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At least 55 records · Page 3

Collaborative Decision Approach for Electricity Pricing-demand Response Stackelberg Game

Demand response programs are considered as a valuable resource in smart grids that provide several advantages of load shifting, peak load reduction, mediating intermittency of renewable energy integration, etc. Flexible price-based incentives have been recognized as a critical strategy in motivating and compensating consumers' load adjustment actions for successful implementation of demand response. Game theoretical approaches, especially Stackelberg games are popularly adopted to model the relationship between electricity price and customers' demand response and solved by the classical centralized backward induction (BI) method. However, the BI method generally requires convexity of the follower's model for necessary optimality conditions, and the computational time of any centralized approach increases sharply with larger problem instances. In this paper, the Stackelberg game of electricity pricing-demand response between a distribution system operator (DSO) and load aggregators (LAs) is decomposed based on a collaborative optimization (CO) framework, where each LA is treated as a discipline with its own domain constraints (e.g. building temperature control), while the DSO at the system level tries to reduce the solution discrepancy and guide the searching towards optimality. Several groups of comparison experiments have demonstrated the effectiveness of the proposed collaborative decision approach in solving the demand response game.

Chen, Yang↗

Evaluating Thermostats' Deadbands Using HVAC Hardware-In-the-Loop Experiment for Advanced Control Strategies

Smart thermostats have gained significant popularity due to their potential for optimizing energy consumption and enhanced user control while ensuring occupants' comfort. The deadband, also referred to as temperature differential, is defined as the temperature difference between the desired setpoint and upper threshold or lower threshold for the HVAC equipment to turn on. It is a key factor influencing energy efficiency and user satisfaction. This paper presents a comparative analysis of the deadbands of five different smart thermostats, tested with a heat pump, aiming to identify variations in their deadband settings and implications for energy management. The experimental study was conducted using a HVAC hardware-in-theloop (HIL) system that integrates smart thermostats with physical HVAC equipment in a simulated house environment. The study explores the trade-offs between energy efficiency and occupant comfort and highlights how different thermostats participating in demand response event cycle differently based on their deadband settings. The findings offer valuable insights into how selecting the right thermostat or configuring smart thermostat with appropriate deadband settings can be leveraged to enhance demand response capabilities, shift loads effectively and improve operational flexibility in HVAC systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A Scalable Hardware-and-Human-in-the-Loop Grid-interactive Efficient Building Equipment Performance Dataset

This project developed a publicly available, high-fidelity dataset about the interactions among humans, homes, and heat pumps supporting grid interactive efficient buildings to balance demand on the grid with comfort for occupants. Laboratory measurements and simulations of the hardware capture the second-scale electric power dynamics of heat pumps providing grid services like load shifting and load shedding. Field measurements, behavior tracking, and qualitative surveys of people in their homes over multiple years—including experimentally adjusting the heating and cooling system to provide grid services—to capture the reciprocal effect of human behavior on grid services, and grid services on human comfort. Taken together, these data capture the complete Hardware and Human in the loop system for residential heat pumps, reducing large uncertainties in simulation for design, and models for control of heat pumps, and grid-interactive buildings.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Evaluating Thermostats' Deadbands Using HVAC Hardware-In-the-Loop Experiment for Advanced Control Strategies: Preprint

Smart thermostats have gained significant popularity due to their potential for optimizing energy consumption and enhanced user control while ensuring occupants' comfort. The deadband, also referred to as temperature differential, is defined as the temperature difference between the desired setpoint and upper threshold or lower threshold for the HVAC equipment to turn on. It is a key factor influencing energy efficiency and user satisfaction. This paper presents a comparative analysis of the deadbands of five different smart thermostats, tested with a heat pump, aiming to identify variations in their deadband settings and implications for energy management. The experimental study was conducted using a HVAC hardware-in-theloop (HIL) system that integrates smart thermostats with physical HVAC equipment in a simulated house environment. The study explores the trade-offs between energy efficiency and occupant comfort and highlights how different thermostats participating in demand response event cycle differently based on their deadband settings. The findings offer valuable insights into how selecting the right thermostat or configuring smart thermostat with appropriate deadband settings can be leveraged to enhance demand response capabilities, shift loads effectively and improve operational flexibility in HVAC systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

The effect of price-based demand response on carbon emissions in European electricity markets: The importance of adequate carbon prices

Price-based demand response (PBDR) has recently been attributed great economic but also environmental potential. However, the determination of its short-term effects on carbon emissions requires the knowledge of marginal emission factors (MEFs), which compared to grid mix emission factors (XEFs), are cumbersome to calculate due to the complex characteristics of national electricity markets. This study, therefore, proposes two merit order-based methods to approximate hourly MEFs and applies them to readily available datasets from 20 European countries for the years 2017–2019. Based on the calculated electricity prices, standardized daily load shifts were simulated which indicated that carbon emissions increased for 8 of the 20 countries and by 2.1% on average. Thus, under specific circumstances, PBDR leads to carbon emissions increases, mainly due to the economic advantage fuel sources such as lignite and coal have in the merit order. MEF-based load shifts reduced the mean resulting carbon emissions by 35%, albeit with 56% lower monetary cost savings compared to price-based load shifts. Finally, by repeating the load shift simulations for different carbon price levels, the impact of the carbon price on the resulting carbon emissions was analyzed. The Spearman correlation coefficient between carbon intensity and marginal cost along the German merit order substantially increased with increasing carbon price. The coefficients were -0.13 for the 2019 carbon price of 24.9 €/t, 0 for 42.6 €/t, and 0.4 for 100.0 €/t. Therefore, with adequate carbon prices, PBDR can be an effective tool for both economical and environmental improvement.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Machine learning-enhanced MPC for demand flexibility in small commercial buildings: An experimental study

Small- and medium-sized commercial buildings (SMCBs) represent the majority of U.S. commercial building stock and a significant share of peak electricity demand, yet they often lack centralized building automation systems, representing a significant untapped resource for urban energy management. This infrastructure gap makes advanced control implementation challenging, limiting the potential for widespread demand flexibility. Model Predictive Control (MPC) has shown strong potential for load shifting, peak demand reduction, and cost savings, but its effectiveness is hindered by unmeasured disturbances such as internal heat gains. This paper presents a Hybrid MPC framework that integrates a physics-based gray-box building thermal model, identified using a lumped disturbance (LD) approach, with a machine learning (ML) model for forecasting unmeasured disturbances. The hybrid approach is designed for buildings with multiple individually controlled heat pump and thermostat pairs, common in SMCBs, and aims to optimize coordinated scheduling of multiple heat pumps under dynamic electricity pricing while respecting comfort constraints. The methodology is validated through both simulations of case study buildings and experimental studies at a highly-instrumented test facility. Simulation results show that the Hybrid MPC achieves substantial load shifting and peak demand reduction, approaching the performance of an ideal MPC with perfect disturbance knowledge, and outperforming a conventional MPC without disturbance forecasting. In experiments, the Hybrid MPC reduced daily HVAC energy costs by 8.7%, peak-price time load (load shifting) by 41.7%, and peak demand by 29.2% compared to baseline control, demonstrating comparable benefits to the 11.6% cost savings, 42.9% load shifting, and 23.2% peak reduction of the ideal MPC. These results demonstrate that the proposed hybrid modeling approach can significantly improve MPC performance in real-world SMCB applications without requiring additional disturbance measurements.

Demand Flexibility↗

Quantifying the Value of Grid-Interactive Efficient Buildings through Field Study: Preprint

Quantifying the annual energy impacts of efficient technologies in commercial buildings has been well established by the building science field. As we move toward enabling grid-interactive efficient buildings (GEB) targeting flexible building operation and carbon reduction, quantification methods to evaluate time-sensitive peak load and emissions impact are much less defined. A number of national laboratories are working to field validate four different GEB software solutions that provide the capability to control multiple building end-use systems in multiple load flexibility modes (i.e., energy efficiency, load shed, load shift, and possible load modulation at the second to sub-second level). To guide the laboratory leads in effective measurement and verification (M&V) practices, two of the laboratories collaborated to define metrics to quantify the impacts of flexible load control on building demand, utility costs, carbon emissions, facility management, and occupant comfort. This paper summarizes the proposed metrics to quantify peak load and emission impacts in the field, decision parameters, approaches to accurately conduct M&V, lessons learned, and outstanding needs and next steps.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗

Decarbonizing the grid: Utilizing demand-side flexibility for carbon emission reduction through locational marginal emissions in distribution networks

Decarbonization of the electric grid has become an important world-wide priority and is actively happening in many ways by introducing innovations and new technologies from the generation sectors to the demand sectors. In particular, one promising pathway toward such net-zero carbon emissions is to utilize the demand-side flexibility with the increasing number of flexible loads in distribution networks. In this paper, we explore a load shifting strategy with the emerging concept of location marginal emissions (LMEs) to reduce carbon emissions. LMEs measure the impact of carbon emissions including the locational aspect in more granular way and thus provide a novel mechanism for the system operator and load aggregators to design the LME-based load shifting strategy, which can efficiently guide consumers and thus adjust their consumption behaviors. Simulation case studies on the IEEE test networks are performed to validate the capability of the proposed load shifting method to reduce carbon emissions. We also compare this with other relevant strategies to discuss multiple scenarios and corresponding results. Finally, while each provides a different level of flexibility, all the explored strategies tested have led to solutions that have lower carbon emissions, indicating the great potential of demand-side flexibility in reducing carbon emissions for future distribution networks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Field Performance of Commercial Building Load Flexibility Using Model Predictive Control

Model Predictive Control (MPC) applied to buildings is starting to see some commercial adoption by companies. However, it is hard to estimate if relative energy cost savings are enough to justify the cost of MPC implementation with few reported demonstrations. In small commercial and residential buildings, a one size-fits-all solution can help reduce implementation costs, while in very large buildings or districts the potential energy cost savings magnitude can cover more tailored solutions. This estimation becomes harder for medium to large commercial buildings, where a one-size-fits-all solution cannot be adopted and potential energy cost savings might not be sufficient to cover a tailored solution. Therefore, value propositions in addition to energy efficiency alone can make MPC technology more attractive through additional energy cost savings. One such value proposition is load shifting in response to dynamic electricity prices. On this aspect, MPC is a key technology to unlock building thermal mass for energy flexibility in response to electric grid conditions. This study shows the experimental results of MPC control of an office building in Berkeley, where different dynamic electricity price profiles were used in the MPC objective function to shift the building load and to calculate hypothetical electricity costs. Results show potential 50% cost savings with respect to the existing controller with the dynamic price scenario.

Zanetti, Ettore↗

Quantifying the Value of Grid-Interactive Efficient Buildings through Field Study

Quantifying the annual energy impacts of efficient technologies in commercial buildings has been well established by the building science field. As we move toward enabling grid-interactive efficient buildings (GEB) targeting flexible building operation and carbon reduction, quantification methods to evaluate time-sensitive peak load and emissions impact are much less defined. A number of national laboratories are working to field validate four different GEB software solutions that provide the capability to control multiple building end-use systems in multiple load flexibility modes (i.e., energy efficiency, load shed, load shift, and possible load modulation at the second to sub-second level). To guide the laboratory leads in effective measurement and verification (M&V) practices, two of the laboratories collaborated to define metrics to quantify the impacts of flexible load control on building demand, utility costs, carbon emissions, facility management, and occupant comfort. This paper summarizes the proposed metrics to quantify peak load and emission impacts in the field, decision parameters, approaches to accurately conduct M&V, lessons learned, and outstanding needs and next steps.

Langner, Rois↗

Latent heat thermal energy storage performance maps enabling fast & accurate building energy simulations

Thermal energy storage (TES) using phase change materials (PCMs) has gained attention as an effective approach to manage energy demand fluctuations and shift peak building loads. PCM embedded heat exchangers (PCM-HXs) offer high energy storage density and low temperature variation during phase change, being suitable for load-shifting applications. However, this component is typically evaluated using computationally expensive methods, which present significant challenges when the ultimate goal is to assess the performance of PCM-HX integrated thermal energy storage systems in the full building context. In this paper, we present a methodology to generate highly accurate and computationally efficient PCM-HX performance maps which can be easily integrated into building energy simulation tools to analyze the feasibility of space conditioning systems with latent heat PCM-based TES. The performance maps are generated using a computationally efficient PCM-HX simulation tool based on a Generalized Resistance-Capacitance Model (GRCM) which can simulate arbitrary PCM-HXs with high accuracy and significantly less computational effort compared to full CFD simulations. The methodology was verified for a case study considering a 5-ton (~17.5 kW) air-to-water heat pump-thermal energy storage system (HP-TES), which was co-simulated in Modelica for a DOE prototype small-office building in Vienna, Austria, using Spawn of EnergyPlus™. The TES performance maps provided accurate predictions of PCM-HX behavior when used as Modelica component, with deviations within 2-4% while also achieving at least 103 computational time reduction. Leveraging this faster prediction capability, four PCMs with different melting temperatures for cooling (12°C, 16°C) and heating (31°C, 36°C) were assessed to investigate their impact on system performance. This work highlights the importance of robust PCM-HX models for efficient and high-fidelity building-level simulations, presenting new opportunities for advanced control strategy development and parametric analysis of TES configurations in a computationally efficient manner

Modelica Building Simulations↗

Accuracy of hourly energy predictions for demand flexibility applications

Decarbonization goals in the United States electricity sector are increasing the levels of renewable energy generation in the electricity supply system, and are driving increased attention to building electrification, which will increase the magnitude and shift the timing of the electricity system peak. These changes are motivating new approaches to coordinate building electricity demand with low-carbon renewable generation, elevating the importance of demand flexibility (DF) in buildings and the need to quantify the temporal impacts of DF. In this paper, we first characterize the hourly predictive accuracy of six commonly used baseline models in an application context of quantifying building-level load shift. Our analysis revealed insights such as hours of the day (afternoons), periods of the week (weekends), and seasons (summer) that were predicted with more accuracy than other time periods. In addition, the analysis showed tendencies toward overprediction or underprediction of load. Secondly, we provide the first published investigation of baseline erosion from repeated dispatch of building load shifting. We observed that as the baseline period is pushed back further from the prediction day, the distribution of errors across baseline model predictions increases, with notable inflection points near the three-week erosion point for two of the three models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Scaling Up: Demonstrating Risk Reduction and Cost Compression for Commercial Heat Pump Water Heaters - CRADA 625 (Abstract)

Commercial heat pump water heater (CHPWH) systems significantly decarbonize the commercial and multifamily sectors by eliminating the reliance on gas-fired water heating. CHPWH systems are also well suited to include load shift controls that enable load-up and shed commands for supporting grid reliability and time-of-use pricing structure. However, they have not had wide adoption due to factors including price, complexity, and perceived risk. Although CHPWHs have been available in the US for decades, they have not made significant market gains in part because the systems have required significant and costly engineering design expertise and proved lackluster performance. Successful widespread market adoption requires a different approach; a shift from the current custom specialized expertise project design and installation to a repeatable approach that requires little specialized knowledge or expertise and can deliver persistent performance. Using this type of holistic systems approach requires effectively integrating four CHPWH system key components: primary air-to-water heat pumps; primary thermal storage tanks, a temperature maintenance system, and a control system which has capabilities to manage the primary heat pump cycles, any back-up, supplemental, or temperature maintenance heating, alarms, and grid connectivity allowing for demand response (DR), and/or load shifting. The project team has developed and will implement a suite of tools to support faster, less expensive, and more reliable field installations of CHPWH technology and with the resulting data used to further improve the tool set. These tools include: (1) A tool for optimizing system size and costs. (2) A tool that predicts annual energy use and overall system efficiency. (3) The Advanced Water Heater Specification (AWHS 8.0) defining the components of a full CHPWH system addressing performance requirements by climate zone. (4) The Qualified Products List: (QPL) of approved products that meet the specifications requirements. (5) Training materials including online on-demand modules, instructor-led training, and virtual interactive video tours of CHPWH installations in multifamily buildings. Demonstration site identification in low-income buildings in underserved communities is currently underway. Preliminarily, the team anticipates having three demonstrations in the Pacific Northwest and three in the Northeast for a total of six sites. After the demonstration sites are finalized, and M&V instrumentation installations are complete, the team will gather performance data and confirm whether the CHPWH systems perform as predicted and use the data to improve the existing tools.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Electrification Options for Multi-Family Water Heating in Cold Climates - Final Report

In multi-family buildings, large water storage tanks in centralized domestic hot water (DHW) systems can serve as thermal energy storage (TES) batteries to mitigate grid impact. These systems offer demand shift and efficiency benefits, significantly reducing peak power consumption, particularly in cold climates. This study evaluates the load-shifting benefits of a centralized heat pump water heater (HPWH) system equipped with a CO2 heat pump in multi-family buildings through simulation. The heat pump system and water storage tank are sized using design-day sizing. A finite-element-based stratified tank model and CO2 heat pump performance map from a commercial DHW product are used. Annual simulations are conducted to assess the benefits of the centralized DHW system for energy efficiency improvements, load shifting, and emission reductions. These simulations incorporate utility tariffs and marginal grid emission data from Los Angeles and Chicago. In Los Angeles, using a water tank as a thermal battery achieves 7.4% utility cost savings and 10.2% emission reduction. In Chicago, compared to HPWH conventional operation without preheating, TES-enabled central HPWH provides 15% utility cost savings and 13% emission reduction. The case study demonstrates that the demand reduction potential of central CO2 HPWHs is significant in cold climate regions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Grid-connected heat pump water heater benefits for low-income households in the Southeastern United States

Energy efficient heat pump water heaters (HPWHs) reduce customer energy costs; grid-connected controls further increase HPWH value by enabling energy storage and shifting demand to cheaper, off-peak periods. However, a HPWH’s first cost premium can put it out of reach for low-income customers. This paper will present results of an ongoing study exploring the benefits of load shifting HPWHs for 24 low-income households, aiming to support increased product deployment in this demographic. The study uses EcoPort modules to shift HPWH load for a study sample consisting primarily of low-income adults and seniors in North Carolina. Building upon prior studies, the study’s HPWH operating schedules are designed to maximize shifted energy and minimize participant electricity costs based on local time-of-use rates. The study documents load profiles of grid-connected HPWHs in a less-studied demographic group and explores how certain groups may have particularly flexible loads due to their unique usage profiles. The results are relevant for HPWH product performance in the Southeast. The study will quantify HPWH load shifting performance during North Carolina’s hot summer, temperate shoulder, and occasionally sub-freezing winter seasons, and the study includes installations in conditioned, semi-conditioned, and unconditioned spaces. Finally, study results will also explore how controlled HPWHs can provide value to the regional grid by reducing seasonal peaks via demand response. Lessons learned include HPWH acceptance for low-income and senior users, best practices for maintaining HPWH connectivity among users without prior product experience, and strategies to leverage remote monitoring to ensure optimal operation and enhance HPWH reliability.

Urigwe, Daniela↗

Influence of Thermal Energy Storage Integration Strategy on System Performance and Refrigerant Charge for Small-Scale R290 Heat Pumps

As small-scale (2.5 – 5 kW) heat pumps across the US increase straining the grid during peak hours, integrated heat pump thermal energy storage (HP-TES) systems can assist by load shifting. However, challenges arise when assembling these systems, as direct refrigerant-phase change material (PCM) heat exchangers (HXs) increase refrigerant charge, and indirect secondary loops degrade HP-TES performance. In this work, direct and indirect HP-TES configurations were sized using commercially available HXs to compare the available load shift time and performance at similar refrigerant charges. For the same indirect 5 kW HP-TES refrigerant charge, directly HP-TES can operate up to 72 and 145 minutes in cooling and heating modes, respectively, using small diameter tubes. Using Modelica, COP increases for direct and indirect HP-TES were 22% and 9%, respectively, given the parasitic losses in the indirect HP-TES. Overall, small-scale direct HP-TES can have comparable refrigerant charges with indirect HP-TES maximizing COP improvements.

25 ENERGY STORAGE↗

Salt Hydrate Eutectic Thermal Energy Storage for Building Thermal Regulation (Final Technical Report)

Thermal energy storage is anticipated to play an important role in developing the power grid of the future - a power grid that meets increasing demands of users, is resistant to disruptions, but also allows for greater penetration of renewable resources. Specifically, thermal energy storage materials can be integrated into HVAC systems and building envelopes, where they can be used to shift power demands for building climate control from periods of peak demand to periods of low demand. Phase change materials (PCMs) are compelling as low-cost, high energy density thermal energy storage materials for building thermal management. However, there is a lack of high performance low-cost PCMs within the specific temperature ranges which would most effectively allow for power load shifting. Inorganic salt hydrates represent a promising class of PCMs, but their inherent limitations cause them to be currently unavailable for reliable building applications. The overarching goals of this research effort are to: 1) Discover low-cost, high volumetric density salt hydrate eutectic PCMs to store low-quality heat (10 to 40 °C); 2) Introduce a high thermal conductivity matrix to reduce the time constant for energy storage to ~0.1 to 1 hr, incorporate nucleation catalysts to decrease undercooling, and utilize microencapsulation and shape stabilization approaches, minimizing moisture loss/gain, mitigating phase separation, and maintaining stable melting behavior over the lifetime of the compounds; 3) Evaluate the impact these systems have on peak load shifting, and the potential for overall energy savings under different climatic scenarios and building configurations. These goals will be achieved by an integrated research program consisting of six cohesive research subtasks: 1) Materials discovery of eutectic salt hydrate PCMs by using computationally predicted thermodynamic equilibria, coupled with high-throughput experimental validation, 2) Rapid experimental screening of nucleation catalysts identified through robust computational databases, 3) Embedding salt hydrate PCM into a low cost and scalable high conductivity matrix, 4) Microencapsulation of salt hydrate microspheres using hybrid inorganic-polymer microencapsulation approach, 5) Shape stabilization by thermoreversible salt hydrate salogels, and 6) Analysis of end-use using thermal simulations, and characterization of mock-up energy storage finished components.

25 ENERGY STORAGE↗