Search NASA⌕ Search

SEARCH · Search NASA

Results for “demand aggregation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Modeling Smart Microgrids for the Developing World with Probabilistic Supply and Demand Inputs

Developing world microgrids often balance insufficient supply with growing, unpredictable demand. Deterministic and probabilistic simulators exist to model these microgrids, and each focuses on different technical aspects. With the addition of smart meters into microgrids, monitoring and control is now available at high granularity, which enriches microgrid planning and operation. This research is designing a new simulator to model smart microgrids with discrete probability distributions as supply and demand inputs. In our model, smart meters allow real-time power clipping for demand side management, effectively smoothing the system load curve as needed. To compare clipping schemes for grid operation and generation mixes for planning, we aggregate inputs by convolution then compute expected energy sold and probability of avoiding power cuts. The simulator plots these values for different combinations of power clipping threshold and number of customers clipped.

13 HYDRO ENERGY↗
DOE OSTI2023

Managing Uncertainty and Flexibility in Day-Ahead Electricity Markets

Net load imbalances from day ahead forecasts can lead to significant grid operations costs and are expected to increase as variable renewable energy adoption grows. We propose a new wholesale market product to manage the risk of net load imbalances called Flexibility Options. This product relies on probabilistic forecasts to estimate flexibility demand and would be co-optimized in the day-ahead market. We also propose stochastic methods that enable DER and flexible load aggregators to participate in flexibility markets while considering the uncertainty in weather and occupant behavior.

day-ahead market↗
DOE OSTI2020

Assessment of Commercial Building Lighting as a Frequency Regulation Resource

This paper evaluates the potential for automated lighting control as a resource for frequency regulation of the electric grid system in the context of current energy policies, economic incentives, and technological trends. The growing prevalence of renewable energy has increased the need for ancillary services to maintain grid frequency and stability. While demand side resources like heating, ventilating, and air-conditioning systems, as well as water treatment plants are already evaluated as regulation service providers, the potential application to electrical lighting systems has largely been ignored. Yet, aggregations of lighting systems that are retrofitted with intelligent controls could conceivably contribute to frequency regulation services with little impact on user comfort. To further explore the feasibility of lighting potential, this paper explores (1) how lighting control systems are limited by visual comfort perception and acceptability, (2) how such limitations impact the performance of the lighting system as an frequency regulation resource, and (3) how the market potential of lighting systems as demand side resources compares in different regional transmission organizations. Finally, the impact of developing technologies on the application of lighting systems for frequency regulation is discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Well-to-Wheels Greenhouse Gas Emission Analysis of High-Octane Fuels with Ethanol Blending: Phase II Analysis with Refinery Investment Options

Higher-octane gasoline can enable increases in an internal combustion engine’s energy efficiency and a vehicle’s fuel economy by allowing an increase in the engine compression ratio and/or by enabling downspeeding and downsizing. Producing high-octane fuel (HOF) with the current level of ethanol blending (E10) could increase the energy and greenhouse gas (GHG) emissions intensity of the fuel product from refinery operations. Alternatively, increasing the ethanol blending level in final gasoline products could be a promising solution to HOF production because of the high octane rating and potentially low blended Reid vapor pressure (RVP) of ethanol at 25% and higher of the ethanol blending level by volume. In our previous HOF well-to-wheels (WTW) report (the so-called phase I report of the HOF WTW analysis), we conducted WTW analysis of HOF with different ethanol blending levels (i.e., E10, E25, and E40) and a range of vehicle efficiency gains with detailed petroleum refinery linear programming (LP) modeling by Jacobs Consultancy and showed that the overall WTW GHG emission changes associated with HOFVs were dominated by the positive impact associated with vehicle efficiency gains and ethanol blending levels, while the refining operations to produce gasoline blendstock for oxygenate blending (BOB) for various HOF blend levels had a much smaller impact on WTW GHG emissions (Han et al. 2015). The scope of the previous phase I study, however, was limited to evaluating PADDs 2 and 3 operation changes with various HOF market share scenarios and ethanol blending levels. Also, the study used three typical configuration models of refineries (cracking, light coking, and heavy coking) in each PADD, which may not be representative of the aggregate response of all refineries in each PADD to various ethanol blending levels and HOF market scenarios. Lastly, the phase I study assumed no new refinery expansion in the existing refineries, which limited E10 HOF production to the volume achievable by the cracking refinery configuration. To be able to satisfy large market demands of E10 HOF, that study arbitrarily relaxed the RVP requirements by replacing reformulated gasoline (RFG) RVP requirement of 7 psi in summer with conventional gasoline (CG) RVP requirement of 9 psi in summer. To examine the response by all refineries in major refinery regions, this phase II of the HOF WTW analysis employed regionally aggregated refinery models for the following six regions: PADDs 1, 2, 3, 4, and 5 excluding California (CA) and CA separately. Using aggregate refinery models, this phase II study examined the impacts of ethanol blending and HOF market shares on the refinery operations in these six regions. Also, this study included refinery expansion to produce a pre-determined HOF volume with 10% ethanol blending. In particular, this study examined several refinery expansion options using refinery configuration models to investigate a practical refinery response to the increase in E10 HOF market demand.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Harmonized geospatial data to support infrastructure siting feasibility planning for energy system transitions

Climate change, energy system transitions, and socioeconomic change are compounding influences affecting the growth of electricity demand. While energy efficiency initiatives and distributed resources can address a significant amount of this demand, the United States will likely still need new utility-scale generation resources. The energy sector uses capacity expansion planning models to determine the aggregate need for new generation, but these models are typically at the state or regional scale and are not equipped to address the wide range of location- and technology-specific issues that are increasingly a factor in power plant siting. To help address these challenges, we have developed the Geospatial Raster Input Data for Capacity Expansion Regional Feasibility (GRIDCERF) data package, a high-resolution product to evaluate siting suitability for renewable and non-renewable power plants in the conterminous United States. GRIDCERF offers 264 suitability layers for use with 56 power plant technologies in a harmonized format that can be easily ingested by geospatially-enabled modeling software allowing for customization to robustly address science objectives when evaluating varying future conditions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Multi-time scale control of demand flexibility in smart distribution networks

This study presents a multi-timescale control strategy to deploy demand flexibilities of electric vehicles (EV) for providing system balancing and local congestion management by simultaneously ensuring economic benefits to participating actors. First, the EV charging problem from consumer, aggregator, and grid operator’s perspective is investigated. A hierarchical control architecture (HCA) comprising scheduling, coordinative, and adaptive layers is then designed to realize their coordinative goal. This is realized by integrating a multi-time scale control, which works from a day-ahead scheduling up to real-time adaptive control. The performance of the developed method is investigated with high EV penetration in a typical distribution network. The simulation results demonstrates that HCA exploit EV flexibility to solve grid unbalancing and congestions with simultaneous maximization of economic benefits by ensuring EV participation to day-ahead, balancing, and regulation markets. For the given network configuration and pricing structure, HCA ensures the EV owners to get paid up to 5 times the cost they were paying without control.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

Distributed Smart Grid Asset Control Strategies for Providing Ancillary Services

With large-scale plans to integrate renewable generation driven mainly by state-level renewable portfolio requirements, more resources will be needed to compensate for the uncertainty and variability associated with intermittent generation resources. Distributed assets can be used to mitigate the concerns associated with renewable energy resources and to keep costs down. Under such conditions, performing primary frequency control using only supply-side resources becomes 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 primary frequency control to maintain the stability of the system at an acceptable cost. The main objective of this project is to develop a novel hierarchical distributed framework for frequency based load control. The framework involves two decision layers. The top decision layer determines the optimal gain for aggregated loads for each load bus. The gains are computed using decentralized robust control methods, and will be broadcast to the corresponding participating loads every control period. The second layer consists of a large number of heterogeneous devices, which switch probabilistically during contingencies so that aggregated power change matches the desired amount according to the most recently received gains. The simulation results show great potential to enable systematic design of demand-side primary frequency control with stability guarantees on the overall power system. The proposed design systematically accounts for the interactions between the total load response and bulk power system frequency dynamics. It also guarantees frequency stability under a wide range of time varying operating conditions. The local device-level load response rules fully respect the device constraints (such as temperature setpoint, compressor time delays of HVACs, or arrival and departure of the deferrable loads), which are crucial for implementing real load control programs. The promise of autonomous, Grid Friendly™ response by smart appliances in the form of under-frequency load shedding was demonstrated in the GridWise Olympic Peninsula Demonstration in 2006. Each controller monitored the power grid voltage signal and requested that electrical load be shed by its appliance whenever electric power-grid frequency fell below 59.95 Hz. The controllers and their appliances responded reliably to each shallow under-frequency event, which was an average of one event per day and shed their loads for the durations of these events. Another objective of this project was to perform extensive simulation studies to investigate the impact of a population of Grid Friendly™ Appliances (GFAs) on the bulk power system frequency stability. The GFAs considered in this report are represented as demonstration units with water heaters individually modeled.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

California Price Response Potential Study

California's energy landscape is undergoing a significant transformation, driven by the increasing integration of renewable energy sources, the increased adoption of distributed energy resources, the electrification of end-use loads, and the growing need for grid efficiency. To address these challenges, recent revisions to the State’s Load Management Standards (LMS) require all of California’s large utilities and community choice aggregators (CCAs) to offer dynamic electricity pricing options to customers by 2027. Dynamic pricing, which involves varying electricity rates based on real-time supply and demand conditions, offers a promising solution for optimizing grid operations, reducing costs, and incentivizing efficient use of grid capacity. Effective implementation of dynamic pricing requires understanding the potential impacts on customer bills, system load, and the cost-effectiveness of automation technologies. This study aims to evaluate the load response of various end-use devices to hourly dynamic prices. The end-uses studied here are space cooling, space heating, water heating, crop irrigation, pool and spa pumps, and electric vehicle (EV) charging, all for both residential and commercial applications, except for crop irrigation. In 2030, these end uses are forecasted to account for 18% of annual electricity demand in the state, but 40% of demand in the peak net load hour. By modeling possible price-responsive load dispatch algorithms and assessing the resulting impacts on both individual bills and the overall grid, we seek to inform policymakers and utilities about the potential benefits and challenges associated with dynamic pricing, and considerations for the design of dynamic pricing tariffs. Additionally, we will explore the cost effectiveness of adopting automation technologies to enable devices to respond more effectively to real-time price signals. This study considers a range of price profiles, accounting for differences across utilities and customer classes, and presents scenarios for dynamic price design via variation in the percentage of total customer electric costs that are allocated dynamically (versus constituting a fixed portion of the hourly volumetric price). We present results focused primarily on 2030, forecasting electricity prices under both low and high-cost scenarios, to inform longer-term tariff design considerations. We design tariffs by starting with 2019 prices that were calculated according to CalFUSE guidance (CPUC, 2022) and that have been used in recent studies; these prices are all-in volumetric rates that vary by utility and are revenue-neutral to each customer class. They are developed by considering six electricity cost components that are allocated hourly based on system load indicators (gross and net load, and wholesale prices). These prices are forecasted to 2030 for low and high cost scenarios, considering recent trends in total electricity costs with and without years of substantial wildfire mitigation investments. These tariffs, which allocate all costs on an hourly basis, are considered our “Full” dynamic tariff design scenario, while two additional scenarios explore allocating a portion of costs as a flat volumetric charge: the “Medium” scenario allocates 50% of revenue dynamically (and keeps 50% flat), while the “Mild” scenario allocates 20% of revenue dynamically. The 20% dynamic allocation on the Mild scenario aims to represent a case where only the marginal operating costs of the grid are included in the dynamic price.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Performance Characterization of InCharge™ ICE-66 V2X Bidirectional DC Fast Charger

Vehicle-to-grid (V2G) technology extends the role of Plug-in Hybrid Vehicle’s (PHEV) and Battery Electric Vehicle’s (BEV) batteries beyond transportation by enabling bidirectional power transfer between the vehicle's onboard energy storage and the utility grid. Modern battery-powered vehicles are equipped with high-capacity battery packs that remain stationary and underutilized for the majority of their operational life. A vehicle used for daily commuting may be parked and connected to charging infrastructure for 18–20 hours per day, representing a significant untapped energy resource. When considered at scale, the aggregated storage capacity of a modest fleet of 10–15 vehicles within a single parking facility can reach the MWh range, sufficient to partially offset peak demand for a mid-size commercial building or contribute meaningful ancillary services to the local grid. The ability to charge during off-peak periods when electricity prices are low and discharge during peak demand when prices are high positions EV batteries as distributed energy arbitrage assets, with the potential to offset vehicle ownership and charging costs. Beyond energy arbitrage, V2G-capable assets can provide ancillary grid services through active and reactive power injections. Active power supports grid frequency regulation, while reactive power supports local voltage regulation. Together, these capabilities establish V2G as a technically promising and economically relevant pathway toward greater integration of battery-powered vehicles into the broader energy system.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

Long-term implications of sustained wind power growth in the United States: Potential benefits and secondary impacts

Here, we model scenarios of the U.S. electric sector in which wind generation reaches 10% of end-use electricity demand in 2020, 20% in 2030, and 35% in 2050. As shown in a companion paper, achieving these penetration levels would have significant implications for the wind industry and the broader electric sector. Compared to a baseline that assumes no new wind deployment, under the primary scenario modeled, achieving these penetrations imposes an incremental cost to electricity consumers of less than 1% through 2030. These cost implications, however, should be balanced against the variety of environmental and social implications of such a scenario. Relative to a baseline that assumes no new wind deployment, our analysis shows that the high-penetration wind scenario yields potential greenhouse-gas benefits of 85–1,230 billion USD in present-value terms, with a central estimate of 400 billion USD. Air-pollution-related health benefits are estimated at 52–272 billion USD, while annual electric-sector water withdrawals and consumption are lower by 15% and 23% in 2050, respectively. We also find that a high-wind-energy future would have implications for the diversity and risk of energy supply, local economic development, and land use and related local impacts on communities and ecosystems; however, these additional impacts may not greatly affect aggregate social welfare owing to their nature, in part, as resource transfers.

17 WIND ENERGY↗
DOE OSTI2018

A review of the potential impacts of climate change on bulk power system planning and operations in the United States

In this paper, climate change might impact various components of the bulk electric power system, including electricity demand; transmission; and thermal, hydropower, wind, and solar generators. Most research in this area quantifies impacts on one or a few components and does not link these impacts to effects on power system planning and operations. Here, we advance the understanding of how climate change might impact the bulk U.S. power system in three ways. First, we synthesize recent research to capture likely component-level impacts of climate change in the United States. Second, given the interconnected nature of the electric power system, we assess how aggregated component-level impacts might affect power system planning and operations. Third, we outline an agenda for future research on climate change impacts on power system planning and operations. Although component-level impacts vary in their magnitude, collectively they might significantly affect planning and operations. Most notably, increased demand plus reduced firm capacity across generation types might require systems to procure significant additional capacity to maintain planning reserve margins, and regional declines in renewable resources might need to be offset by increasing zero-carbon investment to meet decarbonization targets. Aggregated impacts might also affect operations, e.g., through shifts in dispatching and increased operational reserve requirements. Future research should aggregate component-level impacts at operational timescales, quantify impacts on wind and solar variability, and contextualize climate change impacts within ongoing shifts in the electric power system.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Design and Evaluation of a Secure Virtual Power Plant.

For three years, Sandia National Laboratories, Georgia Institute of Technology, and University of Illinois at Urbana-Champaign investigated a smart grid vision in which renewable-centric Virtual Power Plants (VPPs) provided ancillary services with interoperable distributed energy resources (DER). This team researched, designed, built, and evaluated real-time VPP designs incorporating DER forecasting, stochastic optimization, controls, and cyber security to construct a system capable of delivering reliable ancillary services, which have been traditionally provided by large power plants or other dedicated equipment. VPPs have become possible through an evolving landscape of state and national interconnection standards, which now require DER to include grid-support functionality and communications capabilities. This makes it possible for third party aggregators to provide a range of critical grid services such as voltage regulation, frequency regulation, and contingency reserves to grid operators. This paradigm (a) enables renewable energy, demand response, and energy storage to participate in grid operations and provide grid services, (b) improves grid reliability by providing additional operating reserves for utilities, independent system operators (ISOs), and regional transmission organization (RTOs), and (c) removes renewable energy high-penetration barriers by providing services with photovoltaics and wind resources that traditionally were the jobs of thermal generators. Therefore, it is believed VPP deployment will have far-reaching positive consequences for grid operations and may provide a robust pathway to high penetrations of renewables on US power systems. In this report, we design VPPs to provide a range of grid-support services and demonstrate one VPP which simultaneously provides bulk-system energy and ancillary reserves.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Achieving Actionable Results from Available Inputs: Metamodels Take Building Energy Simulations One Step Further

Modeling commercial building energy usage can be a difficult and time-consuming task. The increasing prevalence of optimization algorithms provides one path for reducing the time and difficulty. Many use cases remain, however, where information regarding whole-building energy usage is valuable, but the time and expertise required to run and post-process a large number of building energy simulations is intractable. A relatively underutilized option to accurately estimate building energy consumption in real time is to pre-compute large datasets of potential building energy models, and use the set of results to quickly and efficiently provide highly accurate data. This process is called metamodeling. In this paper, two case studies are presented demonstrating the successful applications of metamodeling using the open-source OpenStudio Analysis Framework. The first case study involves the U.S. Department of Energy's Asset Score Tool, specifically the Preview Asset Score Tool, which is designed to give nontechnical users a near-instantaneous estimated range of expected results based on building system-level inputs. The second case study involves estimating the potential demand response capabilities of retail buildings in Colorado. The metamodel developed in this second application not only allows for estimation of a single building's expected performance, but also can be combined with public data to estimate the aggregate DR potential across various geographic (county and state) scales. In both case studies, the unique advantages of pre-computation allow building energy models to take the place of topdown actuarial evaluations. This paper ends by exploring the benefits of using metamodels and then examines the cost-effectiveness of this approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Poly(vinyl alcohol) Hydrogels with Broad-Range Tunable Mechanical Properties via the Hofmeister Effect

Hydrogels, exhibiting wide applications in soft robotics, tissue engineering, implantable electronics, etc., often require sophisticately tailoring of the hydrogel mechanical properties to meet specific demands. For examples, soft robotics necessitates tough hydrogels; stem cell culturing demands various tissue-matching modulus; and neuron probes desire dynamically tunable modulus. Herein, a strategy to broadly alter the mechanical properties of hydrogels reversibly via tuning the aggregation states of the polymer chains by ions based on the Hofmeister effect is reported. An ultratough poly(vinyl alcohol) (PVA) hydrogel as an exemplary material (toughness 150 ± 20 MJ m -3 ), which surpasses synthetic polymers like poly(dimethylsiloxane), synthetic rubber, and natural spider silk is fabricated. With various ions, the hydrogel's various mechanical properties are continuously and reversibly in situ modulated over a large window: tensile strength from 50 ± 9 kPa to 15 ± 1 MPa, toughness from 0.0167 ± 0.003 to 150 ± 20 MJ m -3 , elongation from 300 ± 100% to 2100 ± 300%, and modulus from 24 ± 2 to 2500 ± 140 kPa. Importantly, the ions serve as gelation triggers and property modulators only, not necessarily required to remain in the gel, maintaining the high biocompatibility of PVA without excess ions. Overall, this strategy, enabling high mechanical performance and broad dynamic tunability, presents a universal platform for broad applications from biomedicine to wearable electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Load Shifting with Space Conditioning Heat Pumps During Heating Season in the Pacific Northwest

Ductless mini- and multi-split heat pumps are commonly deployed in Pacific Northwest residences, which historically have inefficient space heating and no space cooling. Heat pumps provide these residences with more energy-efficient space heating in the winter and space cooling for increasingly warm summer periods. For many of these applications, heat pumps add a new electrical load at the residence, compelling regional utilities to seek to understand their impacts on the electrical grid and opportunities for load management. In coordination with regional utilities, this study examined the winter load-shifting potential of residential multi-split heat pumps with variable capacity technology. The study included nine residential sites with a total of 20 ductless indoor units, which underwent simulated smart grid control. Grid control functionality was provided through the CTA-2045 communication protocol and included offsetting temperature setpoints for individual indoor units in the study. Field data collection included whole-house and heating, ventilation, and air conditioning electrical power consumption; indoor zone temperatures; occupant surveys; and CTA-2045 curtailment data for both baseline and simulated load-shifting events by site. This paper provides an overview of the load-shifting strategies and aggregated results from winter data collection.

ASHRAE, Heat pump, energy effciency, residential b↗
DOE OSTI2020

Genetic algorithm for demand response: a stackelberg game approach

Demand response (DR) has gained a significant recent interest due to its potential for mitigating many power system problems. Game theory is a very effective tool to be utilized in DR management. In this paper, the DR between a distribution system operator (DSO) and load aggregators (LAs) is designed as a Stackelberg game, where the DSO acts as the leader and LAs are regarded as the followers. Due to the limitations of the centralized solution approaches, a genetic algorithm-based decentralized approach is proposed. To demonstrate the proposed approach, a case study concerning a day-ahead optimization for a real-time pricing market with a single DSO and three LAs is designed and optimized. The proposed approach is able to shift the demand peaks and prove that it has a great potential to be used for the Stackelberg game between a DSO and multiple LAs to fully exploit the potential of DR.

Amasyali, Kadir↗
DOE OSTI2019

Chance-Constrained Energy Management System for Power Grids With High Proliferation of Renewables and Electric Vehicles

This paper proposes a two-stage energy management system (EMS) for power grids with massive integration of electric vehicles (EVs) and renewable energy resources. The first stage economic dispatch determines the optimal operating points of charging stations and battery swapping stations (BSS) for EVs under plug-in and battery swapping modes, respectively. The proposed stochastic model predictive control (SMPC) problem in this stage is characterized through a chance-constrained optimization formulation that can effectively capture the system and the forecast uncertainties. A distributed algorithm, the alternating direction method of multipliers (ADMM), is applied to accelerate the optimization computation through parallel computing. The second stage is aimed in coordinating the EV charging mechanisms to continuously follow the first-stage solutions, i.e., the target operating points, and meeting the EV customers' charging demands captured via the Advanced Metering Infrastructure (AMI). In this work, the proposed solution offers a holistic control strategy for large-scale centralized power grids in which the aggregated individual parameters are predictable and the system dynamics do not vary sharply within a short time-interval.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Los Angeles Air Force Base Vehicle-to-Grid Demonstration (Final Project Report)

Electrification of non-tactical vehicle fleets represents a key efficiency and energy security objective for the United States Department of Defense. To achieve electrification, the department targeted vehicle-to-grid services as a way to decrease the overall cost of operating the vehicle fleet and achieve rough parity with traditional internal combustion engine vehicle fleets. This report describes efforts to aggregate a fleet of bi-directional electric vehicles and charging stations to provide regulation up and regulation down in the California Independent System Operator ancillary services market. A 29-vehicle electric vehicle demonstration fleet, consisting of mixed purpose and duty vehicles such as sedans, pickups, vans, and medium-duty trucks, was deployed at the Los Angeles Air Force base. The fleet provided frequency regulation to the California Independent System Operator’s wholesale electricity market to determine the capability of recouping some of the additional costs of procuring electric vehicles and their supporting infrastructure. Lawrence Berkeley National Laboratory, with its partner Kisensum, LLC, developed the fleet scheduling, optimization, and control software to allow the vehicle fleet at the air force base to participate in the ancillary services markets. This report focuses on the control software and market interactions, the significant challenges faced and solutions devised to address them, and examines the potential of using the electric vehicle fleet as an energy storage resource for the base buildings, an application known as vehicle-tobuilding, in providing demand response and emergency backup power. The report discusses key findings related to providing frequency regulation to the California Independent System Operator market, electric vehicle fleet performance, compatibility of varying resource parameters of vehicle fleet aggregation, the need for automated methods for communicating hour-ahead energy bidding, challenges related to battery capacity and charge/discharge rates, and monthly settlement revenue.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗