Adaptive Model Predictive Control of Module Temperature in Photovoltaic Systems

With the growing demand for energy and increasing environmental concerns, there is an imminent requirement for a paradigm shift from nonrenewable sources toward renewable sources for energy generation. Among the emerging technologies, the photovoltaic (PV) system holds immense promise due to the abundant availability of the source (solar irradiance), low installation cost, and ease of scale-up. It is known that the efficiency of a PV system, not-withholding its dependency on other factors, decreases with an increase in the module temperature. The module temperature in turn is a function of disturbances, especially, intermittent weather variables, solar irradiance, ambient temperature, and wind speed. In this work, we propose, develop, and design a module temperature control system consisting of an adaptive model predictive controller that manipulates the flow rate of a water coolant flowing on the module. The controller design is based on (i) an expression for computing the PV efficiency as a function of time and (ii) an improvised comprehensive thermal model that accounts for the effects of disturbances and the coolant flow on the module temperature. The adaptive model predictive controller is tuned based on a trade-off between achieved control error and control effort. Further, an optimal set-point (for the module temperature) that achieves a desirable trade-off between the average power gain (due to temperature control) and the power consumed (due to coolant pump) is determined through numerical/simulation studies. In silico simulations on two geographical regions demonstrate that the proposed control scheme results in a maximum improvement in efficiency of nearly 10%.