Optimized frequency stabilization in hybrid renewable power grids with integrated energy storage systems using a modified fuzzy-TID controller – Nature

Optimized Frequency Stabilization in Hybrid Renewable Power Grids with Integrated Energy Storage Systems Using a Modified Fuzzy-TID Controller

Abstract

This report presents innovative methods to mitigate frequency deviations in hybrid renewable power grids (HRPGs) with high penetration of renewable energy sources (RESs), aligning with the Sustainable Development Goals (SDGs) for affordable and clean energy (SDG 7) and climate action (SDG 13). Two HRPG models are analyzed: a two-area power grid combining conventional power plants and RESs, and the IEEE 39-bus system with a unified power flow controller (UPFC) connected in series with the tie-line.

The study introduces a Fuzzy Integral-Tilt-Derivative (Fuzzy I-TD) controller in the secondary control loop (SCL) and compares its performance with Fuzzy Proportional-Integral-Derivative (Fuzzy-PID) and Fuzzy Integral-Proportional-Derivative (Fuzzy I-PD) controllers. Additionally, integration of controlled energy storage systems (ESSs), such as plug-in electric vehicles (PEVs), is evaluated. The Sea Horse Optimizer (SHO), a recent metaheuristic algorithm, optimizes controller parameters under various operating conditions.

Results demonstrate that the Fuzzy I-TD controller significantly reduces frequency and tie-line deviations by 82.7% and 97.01%, respectively, compared to other controllers. The addition of PEVs further reduces frequency fluctuations by 40%, highlighting the effectiveness of combining advanced control strategies with energy storage solutions to enhance grid stability and sustainability.

Introduction

Background

Electrical power systems face critical challenges due to reliance on fossil fuels, leading to carbon emissions and global warming. Integrating renewable energy sources (RESs) into power systems addresses these challenges, supporting SDG 7 and SDG 13 by promoting clean energy and combating climate change. However, high RES penetration reduces system inertia and introduces frequency stability challenges due to the stochastic nature of renewables.

Load frequency control (LFC) is essential to maintain system stability, ensuring frequency and tie-line power remain within acceptable limits. Incorporating energy storage systems (ESSs), such as plug-in electric vehicles (PEVs) and fuel cell systems (FCSs), supports control capabilities and improves system performance by providing additional power injection.

Flexible alternating current transmission systems (FACTS), including unified power flow controllers (UPFCs), are employed alongside LFC to enhance system stability, particularly in interconnected areas.

State-of-the-Art Review

Various control methods have been explored for frequency stability in power systems, including optimal control, model predictive control, robust control, and intelligent control techniques like fuzzy logic and artificial neural networks. The PID controller remains popular due to its simplicity and cost-effectiveness, but its sensitivity to RES variability limits its performance.

Fractional-order controllers (FOCs), especially the Tilt-Integral-Derivative (TID) controller, offer enhanced flexibility and robustness. Cascaded controller structures (CCS), combination structures (CS), and feed forward-feedback structures (FFS) improve controller performance. The FFS arrangement, used in this study, provides robustness against reference input changes.

Optimization algorithms, including particle swarm optimization, genetic algorithms, and recent metaheuristic techniques, are utilized to tune controller parameters effectively. The Sea Horse Optimizer (SHO) is adopted here for its superior optimization capabilities.

Research Contributions

• Development of a coordination strategy combining robust LFC and controlled hybrid ESSs to enhance HRPG performance.
• Introduction of the Fuzzy I-TD controller for the secondary control loop to effectively mitigate frequency deviations.
• Optimization of controller parameters using the SHO algorithm for improved frequency control.
• Consideration of high RES penetration (>50%) in two-area HRPGs, including wind and solar power integration.
• Comparison of the proposed controller with Fuzzy-PID and Fuzzy I-PD controllers under various scenarios.
• Evaluation of hybrid ESSs’ superiority in managing high RES integration.
• Assessment of UPFC influence on system performance during abnormal operations.
• Validation of controller reliability on the IEEE 39-bus system.

Modeling of Hybrid Renewable Power Grids (HRPGs)

HRPG Configuration

The study considers two HRPG models, each integrating three conventional energy sources (thermal, hydro, gas turbines) and two RESs (photovoltaic and wind power plants). The Area Control Error (ACE) serves as the controller input, aiming to regulate active power generation and maintain frequency stability.

Renewable Energy Sources Modeling

• Wind Farm: Power output is modeled based on wind speed, turbine characteristics, and air density, contributing to SDG 7 by harnessing clean energy.
• Photovoltaic (PV) Station: Power output depends on solar radiation and ambient temperature, supporting sustainable energy goals.

Energy Storage Systems (ESSs)

• Fuel Cell Systems (FCS): Polymer electrolyte membrane fuel cells (PEMFC) are used for frequency control due to high power density and fast response.
• Plug-in Electric Vehicles (PEVs): PEVs act as flexible distributed energy storage through Vehicle-to-Grid (V2G) technology, enabling charging during excess generation and discharging during peak demand, thus enhancing grid stability and renewable energy utilization.

Unified Power Flow Controller (UPFC)

The UPFC is connected in series with the tie-line to regulate power flow, enhance transient stability, and maintain voltage stability. It dampens tie-line power oscillations, improving system reliability and supporting SDG 9 (Industry, Innovation, and Infrastructure).

Problem Identification and Controller Structure

Fuzzy I-TD Controller Design

The Fuzzy I-TD controller combines fuzzy logic with a modified Tilt-Integral-Derivative (TID) controller in a feedback arrangement, offering superior disturbance rejection and flexibility. The controller inputs the ACE signal, with parameters optimized via the Sea Horse Optimizer (SHO) to enhance performance and stability.

Optimization Using Sea Horse Optimizer (SHO)

SHO is a nature-inspired metaheuristic algorithm simulating sea horse behaviors (feeding, reproduction, movement) to efficiently optimize controller parameters, ensuring robust frequency control in HRPGs.

Simulation Results and Discussions

Simulations assess the proposed controller and strategies under various scenarios, emphasizing SDG 7 and SDG 13 by promoting renewable integration and grid stability.

1. Scenario 1: Fuzzy I-TD controller performance in two-area power grids under step load perturbation (SLP).
2. Scenario 2: Controller assessment under step load disturbance (SLD).
3. Scenario 3: Evaluation under random load disruption (RLD).
4. Scenario 4: Impact of high renewable penetration on frequency stability.
5. Scenario 5: Effect of different ESS types and FACTS devices on system performance.
6. Scenario 6: Controller validation in the IEEE 39-bus system.
7. Scenario 7: Strategy assessment combining Fuzzy I-TD with PEVs in the modified IEEE 39-bus system.

Key Findings

• SHO outperforms traditional optimizers (TLBO, AOA, ESAOA) in parameter tuning.
• Fuzzy I-TD controller significantly reduces frequency and tie-line power deviations compared to Fuzzy-PID and Fuzzy I-PD controllers, improving grid stability.
• Integration of PEVs with the Fuzzy I-TD controller further decreases frequency fluctuations by 40%, demonstrating the value of ESSs in renewable-rich grids.
• Inclusion of UPFC enhances system stability by damping tie-line oscillations.
• Controller effectiveness is confirmed in complex IEEE 39-bus system scenarios, supporting scalability and real-world applicability.

Conclusions

This study addresses frequency deviation challenges in HRPGs with high RES penetration, contributing to SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action) by enhancing renewable integration and grid stability. The Fuzzy I-TD controller, optimized via the Sea Horse Optimizer (SHO), outperforms existing control methods in mitigating frequency and tie-line power fluctuations.

Integration of controlled energy storage systems, particularly plug-in electric vehicles (PEVs), further improves frequency regulation, highlighting the synergy between advanced control strategies and energy storage technologies. The use of UPFC devices enhances power flow control and system reliability.

Future research may expand these approaches to larger, multi-area power systems with diverse renewable configurations and explore additional technological integrations to advance sustainable energy systems.

1. Sustainable Development Goals (SDGs) Addressed in the Article

1. SDG 7: Affordable and Clean Energy

   • The article focuses on hybrid renewable power grids (HRPGs) with high penetration of renewable energy sources (RESs) such as wind and solar power.
   • It addresses the integration of renewable energy to reduce reliance on conventional energy sources and fossil fuels.

2. SDG 9: Industry, Innovation, and Infrastructure

   • The development and optimization of advanced controllers (Fuzzy I-TD controller) and the use of metaheuristic algorithms (Sea Horse Optimizer) contribute to innovation in energy infrastructure.
   • The article discusses the enhancement of power grid stability and performance through technological advancements.

3. SDG 13: Climate Action

   • By promoting the integration of renewable energy sources and reducing frequency deviations in power grids, the article contributes to mitigating climate change impacts.
   • It addresses environmental issues related to carbon emissions from conventional energy sources.

4. SDG 11: Sustainable Cities and Communities

   • The use of plug-in electric vehicles (PEVs) as energy storage systems supports sustainable urban energy management and reduces pollution.

2. Specific Targets Under the Identified SDGs

1. SDG 7: Affordable and Clean Energy

   • Target 7.2: Increase substantially the share of renewable energy in the global energy mix.
   • Target 7.3: Double the global rate of improvement in energy efficiency.

2. SDG 9: Industry, Innovation, and Infrastructure

   • Target 9.4: Upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies.

3. SDG 13: Climate Action

   • Target 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries.
   • Target 13.2: Integrate climate change measures into national policies, strategies, and planning.

4. SDG 11: Sustainable Cities and Communities

   • Target 11.2: Provide access to safe, affordable, accessible and sustainable transport systems for all.

3. Indicators Mentioned or Implied in the Article to Measure Progress

1. Frequency Deviation Metrics

   • Frequency deviations in the power grid areas (measured in Hz) are used to assess the stability and performance of the HRPGs.
   • Maximum overshoot (MOS) and maximum undershoot (MUS) values of frequency deviations are quantified to evaluate controller effectiveness.

2. Tie-line Power Deviation

   • Power exchange deviations in tie-lines between interconnected areas (measured in per unit, p.u.) are monitored to assess power flow stability.

3. Renewable Energy Penetration

   • Percentage of renewable energy capacity relative to total grid capacity (exceeding 50% in the study) indicates progress towards renewable energy integration.

4. Energy Storage System Performance

   • Effectiveness of energy storage systems (PEVs, fuel cell systems) in reducing frequency fluctuations and power deviations.
   • Reduction percentages in frequency and tie-line deviations (e.g., 82.7%, 97.01%, and 40%) demonstrate improvements.

5. Optimization Algorithm Performance

   • Convergence curves and optimization parameters from the Sea Horse Optimizer (SHO) are used to measure optimization effectiveness.

4. Table of SDGs, Targets, and Indicators Relevant to the Article

SDGs	Targets	Indicators
SDG 7: Affordable and Clean Energy	7.2: Increase share of renewable energy in global energy mix. 7.3: Double rate of improvement in energy efficiency.	Renewable energy capacity percentage in power grid (e.g., >50%). Frequency stability improvements (Hz deviations).
SDG 9: Industry, Innovation, and Infrastructure	9.4: Upgrade infrastructure to sustainable and clean technologies.	Performance of advanced controllers (Fuzzy I-TD) measured by frequency and tie-line power deviations. Optimization algorithm effectiveness (convergence curves, parameter tuning).
SDG 13: Climate Action	13.1: Strengthen resilience and adaptive capacity to climate hazards. 13.2: Integrate climate change measures into policies and planning.	Reduction in frequency deviations and power fluctuations indicating improved grid resilience. Increased renewable energy integration reducing carbon emissions.
SDG 11: Sustainable Cities and Communities	11.2: Provide access to sustainable transport systems.	Use of plug-in electric vehicles (PEVs) as energy storage to support grid stability. Reduction in frequency fluctuations due to PEV integration.

Source: nature.com