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

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Optimizing space constellations for mobile satellite systems

Designing a mobile satellite system entails many complex trade-offs between a great number of parameters including: capacity, complexity of the payload, constellation geometry, number of satellites, quality of coverage, etc. This paper aims at defining a methodology which tries to split the variables to give rapidly some first results. The major input considered is the traffic assumption which would be offered by the system. A first key step is the choice of the best Rider or Walker constellation geometries - with different numbers of satellites - to insure a good quality of coverage over a selected service area. Another aspect to be addressed is the possible altitude location of the constellation, since it is limited by many constraints. The altitude ranges that seem appropriate considering the spatial environment, the launch and orbit keeping policy and the feasibility of the antenna allowing sufficient frequency reuse are briefly analyzed. To support these first considerations, some 'reference constellations' with similar coverage quality are chosen. The in-orbit capacity needed to support the assumed traffic is computed versus altitude. Finally, the exact number of satellite is determined. It comes as an optimum between a small number of satellites offering a high (and costly) power margin in bad propagation situation and a great number of less powerful satellites granting the same quality of service.

Roussel, T.↗
DOE OSTI2015

Status on the Development of a Modeling and Simulation Framework for the Economic Assessment of Nuclear Hybrid Energy Systems

An effort to design and build a modeling and simulation framework to assess the economic viability of Nuclear Hybrid Energy Systems (NHES) was undertaken in fiscal year 2015 (FY15). The purpose of this report is to document the various tasks associated with the development of such a framework and to provide a status on its progress. Several tasks have been accomplished. First, starting from a simulation strategy, a rigorous mathematical formulation has been achieved in which the economic optimization of a Nuclear Hybrid Energy System is presented as a constrained robust (under uncertainty) optimization problem. Some possible algorithms for the solution of the optimization problem are presented. A variation of the Simultaneous Perturbation Stochastic Approximation algorithm has been implemented in RAVEN and preliminary tests have been performed. The development of the software infrastructure to support the simulation of the whole NHES has also moved forward. The coupling between RAVEN and an implementation of the Modelica language (OpenModelica) has been implemented, migrated under several operating systems and tested using an adapted model of a desalination plant. In particular, this exercise was focused on testing the coupling of the different code systems; testing parallel, computationally expensive simulations on the INL cluster; and providing a proof of concept for the possibility of using surrogate models to represent the different NHES subsystems. Another important step was the porting of the RAVEN code under the Windows™ operating system. This accomplishment makes RAVEN compatible with the development environment that is being used for dynamic simulation of NHES components. A very simplified model of a NHES on the electric market has been built in RAVEN to confirm expectations on the analysis capability of RAVEN to provide insight into system economics and to test the capability of RAVEN to identify limit surfaces even for stochastic constraints. This capability will be needed in the future to enforce the stochastic constraints on the electric demand coverage from the NHES. The development team gained experience with many of the tools that are currently envisioned for use in the economic analysis of NHES and completed several important steps. Given the complexity of the project, preference has been given to a structural approach in which several independent efforts have been used to build the cornerstone of the simulation framework. While this is good approach in establishing such a complex framework, it may delay reaching more complete results on the performance of analyzed system configurations. The integration of the previously reported exergy analysis approach was initially proposed as part of this milestone. However, in reality, the exergy-based apportioning of cost will take place only in a second stage of the implementation since it will be used to properly allocate cost among the different NHES subsystems. Therefore, exergy does not appear at the level of the main drivers in the analysis framework; the latter development of the base framework is the focus of this report.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Primary material supply configurations and domestic recycling for cost-effective battery material production in the US

Battery cathode active material costs hinge on regionally concentrated, price-volatile metal supply. Here. we construct a regional facility-level cost model based on over 80 global lithium, cobalt, and nickel mines, refineries, and battery-grade material plants. Our model yields aggregated lithium, nickel, manganese, and cobalt production material costs from 392 region-based supply configurations for five different cathode active materials. Focusing on the United States, all-domestic supply is 9–34% costlier than global average, increasing by cobalt content, while these shortfalls can be overcome by selective low-cost material imports. Furthermore, we analyze costs of two U.S.-based recycling facilities from primary data and techno-economic modelling and compare resulting cathode active material-level costs to primary supply. Although it is still significantly higher on cathode active material cost-level, rising end-of-life flows and lowered black-mass prices will, however, make secondary supply cost-competitive to domestic and foreign primary supply cost floors. Facility-level benchmarks reveal targeted import, scaling, and production cost optimization as levers for a resilient, cost-effective U.S. battery-material supply chain.

Energy↗
DOE OSTI2014

BEopt-CA (Ex): A Tool for Optimal Integration of EE, DR and PV in Existing California Homes

Opportunities for combining energy efficiency, demand response, and energy storage with PV are often missed, because the required knowledge and expertise for these different technologies exist in separate organizations or individuals. Furthermore, there is a lack of quantitative tools to optimize energy efficiency, demand response and energy storage with PV, especially for existing buildings. As technology costs evolve (e.g., the ongoing reduction in the cost of PV), design strategies need to be adjusted accordingly based on quantitative analysis.

14 SOLAR ENERGY↗
DOE OSTI2023

Technology Strategy Assessment: Findings from Storage Innovations 2030 Thermal Energy Storage

The concept of thermal energy storage (TES) can be traced back to early 19th century, with the invention of the ice box to prevent butter from melting. Modern TES development began with building heating and cooling and concentrated solar thermal technologies for power generation in the early 1900s and late 1970s, respectively. TES systems provide many advantages compared with other long-duration energy storage (LDES) technologies, which include low costs, long operational lives, high energy density, synchronous power generation capability with inertia that inherently stabilizes the grid, and the ability to output both heat and electricity. TES Use Cases TES technologies can couple with most renewable energy systems, including wind, photovoltaic, and concentrated solar thermal energy, and can be used for heat-to-heat, heat-to-electricity, electricity-to-heat, and electricity-to-electricity (bidirectional electricity) applications. The three types of TES that have heat as an input or output are grouped together for the purposes of this report. Retrofitting retired thermal power plants can be a potential cost-effective option for TES with electricity output because they both use a similar thermal-to-electricity type of conversion. Additionally, TES can directly serve heat demand for buildings and industrial processes, displacing fossil fuels to achieve broad decarbonization. Bidirectional Electricity Figure 1 shows a bidirectional electricity TES (ETES) architecture that is emerging as a prime technology for LDES at a grid scale. The ETES technology can utilize existing TES technology infrastructures, has no geological limitations (such as mountains and water for pumped storage hydro, underground natural caverns for compressed-air energy storage, etc.), and is capable of deployment anywhere in the United States and the world for broad uses. Particularly, ETES technology can be placed at retired fossil-fueled thermal power plants to reuse decommissioned assets, protect job security in associated communities, and provide resilient and high-inertia (i.e., spinning) power to the grid. Heat Input and Output There also are many ways to integrate TES within heat-to-electricity, heat-to-heat, and electricity-to-heat applications, such as those used in concentrating solar power (CSP), buildings, district heating, and industry process heat applications. These categories can be further classified for low- and high-temperature applications. High-temperature thermal energy storage (HTTES) heat-to-electricity TES applications are currently associated with CSP deployments for power generation. TES with CSP has been deployed in the Southwestern United States with rich solar resources and has proved its value to the electric grid. Electricity-to-heat and heat-to-heat HTTES applications present great potential for decarbonizing energy-intensive industrial process heat applications [8, 9], such as iron ore processing, iron smelting, cement production, glass manufacturing, mineral processing, and chemical production. Some industrial processes require process heat at temperatures > 1,400°C, so HTTES can be utilized to reduce fuel consumption in those processes through fuel, oxidizer, and process material pre-heating. Thermal energy storage for augmenting existing industrial process heat applications makes a much more attractive economic case because the energy penalty due to thermal-to-electric conversion is eliminated. Co-located applications of power production and heat also can add to the value stacking of integrating utility-scale TES; however, these scenarios are very case specific and not practically possible in many cases. These constraints are primarily attributed to the existing infrastructure being designed, developed, and constructed for many decades around the most economically feasible technologies, such as electricity and a selection of fossil fuels for heat input. Low-temperature TES can be utilized for building and district heating and cooling, as well as some process heat applications in electricity-to-heat and heat-to-heat configurations. Lower temperature TES (LTTES) can be added to heat pump equipment (electric input), either directly interacting with the refrigerant in the condenser or evaporator, or through a secondary heat transfer fluid. It also can be integrated in the building envelope or within the ducts of the heating, ventilation, and air conditioning (HVAC) system. Cost-effective integration of TES into buildings adds significant cost, and it is one of the key barriers preventing the commercialization and deployment of TES. The optimal strategy for integrating TES with buildings has yet to be determined for various applications of TES. Nevertheless, thermal storage materials are far less costly per unit of energy stored than electricity storage materials. This means that thermal storage has the potential to reduce the cost to society of energy storage.

25 ENERGY STORAGE↗
DOE OSTI2023

Assessing the Impact of the Inflation Reduction Act on Nuclear Plant Power Uprate and Hydrogen Cogeneration

On August 16, 2022, Congress passed the Inflation Reduction Act (IRA) to promote investment in new, carbon-free power generation and sustainable operation of existing carbon-free assets. Specifically, the IRA includes both a production tax credit (PTC – Section 45Y of the IRA) and an investment tax credit (ITC – Section 48E) which utilities may leverage to offset the costs of power uprate. Further, the IRA includes a provision (Section 45V) for a PTC associated with carbon-free hydrogen cogeneration. These tax credits, along with recent legislation efforts to decarbonize the country, have re-emphasized the importance of maintaining and optimizing the existing nuclear plant operating fleet. As a result, utilities are reexamining the possibility of uprating their existing nuclear assets to further maximize carbon-free electricity generation.

08 HYDROGEN↗
DOE OSTI2021

Deep Reinforcement Learning for Autonomous Water Heater Control

Electric water heaters represent 14% of the electricity consumption in residential buildings. An average household in the United States (U.S.) spends about USD 400–600 (0.45 ¢/L–0.68 ¢/L) on water heating every year. In this context, water heaters are often considered as a valuable asset for Demand Response (DR) and building energy management system (BEMS) applications. To this end, this study proposes a model-free deep reinforcement learning (RL) approach that aims to minimize the electricity cost of a water heater under a time-of-use (TOU) electricity pricing policy by only using standard DR commands. In this approach, a set of RL agents, with different look ahead periods, were trained using the deep Q-networks (DQN) algorithm and their performance was tested on an unseen pair of price and hot water usage profiles. The testing results showed that the RL agents can help save electricity cost in the range of 19% to 35% compared to the baseline operation without causing any discomfort to end users. Additionally, the RL agents outperformed rule-based and model predictive control (MPC)-based controllers and achieved comparable performance to optimization-based control.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRSDate not supplied

Airport Runway Configuration Management with Offline Model-free Reinforcement Learning

Runway configuration management (RCM) deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction and other environmental variables. RCM is one of the most challenging tasks in air traffic management, as it relies on operational and environmental variables (e.g., weather forecast) that are highly uncertain and complex to model. In this paper, an innovative and automated approach is deployed using offline model-free reinforcement learning to provide decision-support for RCM. The proposed technology processes historical data about variables of interest, decisions made regarding RCM, and their subsequent outcome, to identify a policy that would encourage good decisions and avoid the poor ones. The policy search is guided by an appropriately chosen weighted utility function (e.g., based on minimizing delays and go-arounds). Finally, the performance of the proposed tool is validated using Charlotte Douglas International Airport as the case study, which shows that the proposed method is superior to other conventional rule-based approaches.

Milad Memarzadeh↗
NASA NTRSDate not supplied

Airport Runway Configuration Management with Offline Model-free Reinforcement Learning

Runway configuration management (RCM) deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction and other environmental variables. RCM is one of the most challenging tasks in air traffic management, as it relies on operational and environmental variables (e.g., weather forecast) that are highly uncertain and complex to model. In this paper, an innovative and automated approach is deployed using offline model-free reinforcement learning to provide decision-support for RCM. The proposed technology processes historical data about variables of interest, decisions made regarding RCM, and their subsequent outcome, to identify a policy that would encourage good decisions and avoid the poor ones. The policy search is guided by an appropriately chosen weighted utility function (e.g., based on minimizing delays and go-arounds). Finally, the performance of the proposed tool is validated using Charlotte Douglas International Airport as the case study, which shows that the proposed method is superior to other conventional rule-based approaches.

Milad Memarzadeh↗
DOE OSTI2013

GridWise Transactive Energy Framework (DRAFT Version)

Over the past decade, the use of demand response and other flexible distributed resources for market efficiency and grid reliability has grown dramatically. Federal and state policy objectives point to an important role for customers’ loads, generation and storage in the management of an increasingly unpredictable power system. As we consider the need to substantially scale the use of flexible distributed energy resources, there has been growing attention to the need to address not only the economics, but also the control system implications to ensure grid reliability. This has led to a focus on an area of activity called “Transactive Energy.” Transactive Energy refers to the combination of economic and control techniques to improve grid reliability and efficiency. These techniques may also be used to optimize operations within a customer’s facility. The Department of Energy has supported the GridWise® Architecture Council (“the Council”) in developing a conceptual framework that can be used in developing architectures, and designing solutions related to transactive energy. The goal of this effort is to encourage and facilitate collaboration among the many stakeholders involved in the transformation of the power system and thereby advance the practical implementation of transactive energy.

GridWise↗
DOE OSTI2013

Creating a Transactive Energy Framework: Improving Reliability and Efficiency

The use of demand response and other flexible distributed resources over the past decade for market efficiency and grid reliability has grown dramatically. Federal and state policy objectives point to an important role for customer loads, generation and storage in the management of an increasingly unpredictable power system. As we consider the need to substantially scale the use of flexible distributed energy resources, there has been growing attention to the need to address not only the economics, but also the control system implications to ensure grid reliability. This has led to a focus on an area of activity called “Transactive Energy.” Transactive Energy refers to the combination of economic and control techniques that improve grid reliability and efficiency. These techniques may also be used to optimize operations within a customer’s facility. The Department of Energy has supported the GridWise® Architecture Council (“the Council”) to develop a framework to provide opportunity for collaboration among the many stakeholders involved in the transformation of the power system.

GWAC↗
DOE OSTI2018

Review of City Energy and Emissions Analysis Needs, Methods, and Tools

Currently over $300B is spent in US city economies to pay for energy. Many US cities are taking leading roles in exploring and promoting activities to improve energy efficiency and reduce green house gas (GHG) emissions. This paper summarizes a series of interviews with several leading cities regarding their needs, methods and tools they are using to model energy use and evaluate policies to reduce GHG. We also present a review of several analysis tools evaluated and used to explore urban scale design scenarios for two new major developments in the San Francisco area. We found that cities face great challenges managing data on their building stock, obtaining energy use data, and evaluating the different tools that are available to them. There is a need for better data management systems that allow tools to be more interoperable. The wide variety and features of today’s tools, and the fact that many of them are not open data models, create sub-optimal environments to conduct the energy analysis many cities are seeking to conduct.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Improving energy efficiency while reducing anthropogenic heat from buildings: how retrofits influence the building stock and urban microclimate in Los Angeles

Anthropogenic heat (AH) from buildings contributes to urban overheating, especially during heat waves, yet building retrofit studies usually evaluate energy savings without assessing impacts on AH. This study quantifies how common building retrofit measures affect both building energy use and AH emissions across the City of Los Angeles. Using a bottom-up urban building energy modeling framework coupled with high-resolution local weather from the Weather Research and Forecasting model with Building Effect Parameterization (WRF-BEP), we evaluate eleven retrofit measures and two multi-measure retrofit packages. HVAC and LED lighting retrofits provide the largest city-wide annual site energy savings, while roof coating is most effective for reducing AH. A package optimized for energy savings reduces summer site energy use by about 32% (2.3 TWh), while a package incorporating AH-focused measures reduces the total AH by over 50% (137 PJ) with minimal difference in energy savings. The AH-aware package produces substantially greater urban cooling, reducing mean near-surface air temperature by up to 0.62 ℃ and peak temperature by up to 3.79 ℃. These results show that retrofit strategies selected only for energy savings may overlook major opportunities for urban heat mitigation. The study provides a framework for integrating AH into building retrofit planning and urban heat resilience policy.

Anthropogenic heat↗
DOE OSTI2022

System benefits of industrial battery storage: A comparison of grid and facility control and dispatch

Customer-owned, distributed battery installations are being incentivized by utilities to increase installed battery capacity. In many of these incentive agreements, the battery owner relinquishes battery control to the utility in exchange for incentive money. The industrial sector has lagged in storage installation when compared to the residential and commercial sectors. This study compares the economic advantages to utilities and industrial facilities in different dispatch control situations. The study presents a novel framework for the optimization of multiple systems using load profiles from the industrial, residential, and commercial sectors. Case studies are presented to illustrate different dispatch scenarios. Further, the simulations showed more fiscal benefit for the industrial facilities to dispatch the battery for electrical demand reduction than utility dispatch. In the case studies, facility dispatch control resulted in an increase of facility savings by a factor of about 8.7 when compared to utility dispatch. Battery size plays a significant factor on the impact of the grid’s generating costs, showing that larger batteries can provide significant benefit even if dispatched by the facility. Future policies concerning industrial battery installations should consider overall economic benefits to utility and facility in the form of rate structures and incentive participation based on battery size.

25 ENERGY STORAGE↗
DOE OSTI2024

Potential long-term, global effects of enhancing the domestic terrestrial carbon sink in the United States through no-till and cover cropping

Abstract Background Achieving a net zero greenhouse gas United States (US) economy is likely to require both deep sectoral mitigation and additional carbon dioxide removals to offset hard-to-abate emissions. Enhancing the terrestrial carbon sink, through practices such as the adoption of no-till and cover cropping agricultural management, could provide a portion of these required offsets. Changing domestic agricultural practices to optimize carbon content, however, might reduce or shift US agricultural commodity outputs and exports, with potential implications on respective global markets and land use patterns. Here, we use an integrated energy-economy-land-climate model to comprehensively assess the global land, trade, and emissions impacts of an adoption of domestic no-till farming and cover cropping practices based on carbon pricing. Results We find that the adoption of these practices varies depending on which aspects of terrestrial carbon are valued. Valuation of all terrestrial carbon resulted in afforestation at the expense of domestic agricultural production. In contrast, a policy valuing soil carbon in agricultural systems specifically indicates strong adoption of no-till and cover cropping for key crops. Conclusions We conclude that under targeted terrestrial carbon incentives, adoption of no-till and cover cropping practices in the US could increase the terrestrial carbon sink with limited effects on crop availability for food and fodder markets. Future work should consider integrated assessment modeling of non-CO 2 greenhouse gas impacts, above ground carbon storage changes, and capital and operating cost considerations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Optimal investment and scheduling of distributed energy resources with uncertainty in electric vehicles driving schedules

The large scale penetration of electric vehicles (EVs) will introduce technical challenges to the distribution grid, but also carries the potential for vehicle-to-grid services. Namely, if available in large enough numbers, EVs can be used as a distributed energy resource (DER) and their presence can influence optimal DER investment and scheduling decisions in microgrids. In this work, a novel EV fleet aggregator model is introduced in a stochastic formulation of DER-CAM [1], an optimization tool used to address DER investment and scheduling problems. This is used to assess the impact of EV interconnections on optimal DER solutions considering uncertainty in EV driving schedules. Optimization results indicate that EVs can have a significant impact on DER investments, particularly if considering short payback periods. Furthermore, results suggest that uncertainty in driving schedules carries little significance to total energy costs, which is corroborated by results obtained using the stochastic formulation of the problem.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Poly(ethylene oxide)-assisted energy funneling for efficient perovskite light emission

Poly(ethylene oxide) (PEO) has been demonstrated as an effective polymer additive for fabrication of organic–inorganic hybrid perovskite light emitting devices due to its properties enabling pinhole-free film morphology, improved charge injection, and defect passivation. In this paper, we report a new aspect of PEO in assisting the energy funneling in formamidinium lead bromide (FAPbBr 3 ) thin films, leading to a very high photoluminescence quantum yield of ~62.1%. By mixing PEO with FAPbBr 3 at a desired ratio, self-assembled 3D polycrystals with sizes from ~20 to ~1500 nm were formed. The polycrystals contain inhomogeneous domains with a range of energetic excited states. Through fluorescence lifetime image microscopy, the in situ fluorescence emissions and the lifetimes of the domains were measured simultaneously, indicating energy funneling from the wide-bandgap domains (with small-size grains) into the narrow-bandgap domains (with large-size grains). PL spectroscopy on the nano-second scale was used to study the dynamics of charge transfer, which shows a reduced short-wavelength emission and narrowed full width at half maximum with increasing time. Our results also indicate that the optimal PEO doping ratio of 0.35 :1 can facilitate the most efficient charge carrier cascade for energy funneling. Furthermore, this study establishes the role of PEO-assisted energy funneling avoiding the defect-quenching effect in perovskites, for potential applications in perovskite LEDs and lasers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Drive Electric Vermont Case Study

Currently in the United States, the heavy majority of plug-in electric vehicle (PEV) sales have been in highly conducive, selected, metropolitan areas; opposed to more broad distribution across the country. The U.S. Department of Energy’s EV Everywhere Grand Challenge is looking carefully at the barriers and opportunities that exist to enable small and midsize communities to partake in the PEV market and benefit from the economic and environmental advantages of PEVs. In order to gain insight into these challenges and barriers, DOE selected a success story (i.e., Drive Electric Vermont) as the subject of this case study, as the state of Vermont is tied with Detroit, Michigan in having the highest percentage of 2014 (most recent complete data) PEV registrations for cold weather U.S. cities and has seen more than a sixfold increase in charging stations over the last three years. The overall objective of this case study was to use the lessons learned from Drive Electric Vermont to determine what activities are most effective at encouraging acquisitions of PEVs and deployment of charging infrastructure in small to midsize communities, prioritizing and sequencing their implementation, identifying robust means for extrapolation, and applying this understanding to other small to midsize communities across the nation. The Drive Electric Vermont Program was formed in 2012 with a goal of increasing the use of electrified transportation in Vermont through policy development, education and outreach, and infrastructure development. The Drive Electric Vermont Program can be broadly broken into four components: (1) strategic planning/leadership, (2) stakeholder/partnership development, (3) education and outreach, and (4) incentives. The early phases of the program focused heavily on strategic planning, and stakeholder and partnership development, followed by a transition to education and outreach activities, charging infrastructure development, and grant and incentive programs that support charging infrastructure deployment and PEV purchases. Future plans include an additional round of consumer/dealer PEV incentives, continuation of the marketing campaign, and utilization of consumer surveys to develop new campaign themes and to optimize information distribution channels. In Vermont, the number of PEVs grew from 88 in July 2012 to 1,113 in January 2016, with a high percentage of the PEVs being plug-in hybrids (i.e., 865) versus all electric vehicles (i.e., 248). The most popular PEVs are Toyota’s Prius Plug-in and Ford’s C-Max Energi, which make up a combined 48% of the total PEV ownership in the state. The PEVs are predominantly clustered in four counties: Lamoille, Caledonia, Washington, and Chittenden. There is no evident correlation between community size and PEV penetration; however, over 70% of PEV owners are in communities of 1,000 to 10,000 people. On the other hand, there is a close correlation between where PEVs are registered and where they publically charge. The number of PEV charging stations in Vermont grew from 17 in January 2013 to 111 in January 2016, at a variety of charging venues, including retail, parking (short term and long term), workplace, dealerships, hotels, education, leisure, and hospitals. The vast majority of the charging venues contain Level 2 electric vehicle supply equipment (EVSE) solely or combined with Level 1 EVSE or direct current fast charging. The average power transferred per charging event for communities across Vermont ranged from 4.8 to 13.8 kWh for direct current fast charging and 1.0 to 11.9 kWh for and Level 2 EVSE. Over half the charging locations offer free charging. For those that do require payment, different pricing models are employed, including monthly memberships, hourly payment (where there is often a higher fee for the first hour followed by a lower hourly fee for the remaining hours), and energy use based on kWh drawn from the EVSE. There is no correlation between pricing models and community size and region, the strongest correlation is by network provider. Charging events across Vermont generally correlate with east-west bands of chargers (Figure ES-1) running from Burlington to Montpelier and in the area of Rutland.

33 ADVANCED PROPULSION SYSTEMS↗