when doing these calculations to include losses due to the charger used, the battery charge acceptance, the LED driver, etc.
Calculation 3: Battery
To have enough energy storage to power your light through times of the year when there is not sufficient light available, a system will need to have enough battery capacity. Generally, the more northern or more expected rainy days of the year, the more batteries a light will need.
The Shortcomings of Modelling
From these calculations, engineers then design a system of the appropriate size (sufficiently sized solar panel and enough batteries) to stay on when the estimated least amount of energy is available. But if any assumptions are off or if there are unexpected site conditions, or changes in the site over time, this type of system is hard-pressed to cope.
Additionally, having to base the system design on assumed worst case scenarios means that there is no ability to take advantage of times when there is ample sunlight. This makes these systems inefficient, overly large and unnecessarily expensive.
On top of this, even with all of this modelling, conditions often fall outside of this range and can lead to failure of the light or less than optimal performance. For example, the most common modelling approach takes the average solar energy available for the month of December as the assumed worst case scenario. But this is a faulty assumption given that it is a monthly average and is by no means a worst-case scenario.
Another example is the usual assumption that 3 or 5 days are appropriate for the amount of battery backup required. This is faulty as each site will have its own requirements that should be used and generalizations such as 3 or 5 days are mostly used to minimize the size and cost of batteries while sacrificing long term reliability. Finally, most design assumptions assume that the installation location is perfect from a solar perspective when, in many cases, sites have some level of shading. Any shading will affect the ability to collect energy and greatly compromise the reliability of a system that has been designed without incorporating these factors.
Dealing with Variability – The Advantage of Smart Architectural Solar Lighting
With smart technology disrupting nearly every industry, architectural solar lighting has an incredible opportunity. It allows solar lights to overcome one of the major limitations of traditional solutions – being locked into a model based on assumptions and is inefficient for year-round performance.
In contrast, lights that can learn about real-time conditions and adapt to optimize performance greatly simplifies the lighting design and install process. Instead of preparing for an estimated worst case scenario, smart lights can adapt to short and medium-term changes like weather and season in addition to longer term changes like growing trees. It also allows lights to be much smaller and housed in self-contained fixtures that look just like regular lights.
The overall impact is that lights can leverage the full advantages of solar while providing ample light when users need it. This includes avoiding the need to trench for wires which lowers installation costs by up to 50%. They are also much simpler to install compared to a traditional distributed solution.
With greater reliability and installation cost savings, I’m excited to see solar lighting continue to grow as more organizations see the benefit of considering architectural solar lighting for their projects first.
