PHOTOVOLTAIC ENERGY NETWORK

Organization Name

KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS

Inventor(s)

PHOTOVOLTAIC ENERGY NETWORK - A simplified explanation of the abstract

This abstract first appeared for US patent application 18606697 titled 'PHOTOVOLTAIC ENERGY NETWORK

Simplified Explanation: The patent application describes a system, method, and solar photovoltaic (PV) network designed to reduce solar PV variability with minimal time delays and optimized battery storage.

Moving Regression (MR) filter, State of Charge (SoC) feedback control, and Battery Energy Storage System (BESS) work together to smooth out solar PV variabilities.
MR filter utilizes machine learning linear regression to reduce solar PV variations at each time step.

Key Features and Innovation:

System, method, and solar PV network for reducing solar PV variability.
Moving Regression (MR) filter for smoothing out solar PV variations.
State of Charge (SoC) feedback control for optimized battery storage.
Battery Energy Storage System (BESS) for reducing time delays.

Potential Applications: The technology can be applied in solar energy systems, smart grids, renewable energy integration, and energy storage solutions.

Problems Solved: The technology addresses issues related to solar PV variability, time delays, and battery storage optimization in solar energy systems.

Benefits:

Improved efficiency in solar energy systems.
Enhanced stability and reliability in renewable energy integration.
Optimal battery storage utilization.

Commercial Applications: Commercial applications include solar power plants, grid stabilization systems, energy management solutions, and renewable energy projects.

Prior Art: Readers can explore prior art related to solar PV variability reduction, battery storage optimization, and machine learning in solar energy systems.

Frequently Updated Research: Stay updated on research regarding solar PV variability reduction, battery storage optimization, and machine learning applications in renewable energy systems.

Questions about Solar PV Variability Reduction: 1. How does the Moving Regression (MR) filter contribute to reducing solar PV variations? 2. What are the potential commercial implications of optimized battery storage in solar energy systems?

Original Abstract Submitted

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.