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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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This fact sheet explores how to correctly map building loads to rooftop unit (RTU) capacity to help minimize capital investment and ongoing operational costs while maximizing thermal comfort.
The use of stationary electrochemical energy storage systems utilizing lithium-ion batteries has increased rapidly as the production scale and price for lithium-ion batteries has decreased. These energy storage systems are crucial for maintaining grid resiliency, especially for grids operating with high penetration of renewable energy generation assets or for with a variety of distributed energy generation and storage systems. One challenging factor for the development of battery energy storage systems is estimating the proper sizing, in terms of both power and energy, that minimizes total costs over the lifetime of the systems; this calculation is difficult in simple cases, where a battery is costed independently, but is extremely challenging when building loads and electrical generation by photovoltaic resources are also considered. REopt is a techoeconomic optimization tool developed by NREL to address these challenges. Previously, battery degradation has been priced by simply assuming a 10-year replacement schedule for battery systems. However, this does not account for varying degradation trends observed across real-world batteries, or allow for batteries to be operated in a degradation-aware manner that optimizes battery dispatch based on operating costs. This work incorporates a battery life model into REopt. This battery life model is simple, so that it may be solvable within the constrains of a mixed-integer linear optimization problem, but is fit to accelerated aging data recorded in the lab. To achieve the best possible accuracy for lifetime estimates given these constraints, parameters for the battery life model in REopt are estimated by fitting 20-year simulations of battery life after identifying state-space battery degradation model from accelerated aging data. Comparisons of battery life predicted in REopt and from the state-space battery degradation model to ensure validity of lifetime estimates made by REopt. Battery life and cost is optimized by controlling three decision to minimize system life cost: battery sizing, daily state-of-charge, and daily energy-throughput. The cost of battery degradation as a function of these control variables is then estimated assuming two possible maintenance strategies: replacement, where the entire battery system is replaced if cell reach an end-of-life capacity threshold; and augmentation, which establishes a fund to pay for continual purchase of new batteries to maintain the initial energy capacity of the system. These two strategies offer conservative (for replacement) and optimistic (for augmentation) bounds for total system cost. The degradation cost incurred by these strategies is then used to control battery dispatch decisions, operating the battery in a degradation-aware manner that maximizes battery lifetime while also providing energy when favorable. Because the mixed-integer linear program has perfect foresight of future energy needs, batteries with degradation costs are always operated using 'just-in-time' charging, which is unrealistic, as no energy is left in the storage system to perform other energy services or to serve as emergency back-up power. To combat this, an inequality constraint on the average annual state-of-charge is imposed, and the sensitivity of system cost to average stored energy, e.g., the cost of system resiliency, can be quantified. Analysis of results has several conclusions, for instance, oversizing of battery storage systems is not a cost burden when battery storage is an optimal solution, as any additional battery capacity can simply be utilized to avoid costs of purchasing energy from a utility.
The Distribution System Operator with Transactive (DSO+T) study conducted a large-scale simulation of over 60,000 customers in a region the size of Texas to demonstrate the effective coordination of distributed energy resources (DERs) in commercial and residential buildings. The integrated simulation included both the bulk (wholesale generation and transmission) and distribution systems. The DERs (including batteries, electric vehicles, air conditioning, and water heaters) participated in a transactive energy retail market that was integrated into an existing competitive wholesale market. The engineering and economic performance of the resulting demand flexibility was evaluated over annual simulations for both moderate and high renewable generation scenarios. A detailed parametric cost model was developed to enable detailed economic analysis of key stakeholders. In addition, fixed and dynamic customer tariffs were designed and applied to the customer population. This allowed the impact on annual customer bills to be analyzed for various building types (residential versus commercial; single- versus multi-family). This paper presents results showing the relative flexibility of batteries, electric vehicles, and building loads throughout the year and under different renewable scenarios. This feeds a detailed breakdown of the impact this flexibility has on the operating costs of the grid and the resulting net economic benefit. Finally, the study showed that practically all customer classes (including non-participating customers) save money under the proposed demand flexibility scheme. The study found overall net annual economic savings of $3.3-5.0B for a region the size of Texas equating to average customer bill savings of 10-16%.
Heat pumps have a significant impact on the climate, and numerous studies exist discussing the environmental impact of heat pumps quantitatively. Life Cycle Climate Performance (LCCP) is a widely accepted metric and can evaluate the carbon footprint of heat pumps from cradle to grave. LCCP calculation typically requires inputs like annual energy consumption, the material used, refrigerant type, and charge level, which are usually the intermediate or final results from heat pump simulation tools. Thus, a platform linking the heat pump simulation software, local weather conditions, and LCCP evaluation tool can simplify the heat pump design process, especially for complicated systems. This paper presents an LCCP evaluation tool accessing the outputs from the DOE Heat Pump Design Model and the building loads and temperature bin data from AHRI standard 210/240. As for case studies, this paper investigates the LCCP values and life cycle electricity costs (LCEC) of a given cooling-based heat pump with R410A in five typical US cities. In conclusion, this cooling-based heat pump has the highest LCCP value at Chicago and lowest LCCP value at Seattle. In contrast, the heat pump has the highest LCEC at Seattle and the lowest LCEC at Miami. As a future work, a more comprehensive carbon emission and cost analysis of heat pump systems will be conducted through all major cities around the world.
The U.S. has set the climate goal to achieve net-zero greenhouse gas emissions by 2050. District-scale solutions, which include scale-specific opportunities for energy and emissions savings, can be investigated and implemented to help accelerate decarbonization and progress toward this goal. However, there is currently a lack of district-scale models of buildings and community energy systems that can be used to evaluate potential district-scale technologies and strategies across a range of representative community types. This initial work aims to define and develop prototype district models that can be adapted to support the planning, design, and operation of buildings and energy systems in districts considering the complexity and interactions of diverse building loads, weather impacts, distributed energy resources (e.g., PV, EV, electric and thermal energy storage), electric and thermal grid systems, and pricing signals. An overall workflow for developing these prototype district models is established. Stakeholders and potential users of the prototype district models provided technical feedback. The specifications of the selected high priority districts were defined and documented in a scorecard format. An example prototype district model was implemented with the URBANoptTM platform workflows. A case study was performed to demonstrate the model application.
Managed under the the U.S. Department of Energy (DOE)-funded EVs@Scale Consortium, the High-Power Electric Vehicle Charging Hub Integration Platform (eCHIP) project aims to create an experimental platform for integration and control approaches in a direct current (DC), distribution-based high-power charging (HPC) system. The eCHIP project addresses the crucial need to design and validate efficient, low-cost, reliable, and interoperable solutions for a DC-coupled charging hub ("DC hub" for short). This report explains the design, development, and implementation process of an experimental platform for the DC hub. DC distribution holds significant potential for enhancing the operation of an HPC station architecture. However, there are challenges in establishing a DC hub, including interoperability, commoditization, distributed energy resource integration, stability, DC protection, and a lack of common system-level controllers. To address these challenges, a testing setup is required that accommodates commercial off-the-shelf (COTS) products, as well as novel, in-house designed solutions, to evaluate different use cases at rated power and voltage levels. The developed proof-of-concept charging platform and open-source SEMS allow the development and testing of various controllers and chargers from different vendors. The DC hub platform integrated EVs, an ESS, a PV system, and building load to demonstrate the flexibility of the platform. The open-source SEMS controlled the devices within the hub using a rule-based implementation, realizing available standards.
In high ambient temperature (HAT) countries, summer temperatures exceed 35℃, degrading the performance of air-conditioning systems and straining power grids. In this paper, a feasibility analysis was conducted to investigate potential savings during the peak using latent Thermal Energy Storage (TES) at near room phase-change temperatures, replacing condensers, thus, minimizing temperature lifts. Weather data, building loads, and baseline air-conditioning systems data were gathered for Dubai. A transient vapor-compression model in Modelica was used to compare the COP, total power input and cooling capacity of the air-conditioner at peak hours when ambient temperatures range between 35 – 45 ⁰C versus the TES-Phase-Change Material (PCM) melting temperatures from 22 – 28⁰C. The results indicate that the TES-PCM can enhance the system COP at the peak by a factor 1.4 and 2 for during for outdoor temperatures of 35 – 40⁰C, and 40 – 45⁰C, respectively. Lower melting temperature PCMs were able to reduce the required power input by 30-50%, with more savings occurring at higher temperature days. On the other hand, higher temperature PCMs enhancements were minimal especially at outdoor ambient temperatures ranging between 35 – 40⁰C. Improvements to the cooling capacity range from 8 – 18 % for the outdoor temperature range of 35 – 45 ⁰C. An economic analysis was conducted to find the potential saving in utility costs for 30%, 60%, and 90% of the space cooling demands of Dubai, and find the trade-off points between utility savings and cost of PCM-TES implementation. If the peak loads are to be shifted by 6 hours daily, the percentage utility savings for the city is 18%. Using estimated costs of the PCM-TES, ranging from $200-500/kWh, the daily load shifting hours were estimated to range from 4 hours at the lowest cost systems to 2.5 hours at the highest costs.
The rapid adoption of electric vehicles (EVs) has resulted in significant new household electric loads that have the potential to change how energy costs are incurred by homeowners and the landscape of utility operations and energy infrastructure. Whereas adoption patterns and magnitudes of residential building and EV charging loads are influenced by distinct factors, the loads themselves are tightly coupled with the behavior of the individual occupants and EV owners.
Demand response (DR) of building HVAC load can provide crucial demand-side flexibility for the future smart grid. Compared to direct load control, price-based control can respect the customers’ autonomy and privacy. However, it is challenging for price-based control to attain provable performance guarantees under future uncertainty. In this paper, we propose a framework for a utility to perform price-based control of flexible building load within the utility’s service area, in order to attain competitive performance guarantees in terms of controlling the system peak demand under future uncertainty. By adopting a two-step approach, our online price-based control solution can attain a provable competitive ratio for all possible realizations within a given uncertainty set. Simulation experiments demonstrate that, with a robustification procedure, our solution can perform well not only for worst-case inputs, but also for average-case inputs.
Increasing deployment of advanced sensing, controls, and communication infrastructure enables buildings to provide services to the power grid, leading to the concept of grid-interactive efficient buildings. Since occupant activities and preferences primarily drive the availability and operational flexibility of building devices, there is a critical need to develop occupant-centric approaches that prioritize devices for providing grid services, while maintaining the desired end-use quality of service. In this paper, we present a decision-making framework that facilitates a building owner/operator to effectively prioritize loads for curtailment service under uncertainties, while minimizing any adverse impact on the occupants. The proposed framework uses a stochastic (Markov) model to represent the probabilistic behavior of device usage from power consumption data, and a load prioritization algorithm that dynamically ranks building loads using a stochastic multi-criteria decision-making algorithm. The proposed load prioritization framework is illustrated via numerical simulations in a residential building use-case, including plug-loads, air-conditioners, and plug-in electric vehicle chargers, in the context of load curtailment as a grid service. Suitable metrics are proposed to evaluate the closed-loop performance of the proposed prioritization algorithm under various scenarios and design choices. Scalability of the proposed algorithm is established via computational analysis, while time-series plots are used for intuitive explanation of the ranking choices.
Small and medium-sized commercial buildings (SMCB) are significant demand response resources, and it is important to develop grid-responsive control algorithms that exploit those resources and create financial benefits for building owners and HVAC service providers. Furthermore, unlike large-sized commercial buildings, there is an opportunity to have universally applicable control solutions for many SMCBs since those buildings have a consistent HVAC system configuration: SMCBs are commonly served by multiple-staged air conditioning units controlled by their own thermostats. Despite the demand response potential and scalability, however, very few control solutions are available for SMCBs. Typical model predictive control (MPC) and heuristic control approaches for cooling load shifting that lower thermostat setpoints before an electric price jump are suitable mainly for large-sized commercial buildings where a continuous capacity modulation is possible, e.g., via dampers in variable air volume terminal units. However, those approaches can cause undesired, high peaks for SMCBs due to the nature of ON/OFF unit staging and narrow thermostat deadbands. This could discourage the use of advanced grid-responsive controls for SMCBs due to the concern of high demand charges, and has to be resolved. This paper presents a MPC solution that overcomes this challenge. It has a hierarchical MPC structure where an upper level MPC is responsible for electrical load shifting in response to an electric price signal while a lower level MPC is responsible for coordinating compressor stages to eliminate unnecessary peaks and follows the setpoints determined by the upper level MPC. In this work, two one-month, comprehensive laboratory tests have been carried out to demonstrate load shifting and cost savings for the algorithm. Interesting trade-offs between energy efficiency and load flexibility were observed and are discussed, and lessons learned for applying MPCs for SMCBs are also presented.
Precooling is a recognized technique for reducing cooling energy in buildings during peak hours by shifting load to off-peak hours. This technique is particularly effective in buildings with high thermal mass, because of their large thermal energy storage capacity, and in commercial buildings due to their variable electricity pricing based on time-of-use rates. Precooling in residential buildings has been a matter of limited interest in the past because of their low thermal mass and typically uniform electricity pricing rate. While previous studies on precooling primarily focused on cost savings, an important aspect of precooling is the thermal load modulation, which could be very effective in managing peak demand in lightweight residential buildings integrated with thermal energy storage systems. In this study, we examine different precooling strategies to manage the heat gains in lightweight building walls integrated with phase-change materials. We create nine different precooling profiles by controlling the interior temperature, and then evaluate the influence of the precooling profiles on four key building energy performance parameters: total heat gain, peak heat gain, maximum heat gain during peak hours, and time at which peak occurs. To thoroughly understand the fundamental physics, we first consider hypothetical climates and obtain the optimal precooling strategy required to achieve maximum peak shedding and shifting while minimizing the total heat gains. We then extend the model to Baltimore, Maryland, and estimate the benefits of the optimized precooling strategy under real conditions. The optimal precooling strategy proposed in this study can shift the peak heat gain by up to 14 hours, thereby reducing the heat gain during peak period by up to 95%, at the expense of a 23% increase in the total heat gains.
An overview of using flexible load control in residential buildings to support grid reliability, presented at the IEEE PES T&D Conference and Exposition in May 2024.
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Method of simulating probabilistic windloads on building developed. Numerical results of simulation used to assess reliability of building and risk associated with tendencies of large gusts or high steady winds to cause building to sway, buckle, and/or overturn. Using method to analyze proposed design in iterative design cycle, building designed for specified reliability.
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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.
This work investigates the energy savings and load flexibility capacity of phase change material (PCM) integrated into building envelope under a future energy generation scenario, where 80% of the energy load comes from renewable sources. Using community-scale modeling, we first determine the net thermal storage required to fully manage the demand variability at a utility grid, and then using whole-building simulations we optimize operating parameters - such as PCM thickness, latent heat, interior setpoint profile, PCM distribution, and heat transfer coefficient - to determine the optimal conditions required for maximum energy reduction and load flexibility without compromising occupants' thermal comfort. The optimal PCM-integrated envelope proposed in this study can provide annual load flexibility up to 33.6% and annual energy savings up to 10.8% in a lightweight residential building located in Baltimore, MD. This study is relevant given the increasing contributions of renewable energy in the total energy generation mix, leading to a significant time-imbalance between peak energy demand and peak energy production. While thermal energy storage using PCM is a recognized technique, there is no known prior study dedicated to examining the thermal performance of PCM-integrated envelope under future energy generation scenarios. This study bridges the research gap by investigating a practical way to implement PCMs in the buildings and maximize the energy efficiency as well as load flexibility related benefits.