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This presentation provides updates to the SEPA cybersecurity working group related to IEEE 1547.3 Guide for DER Cybersecurity, DOE Laboratory Coordination Committee, and other standards development efforts.
Power systems around the world are undergoing a major transformation because of the increasing shares of renewable energy, growing deployment of energy storage systems, proliferation of distributed energy resources, electrification of other sectors, and so on. In the United States, wind and solar provided almost 10% of electricity in 2019. The U.S. Energy Information Administration, in its 2020 Annual Energy Outlook, forecasted that the share of electricity from renewables will reach 38% by 2050, of which more than 80% will come from wind and solar. Wind and solar, along with battery energy storage systems, interface with the grid using power electronic inverters; hence, they are collectively referred to as inverter-based resources (IBRs). The increasing annual share of electricity from IBRs in a power system means that during more times of the year, the system will operate at a much higher concentration of IBRs. Figure 1 presents the hourly share of wind and solar generation in the Electric Reliability Council of Texas (ERCOT) system in Texas in 2019. While the annual wind share was at 20%, the instantaneous percentage share was much higher. Moments of high shares of IBRs (>50%) will continue to grow as more IBRs will be deployed in a power system.
The inverter-based resource (IBR) has become avery important component in the distribution system. The impacts on system transient stability introduced by high IBR penetration are not fully addressed because of the lack of high-fidelity models. The aggregate IBR model at the transmission level cannot precisely reproduce the dynamics of distributed IBR at the distribution system because of the oversimplification. In this paper, we will develop a high-penetration fully-connected transmission and distribution (T&D) co-simulation platform that supports the simulation of 10,000+ dispersed IBR models. The interfacing and iterative initialization techniques for the co-simulation have been implemented to maintain stable operation and simulation of large-multitude of IBR models. The phasor-domain IBR models with grid-forming (GFM) and grid-following (GFL) control are implemented in the distribution systems simulators. The developed platform is tested on high-performance computing (HPC) resources and can be utilized to explore the hierarchical control strategies of IBRs for the large-scale T&D hybrid system.
Around the globe, electric power networks are transforming into complex cyber–physical energy systems (CPES) due to the accelerating integration of both information and communication technologies (ICT) and distributed energy resources. While this integration improves power grid operations, the growing number of Internet-of-Things (IoT) controllers and high-wattage appliances being connected to the electric grid is creating new attack vectors, largely inherited from the IoT ecosystem, that could lead to disruptions and potentially energy market manipulation via coordinated load-altering attacks (LAAs). In this article, we explore the feasibility and effects of a realistic LAA targeted at IoT high-wattage loads connected at the distribution system level, designed to manipulate local energy markets and perform energy storage (ES) arbitrage. Realistic integrated transmission and distribution (T&D) systems are used to demonstrate the effects that LAAs have on locational marginal prices at the transmission level and in distribution systems adjacent to the targeted network.
Load growth at the grid edge is driving increased attention to the distribution system and its ability to enable customer technology adoption in an affordable and timely manner. Key industry stakeholders, including electric utilities and regulators, can benefit from strategies to manage and balance customer needs with infrastructure investments, such as demand flexibility. This report focuses on demand flexibility—the ability to reduce, shift, shed, generate, or modulate loads in response to building and grid needs—to reduce the need for costly grid upgrades by deferring investment needs and increase system reliability by shifting electricity usage during periods of high risk. Specifically, we focus on the emerging characteristics of business models for demand flexibility as a framework to understand how demand flexibility programs generate value. In this report, we focus on demand flexibility program stakeholder ecosystem management strategies, which provide information on value delivery and describe how program implementers leverage partner capabilities and collaborate to deliver customer and grid benefits. This report discusses the role of stakeholder ecosystem management in demand flexibility programs, identifies existing challenges to effective stakeholder management, and describes lessons learned. This report is part of a series that includes reports on value propositions, customer relationship management strategies, and program life cycle.
This report is a solar resource and infrastructure assessment for the town of Westhampton, Massachusetts. The assessment was funded through the National Renewable Energy Laboratory, Solar Energy Innovation Network (NREL SEIN) Solar in Rural Communities Program, as part of a project to develop a Community-Informed Proactive Solar Siting and Financing Model. As a first step, the project lead organization, UMass Clean Energy Extension prepared an assessment of existing infrastructure, resources, and potential solar development opportunities in participating municipalities, including Westhampton. This assessment was designed to describe relevant bylaws and infrastructure within the town, identify the types of solar facilities that could be developed, and quantify the total space available for each type of facility. In this report, we reviewed existing electricity grid infrastructure, and the potential to interconnect additional solar facilities. At the present time, both distribution lines providing electricity to Westhampton are oversaturated with authorized and proposed solar projects, and cannot accommodate additional solar projects to interconnect to the grid. Future upgrades could potentially free up additional capacity for new, large projects. Meanwhile, most three-phase lines could likely accommodate additional small-to-medium scale projects (under 200 kW), and most single-phase lines could likely accommodate additional projects under 50 kW in size. This description represents the local grid infrastructure as it is - planning for future scenarios of development could include recommendations for areas of grid infrastructure improvement to allow siting of distributed generation in locations preferred by the community. Future scenarios may also include the addition energy storage and other "non-wires alternatives."
This paper presents the results of an investigation into the value that medium voltage back-to-back (MVB2B) converters can be realized through two potential applications: voltage regulation via the provision of reactive power supply or absorption, and distributed energy resource (DER) hosting capacity improvement via the controlled transfer of real power within the distribution system. Two groups of distribution systems have been prepared for the value investigation: the IEEE 123-bus system for the voltage regulation study and two utility-provided, realistic distribution systems for the DER hosting capacity improvement study. The results demonstrate that the MVB2B converter can realize significant value for the grid through these two applications. In particular, with the MVB2B converter transferring power between two feeders, DER-based power can be routed to nearby loads instead of feed through the distribution substation. Additionally, the size of energy storage elements used to enable higher levels of DERs can be significantly reduced.
Large-scale distributed computing infrastructures such as the Worldwide LHC Computing Grid (WLCG) require comprehensive simulation tools for evaluating performance, testing new algorithms, and optimizing resource allocation strategies. However, existing simulators suffer from limited scalability, hardwired algorithms, lack of real-time monitoring, and inability to generate datasets suitable for modern machine learning approaches. We present CGSim, a simulation framework for large-scale distributed computing environments that addresses these limitations. Built upon the validated SimGrid simulation framework, CGSim provides high-level abstractions for modeling heterogeneous grid environments while maintaining accuracy and scalability. Key features include a modular plugin mechanism for testing custom workflow scheduling and data movement policies, interactive real-time visualization dashboards, and automatic generation of event-level datasets suitable for AI-assisted performance modeling. We demonstrate CGSim’s capabilities through a comprehensive evaluation using production ATLAS PanDA workloads, showing significant calibration accuracy improvements across WLCG computing sites. Scalability experiments show near-linear scaling for multi-site simulations, with distributed workloads achieving 6 × better performance compared to single-site execution. The framework enables researchers to simulate WLCG-scale infrastructures with hundreds of sites and thousands of concurrent jobs within practical time budget constraints on commodity hardware.
The energy system infrastructure that delivers power to a building's loads needs to be resilient to withstand and recover from extreme outages (e.g., grid faults that leave millions of people without power during severe weather events). Building-level electrical distribution systems (BEDSs) distribute power from a building's energy sources - including the grid, solar photovoltaic (PV) panels, and batteries - to its loads, including lighting, HVAC, and plug loads. BEDS with storage can provide resilience by distributing local electricity supply to critical loads during an outage. Quantitative metrics are needed to assess the resilience improvements associated with new BEDS and storage system technologies. In this paper, we apply an existing metric, the probability of outage survival curve (POSC), to BEDS with storage and propose novel metrics that improve upon POSC. Through a simulation-based case study, we demonstrate how these metrics are impacted by the BEDS design and how they can be used to design a resilient system.
This paper presents the laboratory evaluation of a commercial Microgrid Management System (MGMS) implemented in the real-world Bronzeville Microgrid which features a futuristic scenario with high renewable energy integration and the use of multiple Grid-Forming (GFM) inverters. The primary objective of the performance evaluation for the MGMS is to assess the MGMS's capability to dispatch GFM units, including a GFM PV unit and two GFM battery units, to maintain the system stability and ensure economic operation, thus guaranteeing the microgrid's resilience during prolonged outages and dynamic events. The laboratory controller hardware-in-the-loop provides realistic testing environment through detailed electromagnetic transient modeling of the microgrid system, hardware MGMS, and standard communication protocols (DNP3). The CHIL evaluation shows how the MGMS effectively manages the GFM inverters, highlighting its performance in maintaining stability, reliability, and survivability in a microgrid environment with a high penetration of renewable energy sources.
This paper presents the laboratory evaluation of a commercial Microgrid Management System (MGMS) implemented in the real-world Bronzeville Microgrid which features a futuristic scenario with high renewable energy integration and the use of multiple Grid-Forming (GFM) inverters. The primary objective of the performance evaluation for the MGMS is to assess the MGMS's capability to dispatch GFM units, including a GFM PV unit and two GFM battery units, to maintain the system stability and ensure economic operation, thus guaranteeing the microgrid's resilience during prolonged outages and dynamic events. The laboratory controller hardware-in-the-loop provides realistic testing environment through detailed electromagnetic transient modeling of the microgrid system, hardware MGMS, and standard communication protocols (DNP3). The CHIL evaluation shows how the MGMS effectively manages the GFM inverters, highlighting its performance in maintaining stability, reliability, and survivability in a microgrid environment with a high penetration of renewable energy sources.
The deployment of solid-state transformers (SSTs) in medium-voltage (MV) distribution systems is facing various challenges, especially the challenge of insulation coordination design against grid-originated lightning impulses. In this article, two challenges in existing insulation coordination designs for grid-connected SSTs are identified. One challenge is the mismatch between metal-oxide varistor (MOV) protective levels and SST insulation strength, the other challenge is the incompatibility of standard impulse test on SST protective structures. To address the MOV selection challenge, a novel lightning protection scheme is designed to protect a single-stage SST where the semiconductor modules are directly exposed to external lightning impulses. Here, the in-lab lightning impulse tests are performed to verify the overvoltage attenuation performance of the prototyped lightning protection scheme. To address the impulse test challenge, the surge withstand capability of the protected SST is comprehensively evaluated with a complete set of insulation coordination design procedures beyond the basic insulation level (BIL) test. After these two challenges are addressed, a discussion is presented on substituting conventional transformers with the protected SSTs into insulation-coordinated distribution systems to facilitate the field deployment of SSTs.
The Transmission Optimization with Grid Enhancing Technologies (TOGETs) project will guide future research to fill in gaps on publicly available knowledge of Grid Enhancing Technologies (GETs) and conduct a full scale, multi-faceted field exercise. Ultimately the project aims to test, verify and validate deployed GETs in a transmission system.
Load growth at the grid edge is driving increased attention to the distribution system and its ability to enable customer technology adoption in an affordable and timely manner. Key industry stakeholders, including electric utilities and regulators, can benefit from strategies to manage and balance customer needs with infrastructure investments, such as demand flexibility. This report focuses on demand flexibility—the ability to reduce, shift, shed, generate, or modulate loads in response to building and grid needs—to reduce the need for costly grid upgrades by deferring investment needs and increase system reliability by shifting electricity usage during periods of high risk. Specifically, we focus on the emerging characteristics of business models for demand flexibility as a framework to understand how demand flexibility programs generate value. In this report, we focus on demand flexibility value propositions, which provide information on value creation and describe how programs deliver clear benefits that address customer and grid needs. This report discusses the role of value propositions in demand flexibility programs, provides an overview of value propositions for a range of demand flexibility stakeholders, identifies existing challenges to establishing an effective value proposition, and describes lessons learned. This report is part of a series that includes reports on customer relationship management strategies, stakeholder ecosystem management, and program life cycle.
The proliferation of distributed energy resources (DER)—and the ability to intelligently control these assets—is re-defining the electrical distribution system. As the number of controllable devices rapidly expands, grid operators must determine how to incorporate these assets while delivering reliable, equitable, and affordable electricity. One possible approach is to establish distribution-level electricity markets and allow devices/aggregations of devices to participate in price establishment. While this approach purports some of the same benefits as the highly successful wholesale electricity markets (i.e., open competition, efficient price discovery, reduced communication overhead), this needs to be researched and quantified via an analysis platform that models distribution-level markets at the appropriate fidelity. Specifically, the simultaneous evaluation of market performance, DER performance, DER bidding approaches, and distribution feeder power quality requires modeling that spans multiple technical areas. Co-simulation has emerged as a powerful tool in addressing this type of problem, where outputs depend on a range of underlying areas of expertise and associated models. In this paper we describe a solution, as implemented in the HELICS co-simulation platform, where we include (1) high fidelity house models, (2) intelligent bidding agents, (3) a modular market integration/design, and (4) a distribution feeder model. We then present a case study where we test two different market designs: (1) a pseudo-wholesale double-blind auction, and (2) an asynchronous matching market. In this work, the markets are run under two DER penetration levels and economic results are compared to full retail net energy metering and avoided cost net metering scenarios that bookend current approaches to remuneration of DER participation. We show the potential for transactive markets to provide increased value for most customers relative to net metering (and all customers relative to avoided cost scenarios) while decreasing costs for the utility.
Grid integration of renewable energy and energy storage requires forward-looking planning process, and increased emphasizes on reliability, resilience, and equity. Power-electronics based energy generation including solar, wind, distributed energy resources (DERs), and various types of grid-tied energy storage and emerging loads, are reshaping grid operator's understanding on interconnection level performance and responses. This paper will present the ongoing work at PNNL related to power electronics R&D, energy modeling and analysis, and a wide spectrum of grid stability studies and technologies in support of grid integration of renewable energy and energy storage.
This report documents the design and evaluation of an integrated online-learning pipeline developed within the AdCyDER project for Distributed Energy Resource (DER) cybersecurity. The pipeline couples a Reinforcement Learning (RL) attack classifier — which produces an attack-type probability distribution — with a Stackelberg game-theoretic (GT) defense selector that consumes those distributions alongside SME-encoded priors over (defense, attack) effectiveness pairings and perdefense costs to choose grid-health-preserving defenses. The objective is not attack classification per se but production of distributions that drive effective defense selection through the Stackelberg layer, learned from delayed grid-health feedback rather than labeled attack data. AdCyDER as a whole is broader than the work presented here; this report covers the specific RL/GT loop integration and its evaluation. We present the integrated pipeline (SCADA telemetry with Fronius inverter physics, Suricata IDS, time-windowed aggregation, per-facility LSTM classifier, Stackelberg optimizer, OpenC2 actuators), an experimental campaign of 28 eight-hour iterations across three baseline modes, and a pipeline-ordered diagnostic protocol. The protocol identifies two distinct failure modes within the loop: paired supervised ceilings on the same features establish that the deployed online RL classifier (macro F1 ≈ 0.07) sits at least 4.7× below a same-architecture supervised LSTM (≈ 0.34) and 10–11× below a linear feature-signal ceiling (≈ 0.70–0.79 depending on per-facility isolation), localizing the dominant failure to the training procedure; and the reward signal driving online updates carries weak directional coupling with classifier correctness in the methodology-expected direction (multi-lens convergent: top-decile P(true) records produce more frequent state changes and slightly larger improvements, top-vs-bot Cohen’s 𝑑 ≈ −0.19), but at effect magnitudes too small to drive gradient-based learning at the campaign sample size. The original learning hypothesis is not supported by the data. The primary contributions are the diagnostic methodology — proposed as a transferable falsification protocol for online RL/GT defense pipelines learning from delayed environmental reward — and the open, reproducible experimental infrastructure. We outline reward reformulation as the highest-priority aspirational next step given the underpowered-but-aligned Q6 reading, with hardware-in-the-loop evaluation as the broadest scope-expansion option.
Context. High order correlations in the cosmic matter density have become increasingly valuable in cosmological analyses. However, computing these correlation functions is computationally expensive. Aims. We aim to circumvent these challenges by developing a new algorithm called ConKer for estimating correlation functions. Methods. This algorithm performs convolutions of matter distributions with spherical kernels using FFT. Since matter distributions and kernels are defined on a grid, it results in some loss of accuracy in the distance and angle definitions. We study the algorithm setting at which these limitations become critical and suggest ways to minimize them. Results. ConKer is applied to the CMASS sample of the SDSS DR12 galaxy survey and corresponding mock catalogs, and is used to compute the correlation functions up to correlation order n = 5. We compare the n = 2 and n = 3 cases to traditional algorithms to verify the accuracy of the new algorithm. We perform a timing study of the algorithm and find that three of the four distinct processes within the algorithm are nearly independent of the catalog size N , while one subdominant component scales as O ( N ). The dominant portion of the calculation has complexity of O ( N c 4/3 log N c ), where N c is the of cells in a three-dimensional grid corresponding to the matter density. Conclusions. We find ConKer to be a fast and accurate method of probing high order correlations in the cosmic matter density, then discuss its application to upcoming surveys of large-scale structure.