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PurposeThe study was designed to assess outcomes of arteriovenous (AV) accesses after interventional stent-graft deployment in haemodialysis patients.Materials and Methods63 haemodialysis patients with 66 AV fistulas and AV grafts were treated by interventional stent-graft deployment from 2006 to 2012 at our hospital. Data of these patients were retrospectively analysed for location of deployed stent-grafts, occurrence and location of (re-)stenosis and (re-)thrombosis. Complex stenosis was the most frequent indication for stent-graft deployment (45.5 %), followed by complications of angioplasty with vessel rupture or dissection (31.8 %).ResultsA high rate of procedural success was achieved (98.5 %). The most frequent location of the deployed stent-graft was the draining vein (66.7 %). Stent-graft deployment was more frequent in AV grafts than in AV fistulas. Primary patency was 45.5 % at 6 month, 31.3 % at 12 month and 19.2 % at 24 month. Primary patency was significantly better for AV fistulas than for AV grafts with deployed stent-grafts. Patency of the deployed stent-graft was much better than overall AV access primary patency with deployed stent-graft. Re-stenosis with thrombosis was the most frequent indication for re-intervention. Most frequent location of re-stenosis was the draining vein (37.1 %), followed by stenosis at the AV access (29.5 %) and the deployed stent-graft (23.5 %).ConclusionRe-stenosis and re-thrombosis remain frequent in AV fistulas and AV grafts in haemodialysis patients despite stent-graft deployment. Re-stenosis of the deployed stent-graft is, only in the minority of the cases, responsible for AV access dysfunction.
Commercial viability of the marine hydrokinetic (MHK) energy industry is contingent on numerous and diverse factors. A major factor is the effects deployed devices have on animals. This factor is multi-faceted since it is dependent on the availability of appropriate scientific approaches to detect these effects. One of the animal groups with overlapping distributions of MHK devices are fishes. As such, individual fish behavior is likely to be influenced by the presence and operation of MHK devices. Depending on the scale of deployment there are implications for changes to essential fish habitat and effects that can be explored during deployment of a single device yet most changes are likely to be realized when multiple devices are deployed over large areas. It is not only important to document these effects and examine the need for mitigation, but also determine whether the methods involved can be used within the economic constraints of this nascent industry. The results presented in this report benefit the MHK industry by providing transferrable environmental monitoring approaches for MHK projects, specifically related to the interactions between static and dynamic tidal turbines and fish. In addition, some of the data can be used to generalize conditions (e.g., the temporal periodicity of fish presence in tidal regions and probability of fish encountering a device) at other MHK sites with similar physical conditions and fish assemblages. Ocean Renewable Power Company, LLC (ORPC) deployed and tested a prototype OCGen® tidal module in Cobscook Bay, Maine, in the summer of 2014. University of Maine researchers proposed an approach to inform other researchers, regulators, and industry members of the effects of this deployment on fish. While the approach was specifically applied to the OCGen® module, results are applicable to other pilot projects and inform future array deployments. Research funded under this grant allowed us to quantify fish presence as well as individual and group-level behavior changes in the presence of the deployed OCGen® module along with a bottom support frame from a previously deployed device (TidGen®). Specific objectives associated with fish behavior changes were (1) continuation of two long-term datasets: (a) stationary down-looking hydroacoustic dataset near an MHK device (group-level) and (b) stationary side-looking hydroacoustics near the bottom-support frame of a previously deployed MHK device (individual-level); (2) application of new processing methods to down-looking hydroacoustic datasets to improve fish species identification (group-level); and (3) development of an encounter probability model using data on fish abundance, vertical distribution, and behavior.
In 2020, a team from the National Renewable Energy Laboratory (NREL) partnered with the East Carolina University Coastal Studies Institute to develop a small, modular, wave-powered point absorber desalination prototype. That prototype, known as the hydraulic and electric reverse osmosis wave energy converter (HERO WEC), was intended to de-risk the installation activities planned for the Waves to Water Prize sponsored by the Water Power Technologies Office. The NREL team was given approximately 18 months to design, build, bench test, and deploy the HERO WEC prototype. Since the initial HERO WEC development, it has been used for two in-lab test programs and three ocean deployments. The first in-lab test program focused on ensuring that the overall operation of the device occurred as expected and on validating the operation of safety systems such as pressure relief valves or electrical breakers for load mitigation. The second in-lab test program was the first time the NREL team leveraged NREL’s large-amplitude motion platform to characterize the performance of the HERO WEC. Of the three in-water deployments, the first deployment was effectively an installation and recovery practice with no meaningful wave activity during the time that it was installed. The second deployment was a 2-week effort that enabled both the electric and hydraulic configurations of the device to be in the water for approximately 5 days each. This deployment was also the first time that the team had the opportunity to perform a drivetrain swap on the Coastal Studies Institute research vessel. The third deployment had to be split into two separate installations due to minor damage incurred from the WEC spinning during the initial installation. This report summarizes the deployments, the challenges encountered with each deployment, and the lessons learned for future work.
This analysis represents the biogas-to-electricity pathway under the Renewable Fuel Standard (RFS) as a point of purchase incentive and tests the impact of this incentive on EV deployment using a vehicle consumer choice model. The credit value generated under this policy was calculated in a number of scenarios based on electricity use of each power train choice on a yearly basis over the 15 year vehicle lifetime, accounting for the average electric vehicle miles travelled and vehicle efficiency, competition for biogas-derived electricity among electric vehicles (EVs), the RIN equivalence value and the time value of money. The credit value calculation in each of these scenarios is offered upfront as a point of purchase incentive for EVs using the Market Acceptance of Advanced Automotive Technologies (MA3T) vehicle choice model, which tracks sales, fleet size and energy use over time. The majority of the scenarios use a proposed RIN equivalence value, which increases the credit value as a way to explore the analysis space. Additional model runs show the relative impact of the equivalence value on EV deployment. The MA3T model output shows that a consumer incentive accelerates the deployment of EVs for all scenarios relative to the baseline (no policy) case. In the scenario modeled to represent the current biogas-to-electricity generation capacity (15 TWh/year) with a 5.24kWh/RIN equivalence value, the policy leads to an additional 1.4 million plug-in hybrid electric vehicles (PHEVs) and 3.5 million battery electric vehicles (BEVs) in 2025 beyond the no-policy case of 1.3 million PHEVs and 2.1 million BEVs when the full value of the credit is passed on to the consumer. In 2030, this increases to 2.4 million PHEVs and 7.3 million BEVs beyond the baseline. This larger impact on BEVs relative to PHEVs is due in part to the larger credit that BEVs receive in the model based on the greater percentage of electric vehicle miles traveled by BEVs relative to PHEVs. In this scenario 2025 also represents the last year in which biogas-derived electricity is able to fully supply the transportation electricity demand in the model. After 2025, the credit value declines on a per vehicle basis. At the same time a larger fraction of the credit may shift towards biogas producers in order to incent additional biogas production. The expanded 41 TWh/year biogas availability scenarios represent an increase beyond today s generation capacity and allow greater eRIN generation. With a 5.24kWh/RIN equivalence value, when all of the credit is directed towards reducing vehicle purchase prices, the 41 TWh/year biogas scenario results in 4.1 million additional PHEVs and 12.2 million additional BEVs on the road in 2030 beyond the baseline of 2.5 million PHEVs and 6.1 million BEVs. Under this expanded biogas capacity, biogas-derived electricity generation is able to fully supply electricity for a fleet of over 21 million EVs (15.6 million BEVs and 5.8 million PHEVs) on a yearly basis. In addition to assessing the full value credit scenarios described above, multiple scenarios were analyzed to determine the impact if only a fraction of the credit value was passed on to the consumer. In all of these cases, the EV deployment was scaled back as the fraction of the credit that was passed on to the consumer was reduced. These scenarios can be used to estimate the impact if the credit value is reduced in other ways as well, as demonstrated by the scenarios where the current (22.6 kWh/RIN) equivalence value was used. The EV deployment that results from an equivalence value of 22.6 kWh/RIN equivalence value is roughly equivalent to the EV deployment observed in the 25% case using the 5.24 kWh/RIN equivalence value. A higher equivalence value means that a smaller number of credits, and therefore value, is created for each kWh, and therefore the impact on EV deployment is reduced. This analysis shows several of the drivers that will impact eRIN generation and credit value, and tests the impact of an eRIN point of purchase incentive on EV deployment. This additional incentive can accelerate the deployment of EVs when it is used to reduce vehicle purchase prices. However, the ultimate impact of this policy, as modeled here, will be determined by future RIN prices, the extent to which eRIN credit value can be passed on to the consumer as a point of purchase incentive and the equivalence value.
Pacific Northwest National Laboratory (PNNL) operates two AXYS WindSentinel lidar buoys for the U.S. Department of Energy’s Wind Energy Technologies. The purpose of these buoys is to collect hub-height winds and supporting meteorological and oceanographic information to facilitate the development of offshore wind energy in the U.S. In general, each buoy is deployed for a year or more at a given location in order to capture at least a full annual cycle of weather conditions. The initial deployment for one buoy was off the coast of Virginia beginning in 2015, and the other buoy was first deployed off the coast of New Jersey beginning in 2016. Over the last two years, PNNL has had an opportunity to analyze the data collected during these first two deployments. This report describes a substantial analysis of data collected by the two lidar buoys operated off the coasts of Virginia and New Jersey. The centerpiece instrument for each buoy as deployed off Virginia and New Jersey is a lidar system, which is designed to measure the horizontal wind vector from approximately 40 m to 200 m above the sea surface with vertical resolution of 40 m. Since the initial deployments, the original lidars have been replaced with more powerful Leosphere 866 v2 systems. The analyses in this report will apply to the original Vindicator systems. In addition to the wind profiles from the lidars, the buoys collect near-surface measurements of wind speed and direction, air temperature, relative humidity, barometric pressure, and solar irradiance. Oceanographic variables measured include the two-dimensional wave spectrum, water temperature and conductivity, and ocean current vectors to a depth of 90 m. An assessment of overall data recovery and a basic analysis of the data collected was provided in a previous report This report substantially extends that analysis. The various sections describe the development and application of an inertial measurement unit (IMU) data recovery scheme for the New Jersey deployment; a climatological analysis of winds at hub height and at the surface together with thermodynamic variables measured at the surface for the full deployment periods; an analysis of oceanographic observations describe sea state; the development of a refinement for NOAA’s WaveWatch III model to allow its application to near-shore areas; a basic climatology of ocean currents observed from the buoys; an analysis of observed winds in the framework of Monin-Obukhov Similarity Theory; and the development and evaluation of techniques to extract turbulence intensity and turbulence kinetic energy from the lidars. The analyses contained in this report provide a great deal of new information about offshore conditions on the U.S. East Coast. In addition, the experience gained will inform both configurations and analysis of data from future deployments of these lidar buoy systems.
Remote coastal communities, which could be early adopters of wave energy projects, have concerns over costs, conflicts, and potential risks of development. Designers and developers are challenged to address these community concerns as they continue to develop wave energy technologies. One potential means of reducing costs, conflicts, and risks, especially for demonstration and pilot-scale projects, could be planning a deployment that operates for only a portion of the year—a seasonal deployment. Here, in this paper, we examine the impacts of a seasonal deployment in terms of cost, electricity production, operations and maintenance, environmental impacts, and community benefits. We take a holistic, comparative approach to feasibility that can be replicated for other comparative studies. We estimate electricity production using a point absorber WEC modeled near Sitka, AK, USA and optimized for the given sea conditions. We determine that, for remote community sized projects, seasonal deployments could result in small cost savings (less than 10 %), but larger decreases in annual energy production (around 30 % for our case study area). Seasonal deployments could be preferable in places with seasonal energy needs, if failures and device access become a major hindrance to wave energy technology development, or as a cautionary approach to introducing new technology to the oceans. We also determine that a highly seasonal wave resource is not necessarily a requirement for seasonal deployments to be considered. Seasonal deployments are an alternative to year-round deployments that can be considered in places where marine spatial conflict is a seasonal concern.
Dynamic wireless power transfer (DWPT), or dynamic wireless charging technology, enables charging-while-driving and offers opportunities for eliminating range anxiety, stimulating market penetration of electric vehicles (EVs), and enhancing the sustainability performance of electrified transportation. However, the deployment of wireless charging lanes on highways and urban road networks can be costly and resource-intensive. A life cycle assessment (LCA) is conducted here to compare the sustainability performance of DWPT applied in a network of highways and urban roads for charging electric passenger cars. The assessment compares DWPT to stationary wireless charging and to conventional plug-in charging using a case study of Washtenaw County in Michigan, USA over 20 years. The LCA is based on three key sustainability metrics: costs, greenhouse gas (GHG) emissions, and energy burdens, encompassing not only the use-phase burdens from electricity and fuel, but also the upfront deployment burdens of DWPT infrastructure. A genetic algorithm is applied to optimize the rollout of DWPT infrastructure both spatially and temporally in order to minimize life cycle costs, GHG, and energy burdens: (1) spatial optimization selects road segments to deploy DWPT considering traffic volume, speed, and pavement remaining service life (RSL); (2) temporal optimization determines in which year to deploy DWPT on a particular road segment considering EV market share growth as a function of DWPT coverage rate, future DWPT cost reduction, and charging efficiency improvement. Results indicate that optimal deployment of DWPT electrifying up to about 3% of total roadway lane-miles reduces life cycle GHG emissions and energy by up to 9.0% and 6.8%, respectively, and enables downsizing of the EV battery capacity by up to 48%, compared to the non-DWPT scenarios. Roadside solar panels and storage batteries are essential to significantly reduce life cycle energy and GHG burdens but bring additional costs. Breakeven analysis indicates a breakeven year for solar charging benefits to pay back the DWPT infrastructure burdens can be less than 20 years for GHG and energy burdens but longer than 20 years for costs. A monetization of carbon emissions of at least $250 per metric tonne of CO 2 is required to shift the optimal “pro-cost” deployment to the optimal “pro-GHG” deployment. A roadway segment with volume greater than about 26,000 vehicle counts per day, speed slower than 55 miles per hour (1 mile ≈ 1.609 km), and pavement RSL shorter than 3 years should be given a high priority for early-stage DWPT deployment.
Stratospheric aerosol injection (SAI) is a prospective climate intervention technology that would seek to abate climate change by deflecting back into space a small fraction of the incoming solar radiation. While most consideration given to SAI assumes a global intervention, this paper considers an alternative scenario whereby SAI might be deployed only in the subpolar regions. Subpolar deployment would quickly envelope the poles as well and could arrest or reverse ice and permafrost melt at high latitudes. This would yield global benefit by retarding sea level rise. Given that effective SAI deployment could be achieved at much lower altitudes in these regions than would be required in the tropics, it is commonly assumed that subpolar deployment would present fewer aeronautical challenges. An SAI deployment intended to reduce average surface temperatures in both the Arctic and Antarctic regions by 2 °C is deemed here to be feasible at relatively low cost with conventional technologies. However, we do not find that such a deployment could be undertaken with a small fleet of pre-existing aircraft, nor that relegating such a program to these sparsely populated regions would obviate the myriad governance challenges that would confront any such deployment. Nevertheless, given its feasibility and potential global benefit, the prospect of subpolar-focused SAI warrants greater attention.
This three-year demonstration and deployment project sought to address market barriers to accelerating the adoption of Advanced Lighting Controls (ALCs), an underutilized technology with low market penetration. ALCs are defined as networked, addressable lighting control systems that utilize software or intelligent controllers to combine multiple energy-saving lighting control strategies in a single space (e.g., smart-time scheduling, daylight harvesting, task tuning, occupancy control, personal control, variable load-shedding, and plug-load control). The networked intelligent aspect of these systems allows applicable lighting control strategies to be combined in a single space, layered over one another, maximizing overall energy-savings. The project included five real building demonstrations of ALCs across the Northeast US region. The demonstrations provided valuable data and experience to support deployment tasks that are necessary to overcome market barriers. These deployment tasks included development of training resources for building designers, installers, and trades, as well as development of new energy efficiency rebates for the technology from Efficiency Forward’s utility partners. Educating designers, installers, and trades on ALCs is a critical task for reducing the cost of the technology that is currently inflated due to perceived complexity and unfamiliarity with how to design and install the systems. Further, utility and non-utility energy efficiency programs continue to relegate the technology to custom or ill-suited prescriptive program designs that do not effectively deploy the technology at scale. This project developed new, scalable rebate approaches for the technology. Efficiency Forward utilized their DesignLights Consortium® (DLC) brand and network of 81 DLC member utilities to develop and deploy the results of the project. The outputs of the project have included five published case studies, a six-hour ALC technology training curriculum that has already been deployed in five US states, and new rebates offered for the technology that have been deployed by a dozen utilities across the US. Widespread adoption of ALC technology in commercial buildings would provide tremendous benefits. The current market penetration of ALC systems is estimated at <0.1% in commercial buildings. If ALC systems were installed in all commercial buildings, approximately 1,051 TBtu of energy could be saved. This would translate into customer cost savings of approximately $10.7 billion annually.
Connected and Automated Vehicles (CAVs) can be considered to be a disruptive transportation technology, with the potential to significantly improve overall transportation system efficiency; however, CAVs may increase induce vehicle miles traveled (VMT) and bring on greater energy consumption. Further, shared mobility is another disruptive transportation event that is reshaping our travel patterns. The primary goal of this project was to extensively collect data from vehicles and associated infrastructure equipped with CAV technologies from both real-world experiments and simulation studies mainly deployed in California, and develop a comprehensive framework for evaluating energy efficiency opportunities from large-scale (e.g., statewide) introduction of CAVs and a wide deployment of shared mobility systems in a variety of scenarios. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance, and energy efficiency, a unique mesoscopic simulation-based model was developed for mobility and energy efficiency evaluation considering these disruptive transportation technologies. As a complement to existing studies on nationwide evaluation of CAVs’ energy impacts, this project was focused on data collection efforts and CAV applications under congested traffic environments that are frequently experienced on a massive scale across the major metropolitan areas in California. Extensive real-world data collection supplemented with simulation studies were conducted to cover a variety of CAV and shared mobility scenarios, particularly on scenarios less-explored in the existing research. Another key component of this project was to consider the interaction between different CAV technologies and shared mobility models, and the compound effect on energy efficiency. A comprehensive modeling suite was developed to quantify the impact of new mobility technologies on travel behavior and traffic performance. The developed modeling framework includes an energy intensity module, mode choice module and activity generation module that are integrated into an agent-based BEAM simulation platform to perform impact analysis based on a variety of scenarios. In addition, the RouteE model has been upgraded to incorporate the impact of CAVs on traffic flow, VMT and energy intensity, using micro-simulation data collected from both freeways and urban arterials. A novel fundamental influencing factor (FIF) mode choice model was developed to link CAV and shared mobility components with travel behaviors, and adapted into the BEAM-centered model framework. A statewide energy inventory was constructed under various CAV technology deployment scenarios by incorporating datasets and models for predicting vehicle market share and vehicle usage, which are tightly associated with the penetration of shared mobility systems. Based applying this modeling suite to a calibrated network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes. The statewide analysis based on the National Household Travel Survey (NHTS) sample data is consistent with the findings from the Riverside network and validate the developed clustering-prediction modeling methodology. The outcomes from this project will help close the knowledge gap on recognizing the potential performance and energy impacts of a broad deployment of CAV and shared mobility technologies across a wide range of roadway infrastructure with varying levels of congestion. Results from this project: 1) will support policymakers in steering CAV development and deployment towards an energy favorable direction; 2) reduce uncertainties in estimating energy saving opportunities from new mobility technologies and services; 3) increase the confidence of CAV technology investors both on the infrastructure side (i.e., transportation agencies) and on the vehicle side (i.e., OEMs); and 4) expedite the deployment of energy-efficient CAV and shared mobility applications.
Solar-reflective building envelope surfaces, such as cool roofs and walls, can be especially helpful in disadvantaged communities that often have poorly insulated older homes, aging or absent air conditioning units, steep utility bills, polluted air, and high vulnerability and exposure to extreme heat. With support from the U.S. Department of Energy, our project seeks to dramatically increase the climate-appropriate deployment of cool surfaces across the United States with an emphasis on their application to disadvantaged communities. First, we sought to identify cool-surface deployment barriers, opportunities, and models by (a) reviewing the history of cool-surface deployment activities, (b) interviewing cool-surface stakeholders, (c) researching successful energy-efficiency/green building deployment models; and (d) interviewing the actors who have implemented these models. Second, we conducted a workshop to engage stakeholders in development of a deployment plan. Third, we asked several U.S. federal agencies (a) how cool surfaces and cool surface stakeholders could support their missions and (b) how agency activities could support cool-surface deployment. Fourth, we identified a set of transformative ideas that form the core of the deployment plan. Transformative ideas include but are not limited to initiatives to (a) launch an educational campaign to make the general public and building professionals aware of how cool roofs and walls exclude unwanted solar heat; (b) create a “Cool Roof Prize” stimulating the development of affordable, high-performance cool asphalt roofing shingles; (c) conduct high-profile, large-scale demonstration programs that bring cool surfaces to disadvantaged heat-vulnerable communities; and (d) support local, regional, and state climate action (heat mitigation) plans with cool surfaces.
The 2021 U.S. Department of Energy's (DOE) "Thermal Energy Storage Systems for Buildings Workshop: Priorities and Pathways to Widespread Deployment of Thermal Energy Storage in Buildings" was hosted virtually on May 11 and 12, 2021. This report provides an overview of the workshop proceedings. Organized by DOE's Building Technologies Office (BTO), the National Renewable Energy Laboratory, Lawrence Berkeley National Laboratory, and Oak Ridge National Laboratory, the workshop convened more than 600 stakeholders from around the world to discuss the need for advancing the deployment of thermal energy storage (TES) in buildings. This workshop was designed to build on BTO's webinar series and 2019 workshop. The goals of this workshop were to promote discussion on TES related to: 1. Market adoption and deployment barriers, 2. Key applications and value drivers, 3. System cost, performance, and market requirements, and 4. End-use specific needs. The workshop took place over two days and consisted of keynote presentations, moderated panels, breakout sessions, and open discussion forums. The keynote presentations introduced opportunities for building TES, barriers to widespread deployment, and future visions. Moderated panel discussions presented diverse perspectives, breakout sessions provided opportunities for more intimate conversations, and open discussions captured feedback from the entire workshop group. Over the course of the two days, attendees exchanged experiences and ideas surrounding multiple aspects of TES in buildings. The first section of this report provides background on the motivations behind advancing TES for residential and commercial buildings and elaborates on the workshop objectives. The second section details the workshop structure and participant demographics. The third section provides key findings of the discussions that took place during the breakout sessions. Finally, this report details recommendations and actions to increase the TES deployment. Several recommendations from workshop attendees are presented in Table ES-1. TES holds significant potential to help increase building efficiency, grid-interactivity, and energy resilience, as well as reduce associated carbon emissions. The outputs generated from this workshop will aid stakeholders in advancing TES in buildings through a deeper understanding of the opportunities and barriers surrounding widespread deployment.
The National Renewable Energy Laboratory (NREL) hosted the Equitable Demonstration and Deployment Roundtable on August 25, 2021. The roundtable brought together experts in climate and energy justice, demonstration projects, and renewable energy deployment to explore issues that will lead to greater understanding of the barriers to equity at scale and the role that the U.S. Department of Energy (DOE) can play to reduce and mitigate them. A large percentage of the participants included experts in energy equity issues from national and local organizations across multiple sectors, both public and private. These individuals engaged with DOE and national laboratory researchers to identify community-based challenges and propose opportunities for DOE to address those challenges to energy demonstration and deployment. The roundtable examined equity considerations for two phases of the energy development process: demonstration and deployment. These two pathways were selected for discussion during the same workshop because they are inextricably linked within the energy ecosystem. Ensuring the deployment of safe, affordable, and reliable energy deployment is DOE's primary goal and energy demonstration projects are a key driver moving technologies from the laboratory to market to achieve this result.
Sensor networks have been used in a rapidly increasing number of applications in many fields. This work generalizes a sensor deployment problem to place a minimum set of wireless sensors at candidate locations in constrained 3D space to k-cover a given set of target objects. By exhausting the combinations of discreteness/continuousness constraints on either sensor locations or target objects, we formulate four classes of sensor deployment problems in 3D space: deploy sensors at Discrete/Continuous Locations (D/CL) to cover Discrete/Continuous Targets (D/CT). We begin with the design of an approximate algorithm for DLDT and then reduce DLCT, CLDT, and CLCT to DLDT by discretizing continuous sensor locations or target objects into a set of divisions without sacrificing sensing precision. Furthermore, we consider a connected version of each problem where the deployed sensors must form a connected network, and design an approximation algorithm to minimize the number of deployed sensors with connectivity guarantee. For performance comparison, we design and implement an optimal solution and a genetic algorithm (GA)-based approach. Extensive simulation results show that the proposed deployment algorithms consistently outperform the GA-based heuristic and achieve a close-to-optimal performance in small-scale problem instances and a significantly superior overall performance than the theoretical upper bound.
Further deployment of agrivoltaics is likely to require a better understanding of how policies and agreements can shape the outcomes of solar siting on farmland. This study evaluates the Massachusetts agrivoltaics policy in terms of its implications on deployment and stakeholder experiences in adoption. We present findings from interviews with 26 state policymakers, Extension agents, representatives of non-governmental organizations, farm owners and operators, and solar developers. Our findings demonstrate how the policy has mixed effects on deployment processes and outcomes—in some instances, the policy enables deployment by formalizing cross-sector collaboration, increasing farm owner and operator participation in development, and facilitating novel business models. In other instances, the policy constrains deployment by prescribing operational requirements, creating liability risk, and developing dependency on empirical data to inform eligibility decisions. Interviewees explained how these mixed policy effects create both benefits and burdens for adopters, particularly farm owners and operators. These insights indicate the value of cross-sector collaboration during all phases of agrivoltaic policy implementation and project development; the importance of coordination across policy, research, and commercial activities; and the significant role of regulators and policy design in deployment. The evidence presented in this paper can inform decision making for emerging agrivoltaic policies and markets, both in the United States and internationally.
The substantial increase in deployment of customer-sited solar photovoltaics (PV) in the United States has been driven by a combination of steeply declining costs, financing innovations, and supportive policies. Among those supportive policies is net metering, which in most states effectively allows customers to receive compensation for distributed PV generation at the full retail electricity price. The current design of retail electricity rates and the presence of net metering have elicited concerns that the possible under-recovery of fixed utility costs from PV system owners may lead to a feedback loop of increasing retail prices that accelerate PV adoption and further rate increases. However, a separate and opposing feedback loop could offset this effect: increased PV deployment may lead to a shift in the timing of peak-period electricity prices that could reduce the bill savings received under net metering where time-varying retail electricity rates are used, thereby dampening further PV adoption. In this paper, we examine the impacts of these two competing feedback dynamics on U.S. distributed PV deployment through 2050 for both residential and commercial customers, across states. Our results indicate that, at the aggregate national level, the two feedback effects nearly offset one another and therefore produce a modest net effect, although their magnitude and direction vary by customer segment and by state. We also model aggregate PV deployment trends under various rate designs and net-metering rules, accounting for feedback dynamics. Our results demonstrate that future adoption of distributed PV is highly sensitive to retail rate structures. Whereas flat, time-invariant rates with net metering lead to higher aggregate national deployment levels than the current mix of rate structures (+5% in 2050), rate structures with higher monthly fixed customer charges or PV compensation at levels lower than the full retail rate can dramatically erode aggregate customer adoption of PV (from -14% to -61%, depending on the design). Moving towards time-varying rates, on the other hand, may accelerate near- and medium-term deployment (through 2030), but is found to slow adoption in the longer term (-22% in 2050).
This report assesses the feasibility and strategic implications of deploying nuclear power reactors, including large-scale plants, advanced small modular reactors (SMRs), and microreactors, in African countries. Case studies focus on South Africa, Egypt, Kenya, Ghana, and Nigeria, examining nuclear energy’s role in Africa’s rapidly evolving energy landscape, marked by fast-growing demand, significant electricity access gaps, increasing renewable penetration, and strong policy commitments to industrialization and energy security. Several U.S. reactor technologies and designs are considered based on their development status and readiness for deployment. The analysis finds that nuclear power can provide reliable, clean baseload and flexible generation, as well as high-temperature process heat for desalination, hydrogen production, and industrial applications. However, suitability is highly country-specific, depending on grid size and stability, transmission capacity, cooling water availability, regulatory readiness, and fuel supply chains. Near-term deployment opportunities are strongest for light-water reactors (such as NuScale, BWRX-300, AP300, and SMR-300) that use low-enriched uranium and build on proven technology. More advanced concepts, including gas-cooled, sodium-cooled, molten-salt cooled reactors, and microreactors, will likely be relevant for African deployment in the 2030s or later, contingent on demonstration projects, high-assay low-enriched uranium (HALEU) fuel availability, and mature international licensing frameworks. Economic analysis shows that SMRs are capital-intensive, with projected overnight costs for 300 MWe units in 2025 ranging from approximately 1.4 to 2.6 billion USD per module. The levelized cost of electricity (LCOE) is highly sensitive to the weighted average cost of capital (WACC). Given typically higher financing costs and utility balance-sheet weaknesses in many African countries, bankable project structures will require sovereign guarantees, robust offtake arrangements, and layered financing from export credit agencies, development finance institutions, and vendor nations. Comparisons with recent large nuclear projects in the United Arab Emirates (UAE) and Egypt underscore the central role of state-backed loans, long tenors, and concessional terms. Country case studies illustrate a spectrum of readiness and opportunity. South Africa operates two 920 MWe pressurized light water reactors (totaling 1,840 MWe) at Koeberg and has the most mature regulatory and industrial base, positioning it as a prime candidate for both large reactors and SMRs to replace coal, support desalination, and anchor industrial hubs. Egypt is constructing four VVER-1200 units at El Dabaa with strong state leadership and could later complement this fleet with SMRs for coastal and industrial applications. Kenya and Ghana are advancing through IAEA Milestones with growing institutional capacity and clear interest in SMRs that match their smaller grids and industrialization plans. Nigeria has the largest demand potential but faces acute constraints in grid reliability, project bankability, and regulatory capacity; targeted deployments of large reactors and SMRs near coastal or industrial sites could have high impact if accompanied by major grid upgrades and institutional reforms. The report identifies cross-cutting challenges such as financing, political continuity, public acceptance, nonproliferation and security, waste and back-end management, regulatory capacity, grid adequacy, and long deployment timelines for first-of-a-kind designs, and ANL/NSE-26/3 ii proposes broad directions for resolution. These include stronger multifaceted financing for nuclear, long-term national energy strategies that transcend electoral cycles, proactive stakeholder engagement, strengthened regional and national regulators, and systematic workforce development through centers of excellence and expanded training. The United States should develop partnerships with African countries and offer end-to-end nuclear package similar to those used effectively by competitors: coordinated project development, state-backed financing, long-term fuel services, and durable in-country support through regional offices and sustained workforce/regulatory training. With timely planning, sustained political commitment, and appropriate financing and institutional support, nuclear energy, both large reactors and advanced SMRs, can become a meaningful, though not dominant, pillar of Africa’s future power mix, enhancing energy security, enabling industrial growth, and supporting climate goals.