Smart windows, such as those that tap solar cell technology to transform sunlight into electricity, offer the additional opportunity to employ windows as energy sources.
But it is highly difficult to integrate solar cells into windows and simultaneously balancing the other complex, and often conflicting, roles of windows. For instance, complex, strategic techniques to material design are required to juggle between luminosity preferences and energy harvesting goals through varying seasons.
Recently, researchers from the U.S. Department of Energy’s (DOE) Argonne National Laboratory, Northwestern University, the University of Chicago, and the University of Wisconsin-Milwaukee integrated solar cell technology with a new optimization method to create a smart window prototype that increases the design over an extensive range of criteria.
Advanced computational methods and comprehensive physical models are used by the optimization algorithm to improve overall energy usage while balancing lighting requirements and building temperature demands across various locations and through varying seasons.
This design framework is customizable and can be applied to virtually any building around the world. Whether you want to maximize the amount of sunlight in a room or minimize heating or cooling efforts, this powerful optimization algorithm produces window designs that align with user needs and preferences.
Junhong Chen, Scientist, Argonne National Laboratory
Chen is also the Crown Family Professor of Molecular Engineering at the Pritzker School of Molecular Engineering at the University of Chicago.
Advanced Approach to Optimization
The researchers demonstrated a comprehensive technique to develop a window design to improve the overall energy efficiency of buildings while taking temperature and lighting preferences into account.
We can regulate the sunlight in a room to ensure the desired luminosity while managing the amount of energy the building uses for heating and cooling. Additionally, the sunlight that doesn’t pass through is captured by the solar cell in the smart window and converted into electricity.
Wei Chen, Wilson-Cook Professor in Engineering Design, McCormick School of Engineering, Northwestern University
The research group led by Wei Chen advanced the creation of the optimization approach, named multicriteria optimization, which involves adjusting the thicknesses of solar cell layers in window design to fulfill the requirements of users.
For instance, the optimized window design might reduce the energy needed to cool a building in the summer by reducing the amount and type of light that passes through while keeping the preferred luminosity indoors intact.
By contrast, when the priority is to achieve winter savings, the design might improve the amount of sunlight that passes through, thus minimizing the energy needed for heating the building.
“Rather than focusing only on the amount of electricity produced by the solar cell, we consider the entire building’s energy consumption to see how we can best use solar energy to minimize it,” added Wei Chen.
Under certain conditions, for instance, higher energy efficiency could be achieved by permitting a higher amount of light to pass through the window, rather than being converted into electricity by the solar cell, to limit the electricity needed for heating and lighting the building.
The algorithm determines the optimal design by incorporating comprehensive physics-based models of the interactions between the materials in the smart window and light, as well as how the processes impact light transmission and energy conversion. In addition, the algorithm considers the changing angles at which the sun hits the window all through the day—and year—in various geographical locations.
“The model we created allows for exploration of millions of unique designs by an algorithm that mimics biological evolution,” noted Wei Chen. “On top of the physics-based models, the algorithm uses computational mechanisms that resemble reproduction and genetic mutation to determine the optimal combination of each design parameter for a certain scenario.”
The researchers demonstrated the viability of a smart window with such a level of customization by creating a small prototype of the window with an area of a few square centimeters.
Dozens of layers of different materials in the prototype control the amount and frequency of light that passes through, and also the amount of solar energy transformed into electricity. One bunch of layers, made of perovskite material, includes the window’s solar cell, which taps sunlight for energy conversion.
Moreover, the prototype includes a group of layers known as a nanophotonic coating, created by associate professor of mechanical engineering Cheng Sun and his research team at Northwestern’s McCormick School of Engineering. The coating tweaks the frequencies of light that passes through the window.
The thickness of each layer that measures tens of microns is less than the diameter of a grain of sand. The researchers selected an aperiodic design for the layers, which means each layer differs in thickness.
With the change in the angle of the sun’s rays against the window all through the day and year, the aperiodic design allows the window’s performance to differ based on the preferences of the user.
The variation in layer thickness is optimized for a wide spectrum of change in the nature of the sunlight that reaches the window. This enables us to systematically allow less infrared transmission in the summertime and more in the wintertime to save energy consumption for temperature regulation, while optimizing the visible transmission for the purpose of indoor lighting and energy harvesting.
Cheng Sun, Associate Professor of Mechanical Engineering, McCormick School of Engineering, Northwestern University
The prototype used in this study was optimized by the researchers for a 2,000 square foot, single-story home in Phoenix. Using the experimental characterization of the prototype, the researchers computed considerable yearly energy savings over major commercially available window technologies.
For the computations, the team used the EnergyPlus building model—a software created at the National Renewable Energy Laboratory, a DOE Office of Energy Efficiency and Renewable Energy laboratory—that predicts the realistic power consumption over time.
The synthesis techniques employed by the researchers to create the prototype mimic common industrial-level manufacturing processes. According to the researchers, the current commercial processes would enable the successful scaling of the window prototype to full-size.
Future prospects include the advancement of the same technology in a flexible form to enable retrofitting of the smart window materials to cover preexisting windows.
The study was financially supported, in part, by the National Science Foundation.
Wang, C., et al. (2020) Maximizing Solar Energy Utilization through Multicriteria Pareto Optimization of Energy Harvesting and Regulating Smart Windows. Cell Reports, Physical Science. doi.org/10.1016/j.xcrp.2020.100108.