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Grid-connected Solar PV Battery System in MATLAB

Grid-connected Solar PV Battery System in MATLAB


Introduction:

In the pursuit of sustainable energy solutions, the integration of solar photovoltaic (PV) systems and batteries with the grid holds immense promise for enhancing the efficiency and reliability of solar-powered electric vehicles (EVs). This integration not only enables optimal utilization of renewable energy sources but also facilitates grid connectivity for seamless power management. In this discussion, we explore the intricacies of integrating solar PV and battery systems with the grid within the context of solar-powered EVs.

  1. System Overview: The integrated system comprises solar PV panels, batteries, and grid connectivity mechanisms. Solar PV panels harness sunlight to generate electrical energy, while batteries store excess energy for later use. Grid connectivity allows bidirectional power flow, enabling energy exchange between the EV system and the grid.

  2. Control Logic: The operation of the solar PV and battery systems is governed by sophisticated control logic. Incremental conductance maximum power point tracking (MPPT) algorithms are employed to extract maximum power from the solar PV panels, ensuring optimal energy utilization.

  3. Battery Charging Logic: Battery charging logic is contingent upon the power generated by the solar PV system. If the solar PV power exceeds the load demand, surplus energy is utilized to charge the battery. Conversely, if solar PV power is insufficient, the battery discharges to supplement the load.

  4. Bidirectional Converter: A bidirectional converter facilitates power exchange between the solar PV and battery systems and the grid. This converter is controlled by a voltage controller to maintain the grid voltage at a predetermined level, ensuring grid stability.

  5. Inverter Control: Inverter control is crucial for regulating power injection into the grid. DQ frame control algorithms generate reference signals for the inverter, dictating the magnitude and phase of the grid current. These reference signals are derived based on the grid requirements and system constraints.

  6. Current Reference Adjustment: The current reference value determines the amount of power injected into the grid. By adjusting this value, the system can dynamically allocate power between the solar PV, battery, and grid, optimizing energy utilization and grid interaction.

  7. Simulation and Analysis: Simulation of the integrated system allows for the analysis of power flow dynamics and system response to varying conditions. Parameters such as battery charging/discharging rates, grid current sharing, and overall system efficiency can be evaluated through simulation experiments.

  8. Dynamic Response: Changes in the current reference value lead to dynamic adjustments in power flow and grid interaction. By observing the response of the system, stakeholders can fine-tune control parameters to achieve desired performance metrics.

  9. Optimization and Validation: Optimization of control parameters ensures efficient energy management and grid integration. Validation of the integrated system through simulation experiments validates its robustness and reliability under different operating conditions.

  10. Impact and Future Directions: The integration of solar PV and battery systems with the grid in solar-powered EVs heralds a paradigm shift towards sustainable transportation and energy ecosystems. Continued research and development efforts aim to enhance system efficiency, grid stability, and interoperability with emerging smart grid technologies.

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