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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Privacy Preserving Model-Free Optimization and Control Framework for Demand Response from Residential Thermal Loads

We consider the problem of optimizing the cost of procuring electricity for a large collection of homes managed by a load serving entity, by pre-cooling or pre-heating the thermal inertial loads in the homes to avoid procuring power during periods of peak electricity pricing. We would like to accomplish this objective in a completely privacy-preserving and model-free manner, that is, without direct access to the state variables (temperatures or power consumption) or the dynamical models (thermal characteristics) of individual homes, while guaranteeing personal comfort constraints of the consumers. We propose a two-stage optimization and control framework to address this problem. In the first stage, we use a long short-term memory (LSTM) network to predict hourly electricity prices, based on historical pricing data and weather forecasts. Given the hourly price forecast and thermal models of the homes, the problem of designing an optimal power consumption trajectory that minimizes the total electricity procurement cost for the collection of thermal loads can be formulated as a large-scale integer program (with millions of variables) due to the on-off cyclical dynamics of such loads. We provide a simple heuristic relaxation to make this large-scale optimization problem model-free and computationally tractable. In the second stage, we translate the results of this optimization problem into distributed open-loop control laws that can be implemented at individual homes without measuring or estimating their state variables, while simultaneously ensuring consumer comfort constraints. We demonstrate the performance of this approach on a large-scale test case comprising of 500 homes in the Houston area and benchmark its performance against a direct model-based optimization and control solution.

Sivaranjani, S.↗
DOE OSTI2019

A GaN-Based Active Power Decoupling Approach for Enhancing the Efficiency and Reliability of Residential PV Microinverters

It is a common sense that, the reliability of a system is dictated by the life-time of its weakest component. In terms of a single-phase solar photovoltaic (PV) system, including PV panel and the microinverter, the reliability of the system is confined by the electrolytic dc-link capacitor at the input of its microinverter. To enhance the reliability of the system by providing the possibility of employing film capacitors, with higher life-time rather than their electrolytic counterparts, this paper presents a novel modeling, design, and control of a PV-side active power decoupling (APD) approach. Besides, GaN FETs are employed to achieve a superior efficiency and power density of the APD. The proposed methodology is verified through a thorough design, loss modeling, and numerical analysis.

14 SOLAR ENERGY↗
DOE OSTI2019

Residential (Secondary-Use) Energy Storage System with Modular Software and Hardware Power Electronic Interfaces

Secondary-use energy storage systems (ESS) are a potential low-cost energy storage system for the electric grid. These systems propose a low-cost solution to the challenge, what to do with electric vehicle batteries once EV end-of-life has been reached. However, the development of an ESS is complex by nature. Typically, the bidirectional power electronics conversion (PEC) system has hardware which includes power stages, auxiliary circuits, protection circuits, and the control interfaces all embedded and integrated as a system. This leads to interoperability challenges and vendor dependencies that increase balance of system costs. This paper proposes a modular approach of software and hardware interfaces to reduce the costs of an ESS.

Starke, Michael↗
DOE OSTI2019

The Social Cost of Individual Privacy in Aggregated Residential Demand Response

There is an increasing body of experimental and theoretical research related to demand response using smart, connected home equipment in smart grid. However, as technologies are deployed at scale, privacy has become a major concern. One possible approach to address this concern is to model an entire home as an aggregated unit of resource for engaging in demand response. Such an approach would allow residents to participate in demand response while abstracting the specifics of the appliance usage pattern from the utility provider. The benefits of privacy-preserving demand response notwithstanding, it implies a cost in terms of wasted capacity. This paper aims to explore the privacy/capacity tradeoff by simulating a fleet of homes and comparing the results with a fleet of individual appliances. The results show that a fleet of homes only bids about 70% of available capacity, and in the presence of an aggregator this number declines to 50%. Thus, privacy of individual homes comes at a cost of sacrificing part of otherwise available capacity.

Tsybina, Eve↗
DOE OSTI2020

A Model-Predictive Hierarchical-Control Framework for Aggregating Residential DERs to Provide Grid Regulation Services

This paper develops a hierarchical control frame-work to aggregate and to manage behind-the-meter distributed energy resources (DERs), which will be ubiquitous in future distribution systems. In the proposed framework, firstly, each controller in the hierarchy determines the flexibility of the DERs such that the obtained flexibility is feasible with respect to its operational purview. For example, the operational purview of a home energy management system may only consider consumer comfort preferences, while that for an aggregator or a grid controller may consider network voltage management as well. Based on the feasible flexibility, optimal setpoints for the DERs is then determined by the hierarchical controllers to help the distribution power network in voltage regulation, coordination issues with existing transmission-level conventional generators, etc. Therefore, the proposed strategy, which is based on model-predictive control, can be effectively utilized by the distribution network to coordinate several DERs to provide grid regulation services. Numerical simulations performed on the IEEE 37-bus test system demonstrate the efficacy of the proposed approach.

behind-the-meter↗
DOE OSTI2022

Hierarchical Model-Free Transactive Control of Residential Building Loads: An Actual Deployment

The transformation of electricity systems into more sustainable configurations brought some new challenges. The uncertain, intermittent, and variable nature of renewable energy sources require a significant amount of load demand flexibility, in which grid-interactive buildings (GEBs) are important flexible assets for electricity systems. In this regard, many demand response (DR) tools have been developed to harness this demand flexibility. However, such tools are mostly simulation-based or deal with a single load, which may not be sufficient to demonstrate their effectiveness. Towards this end, this paper presents a real-world field implementation and testing of a hierarchical model-free transactive DR control approach on actual GEBs. The control implementation incorporates elements of virtual battery, game theory, and model-free control mechanisms. The proposed approach was tested using a total of five GEBs, each having three zones. The results show that the proposed approach can mostly achieve all intended objectives, including flexibility estimation, peak load reduction, power tracking, and controlling GEBs while maintaining occupants’ comfort.

Amasyali, Kadir↗
DOE OSTI2023

Evidence of Residential Demand Flexibility in a 46 Townhome Neighborhood

Demand response is an important emerging part of smart grids and it has been well researched from the theoretical and modeling perspectives. The empirical evidence on demand response is scarce, resulting in a limited understanding of many important aspects of demand response, including latency, cycling, and overrides - including acceptable impacts to customer comfort, convenience, and productivity in the new remote work era. We attempt to provide additional information to address this knowledge gap by sharing early results from the 46-townhome testbed located in Atlanta, GA. We report our findings from the first three months of experimental work. These focus on the delays associated with device status updates, characteristics of user overrides of control signals, and the share of devices available for demand response. We also provide some information about the duration of demand response events and a discussion of some properties of the testbed neighborhood.

Tsybina, Eve↗
DOE OSTI2016

Control and Coordination of Frequency Responsive Residential Water Heaters

Demand-side frequency control can complement traditional generator controls to maintain the stability of large electric systems in the face of rising uncertainty and variability associated with renewable energy resources. This paper presents a hierarchical frequency-based load control strategy that uses a supervisor to flexibly adjust control gains that a population of end-use loads respond to in a decentralized manner to help meet the NERC BAL-003-1 frequency response standard at both the area level and interconnection level. The load model is calibrated and used to model populations of frequency-responsive water heaters in a PowerWorld simulation of the U.S. Western Interconnection (WECC). The proposed design is implemented and demonstrated on physical water heaters in a laboratory setting. A significant fraction of the required frequency response in the WECC could be supplied by electric water heaters alone at penetration levels of less than 15%, while contributing to NERC requirements at the interconnection and area levels.

Williams, Tess L.↗
DOE OSTI2019

Agent-Based System for Transactive Control of Smart Residential Neighborhoods

Buildings consume 74% of the total electricity produced in the United States. A significant portion of the building electric load includes heating ventilation and air-conditioning (HVAC) systems and water heating (WH) systems. Enabling flexibility in the operations can improve overall electric grid efficiency. This paper describes a multi-agent system for supporting integration, learning, optimization, and control of HVAC and WH in supporting a future smart grid. The architecture supports a transactive-based negotiation strategy between homeowners and a microgrid controller to adjust consumption behavior and reduce electricity costs. The framework is deployed in a neighborhood and preliminary testing is underway. The agent architecture design is discussed along with the preliminary optimization results from the demonstration site.

Starke, Michael↗