Combining PPO and incremental conductance for MPPT under dynamic shading and temperature

As solar generation gains an increased importance in a smart grid, an efficient control of a photovoltaic (PV) array has to be considered. However, energy efficiency of a PV array greatly depends on environmental conditions, such as uneven shading, solar irradiance and temperature. Maximum power point tracking (MPPT) algorithms aim to dynamically find an optimal operation voltage in order to compensate for changes in the environment, as well as degradation of solar panels. Besides generating a maximum amount of power, an MPPT controller aims for stability in order to avoid additional losses. Reinforcement Learning (RL) is a flexible training method that can produce a controller for a complex problem without a detailed prior knowledge of the environment. In this work we propose an approach that combines an optimized neural network, trained through deep reinforcement learning (DRL), with a classical closed-loop control. Experimental results suggest that the proposed approach outperforms a recently proposed DRL network both in terms of efficiency and stability. A compact and discrete version of the proposed controller is also evaluated and shown to further increase performance. The implemented algorithms and the RL simulation environment are made available in an open-source repository.