Framework for Evaluating Resilience in High-Penetration Renewable Energy Power Systems

The Resilience Assessment Framework for High-Penetration Renewable Energy Power Systems

Abstract

The unpredictable and intermittent nature of renewable energy poses significant challenges for power systems, particularly in managing sudden disturbances and extreme events. This study introduces a system network model and cascading failure model that accounts for the power flow relationships among various energy sources. Utilizing complex network theory, we analyze the impact of renewable energy on the resilience of power systems. Additionally, we propose several evaluation indices from both structural and functional perspectives. A resilience curve specifically tailored for renewable energy power systems is developed. Key nodes are identified using electrical degree centrality, allowing us to simulate both random and deliberate attack scenarios. The effectiveness of our evaluation method is validated on the IEEE 118-bus system, revealing that high penetration of renewable energy can lead to a decline in power system resilience by more than 20% in many instances.

1. Introduction

Power systems are frequently impacted by natural disasters and human-induced damage, leading to failures and blackouts. The ongoing integration of renewable energy complicates safety and stability issues due to its inherent randomness and uncertainty. Notable long-term power outages, such as those in Texas in 2021 and the United Kingdom in 2019, were partly attributed to renewable energy policies. Consequently, the evaluation and enhancement of the resilience of renewable energy power systems against extreme events have garnered global attention.

Power system resilience refers to its ability to withstand, adapt to, and recover from low-probability, high-risk events. Various models, including triangular and trapezoidal representations, are employed to illustrate changes in system performance before, during, and after disturbances. Resilience evaluation indicators typically encompass the impact magnitude and performance change rate of the system.

While numerous studies have focused on evaluating system performance, they often overlook the network structure and its effects. Complex network theory serves as a powerful framework for understanding the resilience of renewable energy power systems. Structural resilience pertains to the system’s ability to maintain its topological integrity during extreme events, while state resilience relates to the abnormal changes in power parameters (voltage, frequency, etc.) and the system’s capability to deliver necessary services during both steady-state and dynamic conditions.

2. Resilience Evaluation Framework and Network Modeling

The IEEE 30-bus system serves as an illustrative example, incorporating renewable energy sources such as synchronous generators, photovoltaic (PV) systems, and wind power. A new resilience curve is proposed based on complex network theory, analyzing the system from both structural and functional perspectives.

2.1 Infrastructure and Operational Resilience Indices

Two critical factors for quantifying system performance post-attack are infrastructure resilience and operational resilience. Infrastructure resilience quantifies physical robustness against damages, while operational resilience assesses the system’s ability to maintain uninterrupted service during disasters.

2.2 Resilience Curve Based on Multi-Energy Complementarity

Resilience curves quantify system behavior during disaster phases, including degradation, recovery preparation, and recovery. Existing resilience curves for traditional power systems require adaptation for high-penetration renewable energy systems, as the power fluctuations complicate temporal characteristics.

3. Resilience Evaluation Indicators and Disaster Simulation

Resilience evaluation indicators are based on the resilience trapezoid, defining the system state at any point in time. Three indicators are identified: Largest Cluster Size (LCS), Power Flow Entropy (PFE), and Loss in Power Flow (LP).

• Largest Cluster Size (LCS) quantifies the survival of system topology post-attack.
• Power Flow Entropy (PFE) measures the imbalance in power distribution, crucial for assessing resilience under extreme events.
• Loss in Power Flow (LP) reflects energy transmission decreases during attacks.

4. Case Analysis

Using the IEEE 118-bus system, we analyze the impact of renewable energy penetration levels and distribution characteristics on system resilience. The resilience indexes are calculated for various attack scenarios, revealing that higher penetration levels lead to reduced structural resilience.

4.1 Impact of Attack Timing

We examine two attack times: during maximum renewable energy output and peak load. Results indicate that resilience is generally higher during maximum renewable energy output.

4.2 Different Penetration Levels of Renewable Energy

The analysis confirms that increased renewable energy penetration correlates with reduced resilience.

4.3 Resilience Evaluation Considering Power Fluctuation of Renewable Energy

Daily simulations show that system resilience peaks during times of sufficient renewable energy output, emphasizing the need for evaluation across varying conditions.

5. Conclusions

This study establishes a resilience assessment framework for power systems with a high penetration of renewable energy. Our findings indicate that resilience is lower under deliberate attacks compared to random ones, with increased renewable energy penetration leading to reduced resilience. Future research will focus on developing advanced dispatching techniques and optimal recovery strategies to enhance system robustness against identified vulnerabilities.

This comprehensive approach aims to improve the safe and stable operation of renewable energy power systems, assisting operators and policymakers in addressing the challenges posed by the integration of renewable energy sources.