The precise extraction of the I - V curve of PV cells is necessary for designing an efficient PV system. To achieve this, this paper examines different models to investigate the best fit for the I-V curve of a PV solar module. Those models are based on using different sets of equations and assumptions. New PV singlediode models are then proposed which consist of Lambert Wfunction, iterative loop, and approximate equations. Furthermore, a substitution method is proposed to reduce the complexity that emerges when using Newtown-Raphson method to solve a set of nonlinear equations. Those processes allow solving the complex nonlinear PV model in a simple way. Thus, making the proposed model fast, straightforward, and easy solve.

With the advent of grid-connected Photovoltaic systems for energy generation, new technologies must be created that maintain a continuous and stable balance between the supply and demand of generated electricity. Consequently, accurate prediction of solar energy generation and consumption is required. Solar energy generation and power demand are both stochastic and non-stationary. The lack of balance between demand and supply can be costly and leads to long-term ineffectiveness of power generation and distribution. The aim of this work is to propose methods for maintaining demand-supply balance in PV power generation and distribution systems. To achieve this, we build and combine three different tools: 1) an accurate predictive model for forecasting solar energy generation, 2) an accurate model of demand prediction, and 3) a real-time control algorithm that uses the outputs of prediction models and adjusts the output voltage of the power generation PV systems to maintain demand-supply balance. Our prediction models are based on time-series forecasting tools, Multiple Linear Regression Model, Quadratic Support Vector and Artificial Neural Networks. The control algorithm is called Optimal Power Point Tracking (OPPT) and is based on the Perturb and Observe algorithm. We evaluate the performance of the combined prediction-controller system using real-world data.

A maximum power point tracking (MPPT) controller optimizes power harvesting in photovoltaic (PV) systems under varying conditions. The perturb and observation (P&O) algorithm is commonly used for MPP tracking, but suffers from slow response, loss of tracking direction, and entrapment. The current research proposes a neural network (NN) integrated with the P&O algorithm to enhance tracking performance during sudden variations in solar irradiance. The proposed neural network updates the duty cycle change when detecting sudden changes. It effectively estimates the duty cycle change even when trained with a small dataset. The integration between the NN and P&O significantly improves tracking performance compared with the conventional P&O algorithm, especially under sudden irradiance changes.