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DOE OSTI2023

Analytical harmonic vibrational frequencies with VV10-containing density functionals: Theory, efficient implementation, and benchmark assessments

VV10 is a powerful nonlocal density functional for long-range correlation that is used to include dispersion effects in many modern density functionals, such as the meta-generalized gradient approximation (mGGA), B97M-V, the hybrid GGA, ωB97X-V, and the hybrid mGGA, ωB97M-V. While energies and analytical gradients for VV10 are already widely available, this study reports the first derivation and efficient implementation of the analytical second derivatives of the VV10 energy. The additional compute cost of the VV10 contributions to analytical frequencies is shown to be small in all but the smallest basis sets for recommended grid sizes. Here, this study also reports the assessment of VV10-containing functionals for predicting harmonic frequencies using the analytical second derivative code. The contribution of VV10 to simulating harmonic frequencies is shown to be small for small molecules but important for systems where weak interactions are important, such as water clusters. In the latter cases, B97M-V, ωB97M-V, and ωB97X-V perform very well. The convergence of frequencies with respect to the grid size and atomic orbital basis set size is studied, and recommendations are reported. Finally, scaling factors to allow comparison of scaled harmonic frequencies with experimental fundamental frequencies and to predict zero-point vibrational energy are presented for some recently developed functionals (including r2SCAN, B97M-V, ωB97X-V, M06-SX, and ωB97M-V).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Scaling and performance portability of the particle-in-cell scheme for plasma physics applications through mini-apps targeting exascale architectures

We perform a scaling and performance portability study of the particle-in-cell scheme for plasma physics applications through a set of mini-apps we name "Alpine", which can make use of exascale computing capabilities. The mini-apps are based on Independent Parallel Particle Layer, a framework that is designed around performance portable and dimension independent particles and fields. We benchmark the simulations with varying parameters such as grid resolutions (5123 to 20483) and number of simulation particles (109 to 1011) with the following mini-apps: weak and strong Landau damping, bump-on-tail and two-stream instabilities, and the dynamics of an electron bunch in a charge-neutral Penning trap. We show strong and weak scaling and analyze the performance of different components on several pre-exascale architectures such as Piz-Daint, Cori, Summit and Perlmutter. While the scaling and portability study helps identify the performance critical components of the particle-in-cell scheme in the current state-of-the-art computing architectures, the mini-apps by themselves can be used to develop new algorithms and optimize their high performance implementations targeting exascale architectures.

Muralikrishnan, Sriramkrishnan↗

DOE OSTI2022

Updraft dynamics and microphysics: on the added value of the cumulus thermal reference frame in simulations of aerosol–deep convection interactions

One fundamental question about atmospheric moist convection processes that remains debated is whether, or under which conditions, a relevant variability in background aerosol concentrations may have a significant dynamical impact on convective clouds and their associated precipitation. Furthermore, current climate models must parameterize both the microphysical and the cumulus convection processes, but this is usually implemented separately, whereas in nature there is a strong coupling between them. As a first step to improve our understanding of these two problems, we investigate how aerosol concentrations modify key properties of updrafts in eight large-eddy-permitting regional simulations of a case study of scattered convection over Houston, Texas, in which convection is explicitly simulated and microphysical processes are parameterized. Dynamical and liquid-phase microphysical responses are investigated using the following two different reference frames: static cloudy updraft grid cells versus tracked cumulus thermals. In both frameworks, we observe the expected microphysical responses to higher aerosol concentrations, such as higher cloud number concentrations and lower rain number concentrations. In terms of the dynamical responses, both frameworks indicate weak impacts of varying aerosol concentrations relative to the noise between simulations over the observationally derived range of aerosol variability for this case study. On the other hand, results suggest that thermals are more selective than cloudy updraft grid cells in terms of sampling the most active convective air masses. For instance, vertical velocity from thermals is significantly higher at upper levels than when sampled from cloudy updraft grid points, and several microphysical variables have higher average values in the cumulus thermal framework than in the cloudy updraft framework. In addition, the thermal analysis is seen to add rich quantitative information about the rates and covariability of microphysical processes spatially and throughout tracked thermal lifecycles, which can serve as a stronger foundation for improving subgrid-scale parameterizations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Convergence of sum-up rounding schemes for cloaking problems governed by the Helmholtz equation

In this work, we consider the problem of designing a cloak for waves described by the Helmholtz equation from an integer programming point of view. The problem can be modeled as a PDE-constrained optimization problem with integer-valued control inputs that are distributed in the computational domain. A first-discretize-then-optimize approach results in a large-scale mixed-integer nonlinear program that is in general intractable because of the large number of integer variables that arise from the discretization of the domain. Instead, we propose an efficient algorithm that is able to approximate the local infima of the underlying nonconvex infinite-dimensional problem arbitrarily close without the need to solve the discretized finite-dimensional integer programs to optimality. We optimize only the continuous relaxations of the approximations for local minima and then apply the sum-up rounding methodology to obtain integer-valued controls. If the solutions of the discretized continuous relaxations converge to a local minimizer of the continuous relaxation, then the resulting discrete-valued control sequence converges weakly \(^*\) in \(L^\infty\) to the same local minimizer. These approximation properties follow under suitable refinements of the involved discretization grids. Our results use familiar concepts arising from the analytical properties of the underlying PDE and complement previous results, derived from a topology optimization point of view.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Enabling Realistic Communications Evaluations for ADMS

Increasing penetration levels of inverter-based distributed energy resources (DERs) in distribution systems are creating challenges in system operation. Currently, wired or proprietary wireless communications are used by advanced distribution management systems (ADMS) to detect the state of the system and control the DERs for optimal operation. Wireless communications are an alternative solution that can enable connectivity between the dispersed assets used in distribution system applications. As demand for the seamless integration of distributed energy resources (DERs) continues to increase, proper networking and communications systems are becoming important to support the DER infrastructure. This report summarizes validation of a private wireless LTE network in a laboratory environment. The purpose of this work is to 1) demonstrate the effectiveness of private wireless LTE communication for grid applications and 2) provide a means of evaluating latency and overall performance of wireless communication in grid applications. First, private wireless LTE communication was validated in a direct transfer trip relaying scenario as an example of one-way communications using grid equipment. This study provided a baseline understanding of the variation in latency for seven different kinds of wireless scenarios shown in Figure A below. As expected, weak (attenuated) wireless signal-based communication yielded the widest ranges in latency. When higher priority was set for the trip signal communication, this resulted in lower latency in communication timing. In the final phase of this project, the private wireless LTE network was validated using two-way communications in an advanced distribution management system (ADMS) testbed which was developed in the ESIF at NREL. The purpose of this was to study the impact of wireless communication on grid performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Leveraging graph clustering techniques for cyber‐physical system analysis to enhance disturbance characterisation

Abstract Cyber‐physical systems have behaviour that crosses domain boundaries during events such as planned operational changes and malicious disturbances. Traditionally, the cyber and physical systems are monitored separately and use very different toolsets and analysis paradigms. The security and privacy of these cyber‐physical systems requires improved understanding of the combined cyber‐physical system behaviour and methods for holistic analysis. Therefore, the authors propose leveraging clustering techniques on cyber‐physical data from smart grid systems to analyse differences and similarities in behaviour during cyber‐, physical‐, and cyber‐physical disturbances. Since clustering methods are commonly used in data science to examine statistical similarities in order to sort large datasets, these algorithms can assist in identifying useful relationships in cyber‐physical systems. Through this analysis, deeper insights can be shared with decision‐makers on what cyber and physical components are strongly or weakly linked, what cyber‐physical pathways are most traversed, and the criticality of certain cyber‐physical nodes or edges. This paper presents several types of clustering methods for cyber‐physical graphs of smart grid systems and their application in assessing different types of disturbances for informing cyber‐physical situational awareness. The collection of these clustering techniques provide a foundational basis for cyber‐physical graph interdependency analysis.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Constraining the masses of high-redshift clusters with weak lensing: Revised shape calibration testing for the impact of stronger shears and increased blending

Weak lensing measurements suffer from well-known shear estimation biases, which can be partially corrected for with the use of image simulations. Here we present an analysis of simulated images that mimic Hubble Space Telescope/Advance Camera for Surveys observations of high-redshift galaxy clusters, including cluster specific issues such as non-weak shear and increased blending. Our synthetic galaxies have been generated to have similar observed properties as the background-selected source samples studied in the real images. First, we used simulations with galaxies placed on a grid to determine a revised signal-to-noise-dependent (S/N KSB ) correction for multiplicative shear measurement bias, and to quantify the sensitivity of our KSB+ bias calibration to mismatches of galaxy or PSF properties between the real data and the simulations. Next, we studied the impact of increased blending and light contamination from cluster and foreground galaxies, finding it to be negligible for high-redshift (z > 0.7) clusters, whereas shear measurements can be affected at the ~1% level for lower redshift clusters given their brighter member galaxies. Finally, we studied the impact of fainter neighbours and selection bias using a set of simulated images that mimic the positions and magnitudes of galaxies in Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey (CANDELS) data, thereby including realistic clustering. While the initial SExtractor object detection causes a multiplicative shear selection bias of –0.028 ± 0.002, this is reduced to –0.016 ± 0.002 by further cuts applied in our pipeline. Given the limited depth of the CANDELS data, we compared our CANDELS-based estimate for the impact of faint neighbours on the multiplicative shear measurement bias to a grid-based analysis, to which we added clustered galaxies to even fainter magnitudes based on Hubble Ultra Deep Field data, yielding a refined estimate of ~ –0.013. Our sensitivity analysis suggests that our pipeline is calibrated to an accuracy of ~0.015 once all corrections are applied, which is fully sufficient for current and near-future weak lensing studies of high-redshift clusters. As an application, we used it for a refined analysis of three highly relaxed clusters from the South Pole Telescope Sunyaev-Zeldovich survey, where we now included measurements down to the cluster core (r > 200 kpc) as enabled by our work. Compared to previously employed scales (r > 500 kpc), this tightens the cluster mass constraints by a factor 1.38 on average.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Clean Energy Cybersecurity Accelerator Cohort 1: Authentication and Authorization

In the 2023 National Cybersecurity Strategy, the Biden-Harris Administration defines the need for a "defensible, resilient digital ecosystem where it is costlier to attack systems than defend them." The strategy cites the Clean Energy Cybersecurity Accelerator (CECA) as an exemplary effort to bolster the security and resilience of clean energy generation. These efforts help "secure the clean energy grid of the future and [generate] security best practices that extend to other critical infrastructure sectors" and promise broad and far-reaching impacts to bridge the capabilities of private industry and the needs of energy production. Cohort 1 of CECA launched in the fall of 2022 with a focus on solutions that provide strong authentication and authorization for industrial control systems to mitigate attacks on the energy grid. Authentication and authorization verify that the identity (authentication) and permissions (authorization) of a user or device are aligned with their assigned roles. Weaknesses in either can have serious repercussions. To assess the strength of Cohort 1's solutions, CECA devised threat scenarios grounded in historical precedents: the CECA team reviewed exploits from real-world case studies of state-sponsored actors to match the assessment's attack paths and targets. Cohort 1 results provided the energy industry, product vendors, and related agencies valuable insights into the efficacy and applicability of solutions in common system configurations under realistic threat scenarios. The results of the assessment highlight points for interrogation and improvement in subsequent technology iterations. CECA's evaluations are part of an ongoing conversation and collaboration to bolster U.S. cyber resilience against adversaries today and in the future.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Virtual Power Plant Architecture and Resilient Design

Virtual Power Plants (VPPs) represent a fundamental shift in electric grid operations, aggregating distributed energy resources (DERs) such as solar panels and battery storage to deliver utility-scale grid services traditionally provided by centralized power plants. This report examines the unique architectural, operational, and digital assurance considerations that distinguish VPPs from conventional utility infrastructure as they scale from pilot projects to mainstream deployment across the United States. While VPPs offer significant opportunities for grid modernization and enhanced flexibility, their distributed, multi-stakeholder architecture introduces distinct security challenges that differ fundamentally from traditional generation facilities. The analysis identifies risks in VPP operations, including device-level security gaps, platform vulnerabilities, and communication protocol weaknesses that create expanded attack surfaces compared to centralized power plants. Through examination of real-world incidents and emerging threat patterns, the report demonstrates how some VPPs' reliance on consumer-owned devices, public internet infrastructure, and complex vendor ecosystems require new approaches to digital assurance and operational security. The findings provide practical guidance for utilities, regulators, and aggregators to implement robust security frameworks and operational best practices essential for maintaining grid reliability as VPP deployment accelerates under the Federal Energy Regulatory Commission (FERC) Order 2222 and related regulatory initiatives.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Code for the manuscript "Lagrangian Large Eddy Simulations vis Physics-informed Machine Learning"

We would like to disclose a code based on Python/Pytorch, in which we implement the data-driven physics-informed machine learning of Lagrangian large eddy simulation that is described in the manuscript "Lagrangian Large Eddy Simulations via Physics-Informed Machine Learning ". High-Reynolds number Homogeneous Isotropic Turbulence is fully described within the Navier-Stokes (NS) equations, which are notoriously difficult to solve numerically. Engineers, interested primarily in describing turbulence at a reduced range of resolved scales, have designed heuristics, known as Large Eddy Simulation (LES). LES is described in terms of the temporally evolving Eulerian velocity field defined over a spatial grid with the mean-spacing correspondent to the resolved scale. This classic Eulerian LES depends on assumptions about the effects of sub-grid scales on the resolved scales. Here, we take an alternative approach and design novel LES heuristics stated in terms of Lagrangian particles moving with the flow. Our \emph{Lagrangian LES}, thus L-LES, is described by equations generalizing the weakly compressible Smoothed Particle Hydrodynamics formulation with extended parametric and functional freedom, which is then resolved via Machine Learning training on Lagrangian data from Direct Numerical Simulations of the NS equations. The L-LES model includes physics-informed parameterization and functional form, by combining physics-based parameters and physics-inspired Neural Networks to describe the evolution of turbulence within the resolved range of scales. The sub-grid scale contributions are modeled separately with physical constraints to account for the effects from un-resolved scales. We build the resulting model under the Differentiable Programming framework to facilitate efficient training. We experiment with loss functions of different types, including physics-informed ones accounting for statistics of Lagrangian particles. We show that our Lagrangian LES model is capable of reproducing Eulerian and unique Lagrangian turbulence structures and statistics over a range of turbulent Mach numbers.

Tian, Yifeng↗

DOE OSTI2024

GPU-enabled extreme-scale turbulence simulations: Fourier pseudo-spectral algorithms at the exascale using OpenMP offloading

Fourier pseudo-spectral methods for nonlinear partial differential equations are of wide interest in many areas of advanced computational science, including direct numerical simulation of three-dimensional (3-D) turbulence governed by the Navier-Stokes equations in fluid dynamics. This paper presents a new capability for simulating turbulence at a new record resolution up to 35 trillion grid points, on the world's first exascale computer, Frontier, comprising AMD MI250x GPUs with HPE's Slingshot interconnect and operated by the US Department of Energy's Oak Ridge Leadership Computing Facility (OLCF). Key programming strategies designed to take maximum advantage of the machine architecture involve performing almost all computations on the GPU which has the same memory capacity as the CPU, performing all-to-all communication among sets of parallel processes directly on the GPU, and targeting GPUs efficiently using OpenMP offloading for intensive number-crunching including 1-D Fast Fourier Transforms (FFT) performed using AMD ROCm library calls. With 99% of computing power on Frontier being on the GPU, leaving the CPU idle leads to a net performance gain via avoiding the overhead of data movement between host and device except when needed for some I/O purposes. Memory footprint including the size of communication buffers for MPI_ALLTOALL is managed carefully to maximize the largest problem size possible for a given node count. Detailed performance data including separate contributions from different categories of operations to the elapsed wall time per step are reported for five grid resolutions, from 2048 3 on a single node to 32768 3 on 4096 or 8192 nodes out of 9408 on the system. Both 1D and 2D domain decompositions which divide a 3D periodic domain into slabs and pencils respectively are implemented. The present code suite (labeled by the acronym GESTS, GPUs for Extreme Scale Turbulence Simulations) achieves a figure of merit (in grid points per second) exceeding goals set in the Center for Accelerated Application Readiness (CAAR) program for Frontier. The performance attained is highly favorable in both weak scaling and strong scaling, with notable departures only for 2048 3 where communication is entirely intra-node, and for 32768 3 , where a challenge due to small message sizes does arise. Communication performance is addressed further using a lightweight test code that performs all-to-all communication in a manner matching the full turbulence simulation code. Performance at large problem sizes is affected by both small message size due to high node counts as well as dragonfly network topology features on the machine, but is consistent with official expectations of sustained performance on Frontier. Overall, although not perfect, the scalability achieved at the extreme problem size of 32768 3 (and up to 8192 nodes — which corresponds to hardware rated at just under 1 exaflop/sec of theoretical peak computational performance) is arguably better than the scalability observed using prior state-of-the-art algorithms on Frontier's predecessor machine (Summit) at OLCF. New science results for the study of intermittency in turbulence enabled by this code and its extensions are to be reported separately in the near future.

3D fast Fourier transform↗

DOE OSTI2023

Power Solitons in Inverter-based Electric Energy Systems: Observation, Analysis and Implications

The discovery of an unfamiliar class of nonlinear energy waves in electric energy systems with dominating coupled inverter-based energy resources has been reported in a recent research project. Whenever there are slight but persistent differences among the reference signals used by inverters in pulse-width modulation, i.e., a weak group modulation instability condition, the unfamiliar energy waves appear, which contain not only the familiar frequency components present in electric power systems but also self-organized components in the ultra-high frequency region of the energy spectrum. These components are found to be exploding solitary waves with concentrated energy masses, hence we call them power solitons. This paper explains the work of the discovery reported, and the implications of the presence of power solitons for grid integration of inverter-based resources and power system protection.

14 SOLAR ENERGY↗

DOE OSTI2023

Developing Renewable Energy Applications for Water Treatment Technologies and Transmission

The need for smaller treatment facilities for more isolated or rural municipal entities and industrial water treatment facilities continues to grow. From environmental justice and energy needs in remote communities that have never been serviced by larger scale utilities to smaller onsite treatment requirements to meet discharge regulations, energy efficient treatment systems that can leverage renewable energy sources can be an important asset to owners. Climate change and population dynamics have changed the needs for distributed water treatment systems and the manner in which they function. New treatment technologies that can operate under dynamic conditions and leverage variable energy sources (e.g., renewable energy, RE) can provide substantial efficiencies and cost savings. The National Renewable Energy Laboratory (NREL) is embarking on research projects that will perform multi-scale studies to investigate treatment technologies on varying source water quality that can operate under unsteady state flow conditions (timing) in response to energy availability, supply, and demand. A pilot-scale system will be built to assess modularity and scaling of treatment system operating conditions and performance. Modeling will also be used to determine feasibility and integration into grid emulation platforms to understand the use and value of operation with RE. Optimization will be completed for the treatment and distribution systems to document strengths and weaknesses of using RE power sources. Results of this effort will advance dynamic water treatment understanding under variable conditions and integration of RE sources to power these systems. This will lead to further decarbonization of municipal and private treatment facilities while supporting a paradigm shift in water treatment process operation and system design to achieve net-zero goals and move toward energy-sensitive resilient water treatment systems.

ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Idealized simulations of wind farm interactions with intermittent turbulence in stable boundary layer conditions

Stable atmospheric boundary layer conditions typically correspond to weak turbulence levels, but intermittent periods of elevated turbulence can occur during otherwise quiescent conditions. The interaction between intermittent turbulence and wind turbines is not well understood because of sparse observations, as well as the difficulty in realistically resolving small-scale turbulence during strongly stable conditions with numerical simulations. In this study, an explicit filtering and reconstruction approach for large-eddy simulation (LES) is used to simulate weakly and strongly stable conditions, with surface cooling rates of −0.2 and −2.0 K h −1 , respectively. This approach can sustain resolved background turbulence at relatively coarse grid spacing and stronger stratification compared to conventional closures, permitting more realistic intermittent stable boundary layer (SBL) turbulence. The idealized LES capability of the Weather Research and Forecasting model is employed with turbine rotors parameterized using generalized actuator disks to examine (1) how the presence of turbine wakes affects SBL evolution and (2) the effect of intermittent turbulence on power production and wake recovery. Wakes increase mixing and deepen the SBL, with a stronger effect under strongly stable conditions, primarily because the SBL is shallower and closer to the top of the wind turbine rotor layer. Intermittent turbulence does not have a significant impact on mean power generation and wake recovery because the relevant intermittent turbulent structures in this study only affect the bottom half of the rotor disk. Power production is, however, more variable during periods of elevated turbulence, demonstrating the impact of SBL intermittency. This study uses an idealized configuration, focusing on LES model performance and physical understanding, with the goal of informing future simulations of the conditions observed during the American Wake Experiment.

Energy - Wind↗

DOE OSTI2022

Hardware-Based Randomized Encoding for Sensor Authentication in Power Grid SCADA Systems

Supervisory Control and Data Acquisition (SCADA) systems are utilized extensively in critical power grid infrastructures. Modern SCADA systems have been proven to be susceptible to cyber-security attacks and require improved security primitives in order to prevent unwanted influence from an adversarial party. One section of weakness in the SCADA system is the integrity of field level sensors providing essential data for control decisions at a master station. In this paper we propose a lightweight hardware scheme providing inferred authentication for SCADA sensors by combining an analog to digital converter and a permutation generator as a single integrated circuit. Through this method we encode critical sensor data at the time of sensing, so that unencoded data is never stored in memory, increasing the difficulty of software attacks. We show through experimentation how our design stops both software and hardware false data injection attacks occurring at the field level of SCADA systems.

42 ENGINEERING↗

DOE OSTI2022

The Illumination of Thunderclouds by Lightning: 2. The Effect of GLM Instrument Threshold on Detection and Clustering

Abstract Lightning is measured from space using optical instruments that detect transient changes in the illumination of the cloud top. How much of the flash (if any) is recorded by the instrument depends on the instrument detection threshold. NOAA's Geostationary Lightning Mapper (GLM) employs a dynamic threshold that varies across the imaging array and changes over time. This causes flashes in certain regions and at night to be recorded in greater detail than other flashes, and threshold inconsistencies will impose biases on all levels of GLM data products. In this study, we quantify the impact of the varying GLM threshold on event/group/flash detection, flash clustering, and gridded product generation by imposing artificial thresholds on the event data taken from a thunderstorm with a low instrument threshold (∼0.7 fJ). We find that even modest increases in threshold severely impact event (60% loss by 2 fJ, 90% loss by 10 fJ) and group (25% loss by 2 fJ, 81% loss by 10 fJ) detection by suppressing faint illumination of the cloud top from weak sources and scattering. Flash detection is impacted less by threshold increases (4% loss by 2 fJ), but reductions are still significant at higher thresholds (35% loss by 10 fJ, or 44% if single‐group flashes are removed). Undetected pulses cause individual flashes to be split and severely impact the construction of gridded products. All of these factors complicate the interpretation of GLM data, particularly when trended over time under a changing threshold.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Smoothed aggregation for difficult stretched mesh and coefficient variation problems

Abstract Four adaptations of the smoothed aggregation algebraic multigrid (SA‐AMG) method are proposed with an eye toward improving the convergence and robustness of the solver in situations when the discretization matrix contains many weak connections. These weak connections can cause higher than expected levels of fill‐in within the coarse discretization matrices and can also give rise to suboptimal smoothing within the prolongator smoothing phase. These smoothing drawbacks are due to the relatively small size of some diagonal entries within the filtered matrix that one obtains after dropping the weak connections. The new algorithms consider modifications to the Jacobi‐like step that defines the prolongator smoother, modifications to the filtered matrix, and also direct modifications to the resulting grid transfer operators. Numerical results are given illustrating the potential benefits of the proposed adaptations.

Hu, Jonathan J.↗

DOE OSTI2021

Microgrid's Role in Enhancing the Security and Flexibility of City Energy Systems

Smart cities depend on flexible and secure energy systems to ensure resilient power for critical infrastructure; however, recent weather-related events and cyberattacks have highlighted weaknesses in our energy systems, with the potential for widespread economic and security impacts. As stated by the Executive Office of the President, "the resilience of the US electric grid is a key part of the nation's defense against severe weather." To address the energy delivery security challenge, microgrids are rising as a viable solution that enhances the flexibility and resilience of the distribution grid and boosts the reliability of the local supply for the end-user. Traditionally, high capital investment has been a barrier to large-scale adoption of microgrid technology. Understanding the flexibility and resilience benefits of microgrids and accounting for the associated value streams can make the microgrid's proposition economically viable. In this chapter, microgrids' utility and their potential to serve as a flexible and resilient resource for the utility grid by providing capabilities such as peak shaving, demand response, and frequency regulation is presented. Moreover, other value streams, such as (1) their ability to island during a disaster and sustain critical loads which makes them a robust resilience solution for end-users, in the event of the utility grid outage and (2) microgrids also provide a flexible platform for integrating distributed energy resources in conjunction with storage and conventional generation technologies, strengthen microgrid's role in reducing the over-arching goal of emission reduction. Given the myriad of benefits associated with microgrids, we present strategies which can be employed for making microgrid itself resilient against physical and cyberthreats by employing hardware, software, and personnel training solutions to operate the microgrid before, during, and after a potential disaster. This chapter, thus, provides a holistic study of the microgrid as a resilience resource, for the utility grid, and a self-contained end-user for the end-user.

cyber-physical system↗