Grid Resilience Research Articles

Toward equitable grid resilience: operationalizing climate adaptation strategies to mitigate flooding impacts

Disadvantaged communities are disproportionately affected by flooding, exacerbated by climate change. This paper presents a novel framework for incorporating environmental justice into climate adaptation planning of power grids against flooding. A new energy equity metric is introduced with the vision that addressing environmental justice warrants prioritizing disadvantaged communities that have lower risk thresholds. The framework is applied to a levee-protected IEEE standard test system in northern California. The grid performance disturbed due to flooded substations is investigated under current and future climate. The mathematical model of the framework is structured as a two-stage stochastic mixed-integer programming model. This model aims to minimize the equity gap in grid resilience (EGGR) between disadvantaged and non-disadvantaged communities while enhancing the system resilience by reducing the risk of power outages due to flooding. The results show that climate change undermines grid resilience, with disproportionally worse impacts on disadvantaged communities. A significant EGGR is observed that worsens under a changing climate. For adaptation, the optimal placement of distributed energy resources is determined by maximizing the grid resilience to flooding while minimizing EGGR. The proposed framework can equip decision-makers with a robust tool for operationalizing equitable climate adaptation strategies for power grids.

Modeling and Evaluating Power Grid Resilience Under Extreme Space Weather

AbstractThe geomagnetically induced current (GIC) produced during extreme geomagnetic storms can easily lead to large‐scale blackouts in China due to the increase in the scale of its electric power grid. A power grid's resilience is its capability to resist various natural hazards, withstand primary failures, and quickly resume normal operation. To avoid power grid damages, this study developed a resilient power grid, incorporating failure, power flow calculation and recovery models under a uniform induced geoelectric field. We chose a system's performance loss as the resilience evaluation indicator, which intuitively reflected a system's loss under GIC. In addition, the recovery model was optimized using a genetic algorithm, and two resilience improvement measures were proposed. The IEEE‐RTS‐79 system, consisting of 10 generators, 24 buses and 5 transformers, was chosen as an example to verify the feasibility of this study. The results show that the genetic algorithm and optimization measures effectively enhanced the system's resilience indicator and provided a reference for preventing system damages under GIC and quick recovery after possible failures.

Electrochemical C-N Bond Formation within Boron Imidazolate Cages Featuring Single Copper Sites.

Electrocatalysis expands the ability to generate industrially relevant chemicals locally and on-demand with intermittent renewable energy, thereby improving grid resiliency and reducing supply logistics. Herein, we report the feasibility of using molecular copper boron-imidazolate cages, BIF-29(Cu), to enable coupling between the electroreduction reaction of CO2 (CO2RR) with NO3- reduction (NO3RR) to produce urea with high selectivity of 68.5% and activity of 424 μA cm-2. Remarkably, BIF-29(Cu) is among the most selective systems for this multistep C-N coupling to-date, despite possessing isolated single-metal sites. The mechanism for C-N bond formation was probed with a combination of electrochemical analysis, in situ spectroscopy, and atomic-scale simulations. We found that NO3RR and CO2RR occur in tandem at separate copper sites with the most favorable C-N coupling pathway following the condensation between *CO and NH2OH to produce urea. This work highlights the utility of supramolecular metal-organic cages with atomically discrete active sites to enable highly efficient coupling reactions.

Enhancing the resilience of zero-carbon energy communities: Leveraging network reconfiguration and effective load carrying capability quantification

The integration of distributed energy resources (DERs) into contemporary energy communities (ECs) has revolutionized power systems, fostering sustainable and clean energy infrastructures. This paper focuses on the effective load-carrying capability (ELCC) to enhance grid resilience in the presence of DERs. We introduce an innovative network topology-based optimization framework that seamlessly integrates economic and resilience metrics within ECs while reducing carbon emissions. The Pareto front of non-dominated solutions for the proposed three-objective optimization problems is extracted, providing a comprehensive visualization of the trade-off between economic, resilience, and emission objectives, enabling informed decision-making. Analytical results, validated on the IEEE 33-bus test system, demonstrate the effectiveness of DER-based ELCC quantification in managing load supply during emergencies. Case studies show how the synergy between economic and resilience-based metrics significantly enhances grid resilience. The proposed framework has diverse applications, including enhancing grid adaptability to climate change, promoting sustainable energy integration, optimizing demand response strategies, and supporting the transition to a decarbonized energy community. This work addresses the challenges and opportunities in the evolving energy landscape, emphasizing the importance of our approach in achieving a cleaner and more resilient energy future.

A fast-partitioning decision method for demand side resources based on grid resilience assessment

With the large-scale renewable energy integrated into the distribution grid, the grid’s regulating ability and disturbance tolerance are weakening. When partitioning demand-side resources, it is necessary to enhance resilience to ensure the reliability of electric power. This paper proposes a fast-partitioning method that considers resilience, structure, and functionality to adapt to the evolving requirements of the distribution system. Specifically, the comprehensive partition index system is constructed with the resilience assessment index reflecting the ability of partitions to withstand and mitigate the effects of faults, the modularity index based on electrical distance, and regional power balance indexes. Meanwhile, a modified genetic algorithm is proposed to calculate the comprehensive partition index. The modified algorithm first uses a sensitivity matrix to perform initial partitions and construct initial populations. Then, it utilizes a triangular network adjacency matrix for chromosome encoding, significantly reducing the algorithm’s search space and enhancing partitioning efficiency. Finally, the applicability and effectiveness of the proposed method are verified through simulation analysis of the IEEE 28-node system.

Resilient Operation of Electric Vehicles considering Grid Resiliency and Uncertainties

This paper proposes a novel short-time optimization method based on a resiliency enhancement strategy for a smart distribution network during adverse weather conditions. The key idea is to integrate various electric vehicles (EVs) with different behaviour patterns and other distributed energy resources into emergency tools of distribution network operation such as topology reconfiguration and grid-supported services. In this regard, possible management programs for three types of EVs (with different levels of control potentials), energy resources, reconfiguration, and demand response programs impacts on boosting resiliency are investigated. The framework is organized on coordination and benefit sharing among distribution system operator (DSO), private sector, and EV owners to reduce possible side effects of failures that could be incepted by extreme weather conditions. A Monte Carlo-based stochastic simulation for modelling uncertainties such as EVs movement, state of change, arrival/departure time to charge stations, and weather-based failures is devised. In order to evaluate resiliency, metrics based on a multiphase method are analysed. The performance of the proposed method on extra benefit obtaining for DSO and private sector and also resiliency enhancement in disturbance progress, postdisturbance degraded, and restorative state are carried out on IEEE-33 bus test system, and obtained results are analysed in detail.

Design and experimental validation of an artificial neural network-SVPWM controller for a novel micro grid-tied fuel cell-based 3-phase boost inverter

A grid-tied fuel cell (FC) system demands efficient power conversion, power quality preservation, grid stability, power flow management, renewable energy source (RES) integration, and enhanced grid resilience. Achieving these goals requires a precise inverter circuit switch approach. A boost power converter connected to the FC stack ensures voltage regulation, power conditioning, efficient power transfer, system integration, control, and protection. This enhances FC system adaptability and compatibility across various applications, minimizing input current ripples for prolonged FC lifespan.This study introduces a novel DC-DC boost converter with an artificial neural network (ANN) controller to reduce FC input current ripples and enhance FC stack-generated voltage for grid applications. It also presents a space vector sinusoidal pulse width modulation (SVPWM) technique for FC-based three-phase grid-tied inverters. This offers improved voltage utilization, precise voltage and current control, reduced harmonic distortion, rapid response, flexibility, scalability, reduced total harmonic distortion (THD), and over-modulation capability. The proposed SVPWM technique utilizes a digital signal processing (DSP)-based controller, combining high-speed processing, precision, and real-time capabilities to enhance system performance and efficiency.

Demonstrating clean energy transition scenarios in sector-coupled and renewable-based energy communities

Background Energy communities facilitate several advantages, including energy autonomy, reduced greenhouse gas emissions, poverty mitigation, and regional economic development. They also empower citizens with decision-making and co-ownership prospects in community renewable projects. Integrating renewable energy sources and sector coupling is a crucial strategy for flexible energy systems. However, demonstrating clean energy transition scenarios in these communities presents challenges, including technology integration, flexibility activation, load reduction, grid resilience, and business case development. Methods Based on the system of systems approach, this paper introduces a 4-step funnel approach and a 4-step reverse funnel approach to systematically specify and detail demonstration scenarios for energy community projects. The funnel approach involves four steps. First, it selects demonstration scenarios promoting energy-efficient state-of-the-art renewable technologies and storage systems, flexibility through demand side management techniques, reduced grid dependence, and economic viability. Second, it lists all existing and planned project technologies, analysing energy flows. Third, it plans actions at different levels to implement the demonstration scenarios. Fourth, it validates the strategies using key performance indicators (KPI) to quantify the effectiveness of the planned measures. Furthermore, the reverse funnel approach delves deeper into the demonstration scenarios. The four steps involve identifying stakeholder perspectives, describing scenario scopes, listing conditions for realisation, and outlining business models, including value chains and economic assumptions. Results This approach provides a detailed analysis of the demonstration scenarios, considering actors, objectives, boundary conditions, and business assumptions. The methodologies are exemplified in three diverse European energy communities extending across residential, commercial, tertiary, and industrial establishments, allowing power-to-x and sector coupling opportunities. The paper also suggested thirteen KPIs for validating renewable-focused energy community projects. Conclusions Finally, the paper recommends increased collaboration between energy communities, knowledge sharing, stakeholder engagement, transparent data collection and analysis, continuous feedback, and method improvement to mitigate policy, technology, business, and market uncertainties.

Optimal Siting of EV Fleet Charging Station Considering EV Mobility and Microgrid Formation for Enhanced Grid Resilience

Coordinating infrastructure planning for transportation and the power grid is essential for enhanced reliability and resilience during operation and disaster management. This paper presents a two-stage stochastic model to optimize the location of electric vehicle fleet charging stations (FEVCSs) to enhance the resilience of a distribution network. The first stage of this model deals with the decision to place an FEVCS at the most favorable and optimized location, whereas the second stage aims to minimize the weighted sum of the value of lost load in multiple potential scenarios with different faults. Indeed, the second stage is a joint grid restoration scheme with network reconfiguration and microgrid formation using available distributed generators and fleet electric vehicles. The proposed model is tested on a modified IEEE-33 node distribution network and a four-node transportation network. Case studies demonstrate the effectiveness of the proposed model.

Integration of cloud computing: a new transition for Bangladesh power grid empowerment from reliability to grid resiliency

Integration of cloud computing: a new transition for Bangladesh power grid empowerment from reliability to grid resiliency

Battery Cell Balancing of V2G-Equipped Microgrid in the Presence of Energy Storage Aggregator

High electricity consumption in Sarawak’s cities is a concern, driven by residential, industrial, and commercial users. Greater demand for electrical energy requires more electrical resources, affecting grid stability. To prevent blackouts and enhance grid resilience, electric vehicles can serve as distributed energy storage in microgrids. The biggest problem facing energy storage is that not all batteries within energy storage are the same. Every battery cell in the battery pack has a different rate of self-discharge, capacity, internal resistance, and aging, even though they have the same chemical and physical characteristics. These variations lead to power imbalances in energy storage. Therefore, to address this issue, a unique voltage equalization method using a modular flyback converter is presented. Its core objective is to correct voltage imbalances among batteries. The proposed method is modelled using MATLAB, and the performance of the system has been evaluated. Furthermore, the developed model is implemented in a microgrid and tested for two different cases (with and without energy storage for electric cars). From the simulation outcomes, it has been verified that the developed cell balancing technique performs well in equalizing the voltage of cells than the conventional methods.

Enhancing smart grid resilience with deep learning anomaly detection prior to state estimation

State estimation is a critical process in modern power system control centers that operate under strict real-time requirements. Among its key post-processing components are bad data detection and identification, with the latter being more complex and potentially compromising the system’s real-time performance. This paper proposes a novel component in the state estimation process called Anomaly Detection and Identification Module (ADIM), whose goal is to detect anomalies, or erroneous data points, prior to the state estimation process. Our work presents a deep learning-based method that exhibits a commendable level of accuracy in detecting anomalies within a continuous stream of measurements. ADIM’s capabilities are demonstrated through comprehensive experiments on a set of test cases that effectively encompass various network configurations, transformer types, and load scenarios. It is shown that ADIM can detect and identify anomalies effectively, significantly reducing the need for the bad data detection component and improving overall state estimation responsiveness. Our work lays the foundation for developing a detection- and identification-based anomaly system for power system measurements.

Predictive Fraud Detection: An Intelligent Method for Internet of Smart Grid Things Systems

Today, grid resilience as a feature has become non-negotiable, significantly when power interruptions can impact the economy. The widespread popularity of Intelligent Electronic Devices (IED) operating as smart meters enables an immense amount of fine-grained electricity consumption data to be collected. However, risk can still exist in the Smart Grid (SG), as valuable data are exchanged among SG systems; theft or alteration of this data could violate consumer privacy. The Internet of Things for Smart Grid (IoSGT) is a promising ecosystem of different technologies that coordinate with each other to pave the way for new SG applications and services. As a use case of IoSGT for future SG applications and services, fraud detection, ıNon-technical losses (NTL), emerges as an important application for Smart Grid (SG) scenarios. A substantial amount of electrical energy is lost throughout the distribution system, and these losses are divided into two types: technical and non-technical. Non-technical losses (NTL) are any electrical energy consumed and not invoiced. They may occur due to illegal connections, issues with energy meters such as delay in the installation or reading errors, contaminated, defective, or non-adapted measuring equipment, very low valid consumption estimates, faulty connections, and disregarded customers. Non-technical losses are the primary cause of revenue loss in the SG. According to a recent study, electrical utilities lose $89.3 Billion per year due to non-technical losses. This article proposes ensemble predictor-based time series classifiers for NTL detection. The proposed predictor ministers the user’s energy consumption as a data input for classification, from splitting the data to executing the classifier.It encompasses the temporal aspects of energy consumption data during preprocessing, training, testing, and validation stages. The suggested predictor is Time Series (TS) oriented, from data splitting to the classifier’s performance. Overall, our best results have been recorded in the fraud detection-based time series classifiers (TSC) model scoring an improvement in the empirical performance metrics by 10% or more over the other developed models.

A comprehensive analysis of eight rooftop grid-connected solar photovoltaic power plants with battery energy storage for enhanced energy security and grid resiliency

This study presents the outcome of a utility-run rooftop photovoltaic (PV) power plant with battery energy storage systems (BESS) as a viable solution for enhanced energy storage and grid resiliency at the distribution network level. A comprehensive techno-commercial analysis of PV plants with BESS for commercial and industrial (C&I) consumers of a distribution company (DISCOM) is presented. The analysis is based on consumer data from eight different sectors in India. The impact of voltage rise, thermal loading and reverse flow are analysed for different PV + BESS grid network integration scenarios, which are found to be factored in the individual capacity and number of distributed systems connected to the grid, length and current carrying capacity of the feeder, running load and the location of PV + BESS integration. The cost-benefit analysis results show that the maximum economic benefit from PV + BESS can be attained by managing peak load, reducing diesel generator use, and increasing solar fraction in the energy system. The normalised net benefit is higher when PV + BESS is installed with load profiles, which coincides with the DISCOM load profiles. Results show BESS needs to be an essential element of a PV plant for reliability, flexibility, and grid stability.

Investigating the correlation between extreme temperature events and the increasing frequency of power outages in the Northern Territory of Australia

It has been claimed that the grid of the Northern Territory of Australia suffers from more frequent blackouts because of the increasing occurrence of extreme events. However, the temperature oscillations have not changed since the time measurements started to be collected at the end of 1800. The time series of the monthly maximum and highest temperatures for the two locations of the Northern Territory of Australia which are spanning 140 years shows temperature extremes are not worsening, being the records detrended. The increasing power outages have many other causes. Some are natural such as cyclones, winds, or lightning. Others are human such as equipment failure, animal or human interference, overload, and maintenance or upgrade. One reason for the more frequent power outages is the reduced resilience of the grid forced to accept a growing share of non-dispatchable, intermittent, and irregular solar energy without appropriate energy storage. Their occurrence may be lessened by improving the resilience of the grid by providing adequate energy storage. The study contributes to the understanding of the complex factors influencing power outages and highlights the importance of resilient grid infrastructure.

Component Assessment of the Electric Transmission Grid to Hurricanes

AbstractThe increased frequency and intensity of extreme weather events from climate change necessitates understanding impacts on critical infrastructure, particularly electrical transmission grids. One of the foundational concepts of a grid's resilience is its robustness to extreme weather events, such as hurricanes. Resilience of the electric grid to high wind speeds is predicated upon the location and physical characteristics of the system components. Previous modeling assessments of electric grid failure were done at the systems level with assumptions on location and type of specific components. To facilitate more explicit adaptation metrics, accurate component‐level information is needed. In this study, we build and utilize a data set of location, physical characteristics, and age of transmission structures for nine counties in the Florida Panhandle. These component characteristics were then simulated for failure under a variety of scenarios using fragility curves. Eight hurricanes were modeled using Hazus from the Federal Emergency Management Administration and the resulting impact to the network was assessed. The network was generated using the transmission lines and towers, showing increasing impacts to network efficiency with larger storms. Although modern transmission structures are built under the more stringent extreme wind loading construction standards, the prevalence of older, wooden transmission structures throughout the region poses a substantial risk to reliable electricity transmission during tropical cyclone events from the Gulf of Mexico.

Smart Solar Powered Battery System for EV for IoT Voltage Monitoring

Abstract: This paper presents a Smart Solar-Powered Battery System for Electric Vehicles (EVs) enhanced with Internet of Things (IoT) voltage monitoring, featuring a robust combination of components. Utilizing ESP32, Solar Panels, Breadboard, PCB, Voltage Sensor, Liquid Crystal Display, BJT, Battery, Jumper Wires, USB Cable, Arduino IDE, and BlynkIoT, this system embodies a sustainable and efficient EV charging infrastructure. The integration of solar panels within the system capitalizes on solar energy, diminishing the environmental impact of EV charging while reinforcing grid resilience. The high-capacity battery and IoT voltage sensors ensure continuous and reliable power supply to EVs, facilitated by remote monitoring through the BlynkIoT platform. This smart and grid-independent solution promises to redefine electric vehicle charging infrastructure, mitigating reliance on traditional power sources. In summary, the Smart Solar-Powered Battery System, comprising these pivotal components, demonstrates the potential to revolutionize EV charging, aligning with the global shift towards sustainable and ecofriendly transportation solutions.

Sizing PV and BESS for Grid-Connected Microgrid Resilience: A Data-Driven Hybrid Optimization Approach

This article presents a comprehensive data-driven approach on enhancing grid-connected microgrid grid resilience through advanced forecasting and optimization techniques in the context of power outages. Power outages pose significant challenges to modern societies, affecting various sectors such as industries, households, and critical infrastructures. The research combines statistical analysis, machine-learning algorithms, and optimization methods to address this issue to develop a holistic approach for predicting and mitigating power outage events. The proposed methodology involves the use of Monte Carlo simulations in MATLAB for future outage prediction, training a Long Short-Term Memory (LSTM) network for forecasting solar irradiance and load profiles with a dataset spanning from 2009 to 2018, and a hybrid LSTM-Particle Swarm Optimization (PSO) model to improve accuracy. Furthermore, the role of battery state of charge (SoC) in enhancing system resilience is explored. The study also assesses the techno-economic advantages of a grid-tied microgrid integrated with solar panels and batteries over conventional grid systems. The proposed methodology and optimization process demonstrate their versatility and applicability to a wide range of microgrid design scenarios comprising solar PV and battery energy storage systems (BESS), making them a valuable resource for enhancing grid resilience and economic efficiency across diverse settings. The results highlight the potential of the proposed approach in strengthening grid resilience by improving autonomy, reducing downtime by 25%, and fostering sustainable energy utilization by 82%.

Optimal mitigation and control over power system dynamics for stochastic grid resilience

Optimal mitigation planning for highly disruptive contingencies to a transmission-level power system requires optimization with dynamic power system constraints, due to the key role of dynamics in system stability to major perturbations. We formulate a generalized disjunctive program to determine optimal grid component hardening choices for protecting against major failures, with differential algebraic constraints representing system dynamics (specifically, differential equations representing generator and load behavior and algebraic equations representing instantaneous power balance over the transmission system). We optionally allow stochastic optimal pre-positioning across all considered failure scenarios, and optimal emergency control within each scenario. This novel formulation allows, for the first time, analyzing the resilience interdependencies of mitigation planning, preventive control, and emergency control. Using all three strategies in concert is particularly effective at maintaining robust power system operation under severe contingencies, as we demonstrate on the western system coordinating council 9-bus test system using synthetic multi-device outage scenarios.

Developing a 20-year high-resolution wind data set for Puerto Rico

The purpose of this study is to develop a high-resolution wind resource data set for Puerto Rico as part of the wind resource assessment in the Puerto Rico Grid Resilience and Transition to 100 % Renewable Energy Study (PR100) project. The Weather and Research and Forecasting (WRF) is used to model 20-years (2001–2020) of wind resource data on a 3-km grid for the Puerto Rico region. Because accurately representing the planetary boundary layer (PBL)-physics in the WRF is key to accurately model low-level wind speed, 11 different PBL schemes are examined and evaluated to find a WRF configuration that can provide the most accurate wind data. The modeled wind speeds are validated with buoy observations, and the Shin-Hong PBL scheme is selected to generate the final wind data set. The overall results demonstrate that the high-resolution data correctly represents the climate of Puerto Rico (e.g., a dominant easterly trade wind). At 160 m above ground, the southern regions of Puerto Rico show strong offshore wind speed (>8 m/s on average) throughout the daytime and nighttime. For land-based wind, average wind speed ranges from 5 m/s–7 m/s during daytime, whereas notable high wind speeds (>9 m/s) appear in the mountain regions during nighttime.