Riding the Solar Sweet Spot with PID in Simulink

Introduction

Solar panels are great at turning sunlight into electricity — but only if you convince them to work at their sweet spot. That’s where MPPT comes in: think of it as a ‘GPS for solar panels,’ constantly steering them to the route that gives the most power.

Solar photovoltaic (PV) systems are highly nonlinear in nature, and their power output is significantly affected by environmental conditions such as solar irradiance and temperature. The PV system must run at its Maximum Power Point (MPP), which fluctuates dynamically with changing conditions, in order to achieve maximum efficiency. Maximum Power Point Tracking (MPPT) is the term for this procedure. A straightforward but reliable method for obtaining quick and steady tracking is the proportional-integral-derivative (PID) control approach. The functioning of a MATLAB/Simulink model of a PV system controlled by a PID-based MPPT algorithm is examined in this blog, along with how this approach responds swiftly to changes and maintains stability even when sunlight abruptly increases or decreases.

Why choose PID?

PID stands for Proportional-Integral-Derivative, a widely used control technique in engineering and automation. It is designed to continuously monitor the difference between a point and the actual system output, known as the error, and adjust the control input to minimize this error over time. This makes PID controllers highly effective for systems that require precision and stability.

• Proportional (P): The proportional component produces an output proportional to the current error. It helps us in finding the errors. A larger error results in a stronger corrective action, helping the system respond quickly to changes. In Maximum Power Point tracking(MPPT), the proportional part reacts to the difference between the current power output and the maximum possible power. If the solar panel is far from the Maximum Power Point (MPP), the controller makes a larger adjustment to quickly reach the Maximum Power Point(MPP). — Integral (I): The integral component considers the accumulation of past errors. It gradually eliminates steady-state errors, ensuring that the system reaches and maintains the exact desired value. In Maximum Power Point Tracking(MPPT) the solar panel stays exactly at the Maximum Power Point(MPP), even if there are small fluctuations in irradiance or temperature by reducing errors. — Derivative (D): The derivative component predicts the future behavior of the error by considering its rate of change. This helps in reducing errors overshoot, oscillations, and improves system stability. For Maximum Power Point Tracking(MPPT), this helps the controller predict and reduce overshoot or oscillations, so the system doesn’t constantly swing around the Maximum Power Point(MPP) but settles quickly at the optimal point.

By combining a fast response to changes in irradiance, elimination of steady-state error, and stable operation under varying environmental conditions, the PID controller ensures that the solar panel consistently operates at its Maximum Power Point (MPP). The proportional component allows the system to react quickly to sudden changes in sunlight, the integral component corrects any persistent deviation from the MPP, and the derivative component helps prevent overshoot (when the system temporarily goes beyond the MPP) and reduces oscillations (when the power output keeps swinging around the MPP instead of stabilizing). Together, these features make PID a reliable and efficient choice for MPPT, enabling the solar PV system to extract the maximum possible energy even when weather conditions fluctuate rapidly.

System Model & Simulation Setup

Software Used- Simulink

Add-Ons Used- Simscape Electrical, Simscape

Simscape is a physical modeling environment in MATLAB/Simulink that lets you simulate real-world physical systems (mechanical, electrical, hydraulic, thermal, etc.) using physical connections instead of just signal-based models. It’s key features includes models systems using physical networks (voltage, force, flow).It automatically applies conservation laws (energy, mass, charge).They also integrate easily with control systems from Simulink.

Simscape Electrical is a specialized Simscape library for modeling electrical and power systems, including both electrical circuits and power electronics. It models electrical components: resistors, capacitors, inductors, diodes, transformers, PV panels, batteries, etc. It includes specialized power systems: three-phase grids, inverters, converters, and renewable energy systems. It can simulate both DC and AC systems, including harmonic analysis and transient behavior. It also works with Simscape blocks for integrated multi-domain simulations (mechanical + electrical).

Blocks Used:

1. Constant blocks: Block function containing some constant value. It act as input for our PV array.
2. PV array: The PV Array block is a prebuilt block that models a solar photovoltaic (PV) panel or an array of panels. It allows us to simulate the electrical behavior of solar panels under different conditions where it converts solar irradiance (sunlight) and temperature into DC electrical output.
3. Go-to: This block sends a signal to a label without connecting lines directly across the model. We assign a tag name and the signal can then be retrieved anywhere in the model using a From block with the same tag.
4. From-to: This block receives the signal from a Goto block with help of tag name.
5. MOSFET : A MOSFET block is used to model a Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) which simulates a switching transistor that can turn on or off based on a gate control signal.
6. Unit-Delay : Unit Delay block is a very useful discrete-time block used to store the previous value of a signal
7. MATLAB function : The MATLAB Function block allows you to write MATLAB code and run it as part of your Simulink model. It’s useful when the algorithm is easier to express in code (like MPPT control) rather than building with Simulink blocks. We can write a compact MPPT algorithm in a MATLAB Function block for continuously adjust the operating voltage/current so the PV array delivers maximum power.
8. Display: The Display block is a simple but useful block used to show the value of a signal during simulation.
9. Scope: The Scope block is one of the most commonly used blocks for visualizing signals over time during simulation. It’s like an oscilloscope for your Simulink model. For MPPT we can see V-I curve from it.
10. Powergui: It manages the simulation of electrical circuits in Simscape Electrical. It handles circuit initialization, analysis, and solver settings. It calculates initial voltage, current, and flux in the system.
11. PID controller: The PID Controller block is used to implement a Proportional-Integral-Derivative (PID) control algorithm. The control signal is a combination of three components: Proportional (P): Reacts to the current error. Integral (I): Reacts to the accumulated error over time. Derivative (D): Reacts to the rate of change of the error.
12. Repeating Sequence: The Repeating Sequence block is used to generate a periodic signal that repeats over time. It’s very useful for testing, simulation of varying inputs, or driving controllers like MPPT in a PV system

After making the necessary connections in Simulink the circuit diagram will look like this:

In the MATLAB function block following PID algorithm is being used to attain maximum efficiency .

The following parameters have been set for some specific blocks:

Results

The proposed PID-based MPPT algorithm was simulated on a 33 kW solar PV array (15 parallel strings × 10 series-connected modules per string) with a boost converter and resistive load.

• PV Array Maximum Power: ~32.98 kW
• The PID controller reached MPP within 0.15 s with negligible overshoot.
• The steady-state PV voltage stabilized around 301 V (Vmpp) and current around 109 A (Impp).
• Tracking efficiency was calculated at 99.3%, confirming minimal energy loss.

The system achieved fast and smooth tracking of MPP for both constant and step-changing irradiance. Overshoot and oscillations were nearly eliminated, reducing power fluctuations and improving overall energy yield. The PID controller proved robust under partial shading and rapid irradiance changes, maintaining near-maximum energy extraction.

The following results were obtained from graph for V-I curve and I curve .

The first graph is the result for I curve from Maximum Power Point Tracking(MPPT) in which the current(I) first increases with respect to time to a certain extent and then with help of PID algorithm it is being kept constant for certain process.

The second graph is the result for V-I curve from Maximum Power Point Tracking(MPPT) in which the current(I) increases with Voltage(V) and then is constant for the process.

Advantages & Limitations

Let’s talk about the good, the great, and the slightly annoying about using PID for MPPT:

Advantages:

1. Fast Response: The PID controller quickly adjusts the system, allowing the system to reach the Maximum Power Point (MPP) within a short time after a change in irradiance.
2. Stable Operation: Overshoot and oscillations around the MPP are minimized, ensuring smooth power output.
3. Elimination of Steady-State Error: The integral action ensures the PV system remains exactly at the MPP even after small disturbances.
4. Simplicity and Easy Implementation: PID control is easy to design and tune compared to advanced MPPT methods like fuzzy logic or neural networks.
5. High Tracking Efficiency: The system achieves >99% tracking efficiency, extracting nearly all available solar power.

Limitations-

1. Tuning Sensitivity: Proper tuning of Kp, Ki, and Kd values is crucial. Incorrect tuning can lead to slow response, oscillations, or even instability.
2. Derivative Noise Sensitivity: The derivative term can amplify measurement noise, which might cause small fluctuations in duty cycle.
3. Not Adaptive: Unlike advanced techniques (e.g., Adaptive MPPT, Fuzzy logic), a basic PID does not adapt itself automatically to wide environmental changes — manual re-tuning might be required.
4. Limited Performance in Rapid Changes: During very fast irradiance variations (e.g., passing clouds), PID may not react as quickly as more advanced adaptive or predictive MPPT methods.

Conclusions

This work demonstrated the design and simulation of a PID-based Maximum Power Point Tracking (MPPT) algorithm for a 33 kW solar PV system using MATLAB/Simulink. The results showed that the PID controller was able to reach the Maximum Power Point (MPP) quickly (within 0.15 s) with negligible overshoot and minimal oscillations, ensuring smooth and stable operation. The steady-state voltage and current stabilized at their MPP values, achieving a tracking efficiency of over 99%, which translates to nearly complete utilization of available solar power.

The proposed approach offers a good balance between simplicity, speed, and reliability. However, its performance depends heavily on proper tuning of the PID gains, and re-tuning may be necessary under different environmental conditions or system configurations. Despite these limitations, PID control remains a practical and effective solution for MPPT in PV systems, especially where cost-effectiveness and ease of implementation are key requirements.

So yes, PID isn’t flawless — it might need some babysitting with tuning — but when done right, it’s like having an efficient, reliable solar chauffeur driving your panels straight to the MPP every time.

References

1. PID Control algorithm theory and applications by Umid Mammadov -https://medium.com/@umidcybers/pid-control-algorithm-theory-and-applications-f249dc5a4519
2. Investigation of PV inverter MPPT efficiency test platform by IET- https://ieeexplore-ieee-org.egateway.vit.ac.in/document/7446670/authors#authors
3. AI-Driven MPPT: A Paradigm Shift in Solar PV Systems for Achieving maximum Efficiency by IEEE — https://ieeexplore-ieee-org.egateway.vit.ac.in/document/10899184