Publications

The 2-year Puerto Rico Grid Resilience and Transition to 100% Renewable Energy Study analyzed stakeholder-driven pathways to Puerto Rico’s clean energy future. Outputs relating to electricity demand modeling were partially informed by estimates of electric vehicle adoption across all classes of medium- and heavy-duty vehicles (MHDVs), and the ensuing charging loads. To create these estimates, the team developed a transportation model for MHDVs in Puerto Rico to estimate the amount and geospatial distribution of energy used. Charging schedules for the different end uses of MHDVs were then used to construct electric load shapes assuming a portion of those vehicles would be replaced by battery electric counterparts. Study results showed that, by 2050, electric vehicles may constitute roughly 50% of the MHDV population in Puerto Rico. The resulting electrical demand curve attributable to MHDV charging showed that, for solar energy-based electrical systems with limited energy storage, this demand may create challenges unless appropriately managed either on the demand or supply side.

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Electric Vehicles (EV) present a unique challenge to electric power system (EPS) operations because of the potential magnitude and timing of load increases due to EV charging. Time-of-Use (TOU) electricity pricing is an established way to reduce peak system loads. It is effective at shifting the timing of some customer-activated residential loads – such as dishwashers, washing machines, or HVAC systems – to off-peak periods. EV charging, though, can be larger than typical residential loads (up to 19.2 kW) and may have on-board controls that automatically begin charging according to a pre-set schedule, such as when off-peak periods begin. To understand and quantify the potential impact of EV charging's response to TOU pricing, this paper simulates 10 distribution feeders with predicted 2030 EV adoption levels. The simulation results show that distribution EPS experience an increase in peak demand as high as 20% when a majority of the charging begins immediately after on-peak times end, as might occur if EV charging is automatically scheduled. However, if charging start times are randomized within the off-peak period, EV charging is spread out and the simulations showed a decrease in the peak load to be 5% lower than results from simulations that did not implement TOU rates.

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This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO), including some updates from the previous report SAND2022-0215, to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

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Reducing the duration and frequency of blackouts in remote communities poses an engineering challenge for grid operators. Outage effects can also be mitigated locally through microgrids. This paper develops a systematic procedure to account for these challenges by creating microgrids prioritizing high value assets within vulnerable communities. Nighttime satellite imagery is used to identify vulnerable communities. Using an asset classification and rating system, multi-Asset clusters within these communities are prioritized. Infrastructure data, geographic information systems, satellite imagery, and spectral clustering are used to form and rank microgrid candidates. A microgrid sizing algorithm is included to guide through the microgrid design process. An application of the methodology is presented using real event, location, and asset data.

This guide is meant to assist communities – from residents to energy experts to decision makers – in developing a conceptual microgrid design that meets site-specific energy resilience goals. Using the framework described in this guidebook, stakeholders can come together and start to quantify site-specific vulnerabilities, identify the most significant risks to delivery of electricity, and establish electric outage tolerances across the community. In addition to establishing minimum service needs, this framework encourages communities to consider broader sustainability goals and policy constraints and begin to estimate up-front costs associated with the installation of alternative microgrid solutions. The framework guides a community through data collection and a high-level assessment of its needs, constraints, and priorities, prior to engaging engineers, vendors, and contractors. The first sections of this guidebook provide a high-level primer on electric systems. The latter sections include guidance for step-by-step data gathering and analysis of site conditions. The ultimate product resulting from the stepwise approach is a conceptual microgrid design. A conceptual design is defined as an initial design (10%-20% complete) that considers the specific threats, needs, limitations, and investment options for a given location. Going through this exercise and developing the conceptual microgrid design as a community ensures the same community members who will ultimately live with the solution are the developers of its foundational design. Often, these are also the very same people who understand system tolerances and needs the best and are therefore the ideal candidates for establishing these criteria. Especially when it comes to evaluating critical infrastructure, it is the community that best understands the most critical services. The framework is intended to facilitate a systematic approach to planning for resilience and provide a deeper understanding of how to use a framework to make decisions around microgrid solutions. Like many processes where tradeoffs need to be considered, this is often an iterative process. If this guide serves to help educate and empower communities who are beginning the process of deploying a microgrid, it has met the goal of its authors.

Limited access to transmission lines after a major contingency event can inhibit restoration efforts. After Hurricane Maria, for example, flooding and landslides damaged roads and thus limited travel. Transmission lines are also often situated far from maintained roadways, further limiting the ability to access and repair them. Therefore, this paper proposes a methodology for assessing Puerto Rico's infrastructure (i.e., roads and transmission lines) to identify potentially hard to reach areas due to natural risks or distance to roads. The approach uses geographic information system (GIS) data to define vulnerable areas, that may experience excessive restoration times. The methodology also uses graph theory analysis to find transmission lines with high centrality (or importance). Comparison of these important transmission lines with the vulnerability results found that many reside near roads that are at risk for landslides or floods.

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO) to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

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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.

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Electric vehicles (EVs) represent an important socio-economic development opportunity for islands and remote locations because they can lead to reduced fuel imports, electricity storage, grid services, and environmental and health benefits. This paper presents an overview of opportunities, challenges, and examples of EVs in islands and remote power systems, and is meant to provide background to researchers, utilities, energy offices, and other stakeholders interested in the impacts of electrification of transportation. The impact of uncontrolled EV charging on the electric grid operation is discussed, as well as several mitigation strategies. Of particular importance in many islands and remote systems is taking advantage of local resources by combining renewable energy and EV charging. Policy and economic issues are presented, with emphasis on the need for an overarching energy policy to guide the strategies for EVs growth. The key conclusion of this paper is that an orderly transition to EVs, one that maximizes benefits while addressing the challenges, requires careful analysis and comprehensive planning.

An increase in Electric Vehicles (EV) will result in higher demands on the distribution electric power systems (EPS) which may result in thermal line overloading and low voltage violations. To understand the impact, this work simulates two EV charging scenarios (home-and work-dominant) under potential 2030 EV adoption levels on 10 actual distribution feeders that support residential, commercial, and industrial loads. The simulations include actual driving patterns of existing (non-EV) vehicles taken from global positioning system (GPS) data. The GPS driving behaviors, which explain the spatial and temporal EV charging demands, provide information on each vehicles travel distance, dwell locations, and dwell durations. Then, the EPS simulations incorporate the EV charging demands to calculate the power flow across the feeder. Simulation results show that voltage impacts are modest (less than 0.01 p.u.), likely due to robust feeder designs and the models only represent the high-voltage (“primary”) system components. Line loading impacts are more noticeable, with a maximum increase of about 15%. Additionally, the feeder peak load times experience a slight shift for residential and mixed feeders (≈1 h), not at all for the industrial, and 8 h for the commercial feeder.

The rapid increase in penetration of distributed energy resources on the electric power distribution system has created a need for more comprehensive interconnection modeling and impact analysis. Unlike conventional scenario-based studies, quasi-static time-series (QSTS) simulations can realistically model time-dependent voltage controllers and the diversity of potential impacts that can occur at different times of year. However, to accurately model a distribution system with all its controllable devices, a yearlong simulation at 1-second resolution is often required, which could take conventional computers a computational time of 10 to 120 hours when an actual unbalanced distribution feeder is modeled. This computational burden is a clear limitation to the adoption of QSTS simulations in interconnection studies and for determining optimal control solutions for utility operations. The solutions we developed include accurate and computationally efficient QSTS methods that could be implemented in existing open-source and commercial software used by utilities and the development of methods to create high-resolution proxy data sets. This project demonstrated multiple pathways for speeding up the QSTS computation using new and innovative methods for advanced time-series analysis, faster power flow solvers, parallel processing of power flow solutions and circuit reduction. The target performance level for this project was achieved with year-long high-resolution time series solutions run in less than 5 minutes within an acceptable error.

This paper describes a co-simulation environment used to investigate how high penetrations of electric vehicles (EV s) impact a distribution feeder during a resilience event. As EV adoption and EV supply equipment (EVSE) technology advance, possible impacts to the electric grid increase. Additionally, as weather related resilience events become more common, the need to understand possible challenges associated with EV charging during such events becomes more important. Software designed to simulate vehicle travel patterns, EV charging characteristics, and the associated electric demand can be integrated with power system software using co-simulation to provide more realistic results. The work in progress described here will simulate varying EV loading and location over time to provide insights about EVSE characteristics for maximum benefit and allow for general sizing of possible micro grids to supply EVs and critical loads.

The benefits and risks associated with Volt-Var Curve (VVC) control for management of voltages in electric feeders with distributed, roof-top photovoltaic (PV) can be defined using a stochastic hosting capacity analysis methodology. Although past work showed that a PV inverter's reactive power can improve grid voltages for large PV installations, this study adds to the past research by evaluating the control method's impact (both good and bad) when deployed throughout the feeder within small, distributed PV systems. The stochastic hosting capacity simulation effort iterated through hundreds of load and PV generation scenarios and various control types. The simulations also tested the impact of VVCs with tampered settings to understand the potential risks associated with a cyber-attack on all of the PV inverters scattered throughout a feeder. The simulation effort found that the VVC can have an insignificant role in managing the voltage when deployed in distributed roof-top PV inverters. This type of integration strategy will result in little to no harm when subjected to a successful cyber-attack that alters the VVC settings.

An overall capacity assessment and an analysis of the system's X/R ratios for six actual distribution feeders was conducted to characterize the voltage response to various levels of distributed Electric Vehicle Supply Equipment (EVSE). The evaluation identified the capacity of the system at which a voltage violation occurred. This included a review of the uncontrolled and controlled cases to quantify the value of injecting reactive power as the grid voltage decreases. The evaluation found that the implementation of a Volt-Var curve with a global voltage reference provided a notable increase in capacity. A local reference voltage, measured at the point of common coupling, did not increase the capacity of every feeder in the experiment. The review of the X/R line properties using a Principal Component Analysis (PCA) identified groups within the six feeders that corresponded with each system's voltage response rate. This suggests the X/R ratios provide a direct prediction of the feeder's ability to avoid voltage violations while charging EVs.

Accurate distribution secondary low-voltage circuit models are needed to enhance overall distribution system operations and planning, including effective monitoring and coordination of distributed energy resources located in the secondary circuits. We present a full-scale demonstration across three real feeders of a computationally efficient approach for estimating the secondary circuit topologies and parameters using historical voltage and power measurements provided by smart meters. The method is validated against several secondary configurations, and compares favorably to satellite imagery and the utility secondary model. Feeder-wide results show how much parameters can vary from simple assumptions. Model sensitivities are tested, demonstrating only modest amounts of data and resolutions of data measurements are needed for accurate parameter and topology results.

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Distribution system analysis requires yearlong quasi-static time-series (QSTS) simulations to accurately capture the variability introduced by high penetrations of distributed energy resources (DER) such as residential and commercial-scale photovoltaic (PV) installations. Numerous methods are available that significantly reduce the computational time needed for QSTS simulations while maintaining accuracy. However, analyzing the results remains a challenge; a typical QSTS simulation generates millions of data points that contain critical information about the circuit and its components. This paper provides examples of visualization methods to facilitate the analysis of QSTS results and to highlight various characteristics of circuits with high variability.

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