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At least 127 records · Page 7

VOLTTRON: An Open-Source Software Platform of the Future

Buildings consume over 30% of the total primary energy consumed worldwide and contribute to a third of the world greenhouse gas (GHG) emissions. In the US, buildings consume over 40% of total energy and contribute to almost 38% in GHG emissions. In addition, buildings in the US consume over 75% of electricity generated. The need to mitigate climate change is driving efforts to make the US generation cleaner, which has given new impetus for improving the operating efficiency of buildings at scale and increasing the hosting capacity of distributed renewable generation. Because these renewable generation technologies are variable in nature, they create significant short- and long-term imbalance between supply and demand.

54 ENVIRONMENTAL SCIENCES↗

Power Hardware-in-the-Loop Testing of a Smart Distribution System

This paper presents the results of the third and final phase of the National Renewable Energy Lab (NREL) INTEGRATE demonstration: Smart Distribution. For this demonstration, high penetrations of solar PV and wind energy systems were simulated in a power hardware-in-the-loop set-up using a smart distribution test feeder. Simulated and real DERs were controlled by a real-time control platform, which manages grid constraints under high clean energy deployment levels. The power HIL testing, conducted at NREL's ESIF smart power lab, demonstrated how dynamically managing DER increases the grid's hosting capacity by leveraging active network management's (ANM) safe and reliable control framework. Results are presented for how ANM's real-time monitoring, automation, and control can be used to manage multiple DERs and multiple constraints associated with high penetrations of DER on a distribution grid. The project also successfully demonstrated the importance of escalating control actions given how ANM enables operation of grid equipment closer to their actual physical limit in the presence of very high levels of intermittent DER.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Distribution System Planning for Growth in Residential Electric Vehicle Adoption

Anticipated growth in Electric Vehicles (EV) adoption could stress distribution system circuits beyond their original design limits. Uncertainties related to when households will begin buying EVs in large numbers challenges existing distribution system planning approaches and complicates efforts to assess grid impacts. Anticipated location, timing, and demand for EV charging is critical information for distribution system planners as it guides investment strategies for infrastructure upgrades to ensure continued reliability. This research makes two significant contributions: an EV adoption model that uses socioeconomic data to forecast location and year-specific adoption patterns through a bottoms-up approach, and an EV hosting capacity assessment methodology that offers improvements to current utility planning and asset management practices for infrastructure investments. Both contributions are applied to a Southern California Edison feeder in 7 adoption years from 2025 to 2050, with the results indicating that they are likely valuable additions to distribution systems planning capabilities.

Sridhar, Siddharth↗

Residential Advanced Photovoltaic Inverter Pilot Study Results for Select Distribution Secondaries in Hawai'i

Penetration levels of residential rooftop solar photovoltaic (PV) generation on the Hawaiian island of O'ahu have increased significantly in recent years, with many circuits now at the limits of their PV hosting capacity. NREL and Hawaiian Electric have used this opportunity to deploy advanced inverters at new customer installations that would not otherwise have been approved, as a pilot study to gain first-hand operational field data and experience on the impact of inverter-based grid support functions. At each customer location, the deployed advanced inverters were remotely controlled and monitored. Additionally, customer AMI (smart meter) and secondary distribution transformer data was also available for the analysis. The analysis focused on quantifying the customer behind-the-meter voltage rise due to PV production, determining the customer voltage rise with advanced inverters in unity power factor as well as advanced grid support function modes, and estimating the curtailment when inverters have Volt-VAR and Volt-Watt enabled. The results show that: 1) Behind-the-meter voltage rise is often not negligible. 2) An inverter's operating point on a Volt-VAR/Volt-Watt curve depends strongly on the design of the secondary system on which it is installed. 3) The mitigation of voltage rise on the secondary system using Volt-VAR may not be effective if only a small proportion of inverters have grid support enabled, or if the reactance/resistance (X/R) ratio of the grid is low. 4) Volt-Watt curtailment can be estimated using co-located irradiance sensors but there may be opportunities to estimate it using inverter internal electrical measurements. 5) Estimated curtailment is very low for the locations analyzed to date.

14 SOLAR ENERGY↗

Voltage Calculations in Secondary Distribution Networks via Physics-Inspired Neural Network Using Smart Meter Data

The increasing penetration of distributed energy resources (DERs) leads to voltage issues across distribution networks, necessitating voltage calculations by utilities. Electric model-free voltage calculation offers an enticing solution. However, most researches mainly focus on primary distribution networks ignoring secondary distribution networks and commonly overlook extreme voltage case calculations, which require the model’s extrapolation abilities. Here, in addressing the gaps, this paper presents a customized physics-inspired neural network (PINN) model, the structure of which is inspired by the derived coupled power flow model of primary-secondary distribution networks. To ensure precision and rapid convergence, a crafted training framework for the PINN model is proposed. The PINN’s “structure-mimetic” design enables superior extrapolation for unseen scenarios and enhances physical information awareness. We demonstrate this through two applications: hosting capacity analysis and customer-transformer connectivity. The effectiveness and advantages of the proposed PINN model are validated on two public testing systems and one utility distribution feeder model.

Distribution network↗

Voltage Estimation in Low-Voltage Distribution Grids with Distributed Energy Resources

Present distribution grids generally have limited sensing capabilities and are therefore characterized by low observability. Improved observability is a prerequisite for increasing the hosting capacity of distributed energy resources such as solar photovoltaics (PV) in distribution grids. In this context, this paper presents learning-aided low-voltage estimation using untapped but readily available and widely distributed sensors from cable television (CATV) networks. The cable broadband sensors offer timely local voltage magnitude sensing with 5-minute resolution and can provide an order of magnitude more data on the time-varying state of a secondary distribution system than currently deployed utility sensors. The proposed solution incorporates voltage readings from neighboring CATV sensors, taking into account spatio-temporal aspects of the observations, and estimates single-phase voltage magnitudes at all non-monitored low-voltage buses using random forests. The effectiveness of the proposed approach was demonstrated using a multi-phase 1572-bus feeder from the SMART-DS data set for two case studies passive distribution feeder (without PV) and active distribution feeder (with PV). The analysis was conducted on simulated data, and the results show voltage estimates with a high degree of accuracy, even at extremely low percentages of observable nodes.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Supervised Learning for Distribution Secondary Systems Modeling: Improving Solar Interconnection Processes

The current interconnection process and hosting capacity analysis for distributed energy resources (DERs), such as photovoltaics (PV) and battery energy storage systems, are based on analyzing grid network constraints (voltage and thermal) using only medium-voltage distribution network models. This is because most utilities do not have secondary low-voltage system models that connect service transformers and residential customers. This is important because in many cases the main impact of interconnecting DERs could occur on the low-voltage distribution systems. This paper proposes a supervised learning method to approximate local secondary models to improve the interconnection process. The proposed supervised learning method includes a decision tree model that predicts the secondary topology and a logistic regression model that predicts conductor types. The case studies demonstrate the benefits of including secondary low-voltage circuits in the interconnection process. We report the proposed modeling methodology is readily scalable and thus can reduce the cost and effort of PV interconnection for the industry and stakeholders.

14 SOLAR ENERGY↗

Clustering Method and Representative Feeder Selection for the California Solar Initiative

The screening process for DG interconnection procedures needs to be improved in order to increase the PV deployment level on the distribution grid. A significant improvement in the current screening process could be achieved by finding a method to classify the feeders in a utility service territory and determine the sensitivity of particular groups of distribution feeders to the impacts of high PV deployment levels. This report describes the utility distribution feeder characteristics in California for a large dataset of 8,163 feeders and summarizes the California feeder population including the range of characteristics identified and most important to hosting capacity. The report describes the set of feeders that are identified for modeling and analysis as well as feeders identified for the control group. The report presents a method for separating a utility's distribution feeders into unique clusters using the k-means clustering algorithm. An approach for determining the feeder variables of interest for use in a clustering algorithm is also described. The report presents an approach for choosing the feeder variables to be utilized in the clustering process and a method is identified for determining the optimal number of representative clusters.

14 SOLAR ENERGY↗

Determining the Impact of Steady-State PV Fault Current Injections on Distribution Protection

This report investigates the fault current contribution from a single large PV system and the impact it has on existing distribution overcurrent protection devices. Assumptions are made about the modeling of the PV system under fault to perform exhaustive steady - state fault analyses throughout distribution feeder models. Each PV interconnection location is tested to determine how the size of the PV system affects the fault current measured by each protection device. This data is then searched for logical conditions that indicate whether a protection device has operated in a manner that will cause more customer outages due to the addition of the PV system. This is referred to as a protection issue , and there are four unique types of issues that have been identified in the study. The PV system size at which any issues occur are recorded to determine the feeder's PV hosting capacity limitations due to interference with protection settings. The analysis is carried out on six feeder models. The report concludes with a discussion of the prevalence and cause of each protection issue caused by PV system fault current.

30 DIRECT ENERGY CONVERSION↗

The Cost of Distribution System Upgrades to Accommodate Increasing Penetrations of Distributed Photovoltaic Systems on Real Feeders in the United States

The increasing deployment of distributed photovoltaic systems (DPV) can impact operations at the distribution level and the transmission level of the electric grid. It is important to develop and implement forward-looking approaches for calculating distribution upgrade costs that can be used to inform system planning, market and tariff design, cost allocation, and other policymaking as penetration levels of DPV increase. Using a bottom-up approach that involves iterative hosting capacity analysis, this report calculates distribution upgrade costs as a function of DPV penetration on three real feeders - two in California and one in the Northeastern United States.

14 SOLAR ENERGY↗

Advanced Inverter Voltage Controls: Simulation and Field Pilot Findings

This report describes work performed by the Hawaiian Electric Companies and the National Renewable Energy Laboratory (NREL) to model and simulate advanced inverter grid-support functions and to validate and expand on those simulations through a field pilot study. This work builds on earlier research, referred to as the Voltage Regulation Operational Strategies study. The objective of both is to investigate functionalities available in most photovoltaic (PV) inverters to modulate active and reactive power autonomously based on local voltage measurements for the purpose of mitigating off-nominal grid voltage conditions. Specifically of interest are volt/volt-ampere reactive (VAR) control and volt/watt control, the effect of those functions on quasi-steady-state feeder voltages, and the impact of the functions on PV energy production. Because volt/VAR and volt/watt control autonomously adjust inverter output based on local conditions without requiring communication with any other devices, they are good candidates to increase PV hosting capacity when it is limited by voltage constraints in a feeder with very large numbers of PV systems.

14 SOLAR ENERGY↗

CSI4 Analysis to Inform California Grid Integration Rules for PV

Sandia National Laboratories (Sandia) and Electric Power Research Institute, Inc. (EPRI) are collaborated to develop guidelines for effective use of smart inverters given the unique hosting capacity of the California grid. A key outcome of this project will be recommended settings for the smart inverter functions currently being considered as part of the modification to Rule 21. Additionally, this project will provide thresholds of smart inverter set points for which manufacturers can specify their equipment and suggested default settings that are applicable for most utility needs and feeder configurations in California. These results will directly contribute to the goals of the California Public Utilities Commission (CPUC) to enable and enhance integrating PVs on the electric grid.

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New Approaches to Distributed PV Interconnection: Implementation Considerations for Addressing Emerging Issues

As the cost of installing distributed solar photovoltaics (PV) has fallen, the number of requests to interconnect PV systems to distribution grids has risen substantially in many utility service areas. In response, states and utilities are modifying their interconnection practices to address new DER challenges. This report examines emerging issues associated with interconnecting residential- and commercial-scale PV to facilitate sharing of lessons learned and best practices across jurisdictions. It covers issues related to allocating and understanding costs, understanding grid conditions to inform PV siting, interconnecting storage, automating processes, and requiring the availability of advanced inverter functions that can help increase PV hosting capacity. This report builds on an earlier study that reviews interconnection practices and costs across the western states (Bird et al. 2018). Both reports were developed as part of a larger, joint project between the Western Interstate Energy Board and the National Renewable Energy Laboratory, funded by the U.S. Department of Energy's Solar Energy Technologies Office, that examines barriers to distributed PV in the 11 states within the Western Interconnection.

14 SOLAR ENERGY↗

Open V2X at ESIF: June 2015 - February 2017 (Final Report)

The University of Delaware (UD), and their industrial partners, have carried out testing, documentation, and standards development at ESIF. These activities are intended to help meet the objective of NREL's INTEGRATE program, to enable clean energy technologies to increase the hosting capacity of the grid, by providing grid services in a holistic manner using an open source, interoperable platform. In particular, the 'Open V2X at ESIF' project worked to facilitate standardization and interoperability for Electric Vehicle technologies, including battery electric vehicles and plug-in hybrid vehicles (hence EV) to provide grid services. This was done by interconnecting and testing EV and EVSE (charging station) hardware from different manufacturers, by developing standards and test protocols for standards, and by documenting new protocols to be proposed as standards. Contractor-supplied equipment used in the testing included Vehicle Smart Links (VSLs) installed in an EV and School Bus, single-phase and three-phase charging stations, and an optimizing aggregator (software running on a server, hence just 'Aggregator'). The Aggregator software was developed by U of Delaware and is now licensed to Nuvve Corp for commercial development, and licensed to NRG/EVgo for demonstrations. The Aggregator monitors capacity and operations of the EVs and EVSEs, dispatches them in response to requests, and reports their operations and metered power flow. In operation, as vehicles move, plug-in and unplug, the aggregator tracks the total capacity available within a defined jurisdiction or RTO (Regional Transmission Organization, synonym for ISO or TSO), and dispatches part or all of that capacity based on market or direct dispatch signals from a grid entity. The VSL is the data acquisition and control module in the resource itself (EVSE or EV), with rich communication between a single Optimizing Aggregator and many VSL (one in each resource). All requests and dispatch events are also logged by the aggregator to make the provision of grid power auditable. For these tests, the dispatch was typically a recorded signal, the VSL was in the EV or EVSE, and the recording of experimental performance was often carried out by the normal logging functions of the aggregator.

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DEEP Solar: Data DrivEn Modeling and Analytics for Enhanced System Layer ImPlementation

Realizing the SETO 2030 mission of reducing solar energy costs to 3-5 c/kWh will require innovative enabling research on effective, cost-efficient integration of local PV within distribution systems. However, the intermittent and variable nature of PVs compels operators to impose conservative hosting capacity constraints. Given the extremely high variability of (intermittent and unpredictable) solar energy generation, relaxing the capacity constraints (which are currently around 15%) and achieving 100% or greater integration of renewables will require a fundamental transformation of the power grid via the utilization of exponentially larger amounts of AMI enabled fine-grained data. To address the challenges in increasing the penetration of renewable energy based DERs, this project envisions an Enhanced System Layer (ESL) at the distribution network level that is reliable, cost-effective and scalable to millions of Distributed Energy Resources (DERs)/devices. This includes developing: 1) Transformative and highly scalable machine learning based predictive analytics tools that plug into distribution system planning and provide real-time situational awareness at the distribution level for short and long-term operational planning. The tools will be built using novel data-driven energy models of millions of active nodes with AMI, 2) Adaptive stochastic analysis and optimization algorithms for real-time grid operations, 3) Dynamic Scenario Analysis using parallel Cloudenabled implementations with < 1 minute computational cycle times.

14 SOLAR ENERGY↗

CyDER: A Cyber Physical Co-simulation Platform for Distributed Energy Resources in Smartgrids

The CyDER project aimed at developing an open-source, modular and scalable co-simulation platform for power grids with large shares of Distributed Energy Resources (DERs). The project partners are the Lawrence Berkeley National Lab (LBNL), Lawrence Livermore National Lab (LLNL), PG&E, SolarCity, and ChargePoint. The prime recipient is LBNL; SolarCity and ChargePoint were partners for the project’s first two years. Increased DER integration introduces a number of challenges in power grid operation including a more dynamic interaction between the transmission grid and distribution grids, and increased modeling complexity. Although specialized software exists to precisely model different components of the power system, it is far from trivial to integrate all various models and perform a holistic simulation. Instead of replicating all models in a common simulation program, a commonly accepted approach to tackle this model diversity is to couple third-party simulators and models through a co-simulation platform that coordinates information exchange among the various components. Following this line of research, this project’s objective was to develop a co-simulation platform based on a widely accepted industrial standard called Functional Mockup Interface (FMI). Within this process, the project developed models compliant with the FMI standard, called Functional Mockup Units (FMUs), and used them to perform various operational and planning power system analyses. Relying and building upon an industrial standard is the main differentiation of this project compared with previous or parallel efforts in the co-simulation area. Particular emphasis was put on delivering software utilities to facilitate setting up and running co-simulations by end-users. Furthermore, a strong aspect of this project is demonstrating that co-simulation techniques can be used to perform Hardware-in-the-Loop (HIL) simulations that couple software components (e.g., simulated models) with hardware components (e.g., real devices such PV systems and batteries). The long-term goal of CyDER project is to help establish FMI as a powerful standard for co-simulation and promote adoption by electric utilities and other interested stakeholders. The main accomplishments of the project include the development of several FMUs including distribution and transmission grid models, PV inverters with Volt/Var/Watt controllers, batteries, and predictive optimal controllers. Additionally, a unique software package was developed, called SimulatorToFMU, which is capable of exporting any Python-driven simulator or Python script as an FMU. This is an important contribution towards establishing FMI as one of the main co-simulation standards, because more and more third-party programs for sub-system modeling and simulation are delivered with Python APIs. The CyDER platform was used to perform PV hosting capacity analyses in real utility feeders with and without smart inverter controls, battery storage, and EV charging. Smart inverter controls include conventional Volt/Var/Watt controls for reactive power support and active power curtailment, but also predictive controls that optimize the charging and discharging profile of the battery connected on the DC side in order to minimize the customer’s economic benefit. Finally, an important result of this project is delivering an experimental setup that consists of residential-scale PV inverters with battery storage, a real-time grid simulator with an ideal voltage source as grid emulator, and micro Phasor Measurement Units (PMUs). All these components and additional software modules are coupled to one another using the FMI standard and can be co-simulated with the CyDER platform.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Minnesota Solar Pathways: Illuminating Pathways to 10% Solar (Final Technical Report)

The Minnesota Solar Pathways project, funded in part by the Department of Energy’s Solar Energy Technologies Office, was a three-year project designed to explore least-risk, best-value strategies for meeting the State of Minnesota’s solar goals. As part of this aim, the Pathways Team modeled renewable generation costs, examined ways to streamline interconnection, and evaluated technologies that can increase solar hosting capacity on the distribution grid. Midway through the project period, the Pathways Team expanded the scope of study using the same modeling framework from the Minnesota Solar Potential Analysis to estimate and compare generation costs for high renewables penetration scenarios across the Midcontinent Independent System Operator (MISO) region. The analysis concludes that it can be cheaper to overbuild solar + wind and curtail surplus than to store all generation. In addition, the analysis of individual MISO subregions compared with the whole region highlights the opportunities of enabling market potential compared with impact of ongoing transmission constraints. Beyond the analysis of future scenarios, the partnerships initiated and supported through this project underscored the ongoing need for work with stakeholders to address key issues, particularly around siting, and leverage symbiotic strategies to meet state goals.

14 SOLAR ENERGY↗

Evaluation and Comparison of Machine Learning Techniques for Rapid QSTS Simulations

Rapid and accurate quasi-static time series (QSTS) analysis is becoming increasingly important for distribution system analysis as the complexity of the distribution system intensifies with the addition of new types, and quantities, of distributed energy resources (DER). The expanding need for hosting capacity analysis, control systems analysis, photovoltaic (PV) and DER impact analysis, and maintenance cost estimations are just a few reasons that QSTS is necessary. Historically, QSTS analysis has been prohibitively slow due to the number of computations required for a full-year analysis. Therefore, new techniques are required that allow QSTS analysis to rapidly be performed for many different use cases. This research demonstrates a novel approach to doing rapid QSTS analysis for analyzing the number of voltage regulator tap changes in a distribution system with PV components. A representative portion of a yearlong dataset is selected and QSTS analysis is performed to determine the number of tap changes, and this is used as training data for a machine learning algorithm. The machine learning algorithm is then used to predict the number of tap changes in the remaining portion of the year not analyzed directly with QSTS. The predictions from the machine learning algorithms are combined with the results of the partial year simulation for a final prediction for the entire year, with the goal of maintaining an error <10% on the full-year prediction. Five different machine learning techniques were evaluated and compared with each other; a neural network ensemble, a random forest decision tree ensemble, a boosted decision tree ensemble, support vector machines, and a convolutional neural network deep learning technique. A combination of the neural network ensemble together with the random forest produced the best results. Using 20% of the year as training data, analyzed with QSTS, the average performance of the technique resulted in ~2.5% error in the yearly tap changes, while maintaining a <10% 99.9 th percentile error bound on the results. This is a 5x speedup compared to a standard, full-length QSTS simulation. These results demonstrate the potential for applying machine learning techniques to facilitate modern distribution system analysis and further integration of distributed energy resources into the power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗