Grid‑Connected Solar Farms with Dynamic Reserve Power Point

Tracking

J. Sree Ranganayakulu, Y. Sreeya, B. Vishnu Vardhan, G. Sowmya and T. Vinay

Department of EEE, Annamachrya Institute of Technology and Sciences, Rajampet, Andhra Pradesh, India

Keywords: Adaptable Power Regulation, Power System Assistance, Peak Power Output, Uneven Irradiance Distribution,

Solar Energy Farm, Available Power Capacity, Sub‑Optimal Power Point Management, Artificial Neural

Network (ANN).

Abstract: The RPPT methodology permits dynamic Reserve of Power control for grid-connected Solar Farms, assuring

the requisite Reserve of Power to sustain the grid and elevated PV power intrusion. Using a voltage controller

based on a model and Model Predictive Control (MPC) to control PV voltage and inductor current, the

algorithm switches the point of operation between two preset values within the PV curve. The RPPT

methodology is tested in settings of partial shade, fluctuating power reference, and steady-state performance.

It uses MPP information to control PV reserve power. The algorithm tracks MPP under partial shading,

provides grid frequency support, reduces DC-link capacitor stress, and improves system reliability. It operates

in MPPT, FPPT, or RPPT modes to maintain desired Power Reserve, offering advantages over traditional

methods, enabling flexible power injection and grid frequency support.

1 INTRODUCTION

Reserve Power Point Tracking (RPPT) is introduced

as a new algorithm for controlling power output from

grid-connected PV systems. Unlike traditional

MPPT, RPPT addresses modern grid requirements by

providing ancillary services like frequency regulation

and stable power injection. Key benefits of RPPT

over existing Flexible Power Point Tracking (FPPT)

methods:

No additional hardware needed: Avoids costs

associated with measurement-based FPPT.

• Robust without PV models: Operates

effectively without relying on potentially

inaccurate models.

• Handles partial shading: Effectively tracks

the global Maximum Power Point

• (MPP) even with multiple peaks due to

shading.

• Fast dynamic response: Crucial for grid

frequency support.

• Easily implemented: Requires no hardware

modifications to existing PV inverters.

RPPT's "sweeping" action dynamically switches

the PV system's operational point on the PV curve

between two voltages, providing continuous MPP

tracking, precise power control, and fast dynamic

response.

The RPPT algorithm's use of an Artificial Neural

Network (ANN) controller is not mentioned in the

text. ANNs are not included in the RPPT method that

has been explained, but they may be utilized in other

areas of PV system control. The use of ANNs to

improve RPPT performance may be investigated in

future studies. The control diagram for the current

RPPT for PV plants is displayed in Figure 1.

Figure 1: Control Schematic of the PV Plant's Current Rppt.

202

Ranganayakulu, J. S., Sreeya, Y., Vardhan, B. V., Sowmya, G. and Vinay, T.

Grid-Connected Solar Farms with Dynamic Reserve Power Point Tracking.

DOI: 10.5220/0013880100004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 2, pages

202-207

ISBN: 978-989-758-777-1

2 PV SYSTEM CONNECTED TO

THE GRID

A PV system connected to the grid with two stages

uses the suggested RPPT algorithm. Two power

converters are part of this setup:

• Photovoltaic-side DC-DC boost

conversion.

• Grid-side three-phase DC-AC converter.

• A DC-link capacitor connecting the

converters.

2.1 Grid Support

• The system has a "grid support block" that

adjusts the power reference based on grid

frequency changes.

• Grid support functions give a power

reference to the RPPT algorithm.

2.2 RPPT Algorithm

It establishes the PV array's reference voltage. A

voltage controller based on a model is used to regulate

this voltage. By controlling the inductor current and

DC-DC boost converter switching, Model Predictive

Control (MPC) manages the PV output. While

keeping the average DC-link voltage constant, the

three-phase inverter sends the PV power to the grid.

To summarize, the DC-DC boost converter controls

the PV output based on the RPPT's voltage reference.

In addition to managing the DC link, the inverter

supplies electricity to the grid. In figure 2, it is

depicted.

Figure 2: Operating Principle of RPPT.

3 EXISTING METHODOLOGY

Dynamic Reserve Power Point Tracking (DRPPT) is

an algorithm that controls the power output of solar

panel systems to maintain a desired power reserve. It

operates by either following a power reference (Ppv-

ref) or targeting a specific percentage of power

reserve (%Pref).

3.1 Key Features of DRPPT

• Regular scanning: The algorithm scans

the power-voltage curve between two

voltage limits (Vlim-1 and Vlim-2) to find

the Maximum Power Point (MPP), even

under changing conditions.

• Recording data: During the scan, voltage

and power are recorded. The algorithm

spends calculated amounts of time at

voltage limits and briefly at the MPP using

energy balance equations to match the

desired average power output.

• Simplified process: If the target power is

within a specific range, the system only

scans between two points, simplifying the

process.

• Handling delays: The algorithm accounts

for small system response delays.

• Partial shading management: By

including the global MPP in the scanning

range, RPPT effectively identifies the

highest power point.

• Grid support: RPPT can actively control

the output power to meet the grid

frequency requirements according to the

specific grid requirements (such as the

South African standard), and thus can

contribute to supporting and stabilizing

grid frequency.

4 PROPOSED METHODOLOGY

4.1 Artificial Neural Networks (ANNs)

• Structure: Inspired by the human brain,

consisting of interconnected neurons in

layers (input, hidden, output).

• Learning: Connections between nodes have

weights adjusted during learning, enabling

adaptation to new data.

4.2 Algorithm of DRPPT

DRPPT is an advanced technique for controlling

power reserves in grid-connected solar farms that

Grid-Connected Solar Farms with Dynamic Reserve Power Point Tracking

203

also takes energy generation into account. DRPPT

general algorithm:

1. Initialization

o Set the desired power reserve level

based on grid requirements.

o Initialize the system parameters,

including solar irradiance,

temperature, and panel

characteristics.

2. Data Acquisition

o Keep an eye on the photovoltaic

(PV) system's output voltage (V),

current (I), and power (P) at all

times.

o Measure environmental conditions

like solar irradiance and

temperature.

3. Maximum Power Point Estimation

o with the given conditions, implement

an MPPT algorithm (like Perturb

and Observe/Incremental

Conductance) to determine the PV

system's Maximum Power Point

(MPP).

4. Reserve Power Calculation

o Determine the reserve power by

subtracting the intended power

output from the MPP power.

o Ensure the reserve power meets the

grid's requirements for stability and

frequency regulation.

5. Dynamic Adjustment

o Maintain the intended power

production while saving the

calculated power by adjusting the

PV system's operating point.

o When operating below the MPP,

employ a flexible power point

tracking (FPPT) strategy.

1. Grid Synchronization

o Make sure the power output's

voltage, frequency, and phase are

all in sync with the grid.

o For grid compatibility, convert DC

power to AC electricity using

inverters.

2. Feedback and Optimization

o Continuously monitor the system's

performance and environmental

conditions.

o Update the algorithm parameters

dynamically to adapt to changing

conditions, such as partial shading

or grid frequency deviations.

3. Fault Handling

o Detect and isolate faults in the PV

system to prevent disruptions.

o Reconfigure the system to maintain

optimal performance.

This algorithm ensures that the PV system can

provide a stable power reserve while maximizing

energy efficiency.

4.3 Benefits of ANN Controllers

1. Adaptability: Learn and adapt to changing

conditions.

2. Non-linearity: Model complex, non-linear

relationships.

3. Fault Tolerance: Handle noisy or

incomplete data robustly.

4. Parallel Processing: Process multiple

inputs concurrently for faster computations.

5. Versatility: Used in diverse fields like

medical sciences, engineering, robotics,

finance, and speech recognition.

4.4 Using ANNs in MATLAB

• Neural Network Toolbox: Comprehensive

toolbox for creating, configuring, training,

simulating, and visualizing ANNs.

• Functions: Define network parameters, use

various training algorithms, predict outputs

for new inputs, and evaluate performance.

Figure 3: MATLAB Model of DRPPT Using ANN

Controllers.

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

204

Table 1: Differences Between Existing and Proposed Methodology.

Feature

Existing Methodologies

(Measurement/Estimation

Based

)

Proposed RPPT

Methodology

Potential ANN

Integration with

RPPT

Hardware

Often requires additional

sensors/hardware

No additional

hardware

uire

Could potentially

reduce sensor

needs

Model Dependency

Relies on PV models,

susceptible to inaccuracies

Model-free,

robust to aging

Could improve

model-based

estimation if

neede

Dynamic Response

Can be slow, especially with

sensorless methods

Fast dynamic

response

Could enhance

prediction and

control s

Partial Shading

Challenges in identifying

GMPP

Effectively

handles partial

shadin

Could improve

GMPP

identification

Implementation Can be complex

Simple, control-

based

Could simplify

control logic in

complex

scenarios

Figure 4: Output Voltage and Current Wave Forms of

DRPPT Using ANN Controllers.

Figure 5: Output Power Wave Forms of DRPPT Using

ANN Controllers.

5 OUTPUT WAVE FORM

DESCRIPTION

When solar panels generate electricity, they produce

direct current (DC), which is like water flowing

steadily in one direction through a pipe. However, the

electrical grids and most appliances use alternating

current (AC), where the flow of electricity alternates

direction. To make this transition, the system uses an

apparatus known as an inverter. The inverter

transforms the converting DC power to AC, creating

a waveform that alternates back and forth, similar to

the back-and-forth flow of water in a pipe.

Table 1

shows the Differences Between Existing and

Proposed Methodology.

Initially, the AC waveform created by the inverter

might look a bit blocky or choppy, which isn’t ideal

for the grid. To smooth it out and make it more like

the clean sinusoidal waves we want, the system uses

special filters. These filters remove any unwanted

noise or distortions, leaving behind a nice, smooth

wave. This clean waveform can now be sent to the

electrical grid or used by appliances. The peak values

of output voltage (V

) waveform are +2.200e+02 and

-2. 200e+02.Simillarly the output waveforms current

) are 3.666e+00 and -3.666e+00, active power (P

)

are 4.033e+02 and reactive power (Q

) are -

3.105e+01.

To ensure the generated AC power can merge

seamlessly with the grid, the system fine-tunes the

waveform. It aligns the wave's rhythm, including its

voltage, frequency, and phase, with the grid's

Grid-Connected Solar Farms with Dynamic Reserve Power Point Tracking

205

standards. This process ensures everything works

together smoothly, like synchronized dancers moving

in unison. The pv values of the output wave forms are

=6.447e+02, Ipv=3.325e+01, Vdc=2.975e+01,

Ppv=2.130e+04.

However, solar farms face challenges like sudden

changes in sunlight or shifts in grid requirements.

Dynamic algorithms like RPPT (Reserve Power Point

Tracking) help adjust the waveforms quickly to meet

these changing needs. This ensures that the system

can maintain steady power output, even under

varying conditions, and support the grid efficiently.

It’s a seamless blend of technology and adaptability

to keep the power flowing.

6 SIMULATION RESULTS

Simulation results on a three-phase system also

showed the RPPT algorithm's effectiveness at high

power levels. The tests covered various conditions,

including partial shading, MG predictive capabilities

for enhanced Maximum Power Point (MPP) tracking,

updated output to maintain grid stability under

simulated grid frequency changes, and transient

condition tests confirming seamless sequence to

Utility Mode.

Figure 3 shows the MATLAB model of

DRPPT using ANN controllers.

The results presented in figures 4 and 5 suggest

that RPPT is suitable for large-scale solar power

stations. Artificial Neural Networks (ANNs) to

improve the performance of the proposed algorithm,

such as enhancing the precision of power tracking, the

contribution to the grid, reliability, and optimizing the

control.

But while adding ANNs seems like an attractive

option, it would also add considerable complexity to

the system, so the associated benefits need to be

authenticated and weighed against the additional

costs and effort required. However, the simulations

indicated that ANNs have advantages for large-scale

solar plants in particular.

7 CONCLUSIONS

RPPT is a novel design approach for solar power

plants that maximizes the capacity it can output and

incorporates flexible supply to the grid. It has three

modes of operation: maximum power, fixed amount

of power, or reserve for grid needs. When some

panels are shaded, RPPT has demonstrated its ability

to identify the ideal power point. Further integration

on this version to include Artificial Neural Network

(ANN) can prove beneficial in ensuring accuracy of

power tracking, adaptation to changing conditions,

noisy data handling and prediction of future power

output. But the addition of ANNs to systems would

also enhance complexity. RPPT, for now, works well

without ANNs simple solutions at their best.

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