Power System Resilience Research Articles

Tying energy resiliance and digital upgradation of technology transformation of electric power system: Does financial flexibility matters?

As the energy sector undergoes rapid technological transformation, the resilience of electric power systems has become a focal point in the context of climate change and energy security. This paper examines the interplay between energy resilience and the digital upgradation of electric power systems in China, with a particular emphasis on the role of financial flexibility. Using panel data from 2005 to 2023 across 30 provinces in China, we employ the Generalized Method of Moments (GMM) model to investigate how financial flexibility—measured through capital access and adaptive financial strategies—affects the integration of digital technologies such as smart grids, renewable energy systems, and advanced monitoring infrastructures. Our findings demonstrate that financial flexibility significantly enhances the resilience of electric power systems by facilitating quicker adoption of digital technologies and enabling systems to better withstand external shocks, such as fluctuating energy demands and environmental stressors. The study offers policy insights, underscoring the importance of supportive financial mechanisms to accelerate the digital transformation and sustainability of China's energy infrastructure. This research contributes to the ongoing discourse on energy resilience by highlighting the critical role of financial flexibility in enabling technological advancements within the power sector.

Climate Change and the Impacts on Power and Energy Systems

This collection, extracted from the Special Issue “Climate Change and the Impacts on Power and Energy Systems”, features ten papers that address key topics related to the resilience and adaptation of electrical power and energy systems in response to climate change [...]

Resilience‐oriented load restoration method for power‐gas‐water systems considering public safety impact

AbstractFocusing solely on enhancing the resilience of power systems at a systemic level would lead to a significant underestimation of the actual impact of extreme disasters. Equally vital is the assurance of livelihood security amidst such extreme conditions, which is crucial for the development of a truly resilient power system. Hence, this paper attempts to incorporate quantifiable metrics assessing public safety impacts into resilience enhancement works, thereby guiding the precise allocation of funds. Considering that the residents' intuitive feelings are the most direct reflection of the severity of the disaster, this paper employs the modified prospect theory to formulate functions representing residents' psychological risk perception and risk‐taking willingness to tolerate risks during disruptions in power, gas, and water supplies. Meanwhile, in order to accurately calculate the energy loss duration for each residential customer, a resilience enhancement method for post‐disaster collaborative dispatch of electricity‐gas‐water systems is proposed. With the objective of minimizing the public safety and economic impact of disasters, the optimal multi‐source collaborative emergency restoration strategy is developed. The significant necessity and efficiency of the proposed strategy are verified with exhaustive case studies. Numerical results evince the resilience enhancement by considering the livelihood security in the post‐disaster restoration stage.

An integrated approach for multi-objective differential algorithm based event constrained optimal power flow for power system resiliency enhancement

ABSTRACT Due to climatic changes, the probability of occurrence and the intensity of extreme weather events increase day by day which leads to power outages. The probability of failure events is calculated using Monte Carlo simulation technique which is used to identify the most frequent failure events. The least eigenvalues of the inverse reduced Jacobian matrix decide the sensitive buses for load shedding using V-Q sensitivity analysis. This work presents the multi-objective differential evolution (MODE) algorithm for solving resilient constrained optimal power flow (RCOPF) in a deregulated power system. The control variables namely the real power of generators in a base case and under event, generator bus voltage magnitude, the real and reactive power demand of the selected load buses, tap settings and capacitor bank ratings. This algorithm is validated in IEEE 30 bus test system and the Indian 62 bus utility test system to show the effectiveness in four different cases including tie lines and distributed generation.

FACTS: Present and Future

Flexible AC Transmission Systems (FACTS) are pivotal in modernizing power systems, enhancing their stability, controllability, and efficiency. Currently, FACTS devices such as Static VAR Compensators (SVCs), Static Synchronous Compensators (STATCOMs), and Unified Power Flow Controllers (UPFCs) are employed to manage power flow, mitigate system instabilities, and improve voltage regulation across transmission networks. These technologies address present challenges, including the integration of renewable energy sources, reduction of transmission losses, and enhancement of system reliability in the face of fluctuating power demands. However, the growing complexity of power grids, driven by the increasing penetration of intermittent renewable energy, electric vehicles, and distributed generation, necessitates advanced and scalable solutions. The future of FACTS lies in the development of more sophisticated, adaptive, and intelligent devices that leverage real-time data analytics, artificial intelligence, and machine learning to optimize power flow dynamically. Future advancements are expected to focus on enhancing the interoperability of FACTS with smart grid technologies, improving the resilience of power systems against cyber-physical threats, and facilitating the transition towards more decentralized and sustainable energy systems. Moreover, the integration of energy storage with FACTS devices could revolutionize their functionality, offering not only reactive power compensation but also energy balancing capabilities. This paper explores the current applications of FACTS and envisions their future role in addressing the evolving challenges of global power systems, emphasizing the importance of innovation and strategic investment in the ongoing transformation of electrical networks.

Digitalization and Decarbonization of Sri Lanka's Electric Power System: A Way Forward for Sustainability

The global energy landscape is undergoing a transformative shift driven by the dual imperatives of digitalization and decarbonization, essential for creating sustainable and resilient power systems (Lichtenthaler, 2021, [1]). This transformation is particularly critical for developing nations like Sri Lanka, where the integration of smart technologies and renewable energy sources can significantly enhance system efficiency and contribute to a robust response to climate change challenges (Lichtenthaler, 2021, [1]). This paper examines the potential of leveraging advanced digital technologies—such as artificial intelligence, big data analytics, and the Internet of Things—to optimize energy management and facilitate a swift transition toward a low-emission economy in Sri Lanka, while addressing the socio-economic implications of this transition. Furthermore, this study highlights the importance of fostering a circular economy through these technological advancements, emphasizing that innovative solutions can not only improve energy efficiency but also contribute to broader sustainability goals, ensuring equitable access to resources and combating the adverse effects of climate change. Through this lens, the investigation underscores the necessity of a holistic approach that integrates digital transformation with sustainable practices, enabling the decoupling of economic activity from resource depletion and environmental degradation, thereby fostering a resilient energy future for Sri Lanka.

Robust integral super-twisting controller for enhanced photovoltaic integration with hybrid battery and supercapacitor storage in DC microgrid

This study relates to the enhancement in performance of a Hybrid Energy Storage System (HESS) over a DC microgrid, particularly toward a fast-accurate controller of the DC bus voltage. Conventional control methods are particularly difficult to implement in order to reduce start-up time with minimum overshoot, resulting in a significant disturbance of the DC bus voltage. To circumvent these problems, we propose an innovative hybrid controller method that combines the Integral Super-Twisting Algorithm (ISTA) controller technique with adaptive gain in regulating DC bus voltage and current. The capacity of ISTA to greatly enhance the system's dynamic responsiveness, guaranteeing steady operation with effective output voltage control, is by far its greatest benefit. Comparative testing found that the conventional PI controller had a rise time of 0.0666 s, a settling time of 0.165 s, and a 17 % overshoot with a steady-state error of 0.02 %. The proposed ISTA controller had a rise time of 0.0016 s, a settling time of 0.0667 s, zero percentage overshoot, and a minimal steady-state error of only 0.0003 %. The above-mentioned issues mostly focused on better sharing power in HESS and a more effective controller of the DC bus voltage. Furthermore, we evaluate the practical feasibility of the suggested controller through hardware-in-the-loop testing. This research lays a foundation for advancing renewable energy integration in DC microgrid, offering a promising avenue for sustainable and resilient power systems. Finally, the effectiveness and applicability of the proposed ISTA controller for DC microgrid use are proven with Controller Hardware-in-the-Loop (C-HIL) simulations.

Robust Optimization Research of Cyber-Physical Power System Considering Wind Power Uncertainty and Coupled Relationship.

To mitigate the impact of wind power uncertainty and power-communication coupling on the robustness of a new power system, a bi-level mixed-integer robust optimization strategy is proposed. Firstly, a coupled network model is constructed based on complex network theory, taking into account the coupled relationship of energy supply and control dependencies between the power and communication networks. Next, a bi-level mixed-integer robust optimization model is developed to improve power system resilience, incorporating constraints related to the coupling strength, electrical characteristics, and traffic characteristics of the information network. The upper-level model seeks to minimize load shedding by optimizing DC power flow using fuzzy chance constraints, thereby reducing the risk of power imbalances caused by random fluctuations in wind power generation. Furthermore, the deterministic power balance constraints are relaxed into inequality constraints that account for wind power forecasting errors through fuzzy variables. The lower-level model focuses on minimizing traffic load shedding by establishing a topology-function-constrained information network traffic model based on the maximum flow principle in graph theory, thereby improving the efficiency of network flow transmission. Finally, a modified IEEE 39-bus test system with intermittent wind power is used as a case study. Random attack simulations demonstrate that, under the highest link failure rate and wind power penetration, Model 2 outperforms Model 1 by reducing the load loss ratio by 23.6% and improving the node survival ratio by 5.3%.

The development of resilience research in critical infrastructure systems: A bibliometric perspective.

Critical infrastructure systems (CISs) are the cornerstone of modern cities. Substantial economic losses and social impacts are caused once natural disasters or man-made disruptions attack the CISs. As a "resilient city" has become an essential theme of communities' sustainable development, research on resilience and its practice in industries boost the CISs' capacity to respond and adapt to changing environments. From the Web of Science (WOS) Core Collection, this study screened 1,247 scientific articles related to resilience in CISs and conducted a bibliometric analysis to investigate the evolution and future potential in this field. Topic visualized networks were constructed for CIS resilience using CiteSpace, a dedicated tool for visualizing and analyzing trends and patterns in scientific literature. The results demonstrate collaborative research networks among countries, institutions, main scholar/group networks, and leading journals publishing CIS resilience work. This study also explained how the research interest evolved over the last 20 years and found the current frontiers pointing to "power systems resilience" and "supply chain resilience." The reasons were discussed subsequently from the perspectives of the influence that natural hazards (based on the EM-DAT data) and government policies have upon CISs' resilience work.

Security-constrained economic dispatch of power systems with line outage using firefly algorithm

The Security-constrained Economic Dispatch (SCED) problem is the problem of finding the optimal generation dispatch for a power system that minimizes the operating cost while ensuring the security of the system. The security of the system is defined as the ability of the system to withstand disturbances without violating any operational constraints. Line outage is a common disturbance that can affect the security of a power system. The Firefly Algorithm, inspired by the flashing behavior of fireflies in nature, is a metaheuristic optimization technique known for its effectiveness in solving complex and dynamic optimization problems. In this study, the FA is adapted to the SCED problem with a specific focus on enhancing grid resilience during line outage scenarios. The proposed method aims to simultaneously optimize the economic cost of power generation and the system's ability to withstand line outages. To demonstrate the effectiveness of the proposed approach, extensive simulations are conducted on standard IEEE test systems with varying degrees of complexity and network sizes. The results showcase the superior performance of the SCEDL-Firefly Algorithm compared to traditional optimization methods, as it provides a resilient dispatch solution that can effectively adapt to unexpected line outages while maintaining economic efficiency. Overall, this research contributes to the advancement of secure economic dispatch techniques and power system resilience by leveraging the Firefly Algorithm's capabilities. The findings offer valuable insights for power system operators and planners seeking to enhance grid reliability in the presence of line outages, ultimately promoting a more sustainable and resilient energy infrastructure.

End-to-end learning for stochastic preventive dispatch of renewables-rich power systems in abnormal weather conditions

Power systems are currently increasingly threatened by abnormal weather conditions, which are exacerbated by global warming. In particular, the proliferation of renewable energy sources (RES), which are quite variable, might cause power systems to have insufficient energy supply to cope with the risk of severe power imbalances in such conditions. In this paper, a preventive power system dispatch model is proposed that allows power systems with large RES (specifically wind energy) installations to mitigate excessive load curtailments under abnormal weather conditions. Compared to the conventional predict-then-optimize (PTO) framework of power system dispatch that relies on accurate RES forecasting, the proposed end-to-end learning framework mitigates the impact of forecasting errors on decision-making, which reduces the dependence on the accuracy of RES forecasting and effectively enhances the resilience of the power system. The proposed framework consists of a probabilistic RES forecasting module, a preventive dispatch module, and a resilience-oriented assessment module. A backpropagation mechanism based on Karush-Kuhn-Tucker conditions is proposed to unify the dispatch optimization problem as deep neural networks, which ensures the reliability of optimal solutions as well as provides necessary gradients for backpropagation, successfully bridging the information gap among the three modules. The proposed model is updated iteratively in an online implementation scheme composed of the decision-making part and the reflection & training part. The results of numerical experiments on the IEEE 6- and 118-bus systems verify the effectiveness and superiority of the proposed model in maintaining the resilience of RES-based power systems.

Optimal Resilience and Risk-Driven Strategies for Pre-Disaster Protection of Electric Power Systems against Uncertain Disaster Scenarios

Pre-disaster protection strategies are essential for enhancing the resilience of electric power systems against natural disasters. Considering the budgets for protection strategies, the dependency of other infrastructure systems on electricity, and the uncertainty of disaster scenarios, this paper develops risk-neutral and risk management models of strategies for pre-disaster protection. The risk-neutral model is a stochastic model designed to maximize the expected value of resilience (EVR) of the integrated system. The risk management model is a multi-objective model prioritizing the minimization of risk metrics as a secondary goal alongside maximizing the EVR. A case study conducted on the energy infrastructure systems in the Greater Toronto Area (GTA) validates the effectiveness of the models. The findings reveal the following: (i) increasing the budget enhances the EVR of the integrated system; however, beyond a certain budget threshold, the incremental benefits to the EVR significantly diminish; (ii) reducing the value of the downside risk often results in an increase in the EVR, with the variation in Pareto-optimal solutions between the two objectives being non-linear; and (iii) whether for the risk-neutral or risk management protection strategies, there are reasonable budgets when considering disaster intensity and the cost of protection measures. The models can help decision-makers to select effective protection measures for natural disasters.

The adaptability research and evaluation of digital distance protection based on Python and PSCAD

With the development of power systems, digital distance protection, as an innovative and cutting-edge technology, plays a crucial role in ensuring the stable operation of the power grid. Due to its high precision, rapidity, and reliability, digital distance protection has become a key component of power system safety. Distance protection is widely used in distribution networks at or below 35 kV. Therefore, studying its adaptability and effectiveness as a remote backup protection can provide an effective foundation for improving grid security.With the utilization of Python and PSCAD, the adaptability of digital distance protection is meticulously examined in the context of low voltage side faults in 110 kV/35 kV transformers, specifically as a remote backup protection measure. The core objective of this investigation is to assess the efficacy of this protection strategy in upholding the safety and reliability of power systems. To achieve this, 3 approaches is employed: theoretical analysis, simulation modeling, and experimental validation. This process allows for a comprehensive evaluation of the performance characteristics and application efficacy of digital distance protection. The insights garnered from this meticulous study offer profound understanding into the strengths and potential limitations of this protection paradigm, serving as a solid foundation for the refinement of existing protection strategies and the enhancement of the overall resilience of power systems. In essence, this article aims to evaluate the effectiveness of digital distance protection as a remote backup protection strategy for faults on the low-voltage side of 110 kV/35 kV transformers. The core objective is to assess the efficacy of this protection strategy in maintaining the safety and reliability of the power system.

Enhancing power system stability with adaptive under frequency load shedding using synchrophasor measurements and empirical mode decomposition

This paper introduces an adaptive under frequency load shedding (AUFLS) scheme based on synchrophasor measurements and Empirical Mode Decomposition (EMD) to enhance power system stability during significant disturbances. The traditional UFLS methods, which rely on fixed frequency thresholds, often lead to unnecessary disconnections and a lack of flexibility. The proposed AUFLS scheme dynamically adapts to the magnitude of disturbances, frequency response, and voltage stability index, resulting in a more efficient and responsive system. Various UFLS techniques are reviewed, highlighting the advantages of adaptive strategies. The novelty of this research lies in the innovative application of the EMD algorithm within the AUFLS framework. This method identifies the center of inertia (CoI) of the rate of change of frequency (RoCoF) from frequency signals and calculates the total active power imbalance due to disturbances. Furthermore, EMD is applied to the voltage angle at each bus to identify and form coherent bus groups. These coefficients are subsequently employed to allocate the required active power shedding within each group to maintain system stability. Voltage stability indices are calculated for each group, and load shedding is distributed among the buses based on normalized voltage stability indices, thereby enhancing the precision of load shedding decisions and the overall stability of the power system. Results demonstrate that the proposed scheme provides satisfactory frequency response while requiring less load shedding compared to traditional methods, making it effective for modern power systems with high penetration of renewable energy sources (RES). The performance of the proposed scheme is assessed using the IEEE 39-bus test system, demonstrating its effectiveness in improving system resilience to frequency disturbances. The research concludes that the AUFLS scheme offers a promising approach for enhancing the resilience of power systems to frequency disturbances.

A new comprehensive framework for cascading outage simulation in power systems

Simulating accurately and fast cascading outages play a critical role in power system security and resilience assessment. This paper proposes a new comprehensive framework for cascading outage simulation, articulated around an extended AC power flow. To address the key modelling features of cascading outage simulation, the proposed framework develops new algorithms for: a) detecting possible islanding, b) frequency control in each island: primary frequency control (PFC), under-frequency load shedding (UFLS) and over-frequency generator tripping (OFGT) and c) improved under-voltage load shedding (UVLS) and load models to avoid the divergence of AC power flow and allow handling voltage instability during the cascade. Tests on a realistic 60-bus system show that the proposed cascading outage simulator can successfully handle islanding, PFC, UFLS, OFGT, and UVLS; it simulates the cascade until there is no violation of system frequency, voltage, and power flow limits in islands that do not experience blackout.

Improving interdependent urban power and gas distribution systems resilience through optimal scheduling of mobile emergency supply and repair resources

Urban electric power and natural gas systems, serving as the most critical facilities that provide fundamental support to people's daily life, have been increasingly interdependent in their operation over the past decades. This growing interdependency presents great challenges and demands for enhancing the resilience of the interdependent systems, particularly driven by the recent cold weather events. In this paper, we propose a comprehensive strategy for scheduling various types of emergency resources, including mobile and stationary generators, repair crews, and fuel tankers, with the goal of enhancing the resilience of interdependent power and gas distribution systems in the aftermath of disasters. In particular, the strategy incorporates the routing of mobile resources to orchestrate their dynamic moving behaviors. Fuel supply issue for mobile and stationary distributed generators is also addressed through modeling the fuel exchange processes among generators, fuel tankers, and locations. Furthermore, we present a model of repair process to fully capture the real-world repair activities, allowing varying numbers of repair crews to work in a cooperative way. The proposed strategy is formulated into a tractable mixed-integer second-order cone problem. Finally, case studies are carried out to demonstrate the effectiveness of the strategy in enhancing the resilience of the interdependent power and gas distribution systems.

LoadNet: enhancing energy storage system integration in power system operation using temporal convolutional and recurrent models with self-attention

Introduction: In the context of the evolving energy landscape, the efficient integration of energy storage systems (ESS) has become essential for optimizing power system operation and accommodating renewable energy sources.Methods: This study introduces LoadNet, an innovative approach that combines the fusion of Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) models, along with a self-attention mechanism, to address the challenges associated with ESS integration in power system operation. LoadNet aims to enhance the management and utilization of ESS by effectively capturing the complex temporal dependencies present in time-series data. The fusion architecture of TCN-GRU in LoadNet enables the modeling of both short-term and long-term dependencies, allowing for accurate representation of dynamic power system behaviors. Additionally, the incorporation of a self-attention mechanism enables LoadNet to focus on relevant information, facilitating informed decision-making for optimal ESS operation. To assess the efficacy of LoadNet, comprehensive experiments were conducted using real-world power system datasets.Results and Discussion: The results demonstrate that LoadNet significantly improves the efficiency and reliability of power system operation with ESS. By effectively managing the integration of ESS, LoadNet enhances grid stability and reliability, while promoting the seamless integration of renewable energy sources. This contributes to the development of a more sustainable and resilient power system. The proposed LoadNet model represents a significant advancement in power system management. Its ability to optimize power system operation by integrating ESS using the TCN-GRU fusion and self-attention mechanism holds great promise for future power system planning and operation. Ultimately, LoadNet can pave the way for a more sustainable and efficient power grid, supporting the transition to a clean and renewable energy future.

Resilience to extreme storm conditions: A comparative study of two power systems with varying dependencies on offshore wind

In the next decades, the dependencies on power production from renewable energy sources are expected to increase dramatically. A transition towards large-scale offshore wind farms together with an increased electrification of the industry and transportation sectors introduces new vulnerabilities to society. Further, extreme weather events are expected to increase in intensity and frequency, driven by climate change. However, there are significant knowledge gaps concerning the impacts of severe weather conditions on the resilience of power systems with large dependencies on offshore wind. In the present study, a comparison between two different power systems’ resilience to historical extreme storm conditions has been conducted. The power systems are the IEEE39-bus New England model and the Great Britain model. The results show significant differences between the two power systems, which underlying reasons are analysed and explained. With an offshore wind penetration level of 30 %, the New England model stays intact in terms of connected load. When increasing the penetration level to 40 %, about 10 % of the total connected load gets disconnected, whereas about 33 % of the load gets disconnected with a penetration level of 50 %. The Great Britain model stays intact in terms of connected load with a penetration level of at least 49 %.

Decentralized solutions for island states: Enhancing energy resilience through renewable technologies

Decentralized grid solutions could be a feasible alternative to improve resilience and mitigate cascading effects in island states. Our study explores approaches that reduce the risk of infrastructure failures and promote decentralized utility planning in islands. A novel framework is proposed to conduct a power system resilience assessment by integrating vulnerability assessments and energy system modelling approaches through network analysis. The framework is applied to an island context, where vulnerability to hydroclimatic hazards, geographic isolation, restricted access to energy sources, small population bases inadequate for substantial infrastructure investments, dependence on imported energy, lack of energy source diversification, and fragile ecosystems have exacerbated energy insecurity. As a case study, we have applied the framework to Cuba. We simulate disruptions in vulnerable network nodes in Cuba to determine the municipalities that are most impacted by the simulated cascading failures. We designed and optimized the lowest cost decentralized solutions to increase resilience either by acting as the baseload electricity source or as a complementary backup system to complement in case of a power outage. Then, the resilience of the designed system was assessed using power system resilience metrics. The study results show Regla municipality in Cuba as the most vulnerable hotspot for electricity distribution. Upon the different system comparisons, ancillary systems outperform backup systems in enhancing power system resilience, especially in the context of a disruptive event, supplying up to 53 MWh/day more, although they have higher investment costs. Based on this research, resource planners and policymakers can understand vulnerable node points and prioritize the necessary investments for the preferred system choice to alleviate impacts of energy insecurity on the Island States.

Multi-microgrids system’s resilience enhancement

Nowadays, electricity consumption is increasing rapidly which leads to conventional power systems exhaustion. Therefore, micro-grids (MGs) implantation can enhance the resilience of power systems by implication of new resources, such as renewable energy sources (solar panel and wind power systems), electric vehicles (EV), and energy storage systems (ESS). This paper proposes a new strategy for optimal power consumption inside one microgrid; then, the approach will be extended to optimize the power consumption to enhance the resilience in the case of multi-MGs systems. The system controller of each microgrid has been implemented using ESP32 microcontroller and Raspberry IP4. The proposed approach intends to enhance the resilience of the system to react to any contingency in the system such as loss of power linkage between MG and the network in case of any natural disaster, especially in the rural area. Two controllers are implemented; the first one ensures MG autonomy by the efficient use of its own sources. The second one handles the system resilience cases by demanding/delivering power from/into neighbor microgrids. Hence, this work enhances the system resilience with an optimal cost. Thus, the MG can offer ancillary services for the neighboring MGs.