A novel MPPT approach for photovoltaic system using Pelican optimization and high-gain DC–DC converter – Nature

Report on a Novel Maximum Power Point Tracking Approach for Photovoltaic Systems

Executive Summary

This report details a novel approach to enhance the efficiency of Photovoltaic (PV) solar systems, directly contributing to the United Nations’ Sustainable Development Goal 7 (SDG 7: Affordable and Clean Energy). The study introduces the Pelican Optimization Algorithm (POA), a new Maximum Power Point Tracking (MPPT) technique designed to maximize power extraction from solar panels, particularly under challenging conditions such as partial shading. This innovation is coupled with a high-gain DC-to-DC converter to ensure efficient energy transfer. The performance of the POA was benchmarked against established metaheuristic techniques, including Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO), Gray Wolf Optimization (GWO), and Cuckoo Search (CS). Results from MATLAB/Simulink simulations and experimental validation confirm the POA’s superiority, demonstrating rapid convergence (less than 0.2s), minimal power oscillation, and higher efficiency (99%). These advancements are critical for increasing the viability and adoption of solar energy, supporting SDG 13 (Climate Action) by promoting a transition away from fossil fuels and fostering the development of resilient energy infrastructure for SDG 11 (Sustainable Cities and Communities).

1.0 Introduction: Aligning Solar Technology with Sustainable Development Goals

The global imperative to transition towards sustainable energy systems is driven by the dual challenges of rising energy demand and the adverse effects of climate change, as outlined in SDG 7 and SDG 13. Solar Photovoltaic (SPV) technology is a cornerstone of this transition, offering a direct path to clean energy generation. However, the operational efficiency of SPV systems is often hindered by variable environmental conditions and the high cost of implementation. To maximize the energy yield and improve the economic feasibility of solar power, it is crucial to operate PV panels at their Maximum Power Point (MPP). This report addresses this challenge by presenting an advanced MPPT system designed to enhance the performance of SPV installations.

1.1 The Challenge of Maximizing Clean Energy Output

Conventional MPPT methods like Perturb and Observe (P&O) and Hill Climbing (HC) often suffer from power loss due to oscillations around the MPP and struggle to identify the global maximum power point (GMPP) under partial shading conditions (PSCs). More advanced techniques using Artificial Neural Networks (ANN) or Fuzzy Logic Controllers (FLC) can be complex and costly. This technological gap impedes the widespread deployment of efficient solar energy solutions, slowing progress toward achieving universal access to affordable and clean energy (SDG 7).

1.2 Report Objectives and Contributions to SDGs

This research aims to overcome existing limitations by introducing a novel system that combines the Pelican Optimization Algorithm (POA) with a high-performance DC-DC converter. The key contributions of this work towards sustainable development include:

• Enhanced Energy Efficiency: The POA offers fast convergence and minimal oscillation, ensuring more solar energy is converted into usable electricity, directly supporting Target 7.2 of increasing the share of renewable energy.
• Improved System Robustness: The algorithm’s superior performance in diverse weather and partial shading conditions enhances the reliability of solar power, a crucial factor for building sustainable energy infrastructure (SDG 11).
• Technological Advancement: By providing a more effective and moderately complex MPPT solution, this work facilitates the broader adoption of solar technology, contributing to global climate action (SDG 13).

2.0 System Modeling and Methodology

2.1 Photovoltaic (PV) System Modeling

An accurate single-diode PV model was employed to simulate the system’s electrical characteristics under both uniform and partial shading conditions. This foundational step ensures that the subsequent analysis of the MPPT algorithms is based on a realistic representation of a solar array’s performance. The model accounts for key parameters such as series resistance (voltage loss) and shunt resistance (leakage current), which are critical for evaluating efficiency.

2.2 The Pelican Optimization Algorithm (POA) for MPPT

The core of this study is the application of the POA, a nature-inspired metaheuristic algorithm, for MPPT. The algorithm models the hunting behavior of pelicans to efficiently search for the GMPP on the PV system’s power-voltage curve. The methodology involves two primary phases:

1. Exploration Phase: Pelicans (search agents) identify the location of prey (potential MPP) and move towards it. This phase allows the algorithm to scan the entire search space to avoid getting trapped in local maxima, a common failure point in conventional algorithms under PSCs.
2. Exploitation Phase: Pelicans converge on the prey, refining the search in a localized area. This phase ensures rapid convergence to the precise GMPP with minimal steady-state oscillation.

By balancing exploration and exploitation, the POA provides a robust mechanism for maximizing clean energy harvesting, making solar power a more reliable and efficient contributor to the global energy mix in line with SDG 7.

2.3 High-Gain DC-DC Converter Integration

To effectively transfer the maximized power from the PV source to the load, a modified high-gain, single-switch DC-DC converter was integrated into the system. This converter is essential for stepping up the variable DC voltage from the solar panels to a stable, usable level. Its key features, which support the overall system’s contribution to sustainable energy, include:

• High Voltage Gain: Achieves necessary voltage levels efficiently.
• Low Input Ripple: Ensures stable operation and effective MPP tracking.
• High Power Conversion Efficiency: Minimizes energy losses during the power transfer stage.

3.0 Performance Analysis and Results

The proposed POA-based MPPT system was rigorously tested through simulation and experimental validation. Its performance was benchmarked against four other metaheuristic algorithms (P&O, PSO, CS, and GWO) across various operational scenarios relevant to real-world conditions.

3.1 Performance Under Uniform and Variable Conditions

Under fixed irradiance, the POA demonstrated a superior start-up speed, tracking the MPP in under 0.2 seconds without the steady-state oscillations that cause power loss in conventional methods like P&O. When subjected to step changes in irradiance and temperature, the POA consistently outperformed other algorithms in transient response, quickly re-converging to the new MPP. This rapid adaptability is vital for maintaining high energy yields in fluctuating weather, thereby enhancing the reliability of solar energy systems as envisioned by SDG 7.

3.2 Efficacy in Partial Shading Conditions (PSCs)

Partial shading is a significant challenge that can drastically reduce the output of a PV system. The simulation results confirmed that while conventional algorithms like P&O fail to distinguish between local and global power peaks, the POA successfully identified and tracked the GMPP. This capability is critical for urban and integrated PV installations, ensuring that solar energy remains a viable and efficient power source even in complex environments, contributing to the goals of sustainable cities (SDG 11).

3.3 Comparative Evaluation Summary

A comparative analysis highlighted the distinct advantages of the POA:

• Tracking Time: The POA was approximately 20% faster than P&O.
• Tracking Efficiency: The POA achieved an efficiency of 99%, about 1.02% higher than P&O.
• Steady-State Oscillation: The POA exhibited 25% less oscillation than the CS algorithm, minimizing power loss.
• Complexity: With fewer tuning parameters than PSO, the POA offers a balance of high performance and moderate implementation complexity.

4.0 Conclusion and Contribution to Sustainable Development

This report has successfully demonstrated the efficacy of a novel MPPT system utilizing the Pelican Optimization Algorithm and a high-gain DC-DC converter. The proposed system significantly enhances the performance of photovoltaic energy generation, marking a tangible advancement in the pursuit of clean and affordable energy for all (SDG 7).

The key outcomes of this research directly support global sustainability objectives:

1. Advancing Clean Energy (SDG 7): By improving tracking speed, efficiency, and robustness under challenging conditions, the POA-based system maximizes the electricity generated from solar resources, making solar power more economically viable and accessible.
2. Promoting Climate Action (SDG 13): More efficient solar technology accelerates the displacement of fossil fuel-based power generation, contributing to the reduction of greenhouse gas emissions.
3. Building Sustainable Infrastructure (SDG 11): The system’s ability to handle partial shading and dynamic weather enhances the reliability of solar power, supporting its integration into resilient urban and rural energy grids.

The superior performance of the POA, validated through both simulation and experimental results, establishes it as a highly promising solution for optimizing solar energy systems. This work provides a valuable contribution to the renewable energy sector, offering a practical and effective tool to help achieve a sustainable energy future.

1. Which SDGs are addressed or connected to the issues highlighted in the article?

The article’s focus on advancing photovoltaic (PV) solar cell technology connects directly to several Sustainable Development Goals (SDGs). The research aims to improve the efficiency and reliability of solar energy, which is a cornerstone of sustainable development.

• SDG 7: Affordable and Clean Energy

  This is the most prominent SDG addressed. The article is centered on improving renewable energy generation through more efficient solar PV systems. By developing the Pelican Optimization Algorithm (POA) to maximize power output, the research directly contributes to making solar energy more effective and reliable, which is essential for ensuring access to “affordable, reliable, sustainable and modern energy for all.” The introduction explicitly mentions the goal of meeting “global clean energy demands” and the need for “Sustainable renewable energy resources (RERs).”

• SDG 9: Industry, Innovation, and Infrastructure

  The article is a clear example of promoting innovation and upgrading technological capabilities. It introduces a “novel nature-inspired stochastic optimization technique” and an “energy-efficient high-power DC-to-DC converter.” This research enhances scientific and technological capabilities in the renewable energy sector, contributing to building resilient infrastructure and fostering sustainable industrialization through the adoption of clean and environmentally sound technologies.

• SDG 13: Climate Action

  The motivation for the research is rooted in combating climate change. The introduction highlights the need for renewable energy to counter the “detrimental effects of greenhouse gas emissions” and the “rapid depletion of existing energy supply systems.” By making solar energy more efficient and practical, the technology described in the article serves as a direct tool to reduce reliance on fossil fuels and mitigate the impacts of climate change.

2. What specific targets under those SDGs can be identified based on the article’s content?

Based on the article’s discussion of improving solar energy technology, several specific SDG targets can be identified:

1. Under SDG 7: Affordable and Clean Energy

   • Target 7.2: “By 2030, increase substantially the share of renewable energy in the global energy mix.” The article’s primary goal is to maximize the power output and efficiency of solar PV systems. By making solar technology more effective, especially under challenging conditions like partial shading, this research helps increase the viability and adoption of solar power, thereby contributing to a higher share of renewable energy.
   • Target 7.a: “By 2030, enhance international cooperation to facilitate access to clean energy research and technology, including renewable energy, energy efficiency… and promote investment in energy infrastructure and clean energy technology.” This scientific paper itself is a form of international cooperation that disseminates knowledge on advanced clean energy technology. It presents a novel algorithm and converter design, making this research accessible to the global scientific and engineering community to build upon.

2. Under SDG 9: Industry, Innovation, and Infrastructure

   • Target 9.4: “By 2030, upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies…” The development of a high-efficiency (99%) MPPT technique and a high-gain DC-DC converter represents a direct upgrade to clean energy infrastructure. It improves the resource-use efficiency of solar panels by extracting the maximum possible power from the available sunlight.
   • Target 9.5: “Enhance scientific research, upgrade the technological capabilities of industrial sectors… encouraging innovation…” The entire study is an exercise in enhancing scientific research. By benchmarking the new POA against existing metaheuristic techniques (PSO, GWO, CS) and demonstrating its superior performance, the article pushes the boundaries of technological capabilities in the renewable energy industry.

3. Under SDG 13: Climate Action

   • Target 13.2: “Integrate climate change measures into national policies, strategies and planning.” While the article does not discuss policy, it provides the technological means to achieve climate-related goals. Advanced, more efficient solar technologies like the one proposed make it more feasible for nations to implement policies that favor renewable energy and reduce greenhouse gas emissions, as mentioned in the introduction.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

Yes, the article is rich with specific, quantifiable indicators that can be used to measure progress towards the identified targets. These indicators are primarily performance metrics of the proposed technology.

• Overall Efficiency: The article explicitly states that the proposed MPPT technique coupled with the DC-to-DC converter achieves “higher efficiency (99%).” This is a direct indicator for Target 9.4 (increased resource-use efficiency) and Target 7.2 (making renewables more competitive).
• Response Time and Convergence Speed: The research highlights the “quick response times (less than 0.2 s)” and “rapid convergence” of the POA. The conclusion further quantifies this, stating the POA is “around 20% faster than P&O.” This measures the technological advancement and robustness of the system (Target 9.5).
• Power Output Stability: The article mentions “minimal MPP oscillation” as a key advantage. The conclusion notes “steady-state oscillation (around 25% less than the CS algorithm).” This indicates a more stable and reliable energy output, contributing to the quality of clean energy infrastructure (Target 9.4).
• Tracking Efficiency under Adverse Conditions: A major focus is the ability to track the “global maximum power point (GMPP)” under “partial shading conditions (PSCs).” The article provides detailed simulation results and experimental verification for various shading scenarios, demonstrating the algorithm’s effectiveness. This is a crucial indicator of the technology’s practical applicability and robustness (Targets 7.2 and 9.5).
• Comparative Performance Improvement: The entire study benchmarks the POA against four other algorithms (PSO, HHO, GWO, CS, and P&O). The conclusion states the POA has “approximately 1.02% more [tracking efficiency] than P&O.” This comparative analysis serves as a clear indicator of innovation and technological upgrading (Target 9.5).

4. Create a table with three columns titled ‘SDGs, Targets and Indicators” to present the findings from analyzing the article.

Source: nature.com