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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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DOE OSTI2022

Site demonstration and performance evaluation of MPC for a large chiller plant with TES for renewable energy integration and grid decarbonization

Thermal energy storage (TES) for a cooling plant is a crucial resource for load flexibility. Traditionally, simple, heuristic control approaches, such as the storage priority control which charges TES during the nighttime and discharges during the daytime, have been widely used in practice, and shown reasonable performance in the past benefiting both the grid and the end-users such as buildings and district energy systems. However, the increasing penetration of renewables changes the situation, exposing the grid to a growing duck curve, which encourages the consumption of more energy in the daytime, and volatile renewable generation which requires dynamic planning. The growing pressure of diminishing greenhouse gas emissions also increases the complexity of cooling TES plant operations as different control strategies may apply to optimize operations for energy cost or carbon emissions. This paper presents a model predictive control (MPC), site demonstration and evaluation results of optimal operation of a chiller plant, TES and behind-meter photovoltaics for a campus-level district cooling system. The MPC was formulated as a mixed-integer linear program for better numerical and control properties. Compared with baseline rule-based controls, the MPC results show reductions of the excess PV power by around 25%, of the greenhouse gas emission by 10%, and of peak electricity demand by 10%.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Flexible Ramping Product Procurement in Day-Ahead Markets

Flexible ramping products (FRPs) emerge as a promising instrument for addressing steep and uncertain ramping needs through market mechanisms. Initial implementations of FRPs in North American electricity markets, however, revealed several shortcomings in existing FRP designs. Here, in many instances, FRP prices failed to signal the true value of ramping capacity, most notably evident in zero FRP prices observed in a myriad of periods during which the system was in acute need for rampable capacity. These periods were marked by scheduled but undeliverable FRPs, often calling for operator out-of-market actions. On top of that, the methods used for procuring FRPs have been primarily rule-based, lacking explicit economic underpinnings. In this paper, we put forth an alternative framework for FRP procurement, which seeks to set FRP requirements and schedule FRP awards such that the expected system operation cost is minimized. Using real-world data from U.S. ISOs, we showcase the relative merits of the framework in (i) reducing the total system operation cost, (ii) improving price formation, (iii) enhancing the the deliverability of FRP awards, and (iv) reducing the need for out-of-market actions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Energy Saving Estimation of ASHRAE Guideline 36 Supervisory Setpoint Reset Controls in a Commercial Large Office Building

Designing, commissioning, and retrofitting HVAC control systems for energy efficiency is crucial, but the use of ad-hoc control sequences by designers and contractors, based on scattered information, results in diverse and sub-optimal sequences. ASHRAE Guideline 36 (G36) addresses the challenge by providing standardized, rule-based HVAC control sequences that prioritize energy efficiency. However, there is limited evaluation of their energy performance at the building level, with only a few studies primarily focused on HVAC airside systems in small-to-medium-sized commercial buildings. In this study, the energy performance of ASHRAE Guideline 36 control sequences was assessed using a large office building emulator in Chicago. The emulator features a central plant system with multiple chillers and boilers as well as multiple variable air volume (VAV) systems with terminal reheat. To achieve a high-fidelity representation, we developed a Spawn-of-EnergyPlus-based model for the large office building, maintaining the DOE prototype large office building setup but substituting the HVAC system with its Modelica counterpart. This substitution ensures that the building thermal load, HVAC system's dynamics, and detailed control sequences are all accurately represented. The study involved evaluating and implementing control strategies outlined in ASHRAE Guideline 36-2021 to replace conventional controls. These strategies include the demand-based supply air temperature and duct static pressure setpoint reset and the request logic for demand-based reset of chilled/hot water supply temperature setpoints and pipe static pressure setpoints. Energy performance was evaluated under various load conditions, including cooling, heating, and transitional seasons, both for individual control strategies and in combination. The results indicate that the collective control strategies retrofit yield greater energy savings than the sum of individual strategies, highlighting the synergistic benefits of incorporating both airside and plant-side control retrofits. Additionally, energy savings of up to 41% in the heating season, 18% in the shoulder season, and 20 % in the cooling season were observed compared to baseline control while maintaining the thermal comfort level.

ASHRAE Guideline 36, Commercial buildings, Control↗

DOE OSTI2021

A Component-Sizing Methodology for a Hybrid Electric Vehicle Using an Optimization Algorithm

Many leading companies in the automotive industry have been putting tremendous effort into developing new powertrains and technologies to make their products more energy efficient. Evaluating the fuel economy benefit of a new technology in specific powertrain systems is straightforward; and, in an early concept phase, obtaining a projection of energy efficiency benefits from new technologies is extremely useful. However, when carmakers consider new technology or powertrain configurations, they must deal with a trade-off problem involving factors such as energy efficiency and performance, because of the complexities of sizing a vehicle’s powertrain components, which directly affect its energy efficiency and dynamic performance. As powertrains of modern vehicles become more complicated, even more effort is required to design the size of each component. This study presents a component-sizing process based on the forward-looking vehicle simulator “Autonomie” and the optimization algorithm “POUNDERS”; the supervisory control strategy based on Pontryagin’s Minimum Principle (PMP) assures sufficient computational system efficiency. We tested the process by applying it to a single power-split hybrid electric vehicle to determine optimal values of gear ratios and each component size, where we defined the optimization problem as minimizing energy consumption when the vehicle’s dynamic performance is given as a performance constraint. The suggested sizing process will be helpful in determining optimal component sizes for vehicle powertrain to maximize fuel efficiency while dynamic performance is satisfied. Indeed, this process does not require the engineer’s intuition or rules based on heuristics required in the rule-based process.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Cybersecurity of Wide Area Monitoring, Protection and Control Systems for HVDC Applications

The flexibility provided by High Voltage Direct Current (HVDC) systems can be further extended by Wide Area Monitoring, Protection, and Control (WAMAPC) systems. WAMPAC systems enable many HVDC applications and, on the other hand, inevitably introduce cybersecurity concerns that need to be addressed. In this work, a security domain layer and decision framework is reported to detect and mitigate the impact of false data injection (FDI) attacks targeting HVDC stations. Specifically, a rule-based cyber-attack detection method is introduced and implemented on Raspberry Pi and tested on the real-time HVDC simulation facility on the real-time digital simulator (RTDS) platform at ABB U.S. Corporate Research Center.

cybersecurity↗

DOE OSTI2021

A Zonal Volt/VAR Control Mechanism for High PV Penetration Distribution Systems

This paper presents a zonal Volt/VAR control scheme for regulating voltage in unbalanced 3-phase distribution systems using inverter-based resources (IBR). First, the dependency between nodal voltage changes and IBR reactive power injections is derived via voltage sensitivity studies. Then, a fast-incremental clustering method is used to divide the distribution circuit into weakly-coupled zones based on correlations between nodal voltage sensitivities. The weak-coupling allows the voltage to be regulated independently within each zone using a rule-based voltage controller to dispatch IBR for voltage corrections. Simulation results on actual distribution feeder show that the proposed zone-based Volt/VAR control method maintains system voltages within their operational limits while reducing the runtime of the Volt/VAR controller from tens of second to a couple of milliseconds compared with centralized, optimization-based Volt/VAR control methods.

Alrushoud, Asmaa↗

DOE OSTI2023

Controller Hardware-in-the-Loop Modeling and Operation of a High-Power DC Charging Hub

High-power charging (HPC) concept will be a critical enabler for the wide-scale adoption and integration of electric vehicles (EVs). HPC hubs will have a significant impact on achieving this goal, particularly when considering the charging requirements of next-generation medium and heavy-duty vehicles (MD/HD) used in various vocational scenarios. This emphasizes the significance of evaluating and confirming the technical planning and multi-level operation of HPC hubs through modeling, protocol implementation, and simulation before hardware demonstration. The focus of this study is to develop the architecture, modeling, communication, and real-time controller hardware-in-the-loop (C-HIL) simulation of HPC hubs using the DC distribution system approach. Specifically, this work demonstrates the practical implementation of a custom Smart Energy Management System (SEMS) for a real-time C-HIL HPC hub simulation using the Open Charge Point Protocol (OCPP) for communication. OCPP is implemented on the Node-Red framework and the custom controller is executed in Python. We developed a rule-based controller to demonstrate the proposed approach.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Multi-task deep reinforcement learning for intelligent multi-zone residential HVAC control

In this short communication, a data-driven deep reinforcement learning (deep RL) method is applied to minimize HVAC users’ energy consumption costs while maintaining users’ comfort. The applied deep RL method's efficiency is enhanced by conducting multi-task learning that can achieve an economic control strategy for a multi-zone residential HVAC system in both cooling and heating scenarios. The applied multi-task deep RL method is compared with a rule-based benchmark case and a single-task deep deterministic policy gradient algorithm to verify its effective and generalized application in optimizing HVAC operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Unsupervised Power System Event Detection and Classification Using Unlabeled PMU Data

This paper proposes a novel data-driven power system event detection and classification method based on 5TB of actual PMU measurements collected from the US western interconnect. Firstly, a set of comprehensive power quality rules are proposed to pre-filter the raw data and extract the regions of interest (ROI). Six distinct event categories are defined and corresponding patterns are chosen as references. Meanwhile, detailed characteristics of patterns are summarized to enhance our understanding of the actual events. Then, the time-independent feature vectors are generated by extracting the statistical, temporal, and spectral features from the raw time-series data. Furthermore, an ensemble model is proposed to cluster the events by combining multiple K-means clustering models using a voting strategy. Besides, both system-level and PMU-level clustering models are developed. The accuracy and robustness of the event detection method are further improved through interactive evaluation of the two-level clustering results. This paper summarizes the actual characteristics of each event category and provides a reliable basis for accurate label generation. The experiments demonstrate the effectiveness of the proposed event detection and classification method.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Unsupervised Power System Event Detection and Classification Using Unlabeled PMU Data

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

A deep learning-based battery sizing optimization tool for hybridizing generation plants

Hybrid generation and energy storage systems offer the ability to increase flexibility of the combined asset. This flexibility can be used to increase provision of services already provided by the generation asset, such as timing sale of electricity to the energy market during high price periods, and also enable provision of additional services, such as ancillary services or contribute to resource adequacy. From a generation asset owner perspective, the decision to hybridize includes selecting an energy storage system that, among other factors, maximizes financial performance of the energy storage investment. Yet, existing tools to optimize energy storage sizing are either too rudimentary (i.e., based on “rules of thumb”) or too complex to implement (i.e., require specialized engineering and software knowledge and a high-performance computer to run). This work presents a novel deep learning-based battery sizing optimization tool that is designed to help generation asset owners easily assess preliminary sizing considerations for potential battery investments to hybridize their generation facility. The tool uses deep learning to predict revenue over a broad search space of potential battery sizes, estimates capital and operating costs (including accounting for battery degradation), and computes financial performance of each potential battery system investment, recommending a system with maximum financial performance. The tool is tested and validated for hydropower assets. Finally, this tool will help a greater cross-section of industry consider investments in battery systems, increasing their revenue and helping them compete in rapidly evolving electrify markets.

13 HYDRO ENERGY↗

DOE OSTI2023

Open Building Operating System: An Open-Source Grid Responsive Control Platform for Buildings

Grid-interactive efficient buildings (GEBs) with flexible loads are a promising method to decarbonize buildings, shift loads during peak hours, and lower energy use and electricity costs. Despite the promising benefits of GEBs, automation systems that manage flexible loads in response to energy prices or other grid signals are still uncommon in small and medium commercial buildings. Recent literature demonstrates such control solutions, but they often rely on custom integrations lacking the tools and drivers needed for scalability. To address these gaps, our team has created a fully open-source software stack capable of integrating heterogeneous flexible building loads and implementing integrated portable control applications called the Open Building Operating System (OpenBOS). The software can be deployed over existing control architecture with a small capital cost. OpenBOS leverages semantic models, which have been the subject of recent investigations to facilitate application portability. The use of semantic data reduces the labor and expense required to deploy and update smart control applications, increasing scalability. In this paper, the semantic modeling schema "Brick" was used, but the proposed approach can also be applied to ASHRAE standard 223P, when released. This paper describes the methodology and software components of OpenBOS and demonstrates its functionality with a rule-based demand flexibility control application configured using a semantic model. This application was tested at a real building in NY that uses a dual-fuel heating system made up of five ductless heat pump mini-splits and a central furnace serving a single zone. The demonstration reduced electricity costs at the site by 27%, demand during a shed event by 49%, and furnace usage by 35%.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Adaptation to climate change through strategic integration of long fallow into cropping system in a dryland Mediterranean-type environment

The crop-growing region of Western Australia characterized by a Mediterranean-type climate is projected to become warmer and drier. Appropriate selection of crop sequences will be of importance to cope with these climatic changes for this largest grain-producing region of Australia. Through linking a widely used crop model (APSIM), 26 General Circulation Models (GCMs) with one Shared Socioeconomic Pathway (SSP585) and economic analysis, we explored how the climate change would affect dryland wheat cropping and whether/how long fallow (the practice of leaving a field out of production for an entire growing season) could be integrated into wheat cropping system in Western Australia. The potential adaptation of long fallow into wheat system was assessed with four fixed rotations (fallow-wheat, fallow-wheat-wheat, fallow-wheat-wheat-wheat, and fallow-wheat-wheat-wheat-wheat) and four flexible sowing rule-based rotations (the land was fallowed if sowing rule was not met), compared with continuous wheat. The simulation results at four representing locations show that climate change would have negative impacts on both yield and economic return of continuous wheat cropping in Western Australia. Wheat after fallow out-yielded and out-profited wheat after wheat under future climate. But integrating fallow into wheat cropping systems with the above fixed rotations would lead to yield and economic loss. By contrast, cropping systems in which fallowing took place when sowing condition could not be met at a certain time would achieve comparable yield and economic return to continuous wheat, with wheat yield being only 5% less than continuous wheat and the gross margin being $12ha -1 more than continuous wheat averaged across locations. We highlight strategic integration of long fallow into cropping system in a dryland Mediterranean-type environment would have a great potential to cope with future climate change. These findings can be extended into other Mediterranean-type cropping regions in Australia and beyond.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Utilizing commercial heating, ventilating, and air conditioning systems to provide grid services: A review

The modern power grid faces multiple challenges due to an increase in the adoption of renewable generation, such as dynamically balancing supply and demand at different time scales. Demand side management in buildings plays a vital role in achieving this balance because buildings can provide grid services through a variety of building assets. However, the development of grid-interactive, efficient buildings is still in its infancy, and a systematic and holistic understanding of grid service delivery strategies in terms of energy efficiency, load shifting, load shedding and load modulating is still limited. This paper is a comprehensive review of the development and application of building-level control strategies for utilizing heating, ventilating, and air conditioning systems to provide grid services. These strategies have been investigated through numerical and experimental studies. Control algorithms, such as heuristic rule-based control and model-based control, have been used to enable the automatic control delivery of grid services. The advantages and disadvantages of the strategies are summarized and discussed. Finally, research trends are also identified, which include considering predicted mean vote-based and occupant-based thermal comfort, modeling of occupant behavior, integrating power grid operations with building control, and combining different demand flexibility modes in the control design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Development of A Hardware-In-the-Loop (HIL) Testbed for Cyber-Physical Security in Smart Buildings

As smart buildings move towards open communication technologies, providing access to the Building Automation System (BAS) through the building's intranet, or even remotely through the Internet, has become a common practice. However, BAS was historically developed as a closed environment and designed with limited cyber-security considerations. Thus, smart buildings are vulnerable to cyber-attacks with the increased accessibility. This study introduces the development and capability of a Hardware-in-the-Loop (HIT) testbed for testing and evaluating the cyber-physical security of typical BASs in smart buildings. The testbed consists of three subsystems: (1) a real-time HIL emulator simulating the behavior of a virtual building as well as the Heating, Ventilation, and Air Conditioning (HVAC) equipment via a dynamic simulation in Modelica; (2) a set of real HVAC controllers monitoring the virtual building operation and providing local control signals to control HVAC equipment in the HIL emulator; and (3) a BAS server along with a web-based service for users to fully access the schedule, setpoints, trends, alarms, and other control functions of the HVAC controllers remotely through the BACnet network. The server generates rule-based setpoints to local HVAC controllers. Based on these three subsystems, the HIL testbed supports attack/fault-free and attack/fault-injection experiments at various levels of the building system. The resulting test data can be used to inform the building community and support the cyber-physical security technology transfer to the building industry.

Li, Guowen↗

DOE OSTI2022

Reinforcement Learning for Intelligent Building Energy Management System Control *

A building energy management system (BEMS) is a computer-based system designed to monitor and control a building's energy needs. Modern BEMS rely on the sensing and connectivity capabilities of Internet of Things (IoT) technology to intelligently adjust the energy consumption to reduce cost while respecting the consumers' preferences. Increasingly, control decisions are made based on predictions by models trained using supervised machine learning methods, which still requires control policies to be formulated in a rule-based fashion. When using reinforcement learning (RL) instead, control policies are learned by observing the utility in terms of cost and comfort associated with actions such as a change in the heating system's setpoint. The resulting RL-based controllers can capture not only the dynamics of the building and the associated electrical devices, but also fluctuations in electricity prices and user demand, avoiding the need to combine multiple predictive models with tailored control policies. This chapter will provide an overview of RL-based approaches for BEMS. After sketching the taxonomy of general RL methods, we discuss the implications of relying on the individual methods in a BEMS context. Existing work applying RL is presented along the key devices controlled by BEMS systems. Finally, we summarize the state-of-the-art and sketch limitations and open research directions.

Kotevska, Olivera↗

DOE OSTI2025

CRiSPPy: An advanced hydropower scheduling tool for the Colorado River Storage Project

The Western Area Power Administration (WAPA) plays a vital role in delivering reliable and cost-effective hydroelectric power to millions of customers across the western United States. The Colorado River Storage Project (CRSP) carries out WAPA’s mission in Arizona, Utah, Colorado, New Mexico, Nevada, Wyoming and Texas. Achieving this mission requires effective management of the Colorado River system, and depends on the use of advanced analytical tools and modeling methodologies. For many years, CRSP has relied on the Generation and Transmission Maximization Superlite (GTMax SL) model for its mid-term and long-term hydroscheduling needs. However, the evolving energy market, power system operations, environmental rules, and hydrology conditions, coupled with advancements in computational capabilities, have necessitated the development of a more modern and robust solution. This report introduces the Colorado River Storage Project Python-based (CRiSPPy) model, a new, advanced hydropower scheduling tool developed to address CRSP ever-evolving challenges. CRiSPPy represents a significant leap forward in our ability to model and optimize the operation of the Colorado River system. It incorporates state-of-the-art optimization algorithms, enhanced data management capabilities, and an advanced graphical user interface, providing WAPA CRSP personnel with unprecedented insights and decision-making support. This document details the development, capabilities, and implementation of CRiSPPy. It is intended to serve as a comprehensive resource for WAPA staff, stakeholders, and anyone interested in the future of hydropower scheduling in the Colorado River Basin. We are confident that CRiSPPy will enhance WAPA's mission while adapting to the challenges of a dynamic and increasingly complex environment. The version of CRiSPPy described in this report is the version 2.3. New versions of CRiSPPy will be developed as the tool keeps evolving to address CRSP challenges.

13 HYDRO ENERGY↗

DOE OSTI2021

Fermionic systems for quantum information people

The operator algebra of fermionic modes is isomorphic to that of qubits, the difference between them is twofold: the embedding of subalgebras corresponding to mode subsets and multiqubit subsystems on the one hand, and the parity superselection in the fermionic case on the other. We discuss these two fundamental differences extensively, and illustrate these through the Jordan–Wigner representation in a coherent, self-contained, pedagogical way, from the point of view of quantum information theory. Our perspective leads us to develop useful new tools for the treatment of fermionic systems, such as the fermionic (quasi-)tensor product, fermionic canonical embedding, fermionic partial trace, fermionic products of maps and fermionic embeddings of maps. We formulate these by direct, easily applicable formulas, without mode permutations, for arbitrary partitionings of the modes. It is also shown that fermionic reduced states can be calculated by the fermionic partial trace, containing the proper phase factors. We also consider variants of the notions of fermionic mode correlation and entanglement, which can be endowed with the usual, local operation based motivation, if the parity superselection rule is imposed. We also elucidate some other fundamental points, related to joint map extensions, which make the parity superselection inevitable in the description of fermionic systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗