UPLIFTS: Urban Platooning and Logistics in Future Transportation Systems — Phase I Final Report
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29 ENERGY PLANNING, POLICY, AND ECONOMY↗
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Dispatch optimization of concentrating solar power with utility-scale photovoltaics
Concentrating solar power (CSP) tower technologies capture thermal radiation from the sun utilizing a field of solar-tracking heliostats. When paired with inexpensive thermal energy storage (TES), CSP technologies can dispatch electricity during peak-market-priced hours, day or night. The cost of utility-scale photovoltaic (PV) systems has dropped significantly in the last decade, resulting in inexpensive energy production during daylight hours. The hybridization of PV and CSP with TES systems has the potential to provide continuous and stable energy production at a lower cost than a PV or CSP system alone. Hybrid systems are gaining popularity in international markets as a means to increase renewable energy portfolios across the world. Typically, CSP-PV hybrid systems have been evaluated using either monthly averages of hourly PV production or scheduling algorithms that neglect the time-of-production value of electricity in the market. To more accurately evaluate a CSP-PV-battery hybrid design, we develop a profit-maximizing mixed-integer linear program ($\mathcal{H}$) that determines a dispatch schedule for the individual sub-systems with a sub-hourly time fidelity. We introduce the mathematical formulation of such a model and show that it is computationally expensive to solve. To improve model tractability and reduce solution times, we offer techniques that: (1) reduce the problem size, (2) tighten the linear programming relaxation of ($\mathcal{H}$) via reformulation and the introduction of cuts, and (3) implement an optimization-based heuristic (that can yield initial feasible solutions for (($\mathcal{H}$) and, at any rate, yields near-optimal solutions). Applying these solution techniques results in a 79% improvement in solve time, on average, for our 48-h instances of (($\mathcal{H}$); corresponding solution times for an annual model run decrease by as much as 93%, where such a run consists of solving 365 instances of (($\mathcal{H}$), retaining only the first 24 h' worth of the solution, and sliding the time window forward 24 h. We present annual system metrics for two locations and two markets that inform design practices for hybrid systems and lay the groundwork for a more exhaustive policy analysis. A comparison of alternative hybrid systems to the CSP-only system reflects that hybrid models can almost double capacity factors while resulting in a 30% improvement related to various economic metrics.
14 SOLAR ENERGY↗
Model to inform the expansion of hydrogen distribution infrastructure
A growing hydrogen economy requires new hydrogen distribution infrastructure to link geographically distributed hubs of supply and demand. The Hydrogen Optimization with Deployment of Infrastructure (HOwDI) Model helps meet this requirement. The model is a spatially resolved optimization framework that determines location-specific hydrogen production and distribution infrastructure to cost-optimally meet a specified location-based demand. While these results are useful in understanding hydrogen infrastructure development, there is uncertainty in some costs that the model uses for inputs. Thus, the project team took the modeling effort a step further and developed a Monte Carlo methodology to help manage uncertainties. Seven scenarios were run using existing infrastructure and new demand in Texas exploring different policy and tax approaches. The inclusion of tax credits increased the percentage of runs that could deliver hydrogen at <$\$4$ /kg from 31% to 77% and decreased the average dispensed cost from $\$4.35$ /kg to $\$3.55$ /kg. However, even with tax credits there are still some runs where unabated SMR is deployed to meet new demand as the low-carbon production options are not competitive. Every scenario, except for the zero-carbon scenario (without tax credits), resulted in at least 20% of the runs meeting the $\$4$ /kg dispensed fuel cost target. This indicates that multiple pathways exist to deliver $\$4$ /kg hydrogen.
08 HYDROGEN↗
A Scalable Solution Framework for Stochastic Transmission and Generation Planning Problems. Draft
Current commercial software tools for transmission and generation investment planning have limited stochastic modeling capabilities. Because of this limitation, electric power utilities generally rely on scenario planning heuristics to identify potentially robust and cost effective investment plans for a broad range of system, economic, and policy conditions. Several research studies have shown that stochastic models perform significantly better than deterministic or heuristic approaches, in terms of overall costs. However, there is a lack of practical solution approaches to solve such models. In this paper we propose a scalable decomposition algorithm to solve stochastic transmission and generation planning problems, respectively considering discrete and continuous decision variables for transmission and generation investments. Given stochasticity restricted to loads and wind, solar, and hydro power output, we develop a simple scenario reduction framework based on a clustering algorithm, to yield a more tractable model. The resulting stochastic optimization model is decomposed on a scenario basis and solved using a variant of the Progressive Hedging (PH) algorithm. We perform numerical experiments using a 240-bus network representation of the Western Electricity Coordinating Council in the US. Although convergence of PH to an optimal solution is not guaranteed for mixed-integer linear optimization models, we find that it is possible to obtain solutions with acceptable optimality gaps for practical applications. Our numerical simulations are performed both on a commodity workstation and on a high-performance cluster. The results indicate that large-scale problems can be solved to a high degree of accuracy in at most two hours of wall clock time.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Exploring impacts of electricity tariff on charging infrastructure planning: An activity-based approach
In the past decade, electric vehicles (EVs) have gained popularity for their efficiency and environmental benefits. Advances in battery technology and charging equipment have yielded long-range EVs and fast-charging. However, many major cities lack adequate charging infrastructure for daily EV use. This study addresses this gap by integrating activity-based modeling, charging behavior simulation, and charging infrastructure optimization. The research utilizes the POLARIS agent-based transportation model to accurately capture user activities, trip patterns, and traffic flows. Additionally, the study investigates the impact of fixed and spatiotemporal electricity rate distributions on optimal charging infrastructure deployment. The framework is applied to the Chicago regional area network and analyzed under various EV ownership scenarios. Further, the results reveal significant impacts of the charging pricing strategy on user decision-making and charging demand distribution. There is also a need for consistent pricing policies in charging infrastructure planning and operational phases to avoid drops in service quality.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Least-cost Optimal Distribution Grid Expansion (LODGE): Utility Pilots
The Least-cost Optimal Distribution Grid Expansion (LODGE) model provides the optimal portfolio of distribution system upgrades—e.g., voltage regulators, feeder reconductoring, transformer upgrades and non-wires alternatives (NWA), such as strategic siting of storage and distributed generation—to interconnect distributed energy resources (DERs) and enable load growth. It can be used to assess grid infrastructure costs and explore policy and regulatory solutions for distribution planning and DER valuation.In 2025, Berkeley Lab conducted three pilot analyses to validate LODGE results with empirical utility data before the model’s first release in 2026. The pilots, done with utilities in Washington, Colorado, and New Mexico, provide examples that illustrate how the model works, what it can do, and the value of the analysis.
Heleno, Miguel↗
A multistage distributionally robust optimization approach to water allocation under climate uncertainty
This paper investigates a Multistage Distributionally Robust Optimization (MDRO) approach to water allocation under climate uncertainty. The MDRO is formed by creating sets of conditional distributions (called conditional ambiguity sets) on a finite scenario tree. The distributions in the conditional ambiguity sets remain close to a nominal conditional distribution according a ø-divergence (e.g., Kullback-Leibler divergence, Hellinger distance, Burg entropy, etc.). Here, the paper discusses a decomposition algorithm to solve the resulting MDRO with ø-divergences, which uses the dual formulation and solves only linear subproblems instead of convex ones. Some properties of the algorithm such as generating feasible policies and valid upper/lower bounds are established. The paper then applies the modeling and solution techniques to allocate water in a rapidly-developing area of Tucson, Arizona. Tucson, like many arid and semi-arid regions around the world, faces considerable uncertainty in its ability to provide water for its citizens in the future. The primary sources of uncertainty in the Tucson region include (1) unpredictable population growth, (2) the availability of water from the Colorado River, and (3) the effects of climate variability on water consumption. This paper integrates forecasts for all these sources of uncertainty into a single optimization model for robust and sustainable water allocation. Then, it uses this model to analyze the value of constructing additional treatment facilities to reduce future water shortages. The results indicate that the MDRO approach can be very valuable for water managers by providing insights to minimize their risks and help them plan for the future.
54 ENVIRONMENTAL SCIENCES↗
Agent-Based Coordination Scheme for PV Integration (ABC4PV)
Renewables and especially photovoltaics (PV) have benefitted significantly from a host of incentives and policies targeted toward enhanced integration and adoption of specific energy technologies. However, with the push to move forward into a subsidy-free market framework, behind-the-meter residential PV applications have generally struggled to retain their value (unlike utility scale and commercial projects) [1]. This project focused on developing control-theoretic solutions aimed at improving the integration and interaction of behind-the-meter residential PV with other distribution system assets (controllable and non-controllable) to enhance the integrated value of residential PV. To this end, a suite of decentralized control methodologies have been developed to enable effective coordination and control of behind-the-meter residential load customers’ PV, battery storage systems (BSS), controllable loads and other similar assets within a distribution feeder. This interaction aims at procuring energy savings and, thus, energy bill savings. The main source of savings is drawn from reducing the effect of demand charge pricing and is realized at the feeder level, assuming community level interaction and management among the aforementioned assets. Optimal control of the assets is implemented with a distributed optimization methodology, leveraging consensus-based algorithms. The results gathered from the optimal control simulations demonstrates that the savings can be duly achieved and the algorithm decision times (to dynamically control asset set points, for example) are fast. As for the overall efficiency of PV+BSS systems, to procure energy savings from curtailment of the demand charge pricing effects, the optimal control is set up so as to minimize the variance of the load for all customers, throughout a feeder and throughout time in a rolling horizon scheduling with model predictive control. The control takes into account inter-temporal electrochemical storage (battery) degradation costs: specifically, we have developed a long-term lifetime model for the BSS that weighs in the effect of the degradation factor in the dispatch formulations, thus, a considerable operating cost that affects energy decision making. The levelized cost of energy (LCOE – redefined for the purpose of quantifying asset integration effectiveness through the customers’ energy cost) is shown to be below the threshold set for the combined PV+BSS topology of $ 0.14/kWh for multiple cases of PV penetration all the way up to 50%, provided that a policy of shared ownership of and savings is in place. Further, the LCOE calculated for the case before the deployment PV+BSS systems is also achievable, i.e. the deployment of PV+BSS, if planned and scheduled optimally. will have no effect on customers’ energy costs. From the control methodology viewpoint, the developed consensus-based algorithms are shown to converge for a wide range of problem cases (spanning normal operating scenarios and contingencies), guaranteeing dispatch solutions under forecasting errors, communication break-downs and cyber-security attacks. The proposed control solutions are scalable and real-time implementable, with dispatch computations and device set-point updates converging in less than 2s in most practical instances of the above events.
14 SOLAR ENERGY↗
Optimizing and Extending the Functionality of EXARL for Scalable Reinforcement Learning [Slides]
The main goal of the Co-Design Summer School 2021 is to provide algorithmic improvements to EXARL framework by improving performance and by adding functionalities. This presentation includes an introduction to reinforcement learning and to EXARL. The researchers expanded the capability of EXARL by including additional agents like (Asynchronized) Advantage Actor Critic (A2C/A3C) and Twin Delayed Deep Deterministic Policy Gradient (TD3). They also explored algorithmic improvements such as v-trace and Prioritized Experience Replay. They found that A2C/A3C performed best with v-trace and outperformed Deep Q-Network (DQN) on both the CartPole game and the ExaBooster scientific environment. Additionally, they found that TD3 performed as good as the existing Deep Deterministic Policy Gradient (DDPG) agent and that adding Prioritized Experience Replay to DDPG accelerated convergence.
97 MATHEMATICS AND COMPUTING↗
French Atomic and alternative energies commission decommissioning policy and strategies: What is at stake lessons learned and feedback experience - 15013
Since its creation in 1945, the French Atomic Energy and Alternative Energies Commission has carried out research programmes on civil use of nuclear, and its application. CEA has set up and run various types of installations from research (accelerators, pond reactors, fast reactors, laboratories) to industry (power reactor, processing facilities). There are currently more than several tens of facilities which are presently closed down pending dismantling or in the process of being dismantled by CEA, the governmental body for research and development in nuclear and renewable energies. When operating, all these installations have generated radioactive waste. When they are shutdown, either as a result of the programmes for which they were set up, or due to equipment wearing out or being obsolete, these installations must be cleaned up, dismantled and decommissioned, taking into account their nuclear nature and requirements related to safety and environment. During the decommissioning process, the implementer has to deal with safety and site remediation issues. Dealing with these installations is a long-term programme which must be carefully planned and financed in order to optimise its management. The paper will describe the CEA's decommissioning policy, strategies adopted, and draw up lessons learned and feedback experience, showing that: - Safety is guaranteed, - Dismantling is a technically controllable operation, but R and D actions are to be developed for specific problems as characterization or operation in hostile environment. Moreover all the methods and techniques improve as a result of feedback experience, - The amount of material and waste produced by the dismantling are manageable. The very low, low and intermediate level waste can be stored on existing shallow land burial centres, some new repositories have to be created for specific waste (graphite, radium,...) and for high and intermediate level long live waste, which are stored in interim storage , - The dismantling must be optimized, especially by appliance of a good strategy based on rational industrial choices. - Communication and transparency with stakeholders is essential for global process acceptance, - The lessons learned and how feedback experience could benefit for future decommissioning projects. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Development of an Optimal Water Allocation Decision Tool for the Major Crops During the Water Deficit Period in the Southeast U.S.
We developed a dynamic model to optimize irrigation application in three major crops (corn, cotton and peanuts) grown in the Southeast USA. Water supply amount is generated from an engineering model which is then combined with economic models to find the optimal amount of irrigation water to apply on each crop field during the six critical water deficit weeks in summer. Results indicate that water is applied on the crop with the highest marginal value product of irrigation. Decision making tool such as the one developed here would help farmers and policy makers to find the maximum profitable solution when water shortage is a serious concern.
Paudel, Krishna P.↗
Common Methodology for Efficient Airspace Operations
Topics include: a) Developing a common methodology to model and avoid disturbances affecting airspace. b) Integrated contrails and emission models to a national level airspace simulation. c) Developed capability to visualize, evaluate technology and alternate operational concepts and provide inputs for policy-analysis tools to reduce the impact of aviation on the environment. d) Collaborating with Volpe Research Center, NOAA and DLR to leverage expertise and tools in aircraft emissions and weather/climate modeling. Airspace operations is a trade-off balancing safety, capacity, efficiency and environmental considerations. Ideal flight: Unimpeded wind optimal route with optimal climb and descent. Operations degraded due to reduction in airport and airspace capacity caused by inefficient procedures and disturbances.
Sridhar, Banavar↗
Including operational aspects in the planning of power systems with large amounts of variable generation: A review of modeling approaches
In the past, power system planning was based on meeting the load duration curve at minimum cost. The increasing share of variable generation (VG) makes operational constraints more important in the planning problem, and there is more and more interest in considering aspects such as sufficient ramping capability, sufficient reserve procurement, power system stability, storage behavior, and the integration of other energy sectors often through demand response assets. In VG integration studies, several methods have been applied to combine the planning and operational timescales. We present a four-level categorization for the modeling methods, in order of increasing complexity: (1a) investment model only, (1b) operational model only, (2) unidirectionally soft-linked investment and operational models, (3a) bidirectionally soft-linked investment and operational models, (3b) operational model with an investment update algorithm, and (4) co-optimization of investments and operation. The review shows that using a low temporal resolution or only few representative days will not suffice in order to determine the optimal generation portfolio. In addition, considering operational effects proves to be important in order to get a more optimal generation portfolio and more realistic estimations of system costs. However, operational details appear to be less significant than the temporal representation. Furthermore, the benefits and impacts of more advanced modeling techniques on the resulting generation capacity mix significantly depend on the system properties. Thus, the choice of the model should depend on the purpose of the study as well as on system characteristics.
24 POWER TRANSMISSION AND DISTRIBUTION↗
HERON as a Tool for LWR Market Interaction in a Deregulated Market
This document reports the development of HERON (Holistic Energy Resource Optimization Network), a newly-developed RAVEN (Risk Analysis Virtual ENvironment) plugin for grid and capacity optimization, to technoeconomic analysis in a deregulated market. A short description of the HERON plugin is provided, and the release process is described. HERON as a plugin enables RAVEN to perform stochastic technoeconomic analysis of grid-energy systems in a generic approach. The primary function of HERON is to generate the complex RAVEN workflows necessary to optimize component capacities under stochastic systems. HERON is capable of analyzing systems with complex components transferring a variety of commodities, including production components and varied markets. HERON is capable of optimizing high-resolution dispatch for such systems and guiding stochastic optimization algorithms in RAVEN for finding optimal component capacities. In particular, this document demonstrates the application of HERON to systems with deregulated markets.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Strategies for Optimizing WIP Investment in State Energy Offices
This report outlines strategies for Weatherization and Intergovernmental Programs Office (WIP) investment aimed at improving state energy office programs. The project was funded by WIP within the U.S. Department of Energy (DOE) office of Energy Efficiency and Renewable Energy (EERE). WIP aims "to enable strategic investments in energy efficiency and renewable energy technologies through the use of innovative practices across the United States in partnership with a wide range of stakeholders, including state and local organizations and community-based nonprofits." Across its programs, WIP has created thousands of jobs, maintains an investment ratio of 4.1 to 1, and increases U.S. economic output by 1.2 billion a year. This project was funded in the FY21 budget cycle and originally focused on improving WIP energy efficiency funding and programs. The project gathered information from various state energy offices to understand how WIP funding and processes can be optimized to improve state-level energy efficiency programs. We propose that the conclusions are also broadly applicable to weatherization and renewable energy deployment programs.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Accomplishments and challenges of metrics for sustainable energy, population, and economics as illustrated through three countries
The global Sustainable Development Goals require meeting multiple objectives on energy, population, economics, and ecosystems. Development and economic growth as defined by current metrics requires energy inputs, yet energy growth can also increase negative impacts on natural systems. To achieve sustainable development goals, policymakers and technologists will need energy system solutions that consider not only cost and efficiency but also population, quality of life, natural ecosystems, and culture that accommodates different starting points and transition timelines of various countries. To explore possible approaches, this perspectives paper summarizes energy in the context of economic growth and population, illustrating concepts through the diverse status and direction of three countries--Japan, the United States, and Bangladesh--as potential views into a post-growth sustainable future. Four fundamental questions on long-term energy development are identified, related to optimal energy use per capita, sustainable global energy demand, managing an energy transition with stable population, and the need for generalizable approaches across countries.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Performance Assessment of the Exploration Water Recovery System
A new water recovery system architecture designed to fulfill the National Aeronautics and Space Administration s (NASA) Space Exploration Policy has been tested at the Marshall Space Flight Center (MSFC). This water recovery system architecture evolved from the current state-of-the-art system developed for the International Space Station (ISS). Through novel integration of proven technologies for air and water purification, this system promises to elevate existing system optimization. The novel aspect of the system is twofold. First, volatile organic compounds (VOC) are removed from the cabin air via catalytic oxidation in the vapor phase, prior to their absorption into the aqueous phase. Second, vapor compression distillation (VCD) technology processes the condensate and hygiene waste streams in addition to the urine waste stream. Oxidation kinetics dictate that removing VOCs from the vapor phase is more efficient. Treating the various waste streams by VCD reduces the load on the expendable ion exchange and adsorption media which follows, as well as the aqueous-phase catalytic oxidation process further downstream. This paper documents the results of testing this new architecture.
Carter. D. Layne↗
Manufacturing Bms/Iso System Review
The Quality Management System (QMS) is one that recognizes the need to continuously change and improve an organization s products and services as determined by system feedback, and corresponding management decisions. The purpose of a Quality Management System is to minimize quality variability of an organization's products and services. The optimal Quality Management System balances the need for an organization to maintain flexibility in the products and services it provides with the need for providing the appropriate level of discipline and control over the processes used to provide them. The goal of a Quality Management System is to ensure the quality of the products and services while consistently (through minimizing quality variability) meeting or exceeding customer expectations. The GRC Business Management System (BMS) is the foundation of the Center's ISO 9001:2000 registered quality system. ISO 9001 is a quality system model developed by the International Organization for Standardization. BMS supports and promote the Glenn Research Center Quality Policy and wants to ensure the customer satisfaction while also meeting quality standards. My assignment during this summer is to examine the manufacturing processes used to develop research hardware, which in most cases are one of a kind hardware, made with non conventional equipment and materials. During this process of observation I will make a determination, based on my observations of the hardware development processes the best way to meet customer requirements and at the same time achieve the GRC quality standards. The purpose of my task is to review the manufacturing processes identifying opportunities in which to optimize the efficiency of the processes and establish a plan for implementation and continuous improvement.
Gomez, Yazmin↗