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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Critical Exploration of the Efficiency Impacts of Demand Response From HVAC in Commercial Buildings

Increasing quantities of renewable energy generation has yielded a need for greater energy storage capacity in power systems. Thermal storage in variable air volume (VAV) heating, ventilation, and air conditioning (HVAC) in commercial buildings has been identified as an inexpensive source of grid storage, but the true costs are not known. Recent literature explores the inefficiency associated with providing grid services from these HVAC-based demand response (DR) resources by employing a battery analogy to calculate round-trip efficiency (RTE). Results vary significantly across studies and in some cases reported efficiencies are strikingly low. This article has three objectives to address these prior results. First, we synthesize and expand on insights into existing literature by systematically exploring the potential causes for the discrepancies in results. We reinforce previous work indicating baseline modeling may drive differences across studies and deduce that control accuracy plays a role in the major differences between experiments and simulation. Second, we discuss why the RTE metric is problematic for DR applications, discuss another proposed metric, additional energy consumption (AEC), and propose an extension, which we call uninstructed energy consumption (UEC), to evaluate DR performance. Finally, we explore the merits of different metrics using experimental data and highlight UEC's reduced sensitivity to the characteristics of the DR signal than previously proposed metrics.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Online Dynamic Mode Decomposition Based System Identification of Multi-Zone Building HVAC Systems

Many works have recently been conducted to reduce the electricity consumption of smart buildings and allow them to support various grid services. Most of these works require accurate system models for the various appliances in the building including heating, ventilation, and air conditioning (HVAC) units. In this paper, we investigate a recursive data-driven system identification strategy to construct the thermal model for a time-varying building with a multi-zone HVAC unit. The online dynamic mode decomposition (DMD)-based strategy is employed to identify the multi-zone thermal building dynamics, where a simple information update (rank-1) is selected to avoid computational complexity. The DMD-based identification strategy is validated using a real gymnasium building equipped with a 4-zone HVAC unit, and its performance is compared with that of the traditional nuclear-norm subspace identification (N2SID) strategy.

Wu, Tumin [University of Tennessee, Knoxville (UTK↗
DOE OSTI2020

Stochastic Home Energy Management Systems with Varying Controllable Resources

This paper studies the performance of a model predictive control (MPC) algorithm in a home energy management system (HEMS) as the set of controllable resources varies and under both a constant and a time-of-use (TOU) electricity price structure. The set of controllable resources includes residentially-owned photovoltaic (PV) panels, a home battery system (HBS), an electric vehicle (EV), and a home heating, ventilation, and air conditioning (HVAC) system. The HEMS optimally schedules the set of controllable resources given user preferences such as indoor thermal comfort and electricity cost sensitivity. The home energy management system is built on a chance constrained, MPC-based algorithm, where the chance constraint ensures the indoor thermal comfort is satisfied with a high probability given uncertainty in the outdoor temperature and solar irradiance forecasts. Simulation results for varying sets of controllable resources under two different electricity price structures demonstrate the variation in the HEMS control with respect to HBS operation, electricity cost, and grid power usage.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Battery Equivalent Model for Residential HVAC

Flexible loads, especially heating, ventilation, and air-conditioning (HVAC) systems can be used to provide a battery-like service to the power grid by varying their demand up and down over a baseline. Recent work has reported that the round-trip efficiency for providing grid services with HVAC systems can be close to 1. Inspired by this promising result, in this work, we rigorously develop a battery equivalent model (BEM) for residential HVAC systems. Relying only on typically available data, the proposed BEM will bridge the observability and controllability gaps between utility companies and customers. The proposed BEM will enable the utility companies to manage the flexible HVAC devices as easy as charging/discharging the conventional energy storage devices. Validated physics-based models are used to train the BEM, whose prediction accuracy is illustrated through a simulation study.

Dong, Jin↗
DOE OSTI2020

Deep Reinforcement Learning for Residential HVAC Control with Consideration of Human Occupancy

The Artificial Intelligence (AI) development described herein uses model-free Deep Reinforcement Learning (DRL) to minimize energy cost during residential heating, ventilation, and air conditioning (HVAC) operation. Building cooling loads and HVAC operation are difficult to accurately model due to complexity, lack of measurements and data, and model specific performance, so online machine learning is used to allow for real-time readjustment in performance. Energy costs for the multi-zone cooling unit shown in this work are minimized by scheduling on/off commands around dynamic prices. By taking advantage of precooling events that take place when the price is low, the agent is able to reduce operational cost without violating user comfort. The DRL controller was tested in simulation where the learner achieved a 43.89% cost reduction when compared to traditional, fixed-setpoint operation. The system is now ready for the next phase of testing in a live, real-time home environment.

Mckee, Evan↗
DOE OSTI2020

A Robust Hierarchical Dispatch Scheme for Active Distribution Networks Considering Home Thermal Flexibility

Distribution networks are changing from passive absorbers of electric energy to active distribution networks (ADNs) capable of operating and participating in electricity markets. In the context of residential microgrids, which is a type of ADNs, aggregated home heating, ventilation and air-conditioning (HVAC) loads present a key opportunity to drive operational and economic objectives, facilitate high renewable energy penetration, and enhance both system resiliency and flexibility. A robust, hierarchical dispatch scheme is developed and presented in this paper, which connects an upper level multi-phase distribution optimal power flow (DOPF) to a lower level model predictive control (MPC)-based HVAC fleet controller. The approach is tested and verified on a modified IEEE 13 bus system in an intraday market application. The results demonstrate that the proposed hierarchical dispatch scheme is able to drive both economic and operational objectives for the ADN operator.

Rooks, Cody D.↗
DOE OSTI2021

Transactive HVAC Agent - Design and Performance Evaluation

Transactive energy systems are playing an increasingly important role in the efficient and reliable marketbased operation of the power grid. Since a significant portion of the residential building energy consumption is from heating ventilation and air conditioning (HVAC) systems, HVAC is one of the most promising resources to provide load flexibility. However, utilizing HVAC flexibility to provide various grid services while simultaneously maintaining consumer comfort and cost-reductions is challenging. This paper presents a design of a transactive HVAC agent (T-HVAC) to be used as a supervisory control for the HVAC system that can simultaneously ensure comfort and cost-reduction. In particular, the T-HVAC a) estimates HVAC thermal dynamics, b) ensures optimal operations of the HVAC system, and c) participates into markets, and d) implements a market-based control via controlling the thermostat temperature set-point. The T-HVAC performance is demonstrated through multiple scenarios and illustrations.

Demand flexibility, distribution system, HVAC, Tra↗
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 OSTI2022

Occupancy-Based Controls for an All-Electric Residential Community in a Cold Climate

In residential buildings, rapid improvements in sensors, communication, and information technology have enabled occupancy-based building controls. These controls utilize occupancy information and modify the operation of the heating, ventilation, and air-conditioning (HVAC) system to minimize excess HVAC energy use, especially when the building is unoccupied. This reduces the total building energy consumption and utility bills while maintaining thermal comfort. In this paper, we present two novel occupancy-driven controls - reactive control and predictive control - and compare their performance. We model an all-electric residential community based on a 27- home community in Basalt, Colorado, in the United States. We simulated various scenarios, considering different temperature setback and control algorithms, to analyze the community-scale impact of these occupancy-based controls. The results show that total HVAC energy savings in a building ranges from 1%- 20% compared to the baseline scenario without occupancy-based controls. The energy-saving potential is highly correlated with the occupancy pattern and temperature setback in the building.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2020

A Comprehensive Scheduling Framework using SP-ADMM for Residential Demand Response with Weather and Consumer Uncertainties

This paper presents a comprehensive scheduling framework for residential demand response (DR) programs considering both the day-ahead and real-time electricity markets. In the first stage, residential customers determine the operating status of their responsive devices such as heating, ventilation, and air conditioning (HVAC) systems and electric water heaters (EWHs), while the distribution system operator (DSO) computes the amount of electricity to be purchased in the day-ahead electricity market. In the second stage, the DSO purchases insufficient (or sells surplus) electricity in the real-time electricity market to maintain the supply-demand balance. Due to its computational complexity and data privacy issues, the proposed model cannot be directly solved in a centralized manner, especially with a large number of uncertain scenarios. Therefore, this paper proposes a combination of stochastic programming (SP) and the alternating direction method of multipliers (ADMM) algorithm, called SP-ADMM, to decompose the original model and then solve each sub-problem in a distributed manner while considering multiple uncertain scenarios. The simulation study is performed on the IEEE 33-bus system including 121 residential houses. Here, the results demonstrate the effectiveness of the proposed approach for large-scale residential DR applications under weather and consumer uncertainties.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Automated Control of Transactive HVACs in Energy Distribution Systems

Heating, Ventilation, and Air Conditioning (HVAC) systems contribute significantly to a building’s energy consumption. In the recent years, there is an increased interest in developing transactive approaches which could enable automated and flexible scheduling of HVAC systems based on the customer demand and the electricity prices decided by the suppliers. Flexible and automated scheduling of the HVAC systems make it a prime source for participation in residential demand response or transactive energy systems. Therefore, it is of significant interest to identify an optimal strategy to control the HVAC systems. Here, reducing the energy cost while keeping the comfort level acceptable to the users, we argue that such a control strategy should consider both the energy cost and user comfort simultaneously. Accordingly, we develop the control strategy through the solution of an optimization problem that balances between the energy cost and consumer’s dissatisfaction. This optimization enables us to solve a decision-making problem through first price prediction and then choosing HVAC temperature settings throughout the day based on the predicted price, history of the price and HVAC settings, and outside temperature. More specifically, we formulate the control design as a Markov decision process (MDP) using deep neural networks and use Deep Deterministic Policy Gradients (DDPG)-based deep reinforcement learning algorithm to find the optimal control strategy for HVAC systems that balances between electricity cost and user comfort.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Tri-Level Scheduling Model Considering Residential Demand Flexibility of Aggregated HVACs and EVs Under Distribution LMP

Residential loads, especially heating, ventilation and air conditioners (HVACs) and electric vehicles (EVs), have great potentials to provide demand flexibility which is an attribute of grid-interactive efficient buildings (GEB). Under this new paradigm, first, EV and HVAC aggregator models are developed in this paper to represent the fleet of GEBs, in which the aggregated parameters are obtained based on a new approach of data generation and least squares parameter estimation (DG-LSPE), which can deal with heterogeneous HVACs. Then, a tri-level bidding and dispatching framework is established based on competitive distribution operation with distribution locational marginal price (DLMP). Furthermore, the first two levels form a bilevel model to optimize the aggregators’ payment and to represent the interdependency between load aggregators and the distribution system operator (DSO) using DLMP, and the third level is to dispatch the optimal load aggregation to all residents by the proposed priority list-based demand dispatching algorithm. Finally, case studies on a modified IEEE 33-Bus system illustrate three main technical reasons of payment reduction due to demand flexibility: load shift, DLMP step changes, and power losses. They can be used as general guidelines for better decision-making for future planning and operation of demand response programs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

A Contextually Supervised Optimization-Based HVAC Load Disaggregation Methodology

This paper presents a novel contextually supervised optimization-based approach for disaggregating heating, ventilation, and air-conditioning (HVAC) loads using smart meter or Supervisory Control and Data Acquisition data. To disaggregate the load into HVAC loads, large and infrequently used loads (LIUL), and base loads, we formulate an optimization problem to minimize a set of five loss terms, consisting of the reconstruction errors of the overall load profile, the ramp rate losses, and three distinct loss functions linked with the HVAC load, base load, and LIUL, respectively. To enhance accuracy, we incorporate two forms of contextual information into the problem formulation. First, we utilize mutual information to estimate HVAC energy consumption. Second, we employ a base load dictionary to constrain HVAC load estimation errors. The obtained HVAC load profiles are fine-tuned by abnormal ramp detection followed by binary hypothesis testing. Here, the proposed method is developed and tested using sub-metered residential and commercial building data. Simulation results show that the proposed method outperforms existing methods across various data resolutions and load aggregation levels, showing excellent transferability and generalizability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

An Optimization-Based HVAC Load and PV Disaggregation Methodology With Contextual Supervision

Here, this letter presents an optimization-based Heating, Ventilation, and Air Conditioning (HVAC) and PV disaggregation approach. This letter builds on the previous works of authors, which discuss HVAC disaggregation strategy for aggregated levels without requiring sub-metered information. This letter expands on previous works to discuss optimization-based HVAC disaggregation by varying PV penetration scenarios with joint disaggregation of HVAC load and PV.

Analytics↗
DOE OSTI2025

Multi-Agent Hierarchical Deep Reinforcement Learning for HVAC Control With Flexible DERs

As electricity consumption in commercial and residential buildings continues to rise, reducing energy costs presents an increasing challenge. Heating, ventilating, and air-conditioning (HVAC) systems, which typically account for 40%-50% of a building's energy use, are prime targets for energy savings. Intelligent control of HVAC temperature through the exploitation of HVAC load flexibility brings significant potential to reduce energy consumption and electricity expenses. The nonlinear models of HVAC systems challenge traditional control methods, while the uncertainty introduced by HVAC load flexibility complicates distributed energy resource (DER) management using conventional optimal dispatch techniques. In response to these challenges, we propose a hierarchical multi-agent deep reinforcement learning (DRL) approach. The lower-level agents focus on balancing comfort and energy conservation, while the upper-level DRL agents optimize the use of DERs to reduce peak demand based on the control outcomes of the HVAC by the lower-level agents. Here, in the upper-level agents, we incorporate a multi-agent structure based on ensemble learning, which acts based on historical and current data without relying on precise load forecasting to address the delayed rewarding issue in DRL. This allows for the effective reduction of energy costs. The proposed method is tested using a real-world microgrid comprising 413 buildings in Southern California, and the results demonstrate that our approach can significantly reduce overall electricity bills while ensuring the comfort of consumers and residents.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Peak Power Minimization for Commercial Thermostatically Controlled Loads in Multi-Unit Grid-Interactive Efficient Buildings

The load profiles of most commercial and industrial consumers are characterized by brief periods of very high power consumption followed by intervals of lower demand. To encourage such consumers to flatten their load profiles, power utilities in and around the world often levy a monthly demand charge (DC) on the peak demand measured over brief intervals. In this work, we consider the joint optimization of energy costs (EC) and the instantaneous peak power of a multi-unit building which uses a hydronic heating, ventilation and cooling (HVAC) system and responds to a demand response (DR) program. Despite the non-linear structure of the problem, we show how optimal solutions can be obtained efficiently using linear programming. Next, we study the power demand patterns resulting from our proposed strategy for thermostatically controlled loads (TCLs), and evaluate the strategy’s performance for various climate zones in the US, under both typical and atypical weather conditions. Finally, the results show that depending on the ambient conditions and the tariff structure, our strategy can result in utility bill savings of up to nearly 19% compared to the baseline. The results also indicate that our power control strategy can significantly reduce the instantaneous peak power consumption in commercial TCLs.

HVAC↗
DOE OSTI2021

Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales

While wetlands are the largest natural source of methane (CH 4 ) to the atmosphere, they represent a large source of uncertainty in the global CH 4 budget due to the complex biogeochemical controls on CH 4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH 4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. In this work we used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by ~17 ± 11 days, and lagged air and soil temperature by median values of 8 ± 16 and 5 ± 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH 4 . At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH 4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH 4 emissions.

59 BASIC BIOLOGICAL SCIENCES↗