Grid Connected PV Battery Supercapacitor System in MATLAB
The given model is a sophisticated photovoltaic (PV) system incorporating PV panels, a battery, a supercapacitor, a DC load, an inverter, and an AC load, with the ability to connect to the grid. The system aims to maximize power extraction from PV panels, maintain efficient energy storage and distribution, and seamlessly integrate with the grid.
Components of the System:
1. PV Panel:
A set of eight PV panels, each with a power rating of 250 watts, is connected in series to generate a total power of 2,000 watts.
The boost converter is employed to control the PV system, utilizing a Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) algorithm.
The algorithm dynamically adjusts the duty cycle of the boost converter to optimize power extraction from the PV panels.
2. Battery and Supercapacitor:
A 300-volt battery and a 300-volt supercapacitor are integrated into the system.
The boost converters for both the battery and supercapacitor are controlled by voltage and current control methods.
Proportional-Integral (PI) controllers are utilized to maintain the DC bus voltage at 400 volts.
3. DC Load and Inverter:
The DC load is connected to the system, drawing power as needed.
An inverter is employed to convert DC power to AC power, allowing connection to an AC load.
Inverter control is implemented using a current control concept, generating a reference current based on the power conditions of the PV system.
4. Grid Interaction:
The system can operate both in standalone mode and grid-connected modes.
A control mechanism decides whether to draw power from the grid or supply power based on PV and battery conditions.
Control Strategies:
1. Modified Incremental Conductance:
A modified version of the Incremental Conductance algorithm is utilized for MPPT.
The modification involves introducing the term 'M,' calculated as the absolute value of change in voltage divided by change in power.
The modified algorithm aims to provide improved results compared to the conventional Incremental Conductance method.
2. Inverter Control:
Fuzzy Logic-based Energy Management System controls the inverter.
The system decides whether to draw or supply power to the grid based on fuzzy logic rules considering PV power and battery state of charge.
Simulation Results:
The simulation results demonstrate the system's performance under varying conditions, such as changing irradiation levels. The comparison of the three MPPT algorithms — PSO, Incremental Conductance, and Modified Incremental Conductance — highlights the superiority of the modified approach in achieving maximum power extraction.
Conclusion:
The enhanced photovoltaic system with advanced control strategies showcases the integration of renewable energy sources, efficient energy storage, and intelligent grid interaction. The utilization of sophisticated algorithms for MPPT and inverter control ensures optimal operation under different scenarios. This model serves as a valuable tool for understanding and optimizing renewable energy systems.