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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

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↗
DOE OSTI2022

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

Integrating 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 problems 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 outperforms other benchmark controllers regarding computation time without compromising operational performance.

DER↗
DOE OSTI2020

Combined Heat and Power Deployment Program

Combined heat and power (CHP) is an efficient and clean approach to generating on-site electric power and useful thermal energy from a single fuel source. Instead of purchasing electricity from the distribution grid and burning fuel in an on-site furnace or boiler to produce thermal energy, CHP provides both energy services to a facility in one energy-efficient step.

CHP↗
DOE OSTI2022

North Carolina Water Utility Builds Resilience with Distributed Energy Resources

As the frequency and duration of grid outages increase, backup power systems are becoming more important for ensuring that critical infrastructure continues to provide essential services. Most facilities rely on diesel generators, which may be ineffective during long outages owing to limited fuel supplies and high generator failure rates. Distributed energy resources such as solar, storage, and combined-heat-and-power systems, coupled with on-site biofuel production, offer an alternative source of on-site generation that can provide both cost savings and resilience (i.e., the ability to respond to catastrophic events with longer-term consequences). A mixed-integer linear program minimizes costs and maximizes resilience at a wastewater treatment plant in Wilmington, North Carolina. We find that the plant can reduce life-cycle energy costs by 3.1% through the installation of a hybrid combined-heat-and-power, photovoltaic, and storage system. When paired with existing diesel generators, this system can sustain full load for seven days while saving $664,000 over 25 years and reducing diesel fuel use by 48% compared with the diesel-only solution. This analysis informed a decision by the Cape Fear Public Utility Authority to allocate funds for the implementation of a combined-heat-and-power system at the wastewater treatment plant in fiscal year 2023. Finally, the benefits of deploying hybrid combined-heat-and-power technologies and the utilization of on-site biofuel production extend, on a national scale, to thousands of wastewater treatment facilities and other types of critical infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

ESPC ESA Webinar Series: ESPC IDIQ Contract Vehicle Overview

This webinar provides instructions and resources for financing distributed energy projects (e.g. on-site renewable energy, storage, and combined heat and power) using an energy savings performance contract (ESPC) energy sales agreement (ESA) through the U.S. Department of Energy (DOE) indefinite-delivery, indefinite-quantity (IDIQ) energy savings performance contract (ESPC) vehicle. The DOE IDIQ ESPC contract vehicle uses a streamlined master contract that allows federal agencies to work with 21 energy service companies holding the current DOE IDIQ ESPC. Topics covered in the webinar include: An overview of the DOE IDIQ contract vehicle; ESA-specific considerations for IDIQ contracts; and Specific use cases. The webinar also provides an overview of the resources available on the Federal Energy Management Program website, such as IDIQ tools and templates. This webinar is the fifth in a series that provides information, best practices, and resources for implementing an ESPC ESA project. ESPC ESAs are a great option for federal sites that are motivated to reduce site costs, have no capital investment funding, and cannot use the U.S. Department of Defense 10 USC 2922a authority for long-term contracting.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Demand Response in Industrial Facilities: Peak Electric Demand

The US Department of Energy’s (DOE’s) Better Buildings, Better Plants Program (Better Plants) is a voluntary energy efficiency leadership initiative for US manufacturers and water/wastewater entities. The program encourages organizations to commit to reducing the energy intensity of their US operations over a 10-year period, typically by 25%. Companies joining Better Plants are recognized by DOE for their leadership in implementing energy efficiency practices and for reducing their energy intensity. Better Plants Partners are assigned to a Technical Account Manager, who can help companies establish energy intensity baselines, develop energy management plans, and identify key resources and incentives from DOE, other federal agencies, states, utilities, and other organizations that can enable them to reach their goals. Better Plants Partners are expected to report their progress to DOE once a year. This involves establishing an energy intensity baseline upon joining the program and then tracking their progress over time. Demand Response in Industrial Facilities: Peak Electrical Demand is intended to help companies understand peak demand response programs offering by their local utility. Manufacturing industries can learn about time-varying rates and smart technologies they can use to help them reduce their energy bills. This guidance document is applicable to companies participating at either the program or challenge level. Although this guide is intended primarily to assist companies participating in Better Plants, the methodologies and guidance within the document are applicable to any organization interested in understanding peak demand response programs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Prototype and Codesign of Nascent Flexible Wave Energy Converter Concepts

Prevailing mainstream ocean wave energy converter (WEC) archetypes are often relatively large monolithic rigid bodies with singular means of power take-off (e.g., relative motion between rigid bodies) and solo prime mover mechanisms (e.g. a rotary generator, a hydraulic piston). These mainstream WEC archetypes have existed for decades with some having histories spanning more than a century of significant investments and interests. As an alternative to the domain of mainstream WEC archetypes is the domain of distributed embedded energy converter technologies (also known as DEEC-Tec). Use of DEEC-Tec to create WECs - often called flexWECs - is a nascent domain of research and development that centers itself upon the interconnection of many relatively small energy converters - very small energy transducers - that interconnect and combine to create a much larger, overall, energy converting structure. The DEEC-Tec domain enables promising possibilities such as: direct in situ ocean wave energy harvesting and conversion throughout the volume of an entire flexWEC; inherent broad-banded ocean wave energy capture and conversion arising from near semi-continuous structural degrees of freedom; an innate absence of focusing and concentrating of ocean wave energy into a single prime mover and/or transmission system; and built-in implicit redundancy. As such, the DEEC-Tec domain could revolutionize how we conceptualize ocean wave energy conversion - both in terms of ocean wave energy converter design and what we view as viable environments and circumstances for ocean wave energy conversion. The research and development opportunities for the DEEC-Tec domain, therefore, are vast compared to the prevailing mainstream ocean wave energy converter domain. Correspondingly, there are ample opportunities to invest time and money into the DEEC-Tec domain with an aim to enable a paradigm shift in how ocean waves can be utilized as a more viable form of renewable energy. To that end, two seedling efforts have been enacted providing a very cursory investigation into the DEEC-Tec domain: (1) a preliminary simple fabrication/prototyping effort and (2) a preliminary co-design effort. The prototype seedling centers upon the design of a fabrication and evaluation rig for individual DEEC-Tec transducers; e.g., dielectric elastomer generators. The co-design seedling centers upon the analysis of those methods suitable for DEEC-Tec, ocean wave energy conversion, and fluid-structure interaction co-design efforts.

50 EE - Wind and Water Power Program - Water (EE-4↗
DOE OSTI2022

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

Integrating 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 problems 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 (control-lable 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 outperforms other benchmark controllers regarding computation time without compromising operational performance.

DER↗
DOE OSTI2024

Optimal Electric Vehicle Charging and Discharging Strategies Under DER Compensation Programs: Preprint

The adoption of electric vehicles (EVs) is becoming increasingly popular because of environmental concerns, the greater availability of models, and increased cost-competitiveness with gas vehicles. Because EVs have both charging and discharging capabilities, they provide great potential to help electric utilities with grid operation. When the grid demand is high, EVs can discharge to the grid to reduce the peak load, and vice versa; therefore, electric utilities have designed different policies to encourage EV charging station operators to charge or discharge at certain time periods. The New York State Public Service Commission established the Value of Distributed Energy Resources (VDER), or the Value Stack, to compensate for energy created by distributed energy resources, including EVs. This paper presents an optimization-based approach to identify the "golden hours" and "golden spots," i.e., the effective time periods and geographic locations for EV charging station operators to charge or discharge under the VDER program that can provide them the highest benefit. The proposed methodology can be applied to other compensation mechanisms and distribution systems as well. By working with industry partner NineDot Energy, realistic charging station information is used in this study, and the proposed approach is tested on a distribution feeder. The results from this study can help electric utilities and EV charging station operators determine the ideal charging/discharging time and the ideal locations for the charging station(s) in their distribution systems to achieve maximized benefit.

electric vehicle↗
DOE OSTI2024

lanl-ansi/MG-RAVENS

The MG-RAVENS project with the DOE Office of Electricity Microgrid R&D Program is a project to develop a completely free, open-source data exchange standard (API) for the Department of Energy, targeted at software tools related to infrastructure modeling, particularly the modeling of microgrids and electric power distribution systems that are created with funding from the Microgrid R&D Program. This software produces formal definitions of an API, documentation, contains supporting functions for parsing, validating, etc., and will contain examples of workflows enabled by the developed API.

Fobes, David M↗
DOE OSTI2018

Cascade energy optimization for waste heat recovery in distributed energy systems

The efficiency of distributed energy systems can be significantly increased through waste heat recovery from industry or power generation. The technologies used for this process are typically dependent on the quality and temperature grades of waste heat. To maximize the efficiency of cascade heat utilization, it is important to optimize the choice of waste heat recovery technologies and their operation. Here, a detailed mixed integer linear programming optimization model is proposed for waste heat recovery in a district-scale microgrid. The model can distinguish waste heat quality for planning and operation optimization of distributed energy systems. Heat utilization technologies are formulated in this developed model and categorized in different temperature grades. The developed model is validated using four typical cases under different settings of system operation and business models. It is found that the optimization model, by distinguishing waste heat temperature, can increase energy cost savings by around 5%, compared to models that do not consider waste heat temperature grades. Additionally, the results indicate that the developed model can provide more realistic configuration and technologies dispatch.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

ESPC ESA Webinar Series: ESPC ENABLE Contract Vehicle Overview

This webinar provides instructions and resources for financing distributed energy projects (e.g. on-site renewable energy, storage, and combined heat and power) using an energy savings performance contract (ESPC) energy sales agreement (ESA) through the U.S. Department of Energy's (DOE) ESPC ENABLE contract vehicle. The ESPC ENABLE contract vehicle is a streamlined procurement process that uses a standard investment grade audit tool and standard contract templates. Topics covered in the webinar include: An overview of the ESPC ENABLE contract vehicle; ESA-specific considerations for ENABLE contracts; and Case studies. This webinar also provides an overview of the resources available on the Federal Energy Management Program website, such as ESPC ENABLE templates. This webinar is the fourth of a series that provides information, best practices, and resources for implementing an ESPC ESA project. ESPC ESAs are a great option for federal sites that are motivated to reduce site costs, have no capital investment funding, and cannot use the U.S. Department of Defense 10 USC 2922a authority for long-term contracting.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Co-design optimization of combined heat and power-based microgrids

With the emergent need for clean and reliable energy resources, hybrid energy systems, such as the microgrid, are widely adopted in the United States. A microgrid can consist of various distributed energy resources, for instance, combined heat and power (CHP) systems. Here, the CHP module is a distributed cogeneration technology that produces electricity and recaptures heat generated as a by-product. It is an energy-efficient technology converting heat that would otherwise be wasted to valuable thermal energy. For an optimal system configuration, this study develops a novel co-design optimization framework for CHP-based cogeneration microgrids. The framework provides the stakeholder with a method to optimize investments and attain resilient operations. The proposed co-design framework has a mixed integer programming (MIP) model that outputs decisions for both plant designs and operating controls. The microgrid considered in this study contains six components: the CHP, boiler, heat recovery unit, thermal storage system, power storage system, and photovoltaic plant. After solving the MIP model, the optimal design parameters of each component can be found to minimize the total installation cost of all components in the microgrid. Furthermore, the online costs from energy production, operation, maintenance, machine startup, and disruption-induced unsatisfied loads are minimized by solving the optimal control decisions for operations. Case studies based on designing a CHP-based microgrid with empirical data are conducted. Moreover, we consider both nominal and disruptive operational scenarios to validate the performance of the proposed co-design framework in terms of a cost-effective, resilient system.

42 ENGINEERING↗
DOE OSTI2024

Distribution Grid Impact Study in Highland Park, Michigan: Understanding Rooftop Solar, Behind-the-Meter Energy Storage, Electric Vehicle Charging, and Building Electrification [Slides]

Through the Communities LEAP (Local Energy Action Program) Pilot, the National Renewable Energy Laboratory (NREL) engaged the Highland Park Stakeholder Coalition to scope four technical assistance work areas to address their energy needs and goals. This slide deck addresses the highlighted tasks under work area "B" related to policy analysis, due diligence, and case studies for removing barriers and providing best practices for local clean energy development. Highland Park community members face frequent, long-duration power interruptions due largely to the aging distribution system serving the area and the legacy design standards used in its construction. While degrading physical infrastructure such as poles, crossarms, and transformers can result in this substandard reliability, another notable characteristic of this legacy system is the lower, 4.8 kV, voltage class. This is a legacy construction standard which many utilities, DTE included, are phasing out in favor of higher, 15 or 25 kV, voltage classes instead. The existing 4.8 kV distribution system serving Highland Park may limit significant adoptions of clean-energy technologies like high percentages of building electrification or electric vehicle adoption. The following analysis seeks to quantify these limitations under a variety of clean-energy technology adoption scenarios. It compares the overall system risks of the present system to those of a hypothetical, upgraded 13.2 kV system, using NREL-developed risk metrics and offers upgrade cost considerations. The legacy 4.8 kV voltage class serving Highland park is not, as we have modeled it, a major limitation to the widespread adoption of cost-optimal rooftop solar and/or behind-the-meter energy storage. Within our modeling framework, these technologies namely impact secondary, low-voltage assets, which may be remedied without the need for a system-wide upgrade to a 13.2 kV voltage class. Our model of the current 4.8kV system indicates it is not capable of supporting community-wide electrification efforts. Widespread building electrification, and the resulting large increase in wintertime load, dramatically increases the prevalence of voltage violations and thermal overloading on the current 4.8 kV distribution system. These impacts illustrate the need for system-wide upgrades to a 13.2 kV voltage class to accommodate these technologies. Low to Moderate DER adoption does not adversely impact the grid but does improve undervoltage and asset overloading issues. However, these benefits are insufficient to defer grid upgrades. Higher DER penetration is shown to increase overvoltage and asset overloading in future electrification scenarios.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Regulators’ Financial Toolbox: Behind-the-Meter (BTM) Energy Storage Summary

On October 10, 2023, NARUC’s Center for Partnerships & Innovation (CPI) hosted a Regulators’ Financial Toolbox Webinar on the topic of Behind-the-Meter (BTM) Energy Storage. The webinar featured opening remarks by the Honorable Katherine Peretick, Commissioner, Michigan Public Service Commission; and presentations by Ryan Chan, Principal Strategic Analyst, Pacific Gas and Electric Company (PG&E); Arushi Sharma Frank, Energy Market Design Lead and Senior Counsel for Energy Regulatory Matters and Greg Thurnher, Technical Program Manager, Tesla; and Mark LeBel, Senior Associate, Regulatory Assistance Project (RAP).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Optimal Coordination of Distributed Energy Resources Using Deep Deterministic Policy Gradient

Recent studies showed that reinforcement learning (RL) is a promising approach for coordination and control of distributed energy resources (DER) under uncertainties. Many existing RL approaches, including Q-learning and approximate dynamic programming, are based on lookup table methods, which become inefficient when the problem size is large and infeasible when continuous states and actions are involved. In addition, when modeling battery energy storage system (BESS), the loss of life is not reasonably considered into the decision-making process. This paper proposes an innovative deep RL method for DER coordination considering BESS degradation. The proposed deep RL is designed based on an adaptive actor-critic architecture and employs an off-policy deterministic policy gradient method for determining the dispatch operation that minimizes the operation cost and BESS life loss. Case studies were performed to validate the proposed method and demonstrate the effects of incorporating degradation models into control design.

Das, Avijit↗
DOE OSTI2024

The Iowa Tribe of Kansas and Nebraska: Advancing Clean, Resilient, and Sovereign Energy

The Iowa Tribe of Kansas and Nebraska (ITKN) is a federally recognized Native American Tribe located along the Missouri River on the border of northeast Kansas and southeastern Nebraska. There are over 800 residents (Tribal citizens and non-Tribal) who live on the reservation, as well as more than 500 people who visit or work on the reservation on a daily basis. The ITKN faces many energy challenges, including rising service costs and dozens of power outages annually that impact resident well-being and business activities on Tribal lands. Power service issues are made more challenging by the remoteness of the reservation, which is 20 miles from the nearest town. Despite this, the ITKN has a long history of cultural and economic resilience: Local self-reliance, environmental stewardship, respecting the carrying capacity of the land, and strengthening the community are Tribal communities' traditional strengths. Long-term energy goals for the ITKN are centered around achieving energy sovereignty. Priority actions include: (1) Establishing a Tribal Utility Authority (TUA) to promote social welfare and community development.; (2) Deploying renewable community microgrids with ground-mount solar arrays and sustainable energy storage systems to advance energy sovereignty, resilience, and reliability. To advance these goals, the ITKN partnered with the U.S. Department of Energy's (DOE's) Communities LEAP (Local Energy Action Program) pilot. From August 2022 to March 2024, the ITKN community coalition collaborated with technical assistance providers at DOE's National Renewable Energy Laboratory (NREL) and Sandia National Laboratories to evaluate TUA planning needs and microgrid deployment scenarios. This fact sheet provides an overview of the results and outcomes from the Communities LEAP technical assistance process.

Communities Leap↗
DOE OSTI2018

Optimizing Distributed Photovoltaic System Set Points Under Uncertainty

We explore the potential for using dynamic programming (DP) and approximate dynamic programming (ADP) techniques to optimize the set points of distributed photovoltaic (DGPV) inverters under uncertainty about future DGPV deployment. We consider a case where a large (=1MW) system is installed first, and growth in deployment of small rooftop systems is anticipated, but uncertain. We find that for a real feeder (EPRI's J1 feeder), a significant reduction in the number and severity of voltage violations can be expected when DP or ADP is used compared to selecting set points based on the current conditions (the traditional myopic approach). Additionally, we find that using a simple ADP algorithm, sampled backward induction, is more than twice as fast as DP with similar outcomes.

14 SOLAR ENERGY↗