Scientists from the Argonne National Laboratory of the US Department of Energy (DOE), Northwestern University, the University of Chicago and the University of Wisconsin-Milwaukee recently combined solar cell technology with a new optimization approach to developing a prototype smart window that maximizes design across a wide range of criteria.
The optimization algorithm uses comprehensive physical models and advanced computational techniques to maximize overall energy use while balancing the building’s temperature demands and lighting requirements at all locations and during different seasons.
“This design framework is customizable and can be applied to virtually any building worldwide,” said Junhong Chen, an Argonne scientist and professor of molecular engineering for the Crown family at the University of Chicago Pritzker School of Molecular Engineering. “Whether you want to maximize the amount of sunlight in a room or minimize heating or cooling, this powerful optimization algorithm produces window designs that align with the user’s needs and preferences.”
Scientists demonstrated a comprehensive approach to window design to maximize the overall energy efficiency of buildings by considering lighting and temperature preferences.
“We can regulate sunlight in a room to ensure the desired brightness while managing the amount of energy the building uses for heating and cooling,” said Wei Chen, Wilson-Cook engineering design professor at Northwestern Engineering, whose research group led the development of the optimization approach. “In addition, sunlight that does not pass through is captured by the solar cell in the smart window and converted to electricity.”
Called multi-criteria optimization, the approach adjusts the thickness of solar cell layers in window design to meet user needs. For example, to reduce the energy required to cool a building in the summer, the optimal window design could minimize the amount and type of light that passes while maintaining the desired luminosity indoors. On the other hand, when winter savings are a priority, the design can maximize the amount of sunlight that passes through, thus reducing the energy required to heat the building.
“Instead of focusing only on the amount of electricity produced by the solar cell, we consider the energy consumption of the entire building to see how we can best use solar energy to minimize it,” said Wei Chen.
In some scenarios, for example, it might be more energy efficient to allow more light to pass through the window, rather than being converted to electricity by the solar cell, to decrease the electricity required to light and heat the building.
To determine the optimal design, the algorithm incorporates comprehensive physics-based models of the interactions between light and materials in the smart window, as well as how processes affect energy conversion and light transmission. The algorithm also takes into account the different angles at which the sun hits the window during the day and year at different geographic locations.
“The model we created allows the exploration of millions of unique designs using an algorithm that mimics biological evolution,” said Wei Chen. “On top of physics-based models, the algorithm uses computational mechanisms that resemble the reproduction and genetic mutation to determine the optimal combination of each design parameter for a certain setting.”
To demonstrate the feasibility of a smart window capable of this level of customization, the scientists produced a small prototype of the window with an area of a few square centimetres.
The prototype consists of dozens of layers of different materials that control the amount and frequency of passing light, as well as the amount of solar energy converted to electricity.
A group of layers, made of a type of material called perovskite, comprises the window’s solar cell, which collects sunlight for energy conversion. The window prototype also includes a set of layers called a nanophotonic coating, developed by associate professor of mechanical engineering Cheng Sun and his research group at Northwestern’s McCormick School of Engineering. The cladding tunes in to the frequencies of light that can pass through the window.
Each layer is tens of microns thicker, thinner than the diameter of a grain of sand. Scientists chose an aperiodic design for the layers, which means that the thickness of each layer varies. As the angle of the sun’s rays against the window changes throughout the day and year, the aperiodic design allows the performance of the window to vary according to user preferences.
“The variation in layer thickness is optimized for a broad spectrum of changes in the nature of sunlight reaching the window,” said Sun. “This makes it easier for us to systematically transmit less infrared in the summer and more in winter to save on energy consumption for temperature regulation while allowing us to optimize visible transmission for interior lighting and energy harvesting.”
Scientists optimized the prototype used in this study for a 185 m2 single-story house in Phoenix. Based on the experimental characterization of the window prototype, the scientists calculated significant annual energy savings on the main commercially available window technologies. The calculations used the EnergyPlus building model, software developed at the DOE’s National Laboratory for Renewable Energy, a DOE Office of Energy Efficiency and Renewable Energy Laboratory, which estimates actual energy consumption over time.
The synthesis methods that scientists used to produce the window prototype mimic common manufacturing processes at the industrial level, and scientists believe that these existing business processes would allow for a successful expansion of the window prototype to full size.
Future considerations include developing the same technology in a flexible way so that smart window materials can be adapted to cover pre-existing windows.