100 kW Grid-connected PV System with Fuzzy and P&O MPPT
In this simulation model developed in MATLAB, we explore a grid-tied PV battery system incorporating a C converter. The system consists of PV panels, a battery, an inverter, a filter, and a connection to the grid. The C converter facilitates efficient power conversion between the PV panels and the battery, ensuring optimal energy utilization and grid interaction.
PV Panel Details:
The PV array comprises single panels with a rating of 23.15 watts each.
Key specifications include open-circuit voltage, short-circuit current, voltage at maximum power point, and current at maximum power point.
The array, consisting of 8 panels in a series, can generate a maximum power of approximately 17,705 watts under standard irradiance conditions.
System Configuration:
The PV array is connected to the DC bus via the C converter, which regulates the power flow.
The battery, comprising two 36V 12Ah batteries with a capacity of 40Ah, is directly connected to the DC bus.
The inverter, controlled by MPPT algorithms, converts DC power from the battery into AC power for grid interaction.
Energy management logic, including neural network-based control, optimizes power flow between the PV array, battery, and grid.
Simulation and Analysis:
PV Power Generation:
Simulated PV power generation is observed based on varying irradiation levels, with instantaneous power output reaching up to 850 watts.
Power generation data is analyzed to assess the system's ability to track maximum power points and adapt to changing environmental conditions.
Battery Charging and Grid Interaction:
The battery's state of charge is monitored as it charges from the PV array and discharges to the grid.
In scenarios of low PV power generation, power is drawn from the grid to supplement battery charging, ensuring continuous energy supply.
Inverter Control and Grid Connectivity:
Inverter operations are governed by control algorithms based on reference currents derived from energy management logic.
The power exchange between the battery, inverter, and grid is analyzed, showcasing bidirectional power flow and grid synchronization.
Neural Network Energy Management:
The neural network model, trained with simulated data, governs energy flow decisions based on real-time PV and battery parameters.
By dynamically adjusting reference currents, the system optimizes power utilization and grid interaction, ensuring efficient energy management.
Conclusion: The simulation results demonstrate the effective operation of the grid-tied PV battery system with a C converter in MATLAB. Through sophisticated control algorithms and neural network-based energy management, the system efficiently harnesses solar energy, stores excess power in the battery, and interacts with the grid as needed. This holistic approach ensures a reliable and sustainable energy supply while maximizing system efficiency.