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This webinar covers and introduction to customer rates and tariff design, drivers of utility costs, impacts of evolving grids on rate design, retail price signals, and methods for evaluating rates.
Advanced baseband and microwave switching techniques for large domestic communications satellites operating in the 30/20 GHz frequency bands are discussed. The nominal baseband processor throughput is one million packets per second (1.6 Gb/s) from one thousand T1 carrier rate customer premises terminals. A frequency reuse factor of sixteen is assumed by using 16 spot antenna beams with the same 100 MHz bandwidth per beam and a modulation with a one b/s per Hz bandwidth efficiency. Eight of the beams are fixed on major metropolitan areas and eight are scanning beams which periodically cover the remainder of the U.S. under dynamic control. User signals are regenerated (demodulated/remodulated) and message packages are reformatted on board. Frequency division multiple access and time division multiplex are employed on the uplinks and downlinks, respectively, for terminals within the coverage area and dwell interval of a scanning beam. Link establishment and packet routing protocols are defined. Also described is a detailed design of a separate 100 x 100 microwave switch capable of handling nonregenerated signals occupying the remaining 2.4 GHz bandwidth with 60 dB of isolation, at an estimated weight and power consumption of approximately 400 kg and 100 W, respectively.
The discrete cosine transform (DCT) is widely used in image compression and is part of the JPEG and MPEG compression standards. The degree of compression and the amount of distortion in the decompressed image are controlled by the quantization of the transform coefficients. The standards do not specify how the DCT coefficients should be quantized. One approach is to set the quantization level for each coefficient so that the quantization error is near the threshold of visibility. Results from previous work are combined to form the current best detection model for DCT coefficient quantization noise. This model predicts sensitivity as a function of display parameters, enabling quantization matrices to be designed for display situations varying in luminance, veiling light, and spatial frequency related conditions (pixel size, viewing distance, and aspect ratio). It also allows arbitrary color space directions for the representation of color. A model-based method of optimizing the quantization matrix for an individual image was developed. The model described above provides visual thresholds for each DCT frequency. These thresholds are adjusted within each block for visual light adaptation and contrast masking. For given quantization matrix, the DCT quantization errors are scaled by the adjusted thresholds to yield perceptual errors. These errors are pooled nonlinearly over the image to yield total perceptual error. With this model one may estimate the quantization matrix for a particular image that yields minimum bit rate for a given total perceptual error, or minimum perceptual error for a given bit rate. Custom matrices for a number of images show clear improvement over image-independent matrices. Custom matrices are compatible with the JPEG standard, which requires transmission of the quantization matrix.
Many image compression standards (JPEG, MPEG, H.261) are based on the Discrete Cosine Transform (DCT). However, these standards do not specify the actual DCT quantization matrix. We have previously provided mathematical formulae to compute a perceptually lossless quantization matrix. Here I show how to compute a matrix that is optimized for a particular image. The method treats each DCT coefficient as an approximation to the local response of a visual 'channel'. For a given quantization matrix, the DCT quantization errors are adjusted by contrast sensitivity, light adaptation, and contrast masking, and are pooled non-linearly over the blocks of the image. This yields an 8x8 'perceptual error matrix'. A second non-linear pooling over the perceptual error matrix yields total perceptual error. With this model we may estimate the quantization matrix for a particular image that yields minimum bit rate for a given total perceptual error, or minimum perceptual error for a given bit rate. Custom matrices for a number of images show clear improvement over image-independent matrices. Custom matrices are compatible with the JPEG standard, which requires transmission of the quantization matrix.
Residential solar photovoltaic (PV) output in most states is credited at the retail electricity rate, a policy commonly known as net metering. Twelve states have replaced net metering with alternative rate structures that reduce PV adopter bill savings. Proponents of these revisions argue that net metering increases the electricity rates of customers without PV. Here, we analyze the degree to which the timelines of net metering revisions have correlated with potential electricity rate impacts. We estimate that potential rate impacts at the end of 2023 were less than 1% of typical customer bills in 37 of 44 states that have offered net metering. There are no statistically significant differences in average or median estimated rate impacts between states that have and have not revised net metering. Nine of the states that had revised net metering did so when estimated impacts were less than 1% of typical customer bills. Many states have retained net metering into higher PV deployment levels with increased risk of potential rate impacts. Only two states—California and Hawaii—retained net metering beyond estimated rate impacts of 5%, and both have revised net metering. These findings do not suggest a clear, consistent link between net metering revision timelines and potential rate impacts. The timing and nature of net metering revisions are ultimately policy decisions based on state-level priorities and considerations.
Data supporting the article “The Missing Correlation Between the Potential Rate Impacts of Rooftop Solar and the Timing of State Net Metering Policy Revisions” (https://www.nlr.gov/docs/fy25osti/93543.pdf). Residential solar photovoltaic (PV) output in most states is credited at the retail electricity rate, a policy commonly known as net metering. Twelve states have replaced net metering with alternative rate structures that reduce PV adopter bill savings. Proponents of these revisions argue that net metering increases the electricity rates of customers without PV. Here, we analyze the degree to which the timelines of net metering revisions have correlated with potential electricity rate impacts. We estimate that potential rate impacts at the end of 2023 were less than 1% of typical customer bills in 37 of 44 states that have offered net metering. There are no statistically significant differences in average or median estimated rate impacts between states that have and have not revised net metering. Nine of the states that had revised net metering did so when estimated impacts were less than 1% of typical customer bills. Many states have retained net metering into higher PV deployment levels with increased risk of potential rate impacts. Only two states-California and Hawaii-retained net metering beyond estimated rate impacts of 5%, and both have revised net metering. These findings do not suggest a clear, consistent link between net metering revision timelines and potential rate impacts. The timing and nature of net metering revisions are ultimately policy decisions based on state-level priorities and considerations.
Most U.S. states require utilities to credit residential solar photovoltaic (PV) output at the retail electricity rate, a structure known as net metering. However, 12 states have replaced net metering with alternative rate structures that reduce PV adopter bill savings. The share of households living in states that require net metering fell from around 84% in 2014 to around 57% by the end of 2023. Proponents of net metering revisions have argued that net metering can affect the electricity rates of customers without PV. This report analyzes the relationships between state PV deployment levels, potential electricity rate impacts on PV nonadopters, and the timing of revisions to net metering policy.
Virginia's Department of Motor Vehicles (DMV) serves a customer base of approximately 5.6 million licensed drivers and ID card holders and 7 million registered vehicle owners. DMV has more daily face-to-face contact with Virginia's citizens than any other state agency [1]. The DMV faces a major difficulty in keeping up with the excessively large customers' arrival rate. The consequences are queues building up, stretching out to the entrance doors (and sometimes even outside) and customers complaining. While the DMV state employees are trying to serve at their fastest pace, the remarkably large queues indicate that there is a serious problem that the DMV faces in its services, which must be dealt with rapidly. Simulation is considered as one of the best tools for evaluating and improving complex systems. In this paper, we use it to model one of the DMV centers located in Norfolk, VA. The simulation model is modeled in Arena 10.0 from Rockwell systems. The data used is collected from experts of the DMV Virginia headquarter located in Richmond. The model created was verified and validated. The intent of this study is to identify key problems causing the delays at the DMV centers and suggest possible solutions to minimize the customers' waiting time. In addition, two tentative hypotheses aiming to improve the model's design are tested and validated.
Electric utilities, regulators, and stakeholders face increasing risks of severe storms, freezes, floods, and heat waves damaging grid infrastructure and causing power outages—and increasing risks of utility equipment igniting wildfires. At the same time, customer electricity rates have risen substantially in recent years, due in part to replacing aging infrastructure and improving resilience to natural hazards and physical threats. To address these challenges, utilities are beginning to move beyond traditional, siloed planning processes to balance resilience with other fundamental grid objectives such as affordability, reliability, safety, and serving new loads. This study presents a framework for states and utilities that want to advance integration of resilience and distribution planning processes to improve planning efficiency, better prioritize cost-effective grid expenditures, and balance planning objectives. The framework includes 7 key integration points between these planning processes: -Strategy process -Data -Threat assessments -Solution identification and prioritization -Optimization opportunities -Consideration of other grid needs -Metrics Lawrence Berkeley National Laboratory reviewed utility distribution system plans and interviewed subject matter experts to identify emerging practices for each of the 7 integration points. This report presents these practices, which can be used as a guide toward more holistic planning and cohesive investment strategies. It also includes 3 case studies to provide practical examples of how utilities apply such integrated planning processes: two pole hardening programs and one microgrid planning effort. The report concludes by identifying opportunities for future research.
To demodulate a communication signal, a receiver must recover and synchronize to the symbol timing of a received waveform. In a system that utilizes digital sampling, the fidelity of synchronization is limited by the time between the symbol boundary and closest sample time location. To reduce this error, one typically uses a sample clock in excess of the symbol rate in order to provide multiple samples per symbol, thereby lowering the error limit to a fraction of a symbol time. For systems with a large modulation bandwidth, the required sample clock rate is prohibitive due to current technological barriers and processing complexity. With precise control of the phase of the sample clock, one can sample the received signal at times arbitrarily close to the symbol boundary, thus obviating the need, from a synchronization perspective, for multiple samples per symbol. Sample-clock phase-control feedback was developed for use in the demodulation of an optical communication signal, where multi-GHz modulation bandwidths would require prohibitively large sample clock frequencies for rates in excess of the symbol rate. A custom mixedsignal (RF/digital) offset phase-locked loop circuit was developed to control the phase of the 6.4-GHz clock that samples the photon-counting detector output. The offset phase-locked loop is driven by a feedback mechanism that continuously corrects for variation in the symbol time due to motion between the transmitter and receiver as well as oscillator instability. This innovation will allow significant improvements in receiver throughput; for example, the throughput of a pulse-position modulation (PPM) with 16 slots can increase from 188 Mb/s to 1.5 Gb/s.
Selective Laser Melting (SLM) is a powder bed fusion additive manufacturing process used increasingly in the aerospace industry to reduce the cost, weight, and fabrication time for complex propulsion components. SLM stands poised to revolutionize propulsion manufacturing, but there are a number of technical questions that must be addressed in order to achieve rapid, efficient fabrication and ensure adequate performance of parts manufactured using this process in safety-critical flight applications. Previous optimization studies for SLM using the Concept Laser M1 and M2 machines at NASA Marshall Space Flight Center have centered on machine default parameters. The objective of this work is to characterize the impact of higher throughput parameters (a previously unexplored region of the manufacturing operating envelope for this application) on material consolidation. In phase I of this work, density blocks were analyzed to explore the relationship between build parameters (laser power, scan speed, hatch spacing, and layer thickness) and material consolidation (assessed in terms of as-built density and porosity). Phase II additionally considers the impact of post-processing, specifically hot isostatic pressing and heat treatment, as well as deposition pattern on material consolidation in the same higher energy parameter regime considered in the phase I work. Density and microstructure represent the "first-gate" metrics for determining the adequacy of the SLM process in this parameter range and, as a critical initial indicator of material quality, will factor into a follow-on DOE that assesses the impact of these parameters on mechanical properties. This work will contribute to creating a knowledge base (understanding material behavior in all ranges of the AM equipment operating envelope) that is critical to transitioning AM from the custom low rate production sphere it currently occupies to the world of mass high rate production, where parts are fabricated at a rapid rate with confidence that they will meet or exceed all stringent functional requirements for spaceflight hardware. These studies will also provide important data on the sensitivity of material consolidation to process parameters that will inform the design and development of future flight articles using SLM.
Selective Laser Melting (SLM) is a powder bed fusion additive manufacturing process used increasingly in the aerospace industry to reduce the cost, weight, and fabrication time for complex propulsion components. Previous optimization studies for SLM using the Concept Laser M1 and M2 machines at NASA Marshall Space Flight Center have centered on machine default parameters. The objective of this project is to characterize how heat treatment affects density and porosity from a microscopic point of view. This is performs using higher throughput parameters (a previously unexplored region of the manufacturing operating envelope for this application) on material consolidation. Density blocks were analyzed to explore the relationship between build parameters (laser power, scan speed, and hatch spacing) and material consolidation (assessed in terms of density and porosity). The study also considers the impact of post-processing, specifically hot isostatic pressing and heat treatment, as well as deposition pattern on material consolidation in the higher energy parameter regime. Metallurgical evaluation of specimens will also be presented. This work will contribute to creating a knowledge base (understanding material behavior in all ranges of the AM equipment operating envelope) that is critical to transitioning AM from the custom low rate production sphere it currently occupies to the world of mass high rate production, where parts are fabricated at a rapid rate with confidence that they will meet or exceed all stringent functional requirements for spaceflight hardware. These studies will also provide important data on the sensitivity of material consolidation to process parameters that will inform the design and development of future flight articles using SLM.
The Glen Canyon Dam (GCD) is a principal power-generating asset within the Colorado River Storage Project (CRSP), accounting for approximately 70–80% of CRSP power production over the past two decades. In June 2023, the U.S. Bureau of Reclamation (Reclamation) issued a Notice of Intent to prepare an Environmental Impact Statement (EIS) outlining operational guidelines and strategies for Colorado River Basin reservoirs after 2026 (Reclamation, 2023). Power generation is among CRSP’s statutory purposes under the Colorado River Storage Project Act of 1956 (U.S. Congress, 1956). Assessing how alternative policy frameworks affect CRSP power production and the resulting electricity rates for U.S. customers is therefore essential to inform decision-making. This report evaluates projected electricity rates and the market value of electricity from the Western Area Power Administration (WAPA) CRSP GCD under multiple post-2026 policy scenarios to support Reclamation’s EIS development. Results from advanced econometric and machine learning models indicate that the Enhanced Coordination alternative (EnhanCoor), Maximum Operational Flexibility alternative (CCA), and Supply Driven - 55 alternative (SD55) alternatives yield more favorable hydropower generation and capacity outcomes, which are objectives outlined in Reclamation’s documentations (Reclamation, 2007; Reclamation, 2016). Specifically, these scenarios are associated with higher electricity production, lower projected rate trajectories, and greater economic value to the U.S. power system from CRSP generation. The remaining five scenarios generally produce lower generation, higher rate trajectories, and reduced long-term market values.
This slide presentation describes what it calls "disruptive technologies", i.e., "Low-end disruption" occurs when the rate at which products improve exceeds the rate at which customers can adopt the new performance. Therefore, at some point the performance of the product overshoots the needs of certain customer segments. At this point, a disruptive technology may enter the market and provide a product which has lower performance than the incumbent but which exceeds the requirements of certain segments, thereby gaining a foothold in the market. This concept is viewed in impacting incumbent technologies Rosin Flux, with a pH-neutral water soluble Flux; electrostatic discharge models being disrupted by the charge device model (CDM) concept; and High Density Interconnect Printed Circuit Boards (HDI PCB).
This report examines how battery energy storage systems (BESS) can help utilities accommodate large load growth while protecting affordability for existing ratepayers. Rapid growth in electricity demand from artificial intelligence (AI) data centers is straining the U.S. grid. Furthermore, many new data centers are entering rural markets, which could offer economic benefits but may also pose implementation challenges for smaller utilities. At the same time, retail electricity prices are increasing faster than inflation, elevating customer affordability as a key challenge. While data centers have not been the primary driver of increases in residential prices to date, they have pushed wholesale energy and capacity prices higher in several markets. Fundamental utility cost-allocation principles show that data center growth can be rate-positive for existing customers only if new peak demand grows faster than the costs a utility must incur to serve it. Several factors, including a utility’s degree of wholesale market exposure, forecast uncertainty and stranded-asset risk, and tariff design can determine the outcome of load growth on retail rates. Energy storage can make several affordability contributions in the face of this landscape of uncertainty and market volatility, including deferral of higher-cost grid investments through improved utilization of existing assets and flexibility of new large loads, insulation from volatile wholesale prices through peak shaving, and reliability support to address grid risks stemming from the behavior of AI data center loads. Different potential BESS deployment pathways—utility-scale front-of-the-meter systems, aggregated small-scale storage installations, and data center-sited behind-the-meter storage—are compared against each other and against conventional capacity alternatives. This framework is intended as a conceptual resource to utilities, particularly smaller public utilities with rural service territories, who may be considering the role that energy storage can play in insulating existing ratepayers from data center cost shifts.
Shifting electric load from times of peak demand can be a key strategy to slow price growth as reducing peak demand avoids the cost of upgrading generation, transmission and distribution infrastructure. Utilities are releasing time-varying prices, such as time of use rates or dynamic prices, to incentivize grid-friendly load shifting. New dynamic price programs provide insight into the true cost of operating electricity grids and the potential economic benefits of load shifting. Program developers and device manufacturers need to understand the economic opportunities in terms of 1) the variation in prices across hours, days, and seasons; 2) the change in utility bills for customers who don’t shift load; and 3) the potential load shifted and economic value of different technologies if manufacturers or aggregators deploy price-responsive controls. This paper estimates possible impacts of dynamic price adoption and load shifting controls if customers paid the dynamic rate from one pilot program. Statistical analysis of historical prices identified annual and seasonal metrics as well as representative price curves for each circuit in the pilot. Simulations for residential technologies with price-responsive controls including unitary heat pump water heaters, central multifamily heat pump water heaters, heating & cooling + storage systems, and pool pumps estimated the potential impacts of highly dynamic prices both with and without load shifting controls. Results showed the potential to reduce electricity costs on representative days by 42-94% and reduce consumption during times of high electricity prices by 63-100% compared to baseline operation for those flex-friendly devices.
The Progressing Analysis of Variable Electric Rates (PAVER) study analyzed the impact of a range of time-varying electric rates on the performance of a regional electric grid and the resulting costs for participating and non-participating customers. This analysis leveraged and extended the work of PNNL’s Distribution System Operator with Transactive (DSO+T) study. Five different rate designs were included: a flat volumetric energy charge, a typical Time of Use (TOU) rate, a dynamic energy (DE) rate (based on wholesale locational marginal prices), a dynamic energy and capacity (DE+C) rate, and, finally, a Block and Swing (B&S) rate that billed customers based on their average load profile at constant pricing, but used the DE+C dynamic price for load deviations from their average profile. These rates were analyzed in a large-scale co-simulation of an entire regional grid with a customer population representative of the current state. A large fraction (80%) of residential and commercial customers were assumed to participate in these time-varying rates with automatically controlled HVAC, water heaters, electric vehicles, and batteries. This study assumed no industrial sector participation. The DE and DE+C rates saw system peak loads reduced by 6-7%, while the large participation in the TOU rate case saw a significant rebound effect and a resulting peak load increase of >5%. The impacts to the annual and peak system demand impacted system wholesale prices and the overall grid operating costs. This cost structure determined the revenue needed to be collected from customers by each rate design. Participating customers on the DE and DE+C rates (located in one of the modeled DSOs) saw reductions in average annual electricity bills of 11-17% with average increases in monthly bill variation of no more than 13%. At such high participation levels, TOU customers saw 10% higher average annual bills (due to system-wide rebound effects) and average increased monthly bill variation of 16%. Residential owners of large flexible loads (such as electric vehicles) saw larger bill savings (17-20%) when on a fully dynamic rate. The presence of on-site generation (such as rooftop solar) did not appear to appreciably change customer outcomes. Customers on the Block and Swing rate did see 6% lower monthly bill variation (as intended) than the flat rate case, but at the expense of appreciable bill savings, which were only 3%, comparable to the savings seen by non-participants. Given this finding we recommend that additional research be conducted into how best various bill protection mechanisms can balance minimizing customer bill variation with providing financial incentives commensurate with the flexibility customers provide. We also recommend that customer outcomes be explored across a range of regions using current actual customer and system cost data.
The factory of the future will require an operating methodology which effectively utilizes all of the elements of product design, manufacturing and delivery. The process must respond rapidly to changes in product demand, product mix, design changes or changes in the raw materials. To achieve agility in a manufacturing operation, the design and development of the manufacturing processes must focus on customer satisfaction. Achieving greatest results requires that the manufacturing process be considered from product concept through sales. This provides the best opportunity to build a quality product for the customer at a reasonable rate. The primary elements of a manufacturing system include people, equipment, materials, methods and the environment. The most significant and most agile element in any process is the human resource. Only with a highly trained, knowledgeable work force can the proper methods be applied to efficiently process materials with machinery which is predictable, reliable and flexible. This paper discusses the affect of each element on the development of agile manufacturing systems.