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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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At least 325 records · Page 18

Optimal Energy Management for Microgrid with Stationary and Mobile Storage

This paper studies energy management in a Microgrid (MG) with solar generation, Battery Energy Management System (BESS) and gridable (V2G) EVs. A two-stage stochastic optimization method is proposed to capture the intermittent solar generation and random EV user behaviors. It is subsequently formulated as a Mixed Integer Linear Programming (MILP) problem. To evalutate the proposed method, real solar generation, loads, BESS and EV data is used in Sample Average Approximation (SAA). Computational results show the correctness of the proposed method as well as steady and tightly bounded optimality gap. Comparisons demonstrate that the proposed stochastic method outperforms its deterministic counterpart at the expense of higher computational cost. It is also observed that moderate number of EVs helps to reduce the overall operational cost of the MG, which sheds light on future EV integration to the smart grid.

Battery Energy Management System (BESS)↗

Privacy-Preserving Aggregation of Controllable Loads to Compensate Fluctuations in Solar Power

Cybersecurity and privacy are of the utmost importance for safe, reliable operation of the electric grid. It is well known that the increased connectivity/interoperability between all stakeholders (e.g., utilities, suppliers, and consumers) will enable personal information collection. Significant advanced metering infrastructure (AMI) deployment and demand response (DR) programs across the country, while enable enhanced automation, also generate energy data on individual consumers that can potentially be used for exploiting privacy. Inspired by existing works which consider DR, battery-based perturbation, and differential privacy noise adding, we novelly consider the aggregator (cluster) level privacy issue in the DR framework of solar photovoltaic (PV) generation following. Different from most of the existing works which mainly rely on the charging/discharging scheduling of rechargeable batteries, we utilize controllable building loads to serve as virtual storage devices to absorb a large portion of the PV genertaion while delicately keeping desired noisy terms to satisfy the differential privacy for the raw load profiles at the aggregator level. This not only ensures differential privacy, but also improves the DR efficiency in load following since part of the noisy signal in solar PV generation has been filtered out. In particular, a mixed integer quadratic optimization problem is formulated to optimally dispatch a population of on/off controllable loads to achieve this privacy-preserving DR service.

Dong, Jin↗

Multi-Task with Procter and Gamble (CRADA No. NFE-10-02672)

The purpose of this Cooperative Research and Development Agreement (CRADA) between UT-Battelle, LLC (the “Contractor) and Procter & Gamble Company (the “Participant”) is the development of a research partnership to create new tools, tests and analytical methods to improve the performance, safety and/or environmental quality of chemicals, advanced materials, food products and manufacturing processes. The Participant operates in three global business units: Beauty, Health and Well-Being and Household Care. Some of its worldwide products include Head and Shoulders®, Pantene®, Gillette® razors and personal care products, Crest®, Dawn®, Tide®, Bounty®, Duracell® batteries; and Iams® pet food among others. At its core, however, the Participant is a science driven company. It supports one of the most robust industrial research and development (R&D) programs in the world. The Participant uses this rich foundation of science to drive innovation across all of its product lines. But the innovation process is not confined in-house The Participant pursues an “open innovation” policy, seeking partnerships with scientists and researchers in universities and national laboratories where it can contribute its extensive knowledge assets and collaborate to advance scientific understanding. The research under this multi-task CRADA was directed under the following general task areas and, throughout the duration of this CRADA the work statement was modified to match the needs of the Parties and the direction of the research. (1) Software modeling, simulation and development; (2) Manufacturing Technologies; (3) Supply Chain Optimization, (4) Advanced Materials.

36 MATERIALS SCIENCE↗

Electrifying Critical Mineral Refinement in the US (CRADA Final Report)

The technical founder of Aepnus Technology Inc. was competitively selected for support by Cyclotron Road, a U.S. Department of Energy (DOE) Lab-Embedded Entrepreneurship Program (LEEP) through an approved merit review process. Through this program, Aepnus Technology has collaborated with Lawrence Berkeley National Laboratory (LBNL) to develop and validate a novel electrolysis platform targeting the recycling of sodium sulfate waste, a pervasive byproduct in the battery supply chain. The platform aims to address inefficiencies in conventional electrolysis by leveraging Aepnus' proprietary gas diffusion electrode (GDE) technology, which replaces precious metal catalysts, such as iridium, to achieve high efficiency, reduced operational costs, and enhanced durability.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Cost-Optimal Pathways to 75% Fuel Reduction in Remote Alaskan Villages: Preprint

There are thousands of isolated, diesel-powered microgrids that deliver energy to remote communities around the world at very high energy costs. The Remote Communities Renewable Energy program aims to help these communities reduce their fuel consumption and lower their energy costs through the use of high penetration renewable energy. As part of this program, the REopt modeling platform for energy system integration and optimization was used to analyze cost-optimal pathways toward achieving a combined 75% reduction in diesel fuel and fuel oil consumption in a select Alaskan village. In addition to the existing diesel generator and fuel oil heating technologies, the model was able to select from among wind, battery storage, and dispatchable electric heaters to meet the electrical and thermal loads. The model results indicate that while 75% fuel reduction appears to be technically feasible it may not be economically viable at this time. When the fuel reduction target was relaxed, the results indicate that by installing high-penetration renewable energy, the community could lower their energy costs by 21% while still reducing their fuel consumption by 54%.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Bidding strategy for microgrid in day-ahead market based on hybrid stochastic/robust optimization

In this paper, we propose an optimal bidding strategy in the day-ahead market of a microgrid consisting of intermittent distributed generation (DG), storage, dispatchable DG and price responsive loads. The microgrid coordinates the energy consumption or production of its components and trades electricity in both the day-ahead and real-time markets to minimize its operating cost as a single entity. The bidding problem is challenging due to a variety of uncertainties, including power output of intermittent DG, load variation, day-ahead and real-time market prices. A hybrid stochastic/robust optimization model is proposed to minimize the expected net cost, i.e., expected total cost of operation minus total benefit of demand. This formulation can be solved by mixed integer linear programming. The uncertain output of intermittent DG and day-ahead market price are modeled via scenarios based on forecast results, while a robust optimization is proposed to limit the unbalanced power in real-time market taking account of the uncertainty of real-time market price. Numerical simulations on a microgrid consisting of a wind turbine, a PV panel, a fuel cell, a micro-turbine, a diesel generator, a battery and a responsive load show the advantage of stochastic optimization in addition to robust optimization.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

CyDER: A Cyber Physical Co-simulation Platform for Distributed Energy Resources in Smartgrids

The CyDER project aimed at developing an open-source, modular and scalable co-simulation platform for power grids with large shares of Distributed Energy Resources (DERs). The project partners are the Lawrence Berkeley National Lab (LBNL), Lawrence Livermore National Lab (LLNL), PG&E, SolarCity, and ChargePoint. The prime recipient is LBNL; SolarCity and ChargePoint were partners for the project’s first two years. Increased DER integration introduces a number of challenges in power grid operation including a more dynamic interaction between the transmission grid and distribution grids, and increased modeling complexity. Although specialized software exists to precisely model different components of the power system, it is far from trivial to integrate all various models and perform a holistic simulation. Instead of replicating all models in a common simulation program, a commonly accepted approach to tackle this model diversity is to couple third-party simulators and models through a co-simulation platform that coordinates information exchange among the various components. Following this line of research, this project’s objective was to develop a co-simulation platform based on a widely accepted industrial standard called Functional Mockup Interface (FMI). Within this process, the project developed models compliant with the FMI standard, called Functional Mockup Units (FMUs), and used them to perform various operational and planning power system analyses. Relying and building upon an industrial standard is the main differentiation of this project compared with previous or parallel efforts in the co-simulation area. Particular emphasis was put on delivering software utilities to facilitate setting up and running co-simulations by end-users. Furthermore, a strong aspect of this project is demonstrating that co-simulation techniques can be used to perform Hardware-in-the-Loop (HIL) simulations that couple software components (e.g., simulated models) with hardware components (e.g., real devices such PV systems and batteries). The long-term goal of CyDER project is to help establish FMI as a powerful standard for co-simulation and promote adoption by electric utilities and other interested stakeholders. The main accomplishments of the project include the development of several FMUs including distribution and transmission grid models, PV inverters with Volt/Var/Watt controllers, batteries, and predictive optimal controllers. Additionally, a unique software package was developed, called SimulatorToFMU, which is capable of exporting any Python-driven simulator or Python script as an FMU. This is an important contribution towards establishing FMI as one of the main co-simulation standards, because more and more third-party programs for sub-system modeling and simulation are delivered with Python APIs. The CyDER platform was used to perform PV hosting capacity analyses in real utility feeders with and without smart inverter controls, battery storage, and EV charging. Smart inverter controls include conventional Volt/Var/Watt controls for reactive power support and active power curtailment, but also predictive controls that optimize the charging and discharging profile of the battery connected on the DC side in order to minimize the customer’s economic benefit. Finally, an important result of this project is delivering an experimental setup that consists of residential-scale PV inverters with battery storage, a real-time grid simulator with an ideal voltage source as grid emulator, and micro Phasor Measurement Units (PMUs). All these components and additional software modules are coupled to one another using the FMI standard and can be co-simulated with the CyDER platform.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Applying Utility's Advanced Grid Technologies to Improve Resiliency of a Critical Load

The US DOE Office of Electricity's Energy Storage Program's joint R&D work with the Cordova Electric Cooperative (CEC) has deployed several advanced grid technologies that are providing benefits today to Cordova Alaska's electricity users. Advanced grid technologies deployed through DoE co-funded R&D include a 1MW Battery Energy Storage System (BESS), and enhanced monitoring including Phasor Measurement Units (PMU's) to help better understand the operational impacts of the added BESS. This paper will highlight key accomplishments to-date in deploying and using advanced grid technologies, and then outline the next phase of work that will use these technologies to implement an operating scheme to reconfigure the utility's distribution system and utility resources including BESS to provide emergency back-up power to a critical load: the Cordova Community Medical Center (CCMC). This paper will include additional insights on the use of utility resources to support critical loads via case study examples by the National Rural Electric Cooperative Assoc. (NRECA).

BESS↗

Renewable electricity capacity planning with uncertainty at multiple scales

Abstract We formulate and compare optimization models of investment in renewable generation using a suite of social planning models that compute optimal generation capacity investments for a hydro-dominated electricity system where inflow uncertainty results in a risk of energy shortage. The models optimize the expected cost of capacity expansion and operation allowing for investments in hydro, geothermal, solar, wind, and thermal plant, as well as battery storage for smoothing load profiles. A novel feature is the integration of uncertain seasonal hydroelectric energy supply and short-term variability in renewable supply in a two-stage stochastic programming framework. The models are applied to data from the New Zealand electricity system and used to estimate the costs of moving to a 100% renewable electricity system by 2035. We also explore the outcomes obtained when applying different forms of CO 2 constraint that limit respectively non-renewable capacity, non-renewable generation, and CO 2 emissions on average, almost surely, or in a chance-constrained setting, and show how our models can be used to investigate the merits of a proposed pumped-hydro scheme in New Zealand’s South Island.

Ferris, Michael C.↗

American Made Infrastructure: Evolution of Federal Incentives and Requirements

Foreign Entity of Concern (FEOC) restrictions in the One Big Beautiful Bill Act (OBBB) represent the latest evolution of a multi-year legislative trajectory responding to national security concerns about foreign control – and particularly FEOC control – of energy infrastructure. Beginning with Executive Order 14017 (February 2021), which initiated comprehensive federal review of critical supply chain vulnerabilities in semiconductors, battery energy storage systems, and critical minerals, policymakers have progressively expanded restrictions on foreign participation. The National Defense Authorization Act (NDAA) 2019 established precedent for component-level prohibitions on foreign information and communications technology procurement, while NDAA 2024 extended these restrictions to six major People’s Republic of China (PRC) battery manufacturers. Complementary measures such as the Build America, Buy America (BABA) Act and the Infrastructure Investment and Jobs Act (IIJA) introduced domestic content thresholds and FEOC eligibility criteria for federal funding programs. The Inflation Reduction Act (IRA) 2022 further operationalized FEOC restrictions through electric vehicle tax credit requirements, creating a scalable framework for excluding foreign-controlled components. Recent executive actions and state-level policies have reinforced this trajectory, reflecting sustained alignment across federal and state governments. Collectively, these developments demonstrate a bipartisan policy approach that pairs incentives for advanced energy deployment with safeguards designed to prevent subsidizing adversaries or entities that present foreign-sourcing risk.

99 - GENERAL AND MISCELLANEOUS↗

Center for Electrocatalysis, Transport Phenomena, and Materials (CETM) for Innovative Energy Storage - Final Report

EFRC vision. The direct use of organic hydrides in fuel cells as virtual hydrogen carriers that generate stable organic molecules, protons, and electrons upon electro-oxidation and can be electrochemically charged by re-hydrogenating the oxidized carrier was the major focus of the Center for Electrocatalysis, Transport Phenomena and Materials for Innovative Energy Storage (EFRC-ETM). Compared to a hydrogen-on-demand design that includes thermal decomposition of organic hydrides in a catalytic reactor, the proposed approach is much simpler and does not require additional dehydrogenation catalysts or heat exchangers. Further, this approach utilizes the advantages of a flow battery (i.e., separation of power and energy, ease of transport and storage of liquid fuels) with fuels that have system energy densities similar to current hydrogen PEM fuel cells. EFRC challenges. Two major EFRC challenges were electrocatalysis and transport phenomena. The electrocatalysis challenge addresses fundamental processes which occur at a single molecular catalyst (microscopic level) and involve electron and proton transfer between the hydrogen rich and hydrogen depleted forms of organic liquid fuel and the catalyst. To form stable, non-radical dehydrogenation products from the organic liquid fuel, it is necessary to ensure fast transport of at least two electrons and two protons (per double bond formation). The same is true for the reverse hydrogenation reaction. The transport phenomena challenge addresses transport of electrons to/from the electrocatalyst and the current collector as well as protons across the polymer membrane. Additionally it addresses prevention of organic liquid fuel, water and oxygen transport through the PEM. In this challenge, the transport of protons or molecules involves multiple sites or a continuum (macroscopic level) and water serves as a proton conducting medium for the majority of known sulfonic acid based PEMs. Proton transfer in the presence of prospective organic liquid fuels was studied. During EFRC program various types of electrocatalysts, classes of fuels, and membranes have been investigated.

25 ENERGY STORAGE↗

Roll-to-Roll Advanced Materials Manufacturing DOE Laboratory Collaboration (FY2020 Final Report)

R2R processing is used to manufacture a wide range of products for various applications which span many industrial business sectors. The overall R2R methodology has been in use for decades and this continuous technique traditionally involves deposition of material(s) onto moving webs, carriers or other continuous belt-fed or conveyor-based processes that enable successive steps to build a final version which serves to support the deposited materials. Established methods that typify R2R processing include tape casting, silk-screen printing, reel-to-reel vacuum deposition/coating, and R2R lithography. Products supported by R2R manufacturing include micro-electronics, electro-chromic window films, PVs, fuel cells for energy conversion, battery electrodes for energy storage, and barrier and membrane materials. Due to innovation in materials and process equipment, high-quality yet very low-cost multilayer technologies have the potential to be manufactured on a very cost-competitive basis. To move energy-related products from high-cost niche applications to the commercial sector, the means must be available to enable manufacture of these products in a cost-competitive manner that is affordable. Fortunately, products such as fuel cells, thin- and mid-film PVs, batteries, electrochromic and piezoelectric films, water separation membranes, and other energy saving technologies readily lend themselves to manufacture using R2R approaches. However, more early-stage research is needed to solve the challenge of linking the materials (particles, polymers, solvents, additives) used in ink and slurry formulations and the coating and drying processes to the ultimate performance of the final R2R product, especially for a process that uses multiple layers of deposition to achieve the end product. To solve the problems associated with these challenges, the R2R Collaboration is executing a research program with outcomes that will ultimately link modeling, processing, metrology and defect detection tools, thereby directly relating the properties of constituent particles and processing conditions to the performance of final devices. This collaborative approach was designed to foster identification and development of materials and processes related to R2R for clean-energy materials development. Using computational and experimental capabilities by acknowledged subject matter experts within the supported National Laboratory system, this project leverages the capabilities and expertise at each of five National Laboratories to further the development of multilayer technologies that will enable high-volume, cost-competitive platforms. A typical R2R process has three steps: (1) mixing of particles and various constituents in a slurry, (2) coating of the ink/slurry mixture on a substrate, and (3) drying/curing and processing of the coating. Final performance of devices made via R2R processes is dependent on the active materials (e.g., electrochemical particles in battery or fuel cell electrodes) and the device structure that stems from the governing component interactions within the various steps. However, a fundamental understanding of the underlying mechanisms and phenomena is still lacking, which is why industrial-scale R2R process development and manufacturing is still largely empirical in nature. The FY 2019 through FY 2021 program addresses aspects of the following two targets from the AMO Multi-Year Program Plan: (1) Target 8.1 Develop technologies to reduce the cost per manufactured throughput of continuous R2R manufacturing processes. (A) Increasing throughput of R2R processes by 5 times for batteries (to 50 square feet per minute (50 ft 2 /min)) and capacitors and 10 times for printed electronics and the manufacture of other substrates and MEs used in support of these products. (B) Developing resolution capabilities to enable registration and alignment that will detect, align, and co-deposit multiple layers of coatings and print < 1-micron (1 µm) features using continuous process scalable for commercial production. (C) Developing scalable and reliable R2R processes for solution deposition of ultra-thin (<10 nm) films for active and passive materials. (D) Develop in-line multilayer coating technology on thin films with yields greater than 95%. (2) Target 8.2 Develop in-line instrumentation tools that will evaluate the quality of single and multilayer materials in-process. (A) Developing in-line QC technologies and methodologies for real-time identification of defects and expected product properties “in-use/application” during continuous processing at all size-scales with a focus on the “micro” and “nano” scale traces, lines, and devices, i.e., <1 μm at 300 ft./min for R2R processing in air and <10 nm at 20 ft./min for vacuum (B) Developing technologies to increase the measurement frequency of surface rheology without significant cost increases with a goal of a 10-nanometer in-line profilometry at a production rate of 100,000 square millimeters per minute (100,000 mm 2 /min).

42 ENGINEERING↗

Optimal Scheduling of an Isolated Microgrid With Battery Storage Considering Load and Renewable Generation Uncertainties

By modeling the uncertainty of spinning reserves provided by energy storage with probabilistic constraints, a new optimal scheduling mode is proposed in this paper for minimizing the operating costs of an isolated microgrid (MG) by using chance-constrained programming. The model is transformed into a readily solvable mixed integer linear programming formulation in general algebraic modeling system (GAMS) via a proposed discretized step transformation approach and finally solved by applying the CPLEX solver. By properly setting the confidence levels of the spinning reserve probability constraints, the MG operation can achieve a tradeoff between reliability and economy. The test results on the modified Oak Ridge National Laboratory Distributed Energy Control and Communication lab MG test system reveal that the proposal significantly exceeds the commonly used hybrid intelligent algorithm with much better and more stable optimization results and significantly reduced calculation times.

Li, Yang↗

On-Ramp Wireless: Program Final Report

The objective of the project was to develop and demonstrate a centrally managed, wide area wireless distribution grid sensor and faulted circuit indicator system capable of monitoring underground and other hard-to-reach distribution circuits while delivering the reliability, ease of installation, security, battery life and low cost structure required to be a commercially viable product for the electric utility industry. This integrated wireless grid monitoring system enables utilities across the United States to enhance the reliability of their distribution grids, improve performance metrics (i.e., SAIFI, CAIDI, SAIDI), support dynamic load management and predictive maintenance, and enable future remote monitoring applications necessary to achieve the goals of the Smart Grid. This project scope included: conceptual design; trade studies focused on maximizing ease of installation, sensing capabilities, power budget analysis to reduce maintenance costs and time to replace; sensor product development; and field technology demonstration. The trade studies included Radio Frequency (RF) propagation. The RF Trade Study was completed for the 2.4 GHz ISM-band and not extended to 1 GHz band due to availability of suitable radio solutions and associated impacts to overall commercial value of the solution. The technology demonstrations/field trials were performed in partnership with Southern California Edison and San Diego Gas and Electric. The sensing systems included fault indicator and wireless analog sensor devices from Schweitzer Engineering Labs (SEL) and smart transformer monitoring devices from Gridsense. Both SEL and Gridsense worked with ORW during this project to During the demonstration phase, sensors were deployed in multiple locations. Resulting data was analyzed for the following: RF performance, communication systems reliability, sensor system capabilities, and system level analytics relative to the commercial value to the smart grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

FY2017 annual report: Roll-to-Roll Advanced Materials Manufacturing DOE Laboratory Consortium Project

The following report provides a description of the program structure and technical accomplishments made during FY 2017 to overcome challenges for expanding use of R2R technologies and processing for clean energy applications. Technologies are under development that enable enhanced manufacturing of battery electrodes, fuel cells and water separation membranes that take advantage of advancements in new functional materials. Data mining, visualization, modeling and simulation for the materials, technologies and processes being developed allow for improvements in R2R manufacturing applications. This report documents the research conducted by four DOE national laboratories as a consortium in collaboration with industry partnerships. The research is directly applicable to AMM strategies that apply the National Science and Technology Council’s Materials Genome Initiative (NSTC 2011) approach to enhance the performance of battery, fuel cell and water membrane materials. This effort supports building the foundation of technologies, processes and a U.S. manufacturing base that will enable an order of magnitude in shorter process development cycles with the pathway for initial commercialization within months instead of years.

36 MATERIALS SCIENCE↗

Ames National Laboratory Annual Site Environmental Report for CY2021

The primary purpose of this report is to summarize the performance of Ames National Laboratory’s environmental programs, present highlights of significant environmental activities, and confirm compliance with environmental regulations and requirements for calendar year 2021. This report is a working requirement of Department of Energy Order 231.1B, Environment, Safety and Health Reporting. It includes descriptions of the Laboratory’s site, mission, the status of its compliance with applicable environmental regulations, its planning and activities to maintain compliance, and a comprehensive review of its environmental protection, surveillance and monitoring activities. Ames National Laboratory is located on the campus of Iowa State University (ISU) and occupies 13 buildings owned by the Department of Energy (DOE). See the Laboratory’s Web page for location and Laboratory overview. The Laboratory also leases space in ISU owned buildings. In 2021, the Laboratory accumulated and disposed of hazardous waste under a U.S. Environmental Protection Agency (EPA) issued generator number. All waste was handled according to applicable EPA, State, and local regulations and DOE Orders. The Laboratory operates as a Small Quantity Generator (SQG) of hazardous waste. There were no radiological air emissions or exposures to the general public due to Laboratory activities in 2021 (See U.S. Department of Energy Air Emissions Annual Report in Appendix A.) The Laboratory has an established Environmental Radiological Protection Program (Plan 10200.041) per DOE Order 458.1 requirements. Plans, policies, and procedures are in place to protect the public and the environment against undue risk from radiation associated with DOE radiological activities. As indicated in prior Site Environmental Reports, formal pollution prevention awareness, waste minimization and recycling programs have been in practice since 1990, with improvements implemented most recently in 2017 with Iowa State University’s shift toward single-stream recycling. Included in recycling efforts are items such as batteries, monitors, corrugated cardboard, lamps, miscellaneous electronic office equipment, mixed paper, newsprint, food/beverage containers, and laboratory glassware. Ames National Laboratory also recycles/reuses salvageable metal, used oil, and foamed polystyrene peanuts, and encourages chemical redistribution and sharing among research groups. Ames National Laboratory reported its contractual performance to DOE-Ames Site Office (AMSO) through the Laboratory’s Performance Evaluation Measurement Plan (PEMP), and a performance level of “B+” was achieved in 2021 for Sustain Excellence and Enhance Effectiveness of Integrated Safety, Health, and Environmental Protection As reported in Site Environmental Reports for prior years, the Laboratory’s Environmental Management System (EMS) has been integrated into the Laboratory’s Integrated Safety Management System (ISMS) since 2005. The integration of EMS into Laboratory business practices allows the Laboratory to systematically review, address and respond to environmental impacts. In addition to DOE-identified objectives and targets, the EMS Steering Committee recommends annual environmental goals for the Laboratory. Due to the COVID-19 pandemic and limited onsite work staff, goals of reducing water usage and travel/commuting to promote the reduction of scope 3 greenhouse gases were achieved. All contract deliverables and environmental compliance activities were still met during this time.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Depot Charging Schedule Optimization for Medium- and Heavy-Duty Battery-Electric Trucks

Charge management, which lowers charging costs for fleets and prevents straining the electrical grid, is critical to the successful deployment of medium- and heavy-duty battery-electric trucks (MHD BETs). This study introduces an energy demand and cost management framework that optimizes depot charging for MHD BETs by combining an energy consumption machine learning model and a linear program optimization model. The framework considers key factors impacting real-world MHD BET operations, including vehicle and charger configurations, duty cycles, use cases, geographic and climate conditions, operation schedules, and utilities’ time-of-use (TOU) rates and demand charges. The framework was applied to a hypothetical fleet of 100 MHD BETs in California under three different utilities for 365 days, with results compared to unmanaged charging. The optimized charging solution avoided more than 90% of on-peak charging, reduced fleet charging peak load by 64–75%, and lowered fleet energy variable costs by 54–64%. This study concluded that the proposed charge management framework significantly reduces energy costs and peak loads for MHD BET fleets while making recommendations for fleet electrification infrastructure planning and the design of utility TOU rates and demand charges.

Song, Shuhan↗

Scalable Predictive Control and Optimization for Grid Integration of Large-Scale Distributed Energy Resources: Preprint

Integration of a large number of distributed energy resources (DERs) into the power grid needs a scalable power balancing method. We formulate the power balancing problem as a look-ahead optimization problem to be solved sequentially by a power distribution system aggregator based on a model predictive control (MPC) framework. Solving large-scale look-ahead control problem requires proper configuration of the control steps. In this paper, to solve large-scale control problems, we propose a variable time granularity where control time steps nearby the current control step have finer resolutions. The aggregator objective includes maximization of power production revenue and minimization of power purchasing expense, renewable power curtailment, and mileage costs for energy storage and electric vehicle (EV) charging stations while satisfying system capacity and operational constraints. The control problem is formulated as a mixed-integer linear program (MILP) and solved using the XpressMP solver. We perform simulations considering a copper plate representation of a large distribution network consisting of 2507 devices (controllable DERs) including curtailable photovoltaics (PVs), energy storage batteries, EV charging stations, and buildings with heating, ventilation, and air conditioning units (HVACs). We show the effectiveness of the proposed approach in managing DERs interactively for maximum energy trading profit and local supply-demand power balancing. Finally, we demonstrate that the proposed method outperformed other benchmark controllers regarding computation time without compromising operational performance.

DER↗