Advanced control strategy based on hybrid energy storage system for frequency stability of interconnected power system with high renewables penetration – Nature

Report on an Advanced Control Strategy for Power System Frequency Stability

Abstract

This report details a novel strategy for enhancing frequency stability in hybrid interconnected power systems, a critical challenge for achieving Sustainable Development Goal 7 (Affordable and Clean Energy). The approach addresses the high penetration of renewable energy sources (RESs) by integrating a Hybrid Energy Storage System (HESS) with a Proportional Derivative–Proportional Integral (PD-PI) controller. The HESS combines Plug-in Electric Vehicles (PEVs), supporting SDG 11 (Sustainable Cities and Communities), with Superconducting Magnetic Energy Storage (SMES) units, contributing to SDG 9 (Industry, Innovation, and Infrastructure) by providing rapid response. The controller’s parameters are optimized using the Electric Eel Foraging Optimizer (EEFO) to ensure robust performance. Comparative analysis indicates the proposed strategy improves system performance by up to 55%, mitigating frequency fluctuations and supporting the transition to a resilient, low-carbon energy infrastructure in line with SDG 13 (Climate Action). The strategy’s effectiveness was validated under various disturbances, including cyber-attacks, confirming its potential to secure critical energy infrastructure.

1.0 Introduction

The global transition towards sustainable energy systems, driven by the imperatives of Sustainable Development Goal 7 (Affordable and Clean Energy) and SDG 13 (Climate Action), necessitates the large-scale integration of Renewable Energy Sources (RESs). While RESs are essential for reducing greenhouse gas emissions, their intermittent nature introduces significant challenges to grid stability, particularly frequency fluctuations. High penetration of RESs, connected via power electronics, reduces system inertia, thereby threatening the reliability of energy supply. This report presents an advanced control strategy designed to overcome these challenges, fostering the development of resilient and sustainable energy infrastructure as envisioned in SDG 9 (Industry, Innovation, and Infrastructure).

1.1 Literature Review

Extensive research has focused on Load Frequency Control (LFC) to maintain grid stability. Key approaches include:

• Advanced Control Techniques: Methods such as sliding mode control, artificial neural networks, and model predictive control have been employed. However, these often require significant expertise and computational time, limiting their practical application in rapidly evolving energy systems.
• PID Controllers: Proportional-Integral-Derivative (PID) controllers remain prevalent due to their simplicity and cost-effectiveness. Yet, their performance degrades under the high variability of RESs, failing to provide the robust control needed for a modern, sustainable grid. Cascaded controller arrangements like the proposed PD-PI offer enhanced tuning flexibility and disturbance rejection.
• Energy Storage Systems (ESSs): ESSs are crucial for balancing RES intermittency. Various technologies, including fuel cells, supercapacitors, and batteries, have been deployed. To leverage complementary characteristics, Hybrid Energy Storage Systems (HESSs) have been explored, but often without integrated control strategies, limiting their effectiveness.

1.2 Research Gap and Motivation

Previous studies have often failed to address the combined challenges of high RES penetration, dynamic load changes, and system security in an integrated manner. Conventional controllers lack the robustness required, and the potential of coordinated HESSs remains underexplored. This creates a critical gap in developing technologies that can support the ambitious targets of the SDGs. This research is motivated by the need for an effective, integrated strategy that enhances frequency stability, thereby enabling higher RES integration and contributing directly to several SDGs:

1. SDG 7 (Affordable and Clean Energy): By stabilizing grids with high RES penetration, the strategy facilitates a reliable and clean energy supply.
2. SDG 9 (Industry, Innovation, and Infrastructure): The proposed HESS and advanced control system represent an innovative technological solution for building resilient and sustainable energy infrastructure.
3. SDG 11 (Sustainable Cities and Communities): Integrating PEVs into the grid infrastructure supports sustainable transport and enhances urban energy resilience.
4. SDG 13 (Climate Action): Enabling greater use of renewable energy is a direct action to combat climate change.

1.3 Report Contribution

The primary contributions of this study are aligned with advancing sustainable development through technological innovation. They include:

• A Novel HESS Framework: Integrating PEVs for long-term energy balancing with SMES for rapid transient support, creating a resilient system (SDG 9) that enhances the reliability of clean energy (SDG 7).
• A Realistic High-Renewable Model: Utilizing actual wind and solar data from Egypt to ensure the practical applicability of the solution in real-world conditions.
• An Innovative Dual-Application Controller: The cascaded PD-PI controller is applied simultaneously to both LFC and HESS operations, representing a significant innovation (SDG 9) for managing RES-dominated grids.
• Optimized Control Performance: The use of the Electric Eel Foraging Optimizer (EEFO) ensures precise parameter tuning for superior control, enhancing technological capability.
• Comprehensive Robustness Validation: The strategy is validated against system parameter variations, diverse load patterns, and cyber-attacks, ensuring the development of secure and resilient infrastructure (SDG 9, SDG 11).

2.0 Dynamic System Description

The study is based on a hybrid power grid (HPG) model that incorporates both conventional generation units (gas, hydro, thermal) and significant RES penetration. This reflects the transitional nature of modern power systems moving towards full sustainability under SDG 7. The model also includes advanced ESS technologies to support this transition.

2.1 Renewable Energy Source Models

To ensure practical relevance, the RES models are based on real-world data, reflecting the operational challenges that must be overcome to achieve climate goals (SDG 13).

• Wind Power Plant: The model uses actual wind speed data from the Jabal al-Zeit region in Egypt to simulate the variable power output from a wind farm.
• Solar Power Plant: The model incorporates solar radiation data from the Benban area in Egypt to simulate the output of a photovoltaic (PV) plant.

2.2 Hybrid Energy Storage System (HESS) Models

The HESS is a cornerstone of the proposed strategy, providing the flexibility and resilience required by SDG 9 to support a clean energy grid.

• Plug-in Electric Vehicles (PEVs): PEVs are modeled as a distributed energy storage resource. Their integration supports the grid while also advancing sustainable transportation, a key component of SDG 11. PEVs provide long-duration energy balancing by absorbing surplus energy during low demand and discharging it during peak periods.
• Superconducting Magnetic Energy Storage (SMES): SMES units are modeled for their ability to provide rapid, high-power injections or absorptions of energy. This capability is critical for damping fast frequency fluctuations caused by sudden changes in RES output or load, thereby ensuring grid stability and contributing to a robust infrastructure.

3.0 Proposed Control and Optimization Strategy

3.1 PD-PI Control Arrangement

A cascaded Proportional Derivative–Proportional Integral (PD-PI) controller is proposed to manage both the LFC and the HESS. This advanced control structure is an innovation (SDG 9) designed to overcome the limitations of traditional PID controllers. Its key advantages include:

• Improved Disturbance Rejection: The PI component effectively eliminates steady-state errors caused by load disturbances.
• Enhanced Setpoint Tracking: The PD component improves the transient response, reducing overshoot and settling time.
• Decoupled Tuning: The cascaded structure allows for more flexible and precise tuning, which is essential for managing the complex dynamics of a grid with high RES penetration.

The control objective is to minimize the Integral Time Absolute Error (ITAE), ensuring that frequency and tie-line power deviations are quickly and efficiently suppressed.

3.2 Electric Eel Foraging Optimizer (EEFO)

To achieve optimal performance, the parameters of the PD-PI controller are tuned using the Electric Eel Foraging Optimizer (EEFO). EEFO is a bio-inspired metaheuristic algorithm that effectively navigates complex search spaces to find optimal solutions. Its application represents a sophisticated approach to engineering optimization, aligning with the call for advanced technological solutions in SDG 9. The algorithm balances exploration and exploitation phases to avoid local optima and converge on a globally optimal set of controller parameters, ensuring the control system is precisely configured for maximum stability.

4.0 Results and Discussion

The effectiveness of the proposed strategy was evaluated under four distinct scenarios to validate its performance, robustness, and contribution to building a sustainable and resilient energy system.

4.1 Scenario A: Performance Under Low RES Penetration

This scenario established a baseline by testing the system with low RES penetration and a step load perturbation. The results confirmed the superiority of the EEFO-tuned PD-PI controller over both traditional PID controllers and other optimization algorithms. The proposed LFC & HESSs strategy demonstrated significantly improved damping of frequency oscillations, providing a stable foundation necessary for scaling up clean energy integration in line with SDG 7.

4.2 Scenario B: Performance Under Various Load Patterns

To assess the strategy’s adaptability, the system was subjected to series step load variations and random load variations. These tests simulate the dynamic demand profiles of modern urban and industrial centers. The proposed HESS-based PD-PI controller consistently outperformed other methods, maintaining tight frequency control and demonstrating the resilience required to ensure reliable power for sustainable cities and communities (SDG 11).

4.3 Scenario C: Performance Under High RES Penetration

This scenario represents the core challenge addressed by the research. Under conditions of high RES penetration, the proposed strategy proved highly effective at mitigating severe frequency fluctuations. By dynamically coordinating the PEVs and SMES units, the controller maintained system stability where traditional methods failed. This result provides strong evidence that the strategy is a viable solution for enabling the deep decarbonization of the power sector, directly supporting the objectives of SDG 7 and SDG 13.

4.4 Scenario D: Robustness Against Cyber-Attacks and Parameter Variations

The security and robustness of energy infrastructure are critical for sustainable development. This scenario tested the system’s resilience against simulated cyber-attacks and internal parameter uncertainties. The proposed strategy demonstrated remarkable robustness, maintaining stability even when subjected to malicious data injection. This highlights its potential to secure critical energy infrastructure against emerging threats, a vital aspect of building strong and resilient institutions and infrastructure (SDG 9 and SDG 16).

5.0 Conclusion

This report has presented an advanced control strategy that effectively enhances the frequency stability of power systems with high penetration of renewable energy. By integrating a novel Hybrid Energy Storage System (HESS) with an optimally tuned PD-PI controller, the strategy provides a robust solution to the challenges posed by the variability of RESs.

The findings confirm that the proposed method significantly outperforms conventional approaches, improving system performance by up to 55%. Its effectiveness under various load conditions, high RES penetration, and cyber-attacks demonstrates its practical applicability for modernizing power grids.

Ultimately, this work contributes directly to the achievement of several Sustainable Development Goals. It offers a clear technological pathway to:

• Advance SDG 7 by enabling reliable and affordable clean energy at scale.
• Support SDG 9 by delivering an innovative solution for resilient and sustainable infrastructure.
• Contribute to SDG 11 by integrating sustainable transport (PEVs) into a smarter urban energy system.
• Promote SDG 13 by facilitating the deep integration of renewables needed for climate action.

Future work will focus on scaling this strategy for larger, deregulated power systems and exploring the integration of other emerging energy storage technologies to further accelerate the global transition to a sustainable energy future.

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

SDG 7: Affordable and Clean Energy

• The article’s primary focus is on managing power systems with a “high penetration of renewable energy sources (RESs),” such as wind and solar power. This directly supports the transition to cleaner energy systems. The introduction explicitly states that RESs are installed to “minimize the gas emissions resulting from conventional power plants,” aligning with the goal of providing clean energy.

SDG 9: Industry, Innovation, and Infrastructure

• The paper presents a “novel strategy” and innovative technological solutions, including a “hybrid energy storage systems (HESSs)” and a “cascaded PD-PI controller,” to upgrade and improve the resilience of electrical infrastructure. It addresses the need for advanced technologies to manage modern power grids, thereby promoting innovation and building resilient infrastructure capable of handling the variability of RESs and even “cyber attack conditions.”

SDG 11: Sustainable Cities and Communities

• The proposed solution incorporates “plug-in electric vehicles (PEVs)” as a key component of the energy storage system. This integration of electric vehicles into the power grid infrastructure supports the development of sustainable transportation systems and smarter, more sustainable urban environments. By using PEVs for “long-term energy balancing,” the article highlights a synergy between sustainable transport and energy infrastructure.

SDG 13: Climate Action

• By developing a robust solution that enables higher integration of renewable energy, the article directly contributes to climate change mitigation. The introduction’s rationale for using RESs is to “minimize the gas emissions.” The proposed technology is a practical measure to facilitate the shift away from fossil fuels, thereby helping to integrate climate change measures into energy systems and planning.

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

SDG 7: Affordable and Clean Energy

1. Target 7.2: By 2030, increase substantially the share of renewable energy in the global energy mix.
   • The article is fundamentally about enabling “high penetration of renewable energy sources (RESs)” in power grids. The entire study is designed to solve the frequency stability problems that arise from increasing the share of variable renewables like wind and solar, thus facilitating their large-scale adoption.
2. Target 7.a: By 2030, enhance international cooperation to facilitate access to clean energy research and technology, including renewable energy, energy efficiency and advanced and cleaner fossil-fuel technology, and promote investment in energy infrastructure and clean energy technology.
   • This academic paper itself is a contribution to clean energy research and technology. It proposes and validates a “novel strategy” and an advanced control system, sharing knowledge that can be used to promote investment in and deployment of modern, clean energy infrastructure.

SDG 9: Industry, Innovation, and Infrastructure

1. Target 9.1: Develop quality, reliable, sustainable and resilient infrastructure, including regional and transborder infrastructure, to support economic development and human well-being, with a focus on affordable and equitable access for all.
   • The research aims to enhance the reliability and resilience of power grid infrastructure. The proposed strategy is designed to “mitigating frequency fluctuations” and maintain “system stability,” even under challenging conditions like “load disturbances” and “cyber attack conditions,” which directly contributes to creating more resilient energy infrastructure.
2. Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries, in particular developing countries, including, by 2030, encouraging innovation and substantially increasing the number of research and development workers per 1 million people and public and private research and development spending.
   • The article is a direct output of scientific research that introduces technological upgrades. It details a “novel hybrid energy storage system (HESSs),” a “cascaded PD-PI controller,” and the use of an “electric eel foraging optimizer (EEFO)” for tuning, all of which represent an enhancement of technological capabilities for the power industry.

SDG 11: Sustainable Cities and Communities

1. Target 11.2: By 2030, provide access to safe, affordable, accessible and sustainable transport systems for all, improving road safety, notably by expanding public transport, with special attention to the needs of those in vulnerable situations, women, children, persons with disabilities and older persons.
   • While not focused on access, the article supports the transition to sustainable transport by integrating “plug-in electric vehicles (PEVs)” into the energy system. By demonstrating how PEVs can provide valuable grid services (“long-term energy balancing”), the research helps build a stronger business and infrastructure case for the widespread adoption of electric vehicles, a cornerstone of sustainable urban transport.

SDG 13: Climate Action

1. Target 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries.
   • The article’s focus on creating a stable power grid that can handle the intermittency of renewable energy sources strengthens the resilience of critical energy infrastructure. A more stable grid is better equipped to handle the stresses and unpredictable conditions that may be exacerbated by climate change.
2. Target 13.2: Integrate climate change measures into national policies, strategies and planning.
   • The technology proposed in the article is a practical tool for implementing climate change policies. By solving the technical challenges of high renewable penetration, it enables governments and energy providers to more effectively integrate large-scale renewable energy projects into their national energy strategies as a primary measure for climate action.

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

SDG 7: Affordable and Clean Energy

• Indicator for Target 7.2: The article consistently refers to “high penetration of RESs” and uses “real wind and solar data from Egypt” for its simulations. This implies that the share of renewable energy (wind and solar) in the power generation mix is a key metric. This aligns with official indicator 7.2.1 (Renewable energy share in the total final energy consumption).

SDG 9: Industry, Innovation, and Infrastructure

• Indicators for Target 9.1: The article uses several specific technical metrics to measure the reliability and resilience of the power grid infrastructure. These serve as direct indicators of infrastructure quality.
  • Frequency deviation (Δf): The primary measure of grid stability. The goal is to minimize this deviation.
  • Overshoot (OS) and Undershoot (US): Quantify the maximum deviation from the nominal frequency during a disturbance.
  • Settling Time (Ts): Measures how quickly the system returns to stability, indicating resilience.
  • Integral Time Absolute Error (ITAE): A performance index used as a fitness function to minimize cumulative frequency errors over time.
  • System performance under cyber-attacks: The validation of the system “under cyber attack conditions” is a direct measure of its resilience.

SDG 11: Sustainable Cities and Communities

• Indicator for Target 11.2: The article’s model includes “plug-in electric vehicles (PEVs)” as an energy storage resource. An implied indicator is the capacity of PEVs integrated into the grid for energy balancing services. This reflects the level of synergy between sustainable transport and energy infrastructure.

SDG 13: Climate Action

• Indicator for Target 13.2: The explicit motivation for integrating RESs is to “minimize the gas emissions resulting from conventional power plants.” Therefore, an implied high-level indicator is the reduction in greenhouse gas emissions from the power sector as a result of enabling higher renewable energy penetration.

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

Source: nature.com