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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2026

Rural EVSE Planning and Analysis

The dataset includes detailed anonymized public charging station usage from several rural stations on the ChargePoint and Shell Recharge Solutions (formerly Greenlots) networks situated in and around Athens, Ohio, a rural Appalachian community. Both Level 2 and DC fast charging stations are represented. Historical data in the set date back to 2019; additional data will be uploaded semiannually until the project's completion in 2023. Each charging session recorded includes information on date and time, location, charging station level, session duration, energy delivered, and fuel savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Model-Based Framework to Optimize Charger Station Deployment for Battery Electric Vehicles

The development of battery electric vehicles (BEVs) is accelerating due to their environmental advantages over gasoline and diesel-powered vehicles, including a decrease in air pollution and an increase in energy efficiency. The deployment of charging infrastructure will need to increase to keep pace with demand, especially for large commercial vehicles for which few public chargers currently exist. In this paper, a new flexible framework is proposed for optimizing the placement of charging stations for BEVs, within which different physical models and optimization techniques may be used. Furthermore, a set of metrics is suggested to help enforce complex constraints and facilitate direct comparison between different optimization techniques. Unlike many existing charger placement techniques, the proposed method directly considers the historical driving patterns on a vehicle-by-vehicle basis, using transparent models to assess impacts of candidate charger placements, thus improving the explainability of the results. In the developed framework, modeled BEVs are first generated along the road network to mimic historical traffic data and are simulated traveling along a given route according to a simplified vehicle model. During the simulation, the charger placement problem is initially relaxed to allow vehicles to charge at any node along the road network, and vehicle states are tracked to assess areas of high charging demand. Charging stations are then placed based on the results of the relaxed simulation, and suggested placements are evaluated via road network simulation with fixed charger locations. This proposed framework is applied to a sample problem of placing charging stations along five major highway corridors for Class 8 over-the-road electric trucks. A novel mixed integer programming (MIP) formulation is proposed to optimize charger placements based upon the expected charging demand. Constraints were imposed on the final placement results to limit expected wait times at each station and ensure a minimum threshold of trucking routes are viable for BEVs. The results demonstrate the flexibility and potential effectiveness of the developed model-based framework for scalable charger station deployment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

High-Efficiency, Medium-Voltage-Input, Solid-State-Transformer-Based 400-kW/1000V/400A Extreme Fast Charger for Electric Vehicles

Range anxiety and long battery charging time continue to be critical challenges to mass adaptation of EVs. A major identified gap to wider adoption of BEVs is the ability and availability to refuel quickly or to fast charge. Studies have shown that in areas where drivers have access to 50-kW or 120-kW fast charge stations, annual electric vehicle (EV) miles traveled (i.e., eVMT) increased by over 25%, even in cases where fast charging was used for 1% to 5% of total charging events. Charge stations of higher power not only alleviate the “range anxiety” and reduce the driver’s waiting time, but also requires less investment. Michigan Energy Office completed a study in early 2019 titled “Electric Vehicle Charger Placement Optimization in Michigan: Phase I – Highways”. This study finds a system with 150kW chargers, though more expensive individually, actually has lower total system cost when compared to a 50kW charging system when serving the same battery size EV. To be truly competitive to the ICEV refueling experience, even higher power stations are necessary. However, high power charge stations would create large power draws from the grid. If this occurs during peak demand periods, grid capacity could be overloaded. This problem needs to be addressed to reduce the impact on the electric utility infrastructure. The main goal of this project is to develop a 400-kW/400-A XFC system targeting total efficiency of 96.5 percent from the MVAC grid to a vehicle. The novel SST power cell topology, combined with a new silicon carbide (SiC) MOSFET device, enables a 3.5 percent improvement in system efficiency, a 50-percent smaller equipment footprint, and four times less weight than today’s DCFC systems. The SST technology would directly utilize MVAC at 4.8-kV or 13.2-kV. This would eliminate the line frequency transformer (LFT), which steps down medium-voltage AC to 3-Phase 480-V line-to-line voltage in current DCFC systems.

42 ENGINEERING↗

DOE OSTI2024

A Vehicle-to-Grid planning framework incorporating electric vehicle user equilibrium and distribution network flexibility enhancement

The rapid surge in electric vehicle (EV) adoption, coupled with advancements in charging technologies, emphasizes the critical necessity for expanding EV recharging infrastructure. Simultaneously, the Distribution Network (DN) encounters escalating challenges in meeting charging demand during peak traffic periods. Consequently, there is a mounting demand for the deployment of innovative Vehicle-to-Grid (V2G) technologies to augment the DN’s flexibility in power dispatch and alleviate travel costs for EV users. Hence, this paper proposes an EV-user-equilibrium-(UE)-constrained V2G planning framework that enhances flexibility in the DN. The framework aims to ascertain the optimal placement and capacity of EV charging stations (EVCSs) and V2G charging piles within the Transportation Network (TN). It takes into account the equilibrium condition stemming from competitive EV charging and routing behaviors alongside the optimal expansion of DN energy resources to accommodate the electricity supplied by the V2G piles. This study commences by analyzing EV drivers’ travel decisions, considering the influence of charging and V2G pile locations and sizes. Subsequently, we tackle the Traffic Assignment Problem with User Equilibrium (TAP-UE) model to characterize the steady-state traffic flow distribution of EVs. Following this, we formulate the optimization model for the Coordinated Power and Transportation Network (CPTN), which encompasses the optimal expansion of DN facilities and traffic flow regulation under UE conditions. To mitigate the computational complexity associated with the V2G planning model, we introduce a series of linearization methods to obtain a manageable Mixed-Integer Linear Programming (MILP) solution. Finally, to validate the efficacy of our proposed planning framework, we apply it to two test systems, including a real-world case study. Through these case studies, we explore the necessity and potential benefits of V2G technologies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Grid-Enhanced, Mobility-Integrated Network Infrastructures for Extreme Fast Charging (GEMINI-XFC)

GEMINI-XFC will use first-of-a-kind integrated high-fidelity grid and transport modeling to identify effective pathways for widespread electrification, to design and evaluate integrated vehicle-grid control schemes, and to optimize electric vehicle integration at a full regional scale with individual customer resolution. Control variables will include: Charging station design and planning (where and what kind of charging stations); EV route scheduling considering grid "status"; and Dispatch of behind-the-meter energy storage and legacy voltage control actuators (on-load tap changes, voltage regulators, capacitors).

DIRECT ENERGY CONVERSION,POWER TRANSMISSION AND DI↗

DOE OSTI2026

Cost of convenience in long-dwell public electric vehicle charging

This paper presents a comprehensive methodology for quantifying trade offs between the cost of charging at public Level-2 charging stations and the inconvenience of using those stations. The paper also includes a methodology for generating synthetic travel itineraries of tens of thousands of drivers and for sizing and placing charging infrastructure to support those itineraries. Inconvenience is quantified by a weighted sum of the time a driver spends traveling between their intended destination and the charging station; weights are derived from the activity interrupted by the driver’s need to move their car. We demonstrate a trade off between cost of charging and inconvenience by varying how far away from their destination drivers are willing to charge. Our results demonstrate the importance of access to stations and suggest that other methods for increasing access, such as increasing the spatial density of stations, could significantly impact charging cost and inconvenience. Moreover, average energy cost may be reduced by introducing managed charging, which also affects inconvenience. The framework presented here can be used to evaluate the overlapping effects of both access and charge management. Furthermore, the framework can be easily extended to include additional factors such as capital costs, enabling thorough evaluation and planning of regional Level-2 charging networks.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗

DOE OSTI2020

Fleet-wide Electrification Impacts Assessment for the Valley Transportation Authority

This report explores the long-term electrification opportunities for the Valley Transit Authority’s (VTA) transit bus fleet. The potential for transit bus electrification at VTA as well as the economic impacts of partial and complete electrification are explored. We use the Revenue Operation and Device Optimization model to determine the optimal charging, operation and lowest capital and operating cost solution to achieve different levels of electrification to meet their existing routes. This study finds that, relying on only depot charging, around 70% of the daily trips by VTA’s transit bus fleet can be replaced with battery electric buses (BEBs) today. The benefits and drawbacks of five methods for improving the electrification potential beyond that achievable with only depot charging are discussed including (1) increase charger power, (2) purchase of larger vehicle batteries, (3) en-route charging, (4) purchasing additional buses and swapping them to enable the existing routes/blocks1 to be met, and (5) route/block redesign. A strategy is developed to enable full fleet electrification by increasing charger power or allowing intraday charging as a proxy for the options mentioned above. This method allows us to develop an understanding of the impacts and trade-offs of full fleet electrification. Two charging strategies are examined. Immediate charging, when the bus is charged as soon as it arrives at a depot or en-route charging station, and smart charging, which uses a controller to determine the best times to charge to achieve the lowest charging cost, while maintaining the same trip schedules. Smart charging is effective at reducing the peak power consumption, which can be reduced by between 31% and 65% compared to immediate charging. This translates directly to lower electricity demand charges and lower costs for possible distribution system upgrades. The total lifetime net present value (NPV) costs for different scenarios are presented in Figure ES-1. Scenarios are separated into three sections. The first stacked bar on the left is the base case (business-as-usual) where all buses are diesel hybrids, the next four bars include partial and full fleet electrification utilizing only immediate charging, and the last four bars include partial and full fleet electrification utilizing smart charging. The results show that smart charging scenarios are within ±4% of the lifetime NPV cost of the diesel-hybrid only (business-as-usual) scenario. The scenarios with full fleet electrification (i.e., including intraday charging) are 4% lower cost and those with partial fleet electrification (i.e., without intraday charging) are 2%–3% higher. However, it is important to note that the intraday charging scenarios do not include any additional costs for the equipment necessary to achieve intraday charging (e.g., additional chargers, larger batteries, new route design). Additionally, it is worth noting that the Low Carbon Fuel Standard (LCFS) credit received for implementing electric buses is essential to achieving these results.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Charging location selection of an incoming electric vehicle for large EV depot infrastructure

As the use of renewable energy resources increases within charging infrastructures, the risk that charging an additional load within the charging infrastructure will create a violation of the applicable grid code also increases. In an embodiment, a charging location is selected for an incoming load based on a load flow analysis, to thereby mitigate the risk of a violation. This selection process may also account for the charging schedule of the load and the capacity of available charging stations (e.g., fast charging vs. slow charging).

Hafiz, Faeza↗

DOE OSTI2024

Physical Safety and Security at Electric Vehicle Charging Sites

This help sheet provides an overview of physical safety and security design elements for public EV charging stations and general best practices that can be considered for the safety and comfort of charging station customers.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

DOE OSTI2021

Overview of the Electrification of Transportation in Hawaii

This document is a summary of electric vehicle (EV) experiences in Hawaii. It is meant to be informative but does not present any new technical analysis except for the development of key lessons learned that could be applied in similar contexts. The electrification of transportation is essential for Hawaii's energy goal. An electrification of transportation strategy complements other energy policy goals, increases clean energy impacts, and provides customer value. By the end of 2020, there were over 12,000 EVs registered in Hawaii (about 1 percent of all cars). That number is expected to grow, based on the results from recent surveys and studies in Hawaii. Surveys pointed out the need for more charging stations, especially in places where people do business or park for long periods of the day. Participation in controlled charging programs should have attractive incentives since a majority of EV owners would not be willing to interrupt their EV charging for demand response. Various studies have confirmed the EV potential in Hawaii. For example, the JUMPSmart Maui demonstration project, a public-private partnership with Japan, helped to establish the EV charging station infrastructure in Maui and provided important information about charging behaviors. A critical backbone study commissioned by the utility recommended that 3,600 public chargers be installed by 2030 on the five islands, which confirms the need for infrastructure improvements expressed in earlier surveys. The process that emerged in Hawaii can be an example to other locations, which could heed the lessons from Hawaii's EV experiences: The importance of an overarching energy goal/objective based on a shared vision; planning and pilot projects; a strategic plan (roadmap) leveraging on initial experiences; evaluation of the effectiveness/success of actions; fine-tuning as needed; close regulatory oversight and stakeholder participation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Key Performance Indicators for Vehicle Grid Integration

Electric vehicle (EV) sales account for a rapidly growing portion of the light-duty vehicle market and a portion of medium and heavy-duty fleet vehicles. However, in many locations, charging stations will require costly utility grid upgrades with long lead times. Today, there are a few methods of smart charge management (SCM) which can reduce the costs and wait times for electric vehicle supply equipment (EVSE) interconnection approvals, as well as reduce impacts of the charging stations on the grid and on EV driver transportation costs. It is crucial for the EV charging industry to understand the vehicle grid integration (VGI) requirements for EVSE to prevent adverse impacts from EVSE interconnections and assure that charging loads and interconnections are affordable.

33 - ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2019

International Space Station Spacecraft Charging Hazards: Hazard Identification, Management, and Control Methodologies, with Possible Applications to Human Spaceflight Beyond LEO

In this paper, we present an overview of how the International Space Station (ISS) safety engineering methodology directed to controlling extravehicular activity (EVA) crew electrical shock hazards, caused by ISS spacecraft charging, has evolved over the past 25+ years. Long-term measurements of ISS charging severity and frequency-of-occurrence, combined with detailed probabilistic analysis of EVA electric shock- circuit completion, led to a change in hazard control methodology. The requirement for two-fault tolerant EVA shock hazard control during all EVAs was replaced with a less operationally burdensome and risky EVA shock hazard detection and warning process. The applicability of event probability-based detection-and- warning processes to human spaceflight charging hazard control beyond low-earth orbit (LEO) is also considered.

Koontz, Steve↗

DOE OSTI2024

Kansas City, Missouri, Streetlight Electric Vehicle Charging: Strategies and challenges for site selection of streetlight electric vehicle infrastructure in Kansas City, Missouri (Final Report)

Public streetlight charging, whether on streets in central business districts or residential areas, provides easy charging access for apartment residents and homeowners alike. While most electric vehicle (EV) drivers charge at home, they do so in garages or on driveways they own. For renters and residents of multifamily housing (MFH), however, this may not be an option. EVs have a lower cost of ownership compared to conventional vehicles, and a used EV may be an affordable option for a lower-income household. But without easy access to charging, even a low-cost used EV may not be an option for a prospective buyer. An affordable curbside charging network has the potential to expand EV adoption into neighborhoods that have to date seen minimal interest and uptake of the technology and associated charging infrastructure. Streetlight charging networks can provide an economical, scalable, and effective approach to providing equitable and convenient charging. Metropolitan Energy Center (MEC) is dedicated to the mission of creating resource efficiency, environmental health, and economic vitality in the Kansas City region and beyond. Since 1983, MEC has provided resources, outreach, and training to make alternative fuels and energy efficiency commonplace. MEC led a streetlight charging pilot project that installed limited EV charging infrastructure on the streetlight system in Kansas City, Missouri, to demonstrate and test the benefits of curbside charging for EVs at existing on-street parking locations. The project aimed to cost-effectively expand the charging network in Kansas City to support residential charging and provide infrastructure in one or more charging deserts throughout the city. This pilot evaluates the impact and overall success of streetlight charging based on community feedback, utilization of charging infrastructure, technical feasibility, and cost. The project has pursued a data- and community-driven site selection process designed to identify sites with high demand and high opportunity for EV charging. This project was funded by the U.S. Department of Energy (DOE) and awarded to MEC through a competitive proposal process. The novelty and complexity of this project required an organization that could facilitate collaboration across levels of government, community members, and industry partners. For the past 25 years, through Kansas City Regional Clean Cities, MEC has worked with numerous public and private fleets on a variety of projects to improve the environmental performance and efficiency of the regional vehicle fleet. To advance affordable, efficient, and clean transportation efforts, DOE Clean Cities and Communities coalitions create local networks of public and private sector stakeholders and engage communities. Rooted within their local communities, the coalitions serve as experts and ambassadors, bringing to bear the collective knowledge, experience, and practical know-how of the entire network from within DOE, its national laboratories, and diverse stakeholders in the field. MEC and its project partners made in-kind contributions to leverage federal dollars for the benefit of the Kansas City community. Findings from this project will help determine the best applications for streetlight charging technologies to maximize funding impact and serve community needs. The team evaluated locations based on expected charging demand, technical feasibility, safety considerations, and enhanced charging network siting needs. Throughout the project, the team gathered feedback and evaluated ways to make public charging for EVs available to all community members. The insights will help Kansas City and other communities streamline future efforts to support EV drivers through public charging in the city right-of-way. Furthermore, this project will inform citywide guidance for future installations. MEC is committed to a transparent and publicly accessible approach that encourages the collaborative evaluation of streetlight charging. The project has engaged the community to proactively identify and evaluate the benefits and impacts of streetlight charging. It was a priority for the project to ensure the benefits of this pilot are distributed equitably to all members of the Kansas City community and that new charging opportunities and associated resources are available in diverse neighborhoods across the city. The charging infrastructure supports an affordable curbside charging network that will enable more drivers to choose EVs and provide easy charging access for all community members interested in driving an EV. The community feedback received through this project informed future resources and opportunities to make EVs more accessible to all members of the Kansas City community. MEC worked with several community partners on this project, including Missouri University of Science and Technology (MST), Pennsylvania State University (Penn State), the National Renewable Energy Laboratory (NREL); the city of Kansas City, Missouri; Evergy; Black and McDonald (B&M); LilyPad EV; EVNoire; and Westside Housing Organization (WHO). Project partners contributed to the cost match required for DOE grants through capital expenditures, personnel, and other in-kind contributions. Detailed descriptions of project team organizations can be found in Appendix A. Project Partners. Analysts at NREL and MST/PennState developed site maps based on demand and equity considerations. MEC conducted outreach to community members to garner input on project design and site selection, and received approval from the Missouri Public Service Commission (PSC) for Evergy’s EV charging station ownership. MEC worked with all partners to gather additional siting criteria and developed a site selection evaluation checklist, and partners conducted site visits to proposed installation sites. Next, B&M, Evergy, and the city executed all site agreements, conducted site-specific engineering design, acquired associated permits, and issued notices to proceed site by site or in small batches. Finally, from January to April 2023, the project team installed 23 EV charging stations built on Kansas City’s streetlight system in six council districts. Evergy will own, operate, and monitor the stations for 10 years, sharing charging data with MEC for at least 1 year.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

A Dynamic Pricing Method to Manage the Impact of EV Charging on the Grid Using RL

This work addresses the challenge of managing electrical vehicle (EV) charging loads on distribution feeders with the increase in deployment of fast charging stations. To mitigate the adverse impacts on feeder health, a novel dynamic grid-informed pricing approach is proposed. This approach leverages reinforcement learning (RL) to determine hourly charging prices based on real-time grid conditions. A synthetic environment was developed to train the reinforcement learning agent. A model of an IEEE 34-bus distribution feeder with EV charging stations has been developed in OpenDSS utilizing Caldera for realistic EV charging profiles. Test cases demonstrate that the dynamic pricing strategy achieves higher energy delivery to the EV end user at a lower cost compared to constant pricing methods, while lowering voltage deviations and congestion. This approach offers more granular price adjustments, responding dynamically to feeder conditions and potentially improving grid stability and efficiency. The communication architecture to implement this dynamic pricing method is described. This research contributes to the development of smart grid-informed charging solutions that can reduce the cost of charging to the end user while also helping the grid.

EV charging, dynamic pricing, grid-informed chargi↗

DOE OSTI2025

A dynamic pricing method to manage the impact of EV charging on the grid using RL

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Control and Optimization of Energy Storage System in Power Distribution System

The widespread adoption of electric vehicles (EVs) and transportation electrification is encumbered by two chief barriers: i) the limited driving range of EVs in the market today and ii) inadequate fast-charging infrastructure for long-distance trips. Extreme fast charging (XFC) technology can recharge EVs in less than 10 minutes for 200 miles range. Firstly, a novel robust optimization-based mixed integer linear programming model is proposed to size a battery energy storage system (BESS) and PV system in an XFCS. In this part, it is assumed that the sizing and location of the XFCS are known. Secondly, the aforesaid assumption is relaxed, and a strategic multi-period coordinated planning model is proposed to optimally site and size BESS-assisted charging stations in a highway transportation network and PV systems in a power distribution network by considering the coupling between both networks. Optimal operation and control of BESS-assisted EV charging stations are vital to alleviate the adverse impact of extreme fast charging of EVs on the host power network. A joint solution is proposed to mitigate the steady state and transient impact of extremefast charging of EVs and ensure grid-friendly integration of XFCSs with the host grid. Lastly, to make the operation of the XFCS cost-effective, a multi-layered energy management framework is proposed for the XFCS by considering forecast uncertainties, monthly demand charges reduction, and BESS degradation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

EV Charging Infrastructure Energization An Overview of Approaches for Simplifying and Accelerating Timelines to Processing EV Charging Load Service Requests

The United States has seen significant growth in electric vehicle (EV) adoption, leading to increased demand for EV charging infrastructure. Over the past decade, EV charging infrastructure site developers, site hosts, and electric distribution utilities have navigated the process to integrate chargers onto the electric grid. Site developers and site hosts have raised the alarm that the integration process for high-powered EV charging projects does not meet the needs of the EV market for timeliness or cost. High-powered charging stations typically require a load service request or an agreement with the local utility to connect to the grid. The process of energizing a new high-powered charging site can be complex and time-consuming, often taking up to 2 years. This timeline is the result of current utility energization processes having been designed for construction projects that take longer to build (i.e., buildings). The specific challenges stem from various factors, including compartmentalization in application processes, the integration of EV charging process approvals with other distributed energy resources (DERs), and the need to ensure grid reliability. The energization process needs to evolve to meet the growing demand for high-powered EV charging. This white paper compiles information gathered through various conversations with key stakeholders, including utilities, utility regulators, EV charging operators, site developers, and authorities having jurisdiction (AHJ) as well as through an extensive literature review. This document identifies the challenges and provides potential solutions to streamline the process of connecting EV charging infrastructure to the power grid in the United States, serving as a starting point for future conversations around these solutions. The solutions noted in this white paper require collaborative efforts among utilities, regulators, and EV charging infrastructure developers to streamline the grid connection process for EV charging infrastructure. They are broadly organized into four areas: 1. Increase data access and transparency: Develop automated load service request tools, integrate hosting capacity and load service request analyses, incorporate EV adoption forecasts, and provide transparency on the processing queue. 2. Improve energization processes and timing: Create fast-track options based on prescreening criteria, provide flexibility or phased approvals in the load service request/interconnection process, build internal knowledge within utilities about EV charging technologies, and provide standardized workforce training. 3. Promote economic efficiency: Right size distribution components to accurately reflect the load requirements of EV charging infrastructure, make proactive investments in grid infrastructure based on EV adoption forecasts and growth projections, and consider energy equity and environmental justice factors such as equitable access to EV charging when planning infrastructure. 4. Improve grid reliability and resilience: Use load management/power control systems (PCS) at EV charging stations, adopt and implement harmonized standards for communication protocols and information models between the EV charging and grid control infrastructure, and address cybersecurity considerations by implementing robust security measures and standards for EV charging infrastructure—with particular emphasis on clarifying the security requirements for the interface to the grid. The objective of the solutions proposed in this white paper is to accelerate the timeline and decrease costs associated with connecting EV charging infrastructure to the grid. Electric utilities, utility regulators, EV charging infrastructure developers, and site hosts will first need to understand which solutions are available in their service territory, and if warranted, which combination of solutions would support their specific needs. Through the successful implementations of solutions at scale detailed here, industry will demonstrate a new and innovative ecosystem where timely deployment and energization of EV charging infrastructure with greater grid resiliency and reliability is a reality.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Peer-to-peer communication control for resilient operations of networked cyberphysical systems

This report includes two main accomplishments of the peer-to-peer communication control for resilient operation of networked microgrids project in FY24, which include a scheme for cyberattack-aware coordination of networked microgrids for supporting voltages of bulk power systems and a scheme for price signal-based operations of EV-rich networked microgrids with mixed ownership. First, the cyberattack-aware scheme enables networked microgrids to distributedly determine the amount of reactive power injection to support the voltage of bulk power system (BPS) in a fair manner. In this scheme, a risk-informed algorithm is presented to generate the peer-to- peer (P2P) communication graph with minimal risk of attack on communication links. To deal with cyberattacks on MG controllers, the resilient consensus algorithm (CA) is utilized for MG controllers to robustly estimate the total reactive power headroom, from which the MGs can accurately provide the needed amount of reactive power injection for supporting the voltage of BPS. The CA implementation and performance within the P2P communication framework are demonstrated on the IEEE 39-bus system with 6 microgrids contained in the distribution feeder under different cyberattack scenarios. Second, the price-based scheme enables the usage of the real-time price signal for the operations of electric vehicle (EV)-rich networked-microgrids with mixed ownership, in which not all the microgrids can communicate with the distribution system operator (DSO). In this scheme, a max consensus is introduced to enable the real-time price signal to be propagated from the DSO to all the microgrids, from which each microgrid controller will manage the DERs to balance the load demand and the power injection from the EV charging stations within its microgrid. Numerical results over one day with 288 slots of 5-minute intervals on the modified 123-node test feeder including 3 microgrids with high penetration of EV are presented to evaluate how the price signal affects the operations of networked microgrids under different charging strategies of the EV charging stations. The result indicates that our proposed EVCS (dis)charging strategy, which leverages the flexibility of EVs to support the grid through discharging during peak demand, proves to be a cost-effective solution that reduces operational costs while improving the social welfare of EV charging.

24 POWER TRANSMISSION AND DISTRIBUTION↗