Power System Resilience Enhancement Using Artificial Intelligence

Extreme weather events and natural disasters are the major cause of power outages in the United States. An accurate forecast of component outages and the resultant load curtailment in response to extreme events is an essential task in pre- and post-event planning, recovery and hardening of power systems. Power system resilience improvement is investigated in this work from component outage prediction to identifying the potential power outages in the system to estimating probable load curtailment due to these outages and offering methods for grid hardening. Initially, two machine learning based prediction methods are proposed to determine the potential outage of power grid components in response to an imminent hurricane, namely a second order logistic regression model and a three-dimensional Support Vector Machine (SVM). The logistic regression model defines the decision boundary, which partitions the components\u27 states into two sets of damaged and operational. Two metrics are examined to validate the performance of the obtained decision boundary in efficiently predicting component outages. The proposed three-dimensional SVM furthermore leverages its accuracy-uncertainty tradeoff to achieve highly accurate results, which can be further used to schedule system resources in a predictive manner with the objective of maximizing its resilience. The performance of the model is tested through numerical simulations and validated based on well-defined and commonly-used performance measures. After training the outage estimation model, the predicted component outages are plugged into a load curtailment minimization model to estimate the nodal load curtailments in the system. The standard IEEE 30-bus system with a combination of hurricane path and intensity scenarios are used to study the model where the results demonstrate that the proposed modelling framework is capable of effectively capturing the dynamics of load curtailment estimation in response to extreme events. Furthermore, a machine learning based grid hardening model is proposed with the objective of improving power grid resilience. The predictions from previous stages are fed into the proposed grid hardening model, which determines strategic locations for placement of distributed generation (DG) units. In contrast to existing literature in hardening and resilience enhancement, this work co-optimizes grid economic and resilience objectives by considering the intricate dependencies of the two. The numerical simulations on the standard IEEE 118-bus test system illustrate the merits and applicability of the proposed model. The results further indicate that the proposed hardening model through decentralized and distributed local energy resources can produce a more robust solution that can protect the system significantly against multiple component outages. Finally, a probabilistic load curtailment estimation model is proposed through a three-step sequential method. At first, to determine a deterministic outage state of the grid components in response to a forecasted hurricane, a machine learning model based on TWSVM is proposed. Then, to convert the deterministic results into probabilistic outage states, a posterior probability sigmoid model is trained on the obtained results from the previous step. Finally, the obtained component outages are integrated into a load curtailment estimation model to determine the potential load curtailments in the system. The simulation results on a standard test system illustrate the high accuracy performance of the proposed method

Advancements in Enhancing Resilience of Electrical Distribution Systems: A Review on Frameworks, Metrics, and Technological Innovations

This comprehensive review paper explores power system resilience, emphasizing its evolution, comparison with reliability, and conducting a thorough analysis of the definition and characteristics of resilience. The paper presents the resilience frameworks and the application of quantitative power system resilience metrics to assess and quantify resilience. Additionally, it investigates the relevance of complex network theory in the context of power system resilience. An integral part of this review involves examining the incorporation of data-driven techniques in enhancing power system resilience. This includes the role of data-driven methods in enhancing power system resilience and predictive analytics. Further, the paper explores the recent techniques employed for resilience enhancement, which includes planning and operational techniques. Also, a detailed explanation of microgrid (MG) deployment, renewable energy integration, and peer-to-peer (P2P) energy trading in fortifying power systems against disruptions is provided. An analysis of existing research gaps and challenges is discussed for future directions toward improvements in power system resilience. Thus, a comprehensive understanding of power system resilience is provided, which helps in improving the ability of distribution systems to withstand and recover from extreme events and disruptions

Nexus of thermal resilience and energy efficiency in buildings: A case study of a nursing home

Extreme weather events become more frequent and severe due to climate change. Although energy efficiency technologies can influence thermal resilience of buildings, they are traditionally studied separately, and their interconnections are rarely quantified. This study developed a methodology of modeling and analysis to provide insights into the nexus of thermal resilience and energy efficiency of buildings. We conducted a case study of a real nursing home in Florida, where 12 patients died during Hurricane Irma in 2017 due to HVAC system power loss, to understand and quantify how passive and active energy efficiency measures (EEMs) can improve thermal resilience to reduce heat-exposure risk of patients. Results show that passive measures of opening windows and doors for natural ventilation, as well as miscellaneous load reduction, are very effective in eliminating the extreme dangerous occasions. However, to maintain safe conditions, active measures such as on-site power generators and thermal storage are also needed. The nursing home was further studied by changing its location to two other cities: San Francisco (mild climate) and Chicago (cold winter and hot summer). Results revealed that the EEMs' impacts on thermal resilience vary significantly by climate and building characteristics. The study also estimated the costs of EEMs to help stakeholders prioritize the measures. Passive measures that may not save energy may greatly improve thermal resilience, and thus should be considered in building design or retrofit. Findings from this study indicate energy efficiency technologies should be evaluated not only by their energy savings performance but also by their influence on a building's resilience to extreme weather events

A review of methods to better predict and reduce the risk of hurricane damage to the energy sector

In the event of a hurricane, electricity is the most important utility as it provides heat, water, food, light, communication, and medical care to communities. Research predicts an increase in frequency and strength of hurricanes with time due to climate change, which requires communities and electric utility companies to be prepared for the inevitable. This paper assesses existing methods of hurricane preparation and restoration of the electric power grid in hurricane prone locations with regards to the electric utility companies and electric distribution systems. In this study, I perform a comparative analysis between different methods of planning and forecasting electrical power outages for a hurricane event. Previous research analyzes single models and methods, where this paper compares the many different models and methods to synthesize the most promising results for electric utility companies to implement. Results from this study indicate that hardening the electrical grid and optimizing the electrical forecast models with more promising variables (Estimated maximum wind speed, duration of high winds, previous outages, and tree densities) and model types (General Additive Models and Bayesian Additive Regression Tree models) will reduce response and recovery time of the electrical grid after a hurricane. This study is important as it will guide electrical utility companies on better methods to prepare and respond to hurricanes to facilitate fewer power outages and quicker recovery times after a hurricane, saving money and lives of affected communities and service areas

Resilience assessment and planning in power distribution systems:Past and future considerations

Over the past decade, extreme weather events have significantly increased worldwide, leading to widespread power outages and blackouts. As these threats continue to challenge power distribution systems, the importance of mitigating the impacts of extreme weather events has become paramount. Consequently, resilience has become crucial for designing and operating power distribution systems. This work comprehensively explores the current landscape of resilience evaluation and metrics within the power distribution system domain, reviewing existing methods and identifying key attributes that define effective resilience metrics. The challenges encountered during the formulation, development, and calculation of these metrics are also addressed. Additionally, this review acknowledges the intricate interdependencies between power distribution systems and critical infrastructures, including information and communication technology, transportation, water distribution, and natural gas networks. It is important to understand these interdependencies and their impact on power distribution system resilience. Moreover, this work provides an in-depth analysis of existing research on planning solutions to enhance distribution system resilience and support power distribution system operators and planners in developing effective mitigation strategies. These strategies are crucial for minimizing the adverse impacts of extreme weather events and fostering overall resilience within power distribution systems.Comment: 27 pages, 7 figures, submitted for review to Renewable and Sustainable Energy Review

Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges

This systematic review meticulously explores the transformative impact of digital technologies on the grid planning, grid operations, and energy market dynamics of power distribution grids. Utilizing a robust methodological framework, over 54,000 scholarly articles were analyzed to investigate the integration and effects of artificial intelligence, machine learning, optimization, the Internet of Things, and advanced metering infrastructure within these key subsections. The literature was categorized to show how these technologies contribute specifically to grid planning, operation, and market mechanisms. It was found that digitalization significantly enhances grid planning through improved forecasting accuracy and robust infrastructure design. In operations, these technologies enable real-time management and advanced fault detection, thereby enhancing reliability and operational efficiency. Moreover, in the market domain, they support more efficient energy trading and help in achieving regulatory compliance, thus fostering transparent and competitive markets. However, challenges such as data complexity and system integration are identified as critical hurdles that must be overcome to fully harness the potential of smart grid technologies. This review not only highlights the comprehensive benefits but also maps out the interdependencies among the planning, operation, and market strategies, underlining the critical role of digital technologies in advancing sustainable and resilient energy systems

Data Challenges and Data Analytics Solutions for Power Systems

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