Abstract: A system, method, and solar photovoltaic (PV) network for solar PV variability reduction with reduced time delays and battery storage optimization are described. The system includes a Moving Regression (MR) filter; a State of Charge (SoC) feedback control; and a Battery Energy Storage System (BESS). The MR filter, SoC feedback control and BESS are configured to provide smoothing of solar PV variabilities. The MR filter is a non-parametric smoother that utilizes a machine learning concept of linear regression to smooth out solar PV variations at every time step.

Abstract: An intermittent power system to provide smoothed electric power into a power grid that includes an intermittent power source, a neural network-based predictive controller (NNPC) and a low pass filter (LPF) connected to the power grid to provide the smoothed electric power. The LPF provides a smoothed power reference for the NNPC. The system further includes a neural network predictor connected between the intermittent power source and the NNPC, and a power grid connection. The neural network predictor takes electric power from the intermittent power source as an input and makes a prediction of unsmoothed electric power. A power grid connection provides the smoothed electric power of the NNPC into the power grid. The NN model solves issues related to the mathematical complexity of a conventional MPC model that arises due to the increasing complications in an intermittent power plant, including a PV power plant.

Abstract: A microgrid system is described. The microgrid system includes a Hodrick-Prescott filter configured to generate a filtered power based on an electrical power that is generated from an renewable energy. The microgrid system further includes a battery configured to output a battery power based on the electrical power and the filtered power. Also, the microgrid system includes a state of charge (SoC) control module configured to control charging and discharging of the battery by applying a feedback control on the output battery power.

Abstract: A microgrid system is described. The microgrid system includes a Hodrick-Prescott filter configured to generate a filtered power based on an electrical power that is generated from an renewable energy. The microgrid system further includes a battery configured to output a battery power based on the electrical power and the filtered power. Also, the microgrid system includes a state of charge (SoC) control module configured to control charging and discharging of the battery by applying a feedback control on the output battery power.

Abstract: A system, method, and solar photovoltaic (PV) network for solar PV variability reduction with reduced time delays and battery storage optimization are described. The system includes a Moving Regression (MR) filter; a State of Charge (SoC) feedback control; and a Battery Energy Storage System (BESS). The MR filter, SoC feedback control and BESS are configured to provide smoothing of solar PV variabilities. The MR filter is a non-parametric smoother that utilizes a machine learning concept of linear regression to smooth out solar PV variations at every time step.

Abstract: A system, method, and solar photovoltaic (PV) network for solar PV variability reduction with reduced time delays and battery storage optimization are described. The system includes a Moving Regression (MR) filter; a State of Charge (SoC) feedback control; and a Battery Energy Storage System (BESS). The MR filter, SoC feedback control and BESS are configured to provide smoothing of solar PV variabilities. The MR filter is a non-parametric smoother that utilizes a machine learning concept of linear regression to smooth out solar PV variations at every time step.