Optimising photovoltaic modules for indoor energy-harvesting systems

Abstract By harvesting low-intensity ambient light, indoor photovoltaics (PVs) could soon power countless internet-of-things (IoT) devices and sensors. However, indoor illumination conditions vary from room to room and even hour to hour, leading to inconsistent PV power generation. To overcome this, energy-harvesting circuitry can be used alongside indoor PV modules to recharge batteries or capacitors, forming energy-harvesting systems that enable consistent discharge into IoT devices. The optimisation of such systems is a topic of intense research. In this work, we use thermodynamic principles to model power generation in indoor PV modules based on inorganic, perovskite, and organic semiconductors, before evaluating the efficiency of the whole energy-harvesting system. In these investigations, we account for detailed device physics, including sub-gap absorption, band-filling effects, point defects, and parasitic resistances, while also considering performance under several different light sources. Ultimately, we find that the maximum power point voltage ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:msub> <mml:mi>V</mml:mi> <mml:mrow> <mml:mrow> <mml:mtext>mpp</mml:mtext> </mml:mrow> </mml:mrow> </mml:msub> </mml:mrow> </mml:mrow> </mml:math> ) is pivotal in determining the optimal number of cells for an indoor PV module. Despite some PV materials having a lower <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:msub> <mml:mi>V</mml:mi> <mml:mrow> <mml:mrow> <mml:mtext>mpp</mml:mtext> </mml:mrow> </mml:mrow> </mml:msub> </mml:mrow> </mml:mrow> </mml:math> due to narrower bandgaps or increased voltage losses, we find that this can be compensated for by increasing the number of cells; though too many cells can actually lead to inefficient energy harvesting. As a final case study, we evaluate the power generated and stored in a typical day (where an interplay between daylight and artificial light is present) to determine how stored energy translates to measurements made with an IoT device.