7,423 research outputs found
Optimizing plug-in electric vehicle charging in interaction with a small office building
This paper considers the integration of plug-in electric vehicles (PEVs) in micro-grids. Extending a theoretical framework for mobile storage connection, the economic analysis here turns to the interactions of commuters and their driving behavior with office buildings. An illustrative example for a real office building is reported. The chosen system includes solar thermal, photovoltaic, combined heat and power generation as well as an array of plug-in electric vehicles with a combined aggregated capaci-ty of 864 kWh. With the benefit-sharing mechanism proposed here and idea-lized circumstances, estimated cost savings of 5% are possible. Different pricing schemes were applied which include flat rates, demand charges, as well as hourly variable final customer tariffs and their effects on the operation of intermittent storage were revealed and examined in detail. Because the plug-in electric vehicle connection coincides with peak heat and electricity loads as well as solar radiation, it is possible to shift energy demand as desired in order to realize cost savings. --Battery storage,building management systems,dispersed storage and generation,electric vehicles,load management,microgrid,optimization methods,power system economics,road vehicle electric propulsion
Final report: Workshop on: Integrating electric mobility systems with the grid infrastructure
EXECUTIVE SUMMARY:
This document is a report on the workshop entitled “Integrating Electric Mobility
Systems with the Grid Infrastructure” which was held at Boston University on November 6-7
with the sponsorship of the Sloan Foundation. Its objective was to bring together researchers
and technical leaders from academia, industry, and government in order to set a short and longterm research agenda regarding the future of mobility and the ability of electric utilities to meet
the needs of a highway transportation system powered primarily by electricity. The report is a
summary of their insights based on workshop presentations and discussions. The list of
participants and detailed Workshop program are provided in Appendices 1 and 2.
Public and private decisions made in the coming decade will direct profound changes in
the way people and goods are moved and the ability of clean energy sources – primarily
delivered in the form of electricity – to power these new systems. Decisions need to be made
quickly because of rapid advances in technology, and the growing recognition that meeting
climate goals requires rapid and dramatic action. The blunt fact is, however, that the pace of
innovation, and the range of business models that can be built around these innovations, has
grown at a rate that has outstripped our ability to clearly understand the choices that must be
made or estimate the consequences of these choices. The group of people assembled for this
Workshop are uniquely qualified to understand the options that are opening both in the future of
mobility and the ability of electric utilities to meet the needs of a highway transportation system
powered primarily by electricity. They were asked both to explain what is known about the
choices we face and to define the research issues most urgently needed to help public and
private decision-makers choose wisely. This report is a summary of their insights based on
workshop presentations and discussions.
New communication and data analysis tools have profoundly changed the definition of
what is technologically possible. Cell phones have put powerful computers, communication
devices, and position locators into the pockets and purses of most Americans making it possible
for Uber, Lyft and other Transportation Network Companies to deliver on-demand mobility
services. But these technologies, as well as technologies for pricing access to congested
roads, also open many other possibilities for shared mobility services – both public and private –
that could cut costs and travel time by reducing congestion. Options would be greatly expanded
if fully autonomous vehicles become available. These new business models would also affect
options for charging electric vehicles. It is unclear, however, how to optimize charging
(minimizing congestion on the electric grid) without increasing congestion on the roads or
creating significant problems for the power system that supports such charging capacity.
With so much in flux, many uncertainties cloud our vision of the future. The way new
mobility services will reshape the number, length of trips, and the choice of electric vehicle
charging systems and constraints on charging, and many other important behavioral issues are
critical to this future but remain largely unknown. The challenge at hand is to define plausible
future structures of electric grids and mobility systems, and anticipate the direct and indirect
impacts of the changes involved. These insights can provide tools essential for effective private ... [TRUNCATED]Workshop funded by the Alfred P. Sloan Foundatio
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Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California
This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits
Review of trends and targets of complex systems for power system optimization
Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107
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Trends in life cycle greenhouse gas emissions of future light duty electric vehicles
The majority of previous studies examining life cycle greenhouse gas (LCGHG) emissions of battery electric vehicles (BEVs) have focused on efficiency-oriented vehicle designs with limited battery capacities. However, two dominant trends in the US BEV market make these studies increasingly obsolete: sales show significant increases in battery capacity and attendant range and are increasingly dominated by large luxury or high-performance vehicles. In addition, an era of new use and ownership models may mean significant changes to vehicle utilization, and the carbon intensity of electricity is expected to decrease. Thus, the question is whether these trends significantly alter our expectations of future BEV LCGHG emissions. To answer this question, three archetypal vehicle designs for the year 2025 along with scenarios for increased range and different use models are simulated in an LCGHG model: an efficiency-oriented compact vehicle; a high performance luxury sedan; and a luxury sport utility vehicle. While production emissions are less than 10% of LCGHG emissions for today's gasoline vehicles, they account for about 40% for a BEV, and as much as two-thirds of a future BEV operated on a primarily renewable grid. Larger battery systems and low utilization do not outweigh expected reductions in emissions from electricity used for vehicle charging. These trends could be exacerbated by increasing BEV market shares for larger vehicles. However, larger battery systems could reduce per-mile emissions of BEVs in high mileage applications, like on-demand ride sharing or shared vehicle fleets, meaning that trends in use patterns may countervail those in BEV design
Smart EV Charging for Improved Sustainable Mobility
The landscape of energy generation and utilization is witnessing an unprecedented change. We are at the threshold of a major shift in electricity generation from utilization of conventional sources of energy like coal to sustainable and renewable sources of energy like solar and wind. On the other hand, electricity consumption, especially in the field of transportation, due to advancements in the field of battery research and exponential technologies like vehicle telematics, is seeing a shift from carbon based to Lithium based fuel. Encouraged by 1. Decrease in the cost of Li – ion based batteries 2. Breakthroughs in battery chemistry research - resulting in increased drive range 3. Government incentives and tariff concessions by utilities for EV owners in the form of tax credits, EV – only parking spaces, free charging equipment etc., the automobile market, especially the passenger vehicle market, is witnessing a steady growth in the sale of electric vehicles. This has resulted in Electric Vehicles contributing to the electricity load resulting in two challenges 1. At the supply end, it contributes as a potential micro energy storage system to fit the time gap between the demand for electricity and the supply of renewable and/or low cost electricity generation; and, 2. At the consumer-end, it creates a necessity to make energy consumption as sustainable and renewable as possible, while preserving battery life. In this thesis work we attempt to provide multiple practical solutions to address these needs by advancing existing technologies in the industry. Firstly, we have developed a “Joint EV-Grid Solution for Robust and Low-Complexity Smart Charing”, where we have designed and implemented a distributed smart charging algorithm, which runs in the EV with load and pricing information collected from Grid through the charging station. It is responsible for optimizing the charge plan of the user’s vehicle based on his/her preference and ensure a full charge before departure. The objective could be minimizing the electricity cost per charge session or maximizing the renewable energy usage. For instance, by setting the preference to optimize the algorithm according to “Price”, the additional demand is scheduled to off-peak hours (i.e., incurring the least cost). Alternatively, by setting the preference to “Renewables” the EV charges based on the maximum availability of renewable energy sources, thereby maximizing the utilization of renewable energy resources which may lead to reduced cost, if not minimize it. Furthermore, we have improved on our initial approach by introducing “Smart Charging Solution through Usage/Charging Pattern Learning” where we have used machine learning algorithms like Logistic regression and Fuzzy Logic to enable EVs to learn the usage and charging pattern of users and prepare a charging plan that is personalized at the users’ end and prevents potential smart-changing-caused demand peaks by distributing the net load throughout the day. Through our experiment studies we were successful in creating a distributed Charging algorithm and a Machine Learning system that could cater to the said requirements through innovative charging strategies. Consequently, helping us create a sustainable, win – win situation for both electricity consumer and producer
On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms
We study the interaction between a fleet of electric, self-driving vehicles
servicing on-demand transportation requests (referred to as Autonomous
Mobility-on-Demand, or AMoD, system) and the electric power network. We propose
a model that captures the coupling between the two systems stemming from the
vehicles' charging requirements and captures time-varying customer demand and
power generation costs, road congestion, battery depreciation, and power
transmission and distribution constraints. We then leverage the model to
jointly optimize the operation of both systems. We devise an algorithmic
procedure to losslessly reduce the problem size by bundling customer requests,
allowing it to be efficiently solved by off-the-shelf linear programming
solvers. Next, we show that the socially optimal solution to the joint problem
can be enforced as a general equilibrium, and we provide a dual decomposition
algorithm that allows self-interested agents to compute the market clearing
prices without sharing private information. We assess the performance of the
mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact
on the Texas power network. Lack of coordination between the AMoD system and
the power network can cause a 4.4% increase in the price of electricity in
Dallas-Fort Worth; conversely, coordination between the AMoD system and the
power network could reduce electricity expenditure compared to the case where
no cars are present (despite the increased demand for electricity) and yield
savings of up $147M/year. Finally, we provide a receding-horizon implementation
and assess its performance with agent-based simulations. Collectively, the
results of this paper provide a first-of-a-kind characterization of the
interaction between electric-powered AMoD systems and the power network, and
shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and
Systems XIV and accepted by TCNS. In Version 4, the body of the paper is
largely rewritten for clarity and consistency, and new numerical simulations
are presented. All source code is available (MIT) at
https://dx.doi.org/10.5281/zenodo.324165
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