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150-kW Three-Port Custom-Core Transformer Design Methodology
The design of a 150-kW high-frequency three-port transformer (TPT) for a triple active bridge (TAB) converter application is the focus of this paper. A non-tradeoff inclusive design methodology for custom-core transformers is proposed. Classic transformer design methodologies based on complex trade-offs mechanisms require significant effort to adjust the design for commercially available pre-sized cores. The proposed design is based on the development of a set of design equations that allows to select the core dimensions and the turns numbers for the windings that best accomplish the transformer specifications. Experimental evidence verifying the proposed theoretical design methodology is presented in this work.
Impacts of Voltage-Based Grid Support Functions on Energy Production of PV Customers
This paper presents the impact of inverter grid- support functions (GSFs) on photovoltaic (PV) customer energy production on a real distribution feeder in Oahu, HI. These autonomous GSFs based on local voltage measurements are good alternatives to increase PV hosting capacity. However, these functions can result in PV energy curtailment to the customer, and this study addresses the concerns about the impact of inverter GSFs through detailed quasi-static time series (QSTS) simulations. It proposes four metrics: maximum and average GSF curtailment, average increased generation and average net generation change, to assess the impact of a given control on PV systems located on customer sites. It was found that curtailment of PV production is negligible for customers where peak voltage is within ANSI C84.1 range. A reliable relationship between curtailment and peak customer voltage is demonstrated, suggesting that peak voltage could be used as an indicator of customer curtailment.
Potential Impacts of Dynamic Electricity Pricing in California: Load Shape and Customer Bill Impacts Under Elastic Customer Response
The increasing penetration of renewable energy in California has intensified grid management challenges, exemplified by the “duck curve” and the resulting need for steep ramping and curtailment of renewables. To address these issues, dynamic electricity tariffs that vary in near-real time are being considered to incentivize customers to shift demand and support the grid. This study extends previous work on the bill impacts of such tariffs in the absence of load response by quantifying the system-level and customer impacts of load response based on customer price elasticity. Customer-level load response modeling was conducted using meter data from 411,000 customers across residential, commercial, and industrial sectors. Customer demand elasticity was estimated using literature-based values, with scenarios ranging from low to high elasticity, including an automation-enhanced scenario. Results indicate that universal adoption of, and response to, dynamic tariffs can significantly reduce peak net load (by 15%) and maximum ramping requirements (by 20%) with moderate elasticity, delivering demand response resources comparable to or exceeding current programs at all elasticity levels. Bill analysis shows that, when responding elastically to dynamic prices, most non-PV customers experience modest savings, while PV customers may see higher effective rates due to lower compensation for exports during low-price periods. Emissions analysis reveals a reduction in per-kWh emissions system-wide, with a total absolute load increase of 2% accompanied by a negligible absolute emissions increase. The study concludes that while dynamic tariffs offer substantial grid benefits, customer bill savings under modeled response behaviors may be too modest to drive widespread adoption without additional incentives or enabling technologies. Future research should model flexible loads and advanced control technologies with greater fidelity to better represent the potential opportunities of dynamic tariffs.
A Data-Driven Customer Segmentation Strategy Based on Contribution to System Peak Demand
Not provided.
Protecting Customer Privacy Through Distributed Energy Resource Anonymization
Due to their stochastic nature, the increase of Renewable Energy Resources (RERs) as a primary source of energy for power grids creates challenges regarding the reliability and resilience of the system. In order to combat these obstacles, expansion of Distributed Energy Resources (DERs) and their participation in Demand Response (DR) programs is necessary. Widespread participation requires prioritizing customer privacy and addressing concerns that may arise regarding communication between DERs and the Grid Service Provider (GSP). This paper discusses the use of flow reservation resources to split the operating cycles of DER load profiles into unique phases. The splitting of phases increases anonymization of the DERs by making it more difficult to determine the individual characteristics of the device. We discuss an example of this using simulated DER load profile data and examine the resulting effectiveness by using a machine learning algorithm for classification, called Support Vector Machine (SVM).
Customs: Inspect and Report Python Imports (customs) v1.0.0
We have developed a simple, minimally invasive monitoring framework that enables us to capture key metrics from Python user processes. This is part of a larger effort to collect data across our entire data-intensive science workload at NERSC. The approach for Python is inspired by a similar framework developed for Blue Waters at NCSA. Both frameworks leverage standard Python features, sitecustomize and atexit, to capture Python imports of interest and other job data. The customs package decomposes the information capture into separate inspection and reporting interfaces.
Development and Clustering of Rate-Oriented Load Metrics for Customer Price-Plan Analysis: Preprint
One of the few methods electric utilities can use to motivate and change customer energy consumption is through their retail rate structures. Utilities are increasingly moving towards more dynamic rate-plans to achieve the objectives of motivating energy conservation, increasing self-consumption from onsite renewable generation, reducing peak demand and to flattening the demand profiles. This paper presents a set of rate-oriented customer load metrics that are the determinants of customers bill under four unique rate-plans. These metrics are not only indicative of which rate structure a customer should choose based on their load profiles, but also convey useful information about load consumption behavior. With these metrics, the utility can analyze their customers and identify customer classes that are rewarded under each rate-plan. This can help inform utilities whether the customer profiles being rewarded under each rate-plans are meeting their original objectives. To develop these customer classes, we calculate these rate-oriented load metrics for each customer and perform k-means clustering. The analysis is conducted on from a set of 300 customer profiles, examining the impact of four different rate-plans, different numbers of clusters and looking at customer bills and cluster load profile characteristics.
Development and Clustering of Rate-Oriented Load Metrics for Customer Price-Plan Analysis
One of the few methods electric utilities can use to motivate and change customer energy consumption is through retail rate structures. Utilities are increasingly moving toward more dynamic rate plans to encourage energy conservation, utilization of onsite renewable generation, peak demand reduction and flattening of demand profiles. This paper creates a set of rate-oriented load metrics that are the determinants of customers' bills under four unique rate plans. These metrics are not only indicative of which rate structure can provide customer bill reductions based on their load profile characteristics, but also convey useful information about load consumption behavior. With these metrics, utilities can analyze their customers and identify classes that are rewarded under each rate plan. This can help inform utilities whether the customers rewarded under each rate plans are meeting their original objectives. To develop these customer classes, we calculate these rate-oriented load metrics for each customer and perform k-means clustering. The analysis is conducted on a set of 300 customer profiles, examining four different rate plans, different numbers of clusters, customer bills and cluster load profile characteristics.
Potential bill impacts of dynamic electricity pricing on California utility customers
The rapid growth of renewable generation is creating challenges for the California grid in the form of the “duck curve,” with increasingly steep ramping required for conventional generation resources in the morning and evening, and growing curtailment of solar resources in midday periods. Time-varying electricity tariffs have received considerable attention as a tool to address these challenges, with a renewed recent focus on the potential for dynamic tariffs that vary to reflect conditions on the grid in near-real time. Consideration of dynamic tariffs may raise concerns about the financial impact on utility customers, especially for those who have limited flexibility to modify their electricity consumption in response. Specific areas of concern include electricity bills, bill volatility, and equity implications related to cost shifting among customer groups. In this paper we leverage smart meter data for more than 400,000 California utility customers, spanning residential, commercial, industrial, and agricultural customers, to assess potential customer bill impacts arising from a multi-component dynamic tariff . Specifically, we compute impacts on customer bills and bill volatility under the assumption of fully inelastic demand, i.e., where customers do not change their consumption patterns in response to the tariff. We also assess various approaches designing subscription load shapes that customers can pre-purchase as a hedge that may provide a measure of protection against large negative impacts, while still incentivizing the modification of loads on the margin. We compare and contrast the relative impacts on different customer classes and discuss benefits and pitfalls of different dynamic tariff structures and subscription load shapes.
Effects of Platform Design on the Customer Experience in an Online Solar PV Marketplace
We analyze a unique dataset of residential solar PV quotes offered in an online marketplace to understand how platform design changes affect customer outcomes. Three of the four design changes are associated with statistically significant and robust reductions in offer prices, though none of the policies were designed explicitly to reduce prices. The results suggest that even small changes in how prospective solar PV customers interact with installers can affect customer outcomes such as prices. Specifically, the four changes we evaluate are: 1) a customer map that shows potential new EnergySage registrants the locations of nearby customers; 2) a quote cap that precludes more than seven installers from bidding on any one customer; 3) a price guidance feature that informs installers about competitive prices in the customer's market before they submit quotes; and 4) no pre-quote messaging to prohibit installers from contacting customers prior to offering quotes. We calculate descriptive statistics to investigate whether each design change accomplished its specific objectives. Then, we econometrically evaluate the impacts of the design changes on PV quote prices and purchase prices using a regression discontinuity approach.
Equitable Design of Behind-the-Meter Energy Storage Programs: A Customer Perspective
Utility deployment of energy storage is done as a utility-scale asset connected directly to the grid (front of meter) or in partnership with a customer on the customer’s premises (behind the meter). By offering incentives for customers to install behind-the-meter storage, utilities can gain operational control of the assets and operate them in a manner that benefits all customers. Some states have also begun providing increased incentive levels to low-income customers to ensure that they can equitably share in the benefits of behind-the-meter energy storage programs. This paper evaluates different approaches to energy storage procurement from the customer’s perspective and evaluates how behind-the-meter programs can be equitably structured while keeping customers financially indifferent between front-of-meter and behind-the-meter energy storage procurements.
Estimating Critical Customer Outages Resulting from Extreme Hurricanes
US power outage data has been collected by organizations such as Oak Ridge National Laboratory (ORNL) through Environment for Analysis Geo-Located Energy Infrastructure (EAGLE-I: freely available) and poweroutage.us (commercial data: available to purchase). However, these sources do not provide information specific to outages of critical customers. Critical customers include entities, facilities, and individuals whose continuous access to electricity is essential for public safety, emergency response, disaster recovery, the well-being of vulnerable populations, public safety and order, and public utilities such as natural gas, communications, water and sanitation. Identification and geolocation of critical customers is crucial for understanding and addressing the effects of power outages on essential services and ensuring that necessary measures are taken to maintain their operations during power disruptions. This work is a first step towards estimating the occurrences of critical customer outages and developing a critical customer power outage data repository. This work estimates outage incidents of critical customers through spatiotemporal mapping of power outage data, weather data, building data, and critical infrastructure network data. Our results show that critical customer effects vary across different counties. We provide appropriate mathematical explanations and simplifications to define and systematize the proposed approach.
Business Models for Scaling Demand Flexibility Volume II – Customer relationship management strategies, challenges, and lessons learned from U.S. programs
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 customer relationship management strategies, which provide information on value creation and focus on ensuring customers can navigate programs smoothly. This report discusses the role of customer relationship management strategies in demand flexibility programs, characterizes customer relationship management strategies that can be considered during program design and implementation, identifies existing challenges to customer relationship management strategies, and describes lessons learned. This report is part of a series that includes reports on value propositions, stakeholder ecosystem management, and program life cycle.
Large-Scale Simulation of Regional Demand Flexibility Implementation and Customer Economic Impact
The Distribution System Operator with Transactive (DSO+T) study conducted a large-scale simulation of over 60,000 customers in a region the size of Texas to demonstrate the effective coordination of distributed energy resources (DERs) in commercial and residential buildings. The integrated simulation included both the bulk (wholesale generation and transmission) and distribution systems. The DERs (including batteries, electric vehicles, air conditioning, and water heaters) participated in a transactive energy retail market that was integrated into an existing competitive wholesale market. The engineering and economic performance of the resulting demand flexibility was evaluated over annual simulations for both moderate and high renewable generation scenarios. A detailed parametric cost model was developed to enable detailed economic analysis of key stakeholders. In addition, fixed and dynamic customer tariffs were designed and applied to the customer population. This allowed the impact on annual customer bills to be analyzed for various building types (residential versus commercial; single- versus multi-family). This paper presents results showing the relative flexibility of batteries, electric vehicles, and building loads throughout the year and under different renewable scenarios. This feeds a detailed breakdown of the impact this flexibility has on the operating costs of the grid and the resulting net economic benefit. Finally, the study showed that practically all customer classes (including non-participating customers) save money under the proposed demand flexibility scheme. The study found overall net annual economic savings of $3.3-5.0B for a region the size of Texas equating to average customer bill savings of 10-16%.
Custom-form iron trifluoride Li-batteries using material extrusion and electrolyte exchanged ionogels
Custom-form factor batteries fabricated in non-conventional shapes can maximize the overall energy density of the systems they power, particularly when used in conjunction with energy dense materials (e.g., Li metal anodes and conversion cathodes). Additive manufacturing (AM), and specifically material extrusion (ME), have been shown as effective methods for producing custom-form cell components, particularly electrodes. However, the AM of several promising energy dense materials (conversion electrodes such as iron trifluoride) have yet to be demonstrated or optimized. Furthermore, the integration of multiple AM produced cell components, such as electrodes and separators, along with a custom package remains largely unexplored. In this work, iron trifluoride (FeF 3 ) and ionogel (IG) separators are conformally printed using ME onto non-planar surfaces to enable the fabrication of custom-form Li-FeF 3 batteries. Further, to demonstrate printing on non-planar surfaces, cathodes and separators were deposited onto cylindrical rods using a 5-axis ME printer. ME printed FeF 3 was shown to have performance commensurate with FeF 3 cast using conventional means, both in coin cell and cylindrical rod formats, with capacities exceeding 700 mAh/g on the first cycle and ranging between 600 and 400 mAh/g over the next 50 cycles. Additionally, a ME process for printing polyvinylidene fluoride-co-hexafluoropropylene (PVDF-HFP) based IGs directly onto FeF 3 is developed and enabled using an electrolyte exchange process. In coin cells, this process is shown to produce cells with similar capacity to cells built with Celgard separators out to 50 cycles, with the exception that cycling instabilities are observed during cycles 8–20. When using printed and exchanged IGs in a custom cylindrical cell package, 6 stable high-capacity cycles are achieved. Overall, this work demonstrates approaches for producing high-energy-density Li-FeF 3 cells in coin and cylindrical rod formats, which are translatable to customized, arbitrary geometries compatible with ME printing and electrolyte exchange.
Disaggregating Customer-Level Behind-the-Meter PV Generation Using Smart Meter Data and Solar Exemplars
Customer-level rooftop photovoltaic (PV) has been widely integrated into distribution systems. In most cases, PVs are installed behind-the-meter (BTM), and only the net demand is recorded. Therefore, the native demand and PV generation are unknown to utilities. Separating native demand and solar generation from net demand is critical for improving grid-edge observability. In this paper, a novel approach is proposed for disaggregating customer-level BTM PV generation using low-resolution but widely available hourly smart meter data. The proposed approach exploits the strong correlation between monthly nocturnal and diurnal native demands and the high similarity among PV generation profiles. First, a joint probability density function (PDF) of monthly nocturnal and diurnal native demands is constructed for customers without PVs, using Gaussian mixture modeling (GMM). Deviation from the constructed PDF is utilized to probabilistically assess the monthly solar generation of customers with PVs. Then, to identify hourly BTM solar generation for these customers, their estimated monthly solar generation is decomposed into an hourly timescale; to do this, we have proposed a maximum likelihood estimation (MLE)-based technique that utilizes hourly typical solar exemplars. Leveraging the strong monthly native demand correlation and high PV generation similarity enhances our approach's robustness against the volatility of customers’ hourly load and enables highly-accurate disaggregation. Furthermore, the proposed approach has been verified using real native demand and PV generation data.
Customer Agreement Considerations for Micro-Grids in Sub-Saharan Africa
Micro-grid deployment at a large scale will be necessary for achieving ambitious national and international electrification goals and reaching communities for whom grid extension is prohibitively expensive. The micro-grid industry is growing to fill this need, but barriers remain to reaching scale with sustainable, bankable business models. An important step along the way to industry maturation is creating customer agreements that serve as a foundation for a healthy, long-term relationship between developers, operators, and the communities their micro-grids serve. In addition to directly setting clear expectations with customers, a strong customer agreement can be a valuable tool for prospective funders, system sizing discussions, and customer retention. This report covers considerations for customer agreement design and content, and is intended to serve as a resource for micro-grid developers seeking to develop or re-design customer agreements to support growth and help achieve scale.