Performance Analysis of a Grid-Connected PV-Battery Microgrid with Perturb and Observe MPPT Control
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Abstract
This research presents a comprehensive performance analysis of a resilient, grid-connected photovoltaic (PV) system integrated with a Battery Energy Storage System (BESS). To address the stochastic nature of solar irradiance, a 2 kW PV array is interfaced via a boost converter utilizing the Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) algorithm. A 240 V / 48 Ah battery bank, controlled by a bi-directional DC-DC converter, serves as the primary energy buffer to maintain a stable 400 V DC bus. The control architecture incorporates logic-gated grid interaction, where power flow is dynamically adjusted based on the battery State of Charge (SoC) and a critical PV current threshold of 0.5 A. Simulation results across a full irradiance cycle (1000 W/m² to 10 W/m² and back) validate the system’s ability to achieve power equilibrium. During periods of near-zero generation and low SoC (<10%), the system successfully transitions to a "Grid-Support Mode," importing 10 A to sustain the local load and recharge the storage. The findings confirm that the dual-converter strategy provides robust stability and high efficiency for modern microgrid applications.
Keywords:
Photovoltaic Systems, P&O MPPT, Bi-directional DC-DC Converter, Grid Synchronization, State of Charge (SoC).
2. Introduction
The strategic global transition toward decentralized renewable energy systems has elevated the importance of microgrid resilience. Photovoltaic (PV) generation, while sustainable, remains inherently intermittent due to its sensitivity to atmospheric fluctuations. Consequently, the integration of high-density energy storage and advanced power electronics is critical for ensuring reliable grid operation. To maximize energy harvesting efficiency, Maximum Power Point Tracking (MPPT) algorithms are required to counteract irradiance-induced voltage shifts. Furthermore, maintaining a rigid DC bus voltage in the face of rapid generation drops necessitates sophisticated closed-loop control of both the battery interface and the grid-tied inverter. This study evaluates a hybrid architecture designed to seamlessly balance generation, storage, and grid interaction, ensuring power quality and continuity.
3. System Configuration and Methodology
The system’s technical integrity is predicated on the precise matching of component ratings and the implementation of robust voltage regulation. The PV array consists of eight 250 W panels connected in series, achieving a peak capacity of 2000 W. Each panel is characterized by a maximum power voltage of 30.7 V and a maximum power current of 8.15 A. At standard test conditions, the series string generates approximately 245 V, which the boost converter steps up to the 400 V DC bus reference.
A bi-directional DC-DC converter interfaces a 240 V, 48 Ah battery bank with the DC bus, allowing for autonomous charging and discharging. The system parameters are detailed in Table 1 below.
Table 1: System Design Parameters
Component | Parameter | Rating/Value |
PV Array | Total Power | 2000 W (8 × 250 W) |
Max Power Voltage | 30.7 V (per panel) | |
Max Power Current | 8.15 A (per panel) | |
Battery | Nominal Voltage | 240 V |
Rated Capacity | 48 Ah | |
Low SoC Threshold | 10% | |
DC Bus | Reference Voltage | 400 V |
DC Load | Fixed Resistance | 1000 W |
Inverter | Filter Type | LCL Filter |
The interaction between these components is governed by a control framework that prioritizes DC bus stability and efficient power distribution.
4. Control Strategy and Mathematical Modeling
The microgrid utilizes a dual-layered control strategy to maintain power equilibrium. Closed-loop PI controllers regulate the switching pulses for both the PV boost converter and the bi-directional battery interface.
P&O MPPT Analysis
The Perturb and Observe (P&O) algorithm tracks the peak power point by continuously sampling the current PV voltage and power . The algorithm "observes" the state of the system by comparing these current values against stored previous values and to determine the change and . The duty cycle of the boost converter is adjusted as follows:
· If or , the algorithm decreases to increase PV voltage toward the maximum power point.
· If or , the algorithm increases to decrease PV voltage toward the maximum power point.
Battery and Inverter Control
The bi-directional converter is the "anchor" of the system, operating in voltage control mode. It compares the 400 V reference with the actual DC bus voltage; the error is processed through a PI controller to generate PWM pulses. This ensures the DC bus does not collapse during generation drops.
Grid synchronization is achieved via a single-phase inverter utilizing DQ-transformation-based current control. A Phase-Locked Loop (PLL) ensures the inverter current remains in phase with the grid voltage. By regulating the "Q" (reactive) component to zero, the system maintains a unity power factor, ensuring that only active power is exchanged with the utility grid.
5. Simulation Framework and Implementation
MATLAB/Simulink was employed as the simulation environment to validate the dynamic response of the control loops and power electronic interfaces. The simulation uses a PWM generator and LCL filters to achieve high-fidelity grid synchronization.
To evaluate the system under extreme transients, a dynamic irradiance profile was implemented at 0.3 s intervals. Unlike static models, this profile tests the system's recovery from near-zero generation:
1. 0.0 s – 0.3 s: 1000 W/m² (Standard Test Conditions)
2. 0.3 s – 0.6 s: 500 W/m² (Partial Shading)
3. 0.6 s – 0.9 s: 10 W/m² (Near-zero Solar Availability)
4. 0.9 s – 1.2 s: 500 W/m² (Recovery)
5. 1.2 s – 1.5 s: 1000 W/m² (Full Restoration)
6. Results and Discussion
The results demonstrate that the control architecture successfully manages power flow across the entire irradiance cycle, keeping the DC bus voltage rigid at 400 V.
Comparative Scenario Analysis
1. High Irradiance Mode (1000 W/m²): At peak generation, the PV array produces approximately 2000 W. The system demand totals 1300 W (comprising the 1000 W DC load and 300 W / 2 A export to the grid). The surplus of approximately 700 W is automatically diverted to the battery storage.
2. Grid-Support Mode ( and SoC < 10% ): When irradiance drops to 10 W/m² and the battery is critically depleted (e.g., 9% SoC), the system triggers a logic-gated shift. The inverter reverses flow, importing 10 A from the grid. This 10 A reference is strategically selected to cover the 1000 W local load while providing sufficient headroom to charge the battery from the utility.
Power Balance Validation
System stability is verified by the instantaneous power balance at the DC bus, expressed by the current summation:
Throughout all irradiance transitions, the sum of currents at the DC bus remains zero. This confirms that the bi-directional converter effectively compensates for PV fluctuations by switching between charging and discharging modes, preventing bus voltage instability.
7. Conclusion and Future Scope
This study validates the effectiveness of the P&O MPPT algorithm and the dual-converter control strategy in a grid-connected microgrid. By utilizing a bi-directional converter in voltage control mode, the system maintains a stable 400 V DC bus even during a 90% irradiance drop. The logic-gated transition to grid support ensures that critical loads are met and batteries are protected when solar generation falls below the 0.5 A threshold. The use of DQ-transformation ensures high-quality grid interaction at unity power factor.
Future Scope
· Advanced MPPT: Implementing Incremental Conductance or Fuzzy Logic to reduce oscillation around the peak point during rapid irradiance changes.
· Hardware Expansion: Scaling the model to a three-phase system to support industrial-scale loads.
· Control Optimization: Exploring sliding mode control to improve the transient response of the DC bus during extreme grid fluctuations.
VII. YouTube Video
VIII. Purchase link of the Model
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