Improve System Resilience Research Articles

Two-Stage Optimization of Mobile Energy Storage Sizing, Pre-Positioning, and Re-Allocation for Resilient Networked Microgrids with Dynamic Boundaries

Networked microgrids (NMGs) enhance the resilience of power systems by enabling mutual support among microgrids via dynamic boundaries. While previous research has optimized the locations of mobile energy storage (MES) devices, the critical aspect of MES capacity sizing has been largely neglected, despite its direct impact on costs. This paper introduces a two-stage optimization framework for MES sizing, pre-positioning, and re-allocation within NMGs. In the first stage, the capacity sizing and pre-positioning of MES devices are optimized before a natural disaster. In the second stage, the re-allocation and active power output of MES devices are adjusted post-disaster, with boundary switches operated based on the damage scenarios. The framework restores unserved loads by either forming isolated microgrids using MES or re-establishing connections between microgrids via smart switches. The proposed framework is modeled mathematically and solved using a customized progressive hedging algorithm. Extensive experiments on modified IEEE 33-node and 69-node systems demonstrate the model’s effectiveness and applicability in improving system resilience.

Review of Low Voltage Ride-Through Capabilities in Wind Energy Conversion System

The significance of low voltage ride-through (LVRT) capability in wind energy conversion systems (WECSs) is paramount for ensuring grid stability and reliability during voltage dips. This systematic review delves into the advancements, challenges, and methodologies associated with LVRT capabilities in WECSs. By synthesizing recent research findings, this review highlights technological innovations, control strategies, and regulatory requirements that influence LVRT performance. Key insights include the efficacy of various LVRT techniques, the role of grid codes in shaping LVRT standards, and the integration of advanced control algorithms to improve system resilience. The study offers a comprehensive understanding of the current landscape of LVRT in WECSs and pinpoints future research directions to optimize their performance in increasingly complex grid environments. During the LVRT process, the stator of a double-fed induction generator (DFIG) is directly linked to the power grid. When the external power grid experiences a failure, the stator flux produces a significant transient component, resulting in substantial overvoltage and overcurrent on the rotor side of the DFIG. Failure to implement preventative measures may result in damage to the converter, therefore compromising the safety and stability of how the power system functions.

A comparative analysis of an aerosol dry deposition microscale model for overhead powerlines insulators

Aerosol particles deposition on overhead power line insulators may significantly threaten electric systems, affecting dielectric properties and potentially causing surface discharges and service interruptions, leading to economic consequences. This study addresses this challenge by evaluating a dedicated microscale deposition model that integrated into an advanced air quality (AQ) modeling system. The microscale model integration into the AQ system aims to improve predictions of equivalent salt deposit density (ESDD), a key measure of insulator contamination. This enhancement is crucial for improving system resilience and reliability. The microscale model’s performance in reconstructing particulate matter (PM) deposition fluxes onto XP-70 porcelain-disk electrical insulator is assessed against measured ESDD data collected across various regions in Italy during several experimental campaigns. In order to better understand the specific phenomena impacting PM deposition, the microscale model is compared to the state-of-the-art Zhang deposition model used in CAMx. The analysis reveals that the microscale model tends to overestimate measured ESDD values and predicts higher deposition velocities than the Zhang model. These results suggest potential modelling differences between the two models, including variations in obstacle parameterizations, influence of particle aerodynamics, and dispersion modelling in the near-wall region.

Impact of resilience policies on cape town's water-food nexus: A system dynamics approach

BackgroundClimate change is increasingly affecting the supply of resources such as water and food. From 2015 to 2018, Cape Town endured its most severe drought on record. Yet, resource management often occurs in isolation, which contrasts with the holistic perspective provided by the nexus concept that recognizes the interdependence of resource sectors. This study employs system dynamics modeling, to examine the City of Cape Town’s (CoCT) water-food trade-offs and interactions using qualitative and quantitative approaches. It assesses various policies proposed by the CoCT, to improve system resilience and to boost future water supplies, examining their efficacy and potential drawbacks. These policies are tested against future scenarios including population growth and climate change predictions of different severities.ResultsThe simulation results show an increase in food demand, which is mainly linked to population growth and a significant decrease in water availability. Without intervention, the CoCT is expected to experience serious water shortages within the 40-year simulation period.ConclusionsThe findings indicate that the CoCT’s strategies will effectively secure adequate water for its expanding population. However, a major concern was found to be the proposed intensification of aquifer exploitation. The model predicts that such an approach could lead to overabstraction of some aquifers, compromising their sustainability.

Resilience assessment of FPSO leakage emergency response based on quantitative FRAM

FPSO production process is prone to leakage, and failure to respond promptly and effectively will lead to accident escalation and serious consequences. However, traditional safety assessment methods cannot handle the nonlinear relationships between human, technology, and organization in emergency response process. This study proposes a quantitative FRAM to evaluate the emergency response resilience of FPSO leakage. This method establishes a resilience evaluation framework including three tiers: function, ability, and system, which can quantify system resilience based on the variability of function. First, identify basic functions according to the four stages of monitoring, response, learning and anticipation in the emergency response process, and establish the FRAM model of FPSO leakage emergency response. Then, the quantitative FRAM and Monte Carlo simulation are combined to calculate the variabilities of functions under different operating conditions. Finally, based on the simulation results, the variabilities of basic functions are aggregated and statistically analyzed to quantify system resilience. The implementation process of this method is illustrated by a case study. The influence of different factors on the system resilience is analyzed by setting various operation scenarios, and critical functions are identified by sensitivity analysis, which can provide reference for improving system resilience and ensuring FPSO safety production.

Identifying resilience: a system safety review of trauma and orthopaedic theatres

A prospective, qualitative study, of trauma and orthopaedic theatres was undertaken using the CARe QI handbook and the SEIPS framework, with the aim of preventing future Never Events. The study demonstrated a new approach, focussed on understanding ‘work as done’ to identify opportunities to improve system resilience, tested, using the Model for Improvement. Undertaken during the Covid-19 pandemic, it demonstrates that such conditions should not be a deterrent to observational studies, but requiring greater time and resource than a standard investigation, the approach may not align with current organisational or regulatory expectations. At the conclusion of this study, the mean time between Never Events in theatres had increased from 46 to 224 days, an achievement that had not previously been possible using the regulatory required, safety I, investigatory approach. These findings should be used to inform future PSIRF and Never Event Frameworks, to ensure effective systems-based analysis and improvement.

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 two-stage decision-making approach for optimal design and operation of integrated energy systems considering multiple uncertainties and diverse resilience needs

The enhancement of energy system resilience from a planning perspective is crucial. In this study, a two-stage resilience optimization model is developed to determine the optimal additional configuration and scheduling plan for an integrated energy system, taking into account economic costs, loss of load, and various resilience indicators. Considering the uncertainty of disaster occurrence and component vulnerability, probabilities of multiple energy interruption scenarios are introduced, and a comprehensive system configuration optimization model targeting annual energy supply cost and loss of load is developed. Following which, accounting for the personalized energy demands of end-users, three resilience indicators, namely load importance, energy interruption duration, and the maximum degree of system damage, are proposed. According to the simulation results of an illustrative example, the proposed method effectively improve system resilience. Compared to the baseline solution from pure economic perspective, the total cost increases by 0.38 %, while the load loss decreases by 6.84 %. From the demand-side viewpoints, three resilience indicators are improved by 12.17 %, 11.8 %, and 20 %, respectively. Moreover, the capacity of renewable energy is recognized as the most critical factor determining the load loss over the planning period. Storage devices are more beneficial in reducing the duration of energy interruptions and lowering the maximum damage degree.

Blockchain-Based Joint Auction Model for Distributed Energy in Industrial Park Microgrids

To address the centralized trading demand within industrial parks and the scattered peer-to-peer trading demand outside industrial parks, this paper proposes a blockchain-based joint auction architecture for distributed energy in microgrids inside and outside industrial parks. By combining blockchain technology and auction theory, the architecture integrates the physical energy transactions within industrial parks with the distributed transactions in external microgrids to meet the centralized trading demand within industrial parks and the scattered peer-to-peer trading demand outside industrial parks, optimizing resource allocation and improving system resilience. In the microgrid auction mechanism for industrial parks, considering distributed energy providers (sellers) and distributed energy buyers, an auction mechanism with power transmission distance, average electricity price, and enterprise nature as its main attributes was constructed to maximize social welfare, realizing efficient energy flow in a multi-microgrid environment and enabling coordinated mutual benefits for producers and consumers within the region. Finally, a case study was conducted on the joint auction mechanism for microgrids inside and outside industrial parks, including the impacts of market dynamics and user preferences on electricity prices using different trading methods, the computational results using different trading matching methods (comparing single-attribute and multi-attribute methods), and multi-dimensional verification of user satisfaction with peer-to-peer transactions in a blockchain environment. The effectiveness of the joint trading between physical energy transactions within industrial parks and external microgrids was demonstrated, which could efficiently coordinate energy allocation inside and outside the parks and reduce the cost of energy configuration.

Global-to-Local Dependencies in Phosphorus Mass Flows and Markets: Pathways to Improving System Resiliency in Response to Exogenous Shocks.

Uneven global distribution of phosphate rock deposits and the supply chains to transport phosphorus (P) make P fertilizers vulnerable to exogenous shocks, including commodity market shocks; extreme weather events or natural disasters; and geopolitical instability, such as trade disputes, disruption of shipping routes, and war. Understanding bidirectional risk transmission (global-to-local and local-to-global) in P supply and consumption chains is thus essential. Ignoring P system interdependencies and associated risks could have major impacts on critical infrastructure operations and increase the vulnerability of global food systems. We highlight recent unanticipated events and cascading effects that have impacted P markets globally. We discuss the need to account for exogenous shocks in local assessments of P flows, policies, and infrastructure design choices. We also provide examples of how accounting for undervalued global risks to the P industry can hasten the transition to a sustainable P future. For example, leveraging internal P recycling loops, improving plant P use efficiency, and utilizing legacy soil P all enhance system resiliency in the face of exogenous shocks and long-term anticipated threats. Strategies applied at the local level, which are embedded within national and global policy systems, can have global-scale impacts in derisking the P supply chain.

The First Two Years of COVID-19 Hospitalization Characteristics and Costs: Results from the National Discharge Registry.

The COVID-19 pandemic has emerged as the primary global health challenge of the new millennium. Understanding its impact on health systems and learning from these experiences are crucial for improving system resilience against future health crises. This paper examines hospitalizations related to COVID-19 in Italy from 2020 to 2021, with a specific focus on the costs associated with these admissions. This is a retrospective, population-based study of Italian hospitalizations of patients diagnosed with COVID-19 during the 2020-2021 period, using data extracted from the National Hospital Discharge Registry. The outcome variables considered include hospital admissions, costs, and length of stay. In Italy, hospitalizations for COVID-19 totaled 357,354 in 2020 and 399,043 in 2021, with the transfer rate being three times higher than that of other patients. Hospitalizations were predominantly concentrated in the northern regions, especially during the first year. Mortality rates increased with age, while hospitalization rates peaked in the youngest and oldest age groups. The financial impact of COVID-19 hospitalizations was approximately €3.1 billion in 2020 and €3.6 billion in 2021. The cost per admission was around €8000 for standard care and €24,000 for intensive therapy in both years. Conducting a cost-benefit analysis of implementing a protective pad around the entire health system, which leverages networks of family doctors and nurses connected in real-time, could be an important step in strengthening health system resilience.

Resilience-oriented repair crew and network reconfiguration coordinated operational scheduling for post-event restoration

This paper introduces a post-disaster load restoration approach for the distribution grid, utilizing network reconfiguration (NR) and dispatching of repair crews (RCs) to significantly enhance grid resilience. We propose an RC–NR coordinated model that leverages diverse flexible resources within the active distribution network (ADN), aimed at not only enhancing the grid’s resilience level but also efficiently mending the fault lines. The model introduces fault repairing and sequential NR coupled constraints to devise an optimal resilience strategy within temporal domain cooperation, focusing on minimizing repair and penalty costs associated with the restoration process. To tackle the challenge of computational complexity, the nonlinear model is reformulated into a mixed-integer second-order cone programming model. The efficacy of the approach is validated through case studies on an IEEE 33-bus system, in which simulation results demonstrate a considerable improvement in grid resilience, achieving optimal load recovery with reduced restoration time and costs. The proposed approach outperforms traditional methods with optimal repair sequence and RC scheduling, aligned with NR efforts, and contributes to an improved system resilience level.

The future is transient: Barriers and opportunities for improved UK water resource climate change assessments using the enhanced Future Flows and Groundwater (eFLaG) climate service products

AbstractUK water resources face a number of challenges when planning for an uncertain future. Climate change impacts and what future droughts might look like can be a significant contributor to this uncertainty. Recent and potential future developments (e.g. ever‐finer resolutions) in climate modelling offer the potential for running bias‐corrected transient future scenarios through hydrological, hydrogeological and water supply models, providing users with droughts of differing severity, frequency, spatial extent and duration to those experienced historically, incorporating changes over time and an understanding of climate model uncertainty. The recent enhanced Future Flows and Groundwater (eFLaG) project sought to demonstrate a climate service using these transient scenarios, with the aim of enhancing the resilience of the water industry to drought events and complementing existing approaches. The project demonstrated the use of this transient climate change information within a water resource setting, using a variety of hydrological and water resource models to help illuminate potential gaps and issues with such an approach. If we are to realise the potential of transient scenarios, a number of barriers – both scientific and organisational – need to be overcome. We present a road map for the future based on outcomes from the eFLaG project, as well as ways the eFLaG projections could be used to improve system resilience in the present.

Multi-Indicator Fused Resilience Assessment of Power Grids Considering Wind-Photovoltaic Output Uncertainty during Typhoon Disasters

Extreme weather events such as typhoons pose a serious threat to the safe operation of power grids. In the field of power system resilience assessment during typhoon disasters, a parametric typhoon wind field model combined with actual historical meteorological data has not been well adopted, and the conventional renewable energy uncertainty modeling methods are not suitable for typhoon disaster periods. In this paper, a multi-indicator fused resilience assessment strategy considering wind-photovoltaic uncertainty and component failure during typhoon disasters is proposed. Firstly, based on the actual historical meteorological data of typhoons, an uncertainty model of typhoon wind speed is established by a rolling non-parametric Dirichlet process Gaussian mixture model. Then, a spatial–temporal contingency set is constructed by considering the best-fit wind field model and stress–strength interference model for failure probability of transmission lines. On this basis, a holistic resilience assessment framework is established from the perspectives of priority, robustness, rapidity, and sustainability, and the entropy weight method combined with the technology for order preference by similarity to an ideal solution is leveraged to obtain the comprehensive resilience indicator. Finally, numerical studies are performed on the IEEE-30 bus test system to identify vulnerable lines and improve system resilience during typhoon disasters.

A Comprehensive Review on Integrating Climate Science and Machine Learning for Power System Resilience

This study focuses on how climate science and machine learning techniques may be used to improve power system resilience in the face of climate change. It emphasizes the significance of resilience and the principles of machine learning application in power systems. Predictive models for climate-related disruptions are among the most recent advances in merging climate research with machine learning. The review assesses the efficacy of various models in improving system resilience and their limitations and problems. Future research prospects, policy consequences, and recommendations for moving climate science and machine learning integration forward for power system resilience are highlighted. Overall, the need to integrate these technologies to address climate change concerns and improve power system resilience is emphasized in this analysis.

Analysis on Rapid Charging for Electrified Transportation Systems

Background: To improve system resilience when charging electric vehicles, a new control mechanism for converters that convert voltage from sources in micro-grids is presented. Methods: This deals with an evolving continuous current and stable voltage charging method for electric automobiles (EVs) with the objectives of speedy charging, constant voltage stability, deviation from voltage reduction, and cost reduction. Results: The study uses spectrum evaluation and state of health evaluations to look at the effects of different voltage sag thresholds on the automotive charging parking’s. Conclusions: In the end, the research offers a power management algorithm created for a green energy-based electric vehicle charging station that maximizes the use of sources of Clean Conventional Energy Systems (CCES) with best process while optimizing real-time charging prices.

Dual deep reinforcement learning agents-based integrated order acceptance and scheduling of mass individualized prototyping

Coordinating order acceptance decisions with production scheduling to maximize revenue is challenging for Mass Individualized Prototyping (MIP) service providers. This paper presents a dual deep reinforcement learning agents-based (DDRLA) integrated order acceptance and scheduling (IOAS) for improving revenue. Firstly, a deep reinforcement learning-based virtual production scheduling (VPS) agent together with 8 state features and 11 action rules is designed. The VPS agent quickly and virtually reschedules a dynamically-arriving accepted order to evaluate the overall impact of accepting this order, including consumed capacity and increased revenue. Then, a deep reinforcement learning-based order acceptance decision (OAD) agent is designed. Based on the information guidance resulting from an interaction with the VPS agent, the OAD agent selectively accepts orders to maximize long-term gains, as well as to improve system resilience in the presence of a high ratio of urgent orders. The experiment results show that the proposed DDRLA method has better performance, compared with other IOAS approaches.

Optimization of system resilience in urban rail systems: Train rescheduling considering congestions of stations

In urban rail systems, unexpected events, due to equipment breakdown, extreme weather, etc., frequently cause the delay of trains and overcrowding of platforms, significantly affecting the service quality to passengers. Therefore, improving system resilience to recover to normal conditions more quickly has become a critical problem in the management of urban rail systems. In contrast to most previous studies that focused only on train rescheduling, our paper proposes optimizing the resilience of urban rail systems by considering the influence of delayed trains and overcrowded passenger flows. Specifically, we define a resilience-oriented quantitative evaluation index. Using the evaluation index as the objective function, we construct a mixed-integer nonlinear programming model. In our formulation, we consider a practical case in which the boarding and alighting of passengers at overcrowded stations may further delay trains. To solve the model, we develop a two-phase iterative optimization approach, in which phase 1 focuses on the adjustment of trains to enhance system resilience, and phase 2 evaluates the impact of passengers on the adjusted train schedule. Through the comparison of three different rescheduling methods, we prove the validity of the derived inequalities and our approach balances the computational time and system performance. Case studies are conducted on the Beijing Metro Line 1 to verify the effectiveness of the proposed approach. The results demonstrate that system resilience can be improved by as much as 39.7% using our optimization approach.

Efficiency Optimization and Resilience Improvement in Wireless Motor System With Flexible DC-Link Voltage Regulation

This paper proposes a novel dc-link voltage regulation (DCVR) strategy for the series-series compensated wireless motor (SSWM) system. The steady-state voltage control is presented to optimize the system efficiency and the feedforward voltage boost control is proposed to improve system resilience. The rated capacity identification (RCI) scheme is proposed to design proper parameters for the SSWM system. To achieve steady-state voltage control, the system efficiency characteristic is analyzed in detail and the efficiency optimization can be achieved without an auxiliary dc/dc converter. The influence of impedance matching, the power capacity of the system, and overmodulation of the motor drive are all investigated. Then, the dynamic model of the system is established to analyze the stability of dc-link voltage. In the proposed feedforward voltage boost control, the dc-link voltage is boosted according to the motor operation state to increase the instantaneous power capacity. The dc-link voltage stability is improved and the system resilience is enhanced against power disturbance. Besides, at the end of the acceleration process, the dc-link voltage is smoothly adjusted to the optimal dc-link voltage. The effectiveness of the proposed RCI scheme and DCVR strategy is verified by experiments on a three-phase wireless permanent magnet synchronous motor platform.

Resilience Importance Measure and Optimization Considering the Stepwise Recovery of System Performance

Effective recovery after disruptions is essential to improve system resilience. For many distributed systems such as offshore wind farms and communication networks, spatial location characteristics and limited maintenance resources lead to discontinuous changes in performance during system recovery. However, the stepwise performance is frequently ignored. The coupling relationship between the system and maintenance teams makes it difficult to improve the system resilience from the recovery perspective. In this article, a new method that combines the resilience importance measure and enhanced pigeon-inspired optimization (PIO) is presented to optimize the system resilience under stepwise recovery conditions. First, this study proposes a resilience-oriented importance measure that evaluates the recovery priority for each component. Second, an optimization model is established to minimize the system resilience loss by joint optimization of recovery sequence and task assignment under the conditions of multiple maintenance teams. Third, the resilience importance measure is combined with roulette wheel selection to form a high-quality intitle swarm of PIO. Fourth, an importance measure-based PIO with a Gaussian mutation and adaptive crossover operator is designed to find an optimization solution for system resilience. Finally, the recovery of a network system consisting of 32 nodes and 71 edges is studied. Compared with the stochastic method and traditional PIO, the proposed method in this study reduces the system resilience loss by 48.87 and 15.54%, respectively.