Improved fault-clearing strategy for large renewable energy systems using advanced optimization and FLC – Nature

 

Executive Summary

This report details a novel fault-clearing strategy for large-scale hybrid renewable energy systems, designed to enhance grid stability and support the achievement of the United Nations Sustainable Development Goals (SDGs). Specifically, this work advances SDG 7 (Affordable and Clean Energy) by improving the reliability and efficiency of hybrid photovoltaic, wind, and battery power systems (HPVWBPS). A Modified Fault-Clearing Strategy (MFCS) was developed using a Manta Ray Foraging Optimization (MRFO) algorithm and a Fuzzy Logic Controller (FLC). The MRFO algorithm optimizes the FLC gains and power dispatch during faults, ensuring maximum power point tracking (MPPT) and minimizing the impact of disruptions. This innovation in resilient energy infrastructure directly contributes to SDG 9 (Industry, Innovation, and Infrastructure) and SDG 13 (Climate Action) by making renewable energy sources more viable for large-scale integration. Comparative simulations demonstrate the superiority of the proposed FLC-MRFO framework over conventional PI-based and GWO-optimized controllers. The FLC-MRFO configuration reduced current regulator settling time by 1.3% and rise time by 33.2%, while also lowering maximum overshoot. These enhancements confirm the strategy’s effectiveness in creating more robust, efficient, and stable renewable energy systems, which are critical for a sustainable energy future.

Introduction: Aligning Renewable Energy with Sustainable Development Goals

The global transition towards sustainable energy systems is a cornerstone of the 2030 Agenda for Sustainable Development, particularly addressing SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). Hybrid renewable energy systems (HRES), which combine sources like solar and wind with battery storage, are pivotal to this transition. However, their inherent intermittency and vulnerability to faults pose significant challenges to grid stability and reliability. This report introduces an advanced fault-clearing strategy that leverages intelligent optimization to overcome these challenges, thereby strengthening the viability of large-scale renewable energy integration.

Motivation: Addressing Challenges in Renewable Energy for SDG 7

The motivation for this research is rooted in the need to overcome the operational uncertainties that hinder the widespread adoption of renewable energy. Achieving SDG 7 requires energy systems that are not only clean but also reliable and resilient. Key challenges include:

• Intermittency of Renewables: The variable nature of solar and wind power generation complicates system stability and fault management.
• Energy Storage Uncertainties: Inefficiencies in managing the state of charge (SoC), degradation, and lifespan of battery systems affect overall system reliability.
• Complex System Interactions: The integration of multiple energy sources, storage, and variable loads introduces complexities that traditional control systems struggle to manage, especially during fault conditions.

Addressing these issues is essential for building the resilient energy infrastructure required by SDG 9 and ensuring a consistent supply of clean energy to support sustainable communities as envisioned in SDG 11.

Research Objectives and Contributions to Sustainable Innovation (SDG 9)

This research aims to develop and validate an intelligent fault-clearing strategy that enhances the stability and reliability of large-scale HPVWBPS. The primary objectives and contributions to sustainable innovation are:

1. To design a novel fault-clearing strategy that improves the stability of hybrid renewable systems, directly supporting the reliability targets of SDG 7.
2. To optimize the fault-clearing process using the MRFO algorithm to determine optimal FLC gains, representing an innovation in control systems for resilient infrastructure (SDG 9).
3. To optimize power dispatch during faults by considering all system variables, thereby maximizing the availability of clean energy and contributing to SDG 13.
4. To validate the proposed strategy’s effectiveness through comprehensive simulations, demonstrating significant improvements in fault recovery, overshoot reduction, and settling time compared to conventional methods.

System Configuration and Methodology for Sustainable Energy Grids

The methodology focuses on a large-scale HPVWBPS, a configuration that is critical for providing consistent and reliable renewable energy, thereby advancing SDG 7. The system integrates solar PV panels, wind turbines, and a battery energy storage system (BESS) to ensure a stable power supply under varying environmental conditions and load demands.

Hybrid Power System (HPVWBPS) Model

The HPVWBPS model combines complementary renewable sources to smooth power output and enhance reliability. The battery storage component is crucial for balancing energy supply and demand, storing surplus energy, and providing backup power. This integrated approach is fundamental to creating the dependable, modern energy infrastructure promoted by SDG 9. The power balance of the system ensures that the total generated and stored power meets the load demand at all times, a critical function for grid stability.

Advanced Control Strategy for Enhanced Grid Resilience

A sophisticated control strategy is required to manage the complex power flows within the HPVWBPS and ensure alignment with sustainability goals. The strategy aims to optimize power allocation to meet load demands, minimize losses, and extend the lifespan of components like the battery bank. By employing intelligent algorithms, the control system can adapt to the uncertainties of renewable generation and load, improving the overall efficiency and stability of the system. This contributes to SDG 7 by maximizing the utilization of clean energy and to SDG 9 by creating more robust and intelligent energy infrastructure.

Optimization and Control Framework

To achieve superior performance and resilience, a hybrid control framework combining the Manta Ray Foraging Optimization (MRFO) algorithm with a Fuzzy Logic Controller (FLC) was developed. This framework represents a significant technological upgrade for managing renewable energy systems, in line with the innovation targets of SDG 9.

Manta Ray Foraging Optimization (MRFO) Algorithm

The MRFO is a bio-inspired optimization algorithm that emulates the intelligent foraging behavior of manta rays. It is employed to explore the solution space efficiently and determine the optimal control parameters for the FLC. Its ability to balance exploration and exploitation allows it to find superior solutions for complex, non-linear problems typical of HRES control, leading to more efficient energy harvesting and contributing to the objectives of SDG 7.

Fuzzy Logic Controller (FLC) Optimized by MRFO

The FLC uses rule-based logic to handle the imprecision and uncertainty inherent in renewable energy systems. By optimizing the FLC’s membership functions and rules with the MRFO algorithm, the controller’s performance is significantly enhanced. The FLC-MRFO framework provides adaptive and robust control, ensuring Maximum Power Point Tracking (MPPT) for both solar and wind subsystems and executing rapid and effective fault-clearing actions. This intelligent control is vital for maintaining the stability required for modern, sustainable energy grids.

Performance Evaluation and Results

The proposed FLC-MRFO strategy was evaluated through extensive simulations in MATLAB/SIMULINK and compared against PI-GWO, PI-MRFO, and FLC-GWO configurations. The results validate its superior performance in enhancing the stability and efficiency of the HPVWBPS, thereby demonstrating its potential to help achieve key SDG targets.

System Performance Under Normal Operating Conditions

Under normal conditions, the FLC-MRFO controller demonstrated faster convergence to the maximum power point, reduced overshoot, and shorter settling times. This leads to higher energy harvesting efficiency, maximizing the output of clean energy and directly supporting SDG 7. The MRFO algorithm consistently outperformed the GWO algorithm in finding optimal controller gains, confirming its effectiveness for this application.

System Resilience Under Fault Conditions

The system’s resilience was tested under severe fault conditions, including three-phase-to-ground and single-line-to-ground faults.

• Three-Phase-to-Ground Fault: The FLC-MRFO strategy enabled the system to mitigate the fault and restore normal operation swiftly. While the fault caused a significant drop in power output, the controller stabilized the system and returned parameters to their nominal values faster and with less oscillation than the GWO-based controller.
• Single-Line-to-Ground Fault: During this asymmetrical fault, the FLC-MRFO controller demonstrated a targeted and rapid response, isolating the fault and restoring voltage and current stability with minimal disruption to the overall system.

This enhanced fault ride-through capability is crucial for building resilient energy infrastructure (SDG 9) and ensuring the uninterrupted supply of clean energy (SDG 7).

Conclusion: Advancing SDGs through Innovative Energy System Control

This report has presented an improved fault-clearing strategy for large-scale hybrid renewable energy systems using an FLC optimized by the MRFO algorithm. The findings confirm that this innovative approach significantly enhances system performance, stability, and resilience, making substantial contributions to several Sustainable Development Goals.

• SDG 7 (Affordable and Clean Energy): The strategy improves the efficiency and reliability of renewable energy systems, making clean energy a more viable and dependable source for widespread use.
• SDG 9 (Industry, Innovation, and Infrastructure): The development of the FLC-MRFO framework represents a technological innovation that builds more resilient and intelligent energy infrastructure capable of handling the complexities of renewable energy integration.
• SDG 13 (Climate Action): By improving the stability of large-scale renewable systems, this work facilitates the transition away from fossil fuels, thereby contributing to climate change mitigation efforts.

The FLC-MRFO configuration consistently outperformed other methods, achieving faster response times, lower overshoot, and quicker fault recovery. For the current regulator, it achieved a 33.2% decrease in rise time and a 2.17% reduction in maximum overshoot. These quantitative improvements translate into a more robust and efficient power system. Future work will focus on developing adaptive FLC schemes and further enhancing the MRFO algorithm to address the dynamic challenges of future energy grids.

Analysis of Sustainable Development Goals (SDGs) in the Article

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

1. SDG 7: Affordable and Clean Energy

   • The article’s primary focus is on improving the performance of large-scale hybrid renewable energy systems, specifically those combining photovoltaic (solar), wind, and battery storage (HPVWBPS). This directly contributes to making clean energy sources more reliable and efficient, which is central to SDG 7. The text states, “The motivation…stems from the increasing integration of renewable energy sources and energy storage systems into power grids.”

2. SDG 9: Industry, Innovation, and Infrastructure

   • The research introduces a “novel fault-clearing strategy” using advanced optimization algorithms (Manta Ray Foraging Optimization – MRFO) and control systems (Fuzzy Logic Controller – FLC). This represents a significant technological innovation aimed at creating more resilient and sustainable energy infrastructure. The paper’s contribution is described as contributing “to developing intelligent and adaptive strategies to effectively handle uncertainties, ensuring optimal fault management and system stability in renewable energy integration.”

3. SDG 11: Sustainable Cities and Communities

   • While not a direct focus, the development of reliable large-scale renewable energy systems is a foundational element for creating sustainable cities. By improving the stability of the power grid with clean energy sources, this research supports the infrastructure needed for sustainable urban development. One of the cited articles explicitly mentions “Optimal planning of hybrid renewable energy infrastructure for urban sustainability.”

4. SDG 13: Climate Action

   • The entire premise of the article—enhancing the functionality of solar and wind power systems—is a direct action against climate change. By making renewable energy more stable and reliable, the technology facilitates a greater shift away from fossil fuels, thereby helping to reduce greenhouse gas emissions. The article notes that a key advantage of such systems is “reducing reliance on fossil fuels and minimizing greenhouse gas emissions.”

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

1. 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 supports this target by developing technology that makes large-scale renewable systems more stable and efficient, which is crucial for increasing their integration and share in the power grid. The research aims to “improve the resilience and reliability of large-scale HPVWBPS.”
   • Target 7.a: By 2030, enhance international cooperation to facilitate access to clean energy research and technology… and promote investment in energy infrastructure and clean energy technology. This academic paper is a direct contribution to the body of research on clean energy technology, presenting an advanced strategy that can be adopted and built upon globally.

2. 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 proposed fault-clearing strategy is an advanced, clean technology designed to upgrade energy infrastructure, making it more efficient, reliable, and sustainable.
   • Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors… encouraging innovation. The paper is a clear example of scientific research that introduces a novel method (“introduces a novel hybrid fault-clearing strategy combining MRFO and FLC techniques”) to solve a critical technical challenge in the renewable energy industry, thereby upgrading its technological capabilities.

3. SDG 13: Climate Action

   • Target 13.2: Integrate climate change measures into national policies, strategies and planning. Technologies that improve the viability of large-scale renewable energy are essential components of any national or international strategy to combat climate change. This research provides a technical solution that can be integrated into such plans.

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

1. Indicators for System Efficiency and Reliability (Supporting SDG 7 and SDG 9)

   • Settling Time (ST): The article uses ST to measure how quickly the system stabilizes after a fault. A lower ST indicates better performance and reliability. The proposed FLC-MRFO method achieved an “ST of 1.0020 ms, compared to 1.0033 ms for the PI-GWO controller, marking a 1.3% improvement.”
   • Rise Time (RT): This measures the system’s response speed. The article states, “The RT is reduced from 3.2755 μs to 2.1885 μs, a 33.2% decrease,” indicating a much faster and more efficient system response.
   • Maximum Overshoot (MOST): This measures the extent to which the system’s output exceeds its final, steady-state value. A lower MOST implies less stress on components and greater stability. The article reports that “MOST is also lowered from 143.22% to 140.12%, a 2.17% reduction.”
   • Power Harvesting Efficiency: The article implies this indicator by stating that the proposed FLC-MRFO setup demonstrates “power harvesting efficiency exceeding 98%,” which directly measures the effectiveness of the renewable energy system.

2. Indicators for Innovation and Technological Advancement (Supporting SDG 9)

   • Development of Novel Algorithms and Strategies: The creation and successful simulation of the “modified fault-clearing strategy (MFCS)” using the “Manta ray foraging optimization (MRFO) algorithm and a fuzzy logic controller (FLC)” serves as a direct indicator of innovation and enhanced scientific research in the field.

4. Table of SDGs, Targets, and Indicators

SDGs	Targets	Indicators (Mentioned or Implied in the Article)
SDG 7: Affordable and Clean Energy	7.2: Increase the share of renewable energy. 7.a: Promote access to clean energy research and technology.	Improved system stability and reliability for large-scale renewable energy integration. Power harvesting efficiency (exceeding 98%). Contribution of new research to clean energy technology.
SDG 9: Industry, Innovation, and Infrastructure	9.4: Upgrade infrastructure with clean and sustainable technologies. 9.5: Enhance scientific research and upgrade technological capabilities.	Reduction in Settling Time (ST) by 1.3%. Reduction in Rise Time (RT) by 33.2%. Reduction in Maximum Overshoot (MOST) by 2.17%, leading to reduced component stress. Development of a novel fault-clearing strategy (MFCS) using MRFO and FLC.
SDG 11: Sustainable Cities and Communities	11.6: Reduce the adverse environmental impact of cities.	Implied improvement in urban air quality by enabling more reliable renewable energy, displacing fossil fuels.
SDG 13: Climate Action	13.2: Integrate climate change measures into policies and strategies.	Development of technology that makes renewable energy systems more viable, supporting strategies to reduce reliance on fossil fuels and minimize greenhouse gas emissions.

Source: nature.com