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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Big-Data Analytics for Electric Grid and Demand-Side Management

This report describes research to characterize the status of, and trends in, big-data analytics for the electricity grid. The research focused on 1) identifying power-grid big-data sources, types, and characteristics; and 2) characterizing big-data architecture, analytic methods, technology applications, and challenges. The first part of this report describes the main sources and characteristics of big data for the smart grid and comprehensively reviews big-data architecture, technologies, and applications in the power sector. The second part of this report presents case studies of big data applications in the power industry: (1) a smart-meter data and predictive analytics method for demand response (DR), (2) synchrophasor data analytics for the distribution grid, and (3) utility data for peak-demand management. For the predictive analytics case study, smart-meter-data-driven and physical models were developed to predict the potential kilowatt (kW) capacity reduction from DR. The DR estimation framework that was developed works for both small and large-scale customers. The synchrophasor case study demonstrates use of an algorithm applied to time-series data to detect events that appear as significant changes, known as “edges,” in voltage magnitude measurements. The synchrophasor case study also introduces an approach for clustering sets of events to reveal unique features that distinguish them (e.g., distinguishing capacitor bank switching from transformer tap changes). The peak-demand management case study describes the use of the data analytics to enable DR programs to limit forecasted peak demand, resulting in cost savings to the utility. The findings from the research described in this report support identification of opportunities and technologies for big-data and analytics applications for demand-side management in the power sector as well as other approaches to modernizing the electricity grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Demand-side solutions in the US building sector could achieve deep emissions reductions and avoid over $100 billion in power sector costs

Buildings are energy-intensive and a primary source of US end-use sector carbon emissions. Although building emissions today are 25% below their 2005 peak, far deeper reductions are needed to reach the US 2050 net-zero emissions goal. However, plausible decarbonization pathways that consider both buildings and their interactions with the power grid remain poorly understood. Here, we couple detailed modeling of building energy use and the grid to quantify building decarbonization potential and associated grid impacts. We find up to a 91% reduction in building CO 2 emissions from 2005 levels by 2050 using a portfolio of building efficiency, demand flexibility, and electrification measures alongside rapid grid decarbonization. Building efficiency and flexibility could generate up to $107 billion in annual power system cost savings by 2050, offsetting over a third of the incremental cost of full grid decarbonization. Our results underscore multiple benefits of demand-side solutions for deep decarbonization of US buildings.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

The Demand-Side Grid (dsgrid) Model Documentation

Electrical load is the backdrop for all power systems analysis. As a coequal partner in the supply-demand balance that must be maintained on electrical grids at all times and for all timescales, load is a major source of variability and uncertainty in grid operations. Despite this, utility load forecasting is typically done in a top-down manner that lacks the granularity in time, geography, end use, and technology that is needed to explore the potential impact of technological shifts; and many studies of future grid systems employ relatively simple scaling of historical loads, placing more emphasis on supply-side resources such as generation and transmission that are fewer in number and are better understood in terms of cost and performance. The primary purpose of the demand-side grid model (dsgrid) is to fill these gaps by creating comprehensive load data sets at a sufficient temporal, geographic, sectoral, and end-use resolution to enable detailed analyses of current patterns and future projections of end-use load. Furthermore, the dsgrid platform uses a bottom-up methodology that allows highly resolved analysis of 'what-if' scenarios. dsgrid leverages detailed sectoral energy models to provide hourly time series of load by subsector, end use, and county covering a full year. Although dsgrid currently emphasizes electricity load data, its component sector models for residential buildings, commercial buildings, and industry provide information on other fuel use, including natural gas. The data sets can thus be leveraged to support analysis of numerous demand-side technology-driven changes, such as energy efficiency, electrification, and operational flexibility (i.e., demand response). The electricity use data are time-synchronized with solar and wind data sets so as to be suitable for use in power systems analysis. This report documents dsgrid and its initial data set, which covers electricity demand in the contiguous United States for the historical year of 2012. We start with a historical year to enable model calibration and validation, as well as the development of a mathematical description of our model residuals that can be applied to future-year 'what-if' scenarios to remove known errors between our bottom-up modeling and historical load shapes. This work is part of the Electrification Futures Study (EFS), for which the dsgrid team will be developing future load snapshots that describe projected year 2050 under baseline and a range of electrification assumptions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Impacts of Demand-Side Resources on Electric Transmission Planning

Will demand resources such as energy efficiency (EE), demand response (DR), and distributed generation (DG) have an impact on electricity transmission requirements? Five drivers for transmission expansion are discussed: interconnection, reliability, economics, replacement, and policy. With that background, we review the results of a set of transmission studies that were conducted between 2010 and 2013 by electricity regulators, industry representatives, and other stakeholders in the three physical interconnections within the United States. These broad-based studies were funded by the US Department of Energy and included scenarios of reduced load growth due to EE, DR, and DG. While the studies were independent and used different modeling tools and interconnect-specific assumptions, all provided valuable results and insights. However, some caveats exist. Demand resources were evaluated in conjunction with other factors, and limitations on transmission additions between scenarios made understanding the role of demand resources difficult. One study, the western study, included analyses over both 10- and 20-year planning horizons; the 10-year analysis did not show near-term reductions in transmission, but the 20-year indicated fewer transmission additions, yielding a 36percent capital cost reduction. In the eastern study the reductions in demand largely led to reductions in local generation capacity and an increased opportunity for low-cost and renewable generation to export to other regions. The Texas study evaluated generation changes due to demand, and is in the process of examining demand resource impacts on transmission.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Models and Strategies for Optimal Demand Side Management in the Chemical Industries

Deregulation and the increase of renewable electricity generation from wind and solar photovoltaics have transformed the U.S. electricity market. Economic and environmental benefits notwithstanding, the presence of renewables has increased variability and uncertainty on the supply side of the grid. Managing demand, rather than generation – a strategy referred to as “demand response (DR)” – is an attractive approach for mitigating this imbalance. DR efforts aim to reduce electricity usage during peak demand times, lessening stress on the grid. Industrial users are particularly attractive entities for DR participation since they present large, localized loads that can provide significant relief on grid demand and –unlike other large loads, such as buildings – are minimally dependent on human needs and preferences. In this project, we accomplished three main objectives. (1) We developed data-driven low-order DR scheduling-relevant dynamic models of chemical processes. Concurrently, we studied the formulation and solution of the associated optimal DR production scheduling problems. (a) A prototype air separation unit (ASU) model was used to generate simulated operating data for initial modeling efforts, which enabled the later use of industrial data for data-driven modeling. (b) We utilized Hammerstein-Wiener (HW) and Finite Step Response (FSR) models to represent nonlinear plant dynamics. (c) The HW models were linearized using exact linearization so they could potentially be embedded in power system models, which are formulated as mixed integer linear programs (MILPs). (d) We solved DR optimization problems under uncertainty and found that even naïve predictions of electricity price and product demand led to significant cost savings benefits. (2) Our DR scheduling optimization problem formulations are amenable to real-time solution. (a) We utilized Lagrangian Relaxation (LR) to efficiently solve the optimization problem by decoupling subproblems linked by complicating constraints. (b) We have achieved computation times for the 3-day DR scheduling problem of an ASU as low as 1.88 minutes. (3) Our representations of the DR behavior of chemical process as grid-level batteries were embedded in power system models. (a) For a small-scale grid, we found that incorporating the dynamics of the chemical plant in the optimal power flow calculations resulted in better resource management leading to up to 15% and 46% cost reduction for the grid and chemical plant operations, respectively, during periods of power line congestion. We have published several works dedicated to modeling and solving DR optimization problems from the user side. These were published in top peer-reviewed journals and are summarized in this report. The most recent work (and papers in preparation) considers DR scheduling from the grid side. Future efforts will consider networked plants (e.g., air separation units operating on a common pipeline) for DR participation, which is expected to amplify the capabilities of industrial DR participants to perform load-shifting. Our consideration of uncertainty in DR has inspired future directions in this area as well: we plan to develop multistage methods to fully account for the effects of uncertainty in DR scheduling.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

An energy storage dispatch optimization for demand-side management in industrial facilities

An energy storage (ES) dispatch optimization was implemented to test lithium-ion battery ES, supercapacitor ES, and compressed air ES on two different industrial facilities – one intermittent process facility and one continuous process facility. The model first shows the capability of optimizing the size of a single technology on a single industrial facility to maximize the return on investment. Then, for the same profile, the model identifies significant differences in the optimal size for different technologies based on their performance parameters. Third, the model identifies differences in the optimal size for each technology based on the facilities profile. Here the results show compressed air ES yields the highest return on investment for both facilities and provides insight for future development of ES systems.

25 ENERGY STORAGE↗
DOE OSTI2018

The Electrification Futures Study: Demand-Side Scenarios

Webinar presented July 26, 2018, on the second report in NREL's Electrification Futures Study series, which focuses on scenarios reflecting a wide range of possible electricity demand growth through 2050 driven by the adoption of end-use electric technologies.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

Buildings Sector Scenarios: Demand-side data to support energy system planning in the United States

The US energy system is in a period of high uncertainty about load growth, its implications for the energy generation mix, and downstream impacts on customer energy costs. In this context, there is a need for comprehensive, credible, and readily-customized projections of energy demand to ensure that planning decisions account for end-use management opportunities to improve system reliability and affordability. Here we introduce the Buildings Sector Scenarios (BSS) dataset, which includes a benchmark suite of such projections for the buildings sector — a key source of energy consumption, peak electricity demand, and consumer energy expenditures. The dataset contains projections through 2050 covering the contiguous United States (CONUS) resolved down to the county, hourly level by sector and end use for electricity demand and to the state, annual level by sector and end use for non-electric fuels. We summarize the BSS analysis workflow and the tools and datasets that support it, document key BSS scenario inputs and modeling assumptions, and outline BSS scenario outputs. We assess the technical quality of the dataset against historical surveys and projected estimates of buildings sector demand. Finally, we provide guidance on how stakeholders can access, use, and reproduce the dataset, and/or create new scenarios to explore their own analysis questions.

Langevin, Jared↗
DOE OSTI2014

A Hierarchical Framework for Demand-Side Frequency Control

With large-scale plans to integrate renewable generation, more resources will be needed to compensate for the uncertainty associated with intermittent generation resources. Under such conditions, performing frequency control using only supply-side resources become not only prohibitively expensive but also technically difficult. It is therefore important to explore how a sufficient proportion of the loads could assume a routine role in frequency control to maintain the stability of the system at an acceptable cost. In this paper, a novel hierarchical decentralized framework for frequency based load control is proposed. The framework involves two decision layers. The top decision layer determines the optimal droop gain required from the aggregated load response on each bus using a robust decentralized control approach. The second layer consists of a large number of devices, which switch probabilistically during contingencies so that the aggregated power change matches the desired droop amount according to the updated gains. The proposed framework is based on the classical nonlinear multi-machine power system model, and can deal with timevarying system operating conditions while respecting the physical constraints of individual devices. Realistic simulation results based on a 68-bus system are provided to demonstrate the effectiveness of the proposed strategy.

Moya, Christian↗
DOE OSTI2019

Secondary Voltage Control Via Demand-Side Energy Storage with Temporal Logic Specifications

This paper proposes a secondary voltage control method that can handle the temporal logic specifications (TLSs). The control objective is to schedule a control signal for energy storage system (ESS) such that the voltage variation of a defined critical bus satisfies TLSs. The TLS allows definitions of set and timing constraints at the same time, such as a finite-time restoration. A simplified analytical model is derived to describe the voltage variation of critical buses. Based on this model, a numerical optimal control problem is formulated as a mixed integer linear program to generate the proper control input for the ESS. The proposed control is verified on a lumped distribution system model. With this control diagram, supportive controllers can be easily designed to make voltage behaviors comply with grid codes and avoid unnecessary relay actions.

Zhang, Yichen↗
DOE OSTI2016

Hierarchical Decentralized Control Strategy for Demand-Side Primary Frequency Response

The Grid Friendly$^\textrm{TM}$ Appliance~(GFA) controller, developed at Pacific Northwest National Laboratory, was designed for the purpose of autonomously switching off appliances by detecting under-frequency events. In this paper, a new frequency responsive load~(FRL) controller is first proposed by extending the functionality of the original GFA controller. The proposed FRL controller can autonomously switch on (or off) end-use loads by detecting over-frequency (or under-frequency) events through local frequency measurement. Then, a hierarchical decentralized control framework is developed for engaging the end-use loads to provide primary frequency response with the proposed FRL controller. The developed framework has several important features that are desirable in terms of providing primary frequency control. It not only exclusively maintains the autonomous operation of the end-use loads, but also effectively overcomes the stability issue associated with high penetration of FRLs. The simulation results illustrate the effectiveness of the developed hierarchical control framework for providing primary frequency response with the proposed FRL controller.

Lian, Jianming↗