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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

An empirical study of moving horizon closed-loop demand response scheduling

The potential of electricity-intensive chemical plants to engage in demand response (DR) initiatives in support of power grid operations has been the subject of many conceptual studies. In this work, using an industrially-relevant model of an air separation unit, we undertake an extensive simulation study of moving horizon (MH) rescheduling approaches, where we "close the scheduling loop" based on updated information regarding disturbances such as changes in electricity prices, ambient conditions and chemical product demand. Our study produces several unexpected findings regarding the non-periodic nature of rescheduling solutions, the impact of the accuracy of disturbance forecasts on the economics of DR scheduling, and on the interplay of simultaneously dealing with fluctuations on both the supply side (i.e., electricity prices) and the product demand side of the plant. Furthermore, we posit that the latter fluctuations pose significant limitations to the potential of a chemical plant to engage in DR.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Optimization Framework to Assess the Demand Response Capacity of a Water Distribution System

As large electricity consumers, water distribution system (WDS) pumping stations have the potential to become meaningful participants in demand response (DR) programs. The authors propose an optimization framework for assessing the DR capacity of a WDS and identifying the optimal bidding strategy for maximizing WDS revenue in the DR spot market. The proposed mixed integer linear programming (MILP) model overcomes computational constraints of previous DR optimization models by adopting a preprocessing procedure to minimize the number of binary variables and implementing a convex relaxation technique to linearize the hydraulic equations. The proposed MILP model also explicitly accounts for varying levels of risk tolerance of WDS operators by varying the recovery period over which pumping returns to business-as-usual operation. The optimization framework is implemented on a skeletonized 48-node WDS model that includes 7 pumps, 6 tanks, and 39 pipes. Using a simulated DR event and water consumption profile, the authors derive the optimal DR supply curves (i.e., compensation price versus load curtailment quantity) and revenue potential of the WDS under six scenarios for DR participation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Demand response roadmap for the New Jersey Board of Public Utilities [Slides]

The New Jersey Board of Public Utilities (BPU) requested technical assistance from Berkeley Lab to inform its strategy for advancing demand response through its demand-side program planning process. Berkeley Lab developed this roadmap to delineate milestones for the portfolio's development and to identify the utility investments and BPU actions that achieve those milestones.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Distributionally Robust Bilevel Optimization Model for Distribution Network With Demand Response Under Uncertain Renewables Using Wasserstein Metrics

Here, we consider a distribution network integrating demand response (DR) participants in the presence of uncertain renewable suppliers and outdoor temperatures. A bilevel optimization model is proposed to capture the intricate dynamics between price-incentivized DR participants and distribution system operations, including energy procurement and active/reactive power flows. The model is formulated as a distributional robust bilevel optimization using Wasserstein metrics. We show favorable data-driven properties including out-of-sample guarantee and asymptotic consistency. Furthermore, we present a tractable mixed-integer linear programming reformulation and characterize the worst-case distribution. Computational experiments are conducted on a modified 33-bus system. Our findings underscore the efficacy of the pricing strategies derived from the proposed bilevel optimization model. These strategies not only effectively manage DR participants' behavior but also bring equity considerations among households with various characteristics to light. The results contribute to a deeper understanding of the interplay between distribution system operators and DR participants.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A Scalable and Distributed Algorithm for Managing Residential Demand Response Programs using Alternating Direction Method of Multipliers (ADMM)

For effective engagement of residential demand-side resources and to ensure efficient operation of distribution networks, we must overcome the challenges of controlling and coordinating residential components and devices at scale. To overcome this challenge, we present a distributed and scalable algorithm with a three-level hierarchical information exchange architecture for managing the residential demand response programs. First, a centralized optimization model is formulated to maximize community social welfare. Then, this centralized model is solved in a distributed manner with alternating direction method of multipliers (ADMM) by decomposing the original problem to utility-level and house-level problems. The information exchange between the different layers is limited to the primary residual (i.e., supply-demand mismatch), Lagrangian multipliers, and the total load of each house to protect each customer’s privacy. Simulation studies are performed on the IEEE 33 bus test system with 605 residential customers. The results demonstrate that the proposed approach can save customers’ electricity bills and reduce the peak load at the utility level without much affecting customers’ comfort and privacy. Finally, a quantitative comparison of the distributed and centralized algorithms shows the scalability advantage of the proposed ADMM-based approach, and it gives benchmarking results with achievable value for future research works.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Value-based Insights from the Implementation of Hierarchical Control for Energy Savings and Demand Response in Residential Premises

As the adoption of distributed energy resources and electric vehicles at residential customer premises increases exponentially, behind-the-meter assets can be utilized to achieve energy cost reduction and demand response through coordination and control strategies. A hierarchical control architecture from the utility headend to residential premises is implemented to attain these objectives. This paper extracts the values from the development, implementation, and deployment of that control hierarchy. The development of the control philosophy is built upon the existing advanced metering infrastructure, communication protocols, and industry-compatible application programming interfaces. Results are presented visually with analytical insights by utilizing the data from hardware-in-the-loop testing and simulation analysis out of the collected data from the field.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Intelligent industrial demand response to increase grid flexibility and reliability: A review

The rapid transition toward renewable energy has introduced challenges in grid stability due to the intermittency of non-dispatchable sources like solar and wind. Industrial Demand Response (IDR) offers a promising, cost-effective solution that adjusts energy consumption patterns to align with supply, increases renewable utilization, and reduces costs. This review provides an updated analysis of IDR, sorting technologies into five categories: energy storage, scheduled energy usage, operational flexibility, on-site generation, and intelligent operations. Energy storage solutions, while requiring little flexibility, often have the longest payback periods. While slightly better, on-site generation also has longer payback periods, ranging from 5 to 20 years or more. Scheduled energy usage, operational flexibility, and intelligent operations allow significant peak reduction at lower capital costs but require greater flexibility. While 15–20 % peak reduction is within the range of all five categories, scheduled energy use and on-site energy generation are shown to have reductions of up to 70–80 % in select scenarios. Combining multiple IDR strategies from these five categories maximizes both financial and operational benefits. Synergistic approaches are shown to enhance grid stability while reducing costs. As the grid evolves, IDR will enable a more flexible, renewable-powered future that will benefit industrial facilities and the broader energy system.

Demand flexibility↗
DOE OSTI2020

Modeling the Interaction Between Energy Efficiency and Demand Response on Regional Grid Scales: Preprint

With increasing penetration of intermittent renewable generation at grid and distributed scales, flexible building loads can provide significant system value and support the evolving needs of the grid. The growing value of load flexibility may complicate the traditional separation between energy efficiency (EE) and demand response (DR). EE measures may compete in some cases with a building’s DR capabilities but complement one another in other cases. EE can also increase or decrease the need for DR at the system level and change the availability of DR to meet system needs. In this study we present a bottom-up approach to modeling interactive effects between EE and DR in buildings within two regions of the US electricity grid. From a library of building simulation models for different buildings and climates, we synthesize system-level demand profiles and the impacts of potential future EE portfolios. Coupling the underlying building models with a database of DR-enabling technologies, we then compute the quantity of DR that can be delivered in each scenario. The results show that EE and DR interactions are largely driven by the timing of EE savings that are measure-specific and the coincidence with system peak demand that is region-specific. We also find that perspective of the impacts matters – for instance that some EE measures reduce the system need for DR but also reduce the DR potential. Our results imply that utility EE and DR programs developed without considering interactive effects may lead to increased grid-management challenges over the long term.

buildings↗
DOE OSTI2022

Assessing the Interactive Impacts of Energy Efficiency and Demand Response on Power System Costs and Emissions

Utilities are increasingly interested in integrating energy efficiency (EE) and demand response (DR) measures and technologies (as well as other distributed energy resources) as a strategic approach to improve their cost-effectiveness and performance. However, the specific EE and DR features that may be best integrated, the interplay between changing EE and DR resource potential, and resulting utility system impacts are not well understood. We quantify the impacts of EE and DR in isolation and in combination on bulk power system costs and emissions based on changes in generation expansion, transmission expansion, and dispatch patterns. The study methodology mimics utility resource planning and uses more novel techniques to select least-cost generation and transmission capacity from supply-and demand-side resources, as compared to more commonly used utility approaches that only consider supply-side resources. The results identify key EE and DR characteristics affecting each other’s power system value and the most valuable technologies and strategies that can be jointly deployed. We discuss implications for EE and DR cost-effectiveness frameworks, program design, and integrated resource planning.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Optimal Control of SOEC-Based Hydrogen Production Systems for Demand Response Using Deep Reinforcement Learning in Smart Grids

Solid oxide electrolysis cell (SOEC) hydrogen production technology can range in size from small, appliance-size equipment to large-scale, central production facilities that can be tied directly to renewable or non-greenhouse-gas-emitting forms of electricity production, making it an ideal resource for demand response (DR). The SOEC hydrogen production system is a complex integrated system that encompasses fluid dynamics, electrical dynamics, and electrochemical and thermal dynamics, all of which involve non-linearity and non-convexity. Proper control of the SOEC hydrogen production system is crucial to enable its participation in the DR program. Here, to overcome the difficulty of designing an explicit control law for such nonlinear systems with nonconvex optimization features in DR applications, deep reinforcement learning (DRL) is explored to achieve the optimal control of the SOEC system for DR participation. Specifically, a twin delayed deterministic policy gradient (TD3) control framework is applied to achieve optimal response performance during DR events by considering power tracking error and hydrogen production efficiency with a suitable reward function. Two case studies with grid connections for tracking different DR commands were investigated. The first case study involved operating conditions reaching the boundaries, while the second involved operating conditions within the boundaries. The results showed that the proposed DRL-based control for SOEC can track the DR signal in a timely manner while maintaining high energy efficiency.

08 HYDROGEN↗
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 OSTI2022

Learning-Based Demand Response in Grid Interactive Buildings via Gaussian Processes

This paper presents a predictive controller for a grid-interactive multi-zone building where the temperature dynamics are learned via Gaussian Process (GP) regression. We investigate the development of a learning-based predictive control with two main objectives: (i) continuously learn the temperature dynamics of the building based on data; and, (ii) use the learned dynamics to solve a multi-objective predictive control problem to guarantee occupants' comfort and energy efficiency during normal conditions and demand response events. We leverage the probabilistic non-parametric properties of GPs to estimate the (unknown) non-linear temperature dynamics of the building and to incorporate the uncertainty of those predictions in a multi-objective optimization problem. The GP-based predictive control is solved via a zero-order primal-dual projected-gradient algorithm. We evaluate numerically the performance of the proposed controller using a five-zone commercial building.

demand response↗
DOE OSTI2020

Custom Controls for Improved Demand Response from Heat Pump Water Heaters

As part of a Grid Modernization Laboratory Consortium (GMLC) project looking at defining grid services for a range of devices, NREL used a custom controller to investigate the ability of a HPWH to provide load add and load shed in hybrid and electric resistance mode using standard set point control. Based on those results, changes were made to the controls to increase the amount of grid services delivered, especially in the load-add case, and speed up the recovery from those events. The custom controls also include less common methods, such as duty cycling of the heat pump or elements. The improved controls led to an increase the amount of demand response that could be provided and enabled faster recovery. Results from the laboratory experiments will be presented, along with details about the custom controls that were implemented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Evaluating the demand response potential of ammonia plants

Demand-side management/demand response (DSM/DR) are key strategies for mitigating the inherent variability in electricity generation rates by renewable sources. This article represents—to our knowledge—the first foray into assessing the DR potential of ammonia plants. Ammonia plants are interesting candidates for DR initiatives because of their significant electricity use (for operating compressors driving the synthesis loop) and the ability to store the ammonia product relatively easily and safely. Our approach is based on formulating and solving an optimal DR scheduling problem for an ammonia plant while accounting for the process dynamics. To this end, we introduce a new Hammerstein–Wiener-inspired modeling framework based on injecting linear dynamics in a first-principles static nonlinear model of the process. The results are encouraging; for the cases considered, peak-time power consumption decreases between 3.57% and 7.40%, coupled with 1.39% to 3.70% reductions in operating cost.

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

The use of price-based demand response as a resource in electricity system planning

Utilities have conducted IRP for decades to consider both supply- and demand-side resources to meet bulk power system needs. More recently, an increasing number of states are requiring regulated utilities to file plans that identify distribution system needs, including DERs that can avoid or defer certain types of traditional utility investments cost-effectively. Price-based demand response (DR) is an underutilized resource that could substantially contribute to load flexibility. We find that evaluation of price-based DR as a solution to meet identified electricity system needs is uncommon at both planning processes. Where price-based DR is considered as a solution, methodologies are often deficient in the characterization and treatment of this resource. Based on our analysis of utility filings and state requirements, this brief provides recommendations for improving consideration of price-based DR in the context of long-term planning for bulk power and distribution systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Empirical Assessment of the Appliance-Level Load Shape and Demand Response Potential in India

Over the next 15 years, electricity demand from the key residential and commercial appliances is projected to be nearly 300 GW or ~65% of India’s total peak demand. The objective of this study is to characterize appliance level demand and temporal variation, and identify the overall DR potential in India. We use Bangalore Electricity Supply Company territory (peak load of 3,505 MW in 2016) as a case study, using actual one-minute resolution load data for 2,979 distribution feeders and a detailed load survey. Our results show that agricultural pumping and space cooling (residential, commercial, and industrial) are the main contributors to the peak demand – with shares of 23-27% and 14-23%, respectively. Both sectors have about 1,000 MW of DR potential – agricultural pumps offering load shifting service while space cooling offering shimmy service that is capable of dynamically adjusting to react to short-run ramps and grid disturbances. Residential electric water heaters contribute nearly 18% of the winter morning peak demand and can also offer about 500 MW in shimmy service. Overall, we find that shifting and shimmy services offer 1,199 MW and 1,511 MW total DR potential, respectively.

demand response↗
DOE OSTI2020

Optimizing HVAC Operations in Multi-unit Buildings for Grid Demand Response

The thermal inertia of buildings, along with the flexibility associated with thermostatically controlled loads (TCL) allows heating ventilation and cooling (HVAC) systems to be used for grid demand response (DR). In this work, we consider an HVAC system that serves multiple units in a residential building to meet their space heating requirements. We aim to determine the optimal power flow to each unit that minimizes the power costs incurred by the building's occupants while keeping in consideration their thermal comfort. The DR program is assumed to allow the building temperatures to deviate from the set-points up to a maximum limit. Despite the complex, non-linear structure of the problem, we show how the optimal solutions can be obtained efficiently using quadratic programming. Since HVAC systems can run on either electricity or natural gas, we study the efficacy of the DR regime for both hourly electricity prices and flat gas prices over the course of 24 hours. Finally, we run simulations to determine the optimal thermal power and the evolution of unit temperatures for various energy pricing schemes.

Naqvi, Syed A.↗
DOE OSTI2021

EFFORT (EFFectiveness Of Rate sTructure for enabling demand response)

This tool helps utilities evaluate potential time of use price-plans and find effective hours for price plan on peak periods and the potential price-elastic demand response. An optimization model can be used to find tiered prices for the time of use tariffs and analyze the impact on load profiles, energy consumption, utility, and customer savings. The tool is equipped with an optimization model to determine the energy consumption of electric appliances that can leverage survey data containing information on the number of appliances owned by customers. Lastly, a statistical model can be used to predict system-level load based on exogenous parameters such as weather, time of day, month, type of day, and year. The tool is scripted in Python and uses the Pyomo python optimization language.

Duwadi, Kapil↗