13,713 research outputs found
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
A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing
Edge computing is promoted to meet increasing performance needs of
data-driven services using computational and storage resources close to the end
devices, at the edge of the current network. To achieve higher performance in
this new paradigm one has to consider how to combine the efficiency of resource
usage at all three layers of architecture: end devices, edge devices, and the
cloud. While cloud capacity is elastically extendable, end devices and edge
devices are to various degrees resource-constrained. Hence, an efficient
resource management is essential to make edge computing a reality. In this
work, we first present terminology and architectures to characterize current
works within the field of edge computing. Then, we review a wide range of
recent articles and categorize relevant aspects in terms of 4 perspectives:
resource type, resource management objective, resource location, and resource
use. This taxonomy and the ensuing analysis is used to identify some gaps in
the existing research. Among several research gaps, we found that research is
less prevalent on data, storage, and energy as a resource, and less extensive
towards the estimation, discovery and sharing objectives. As for resource
types, the most well-studied resources are computation and communication
resources. Our analysis shows that resource management at the edge requires a
deeper understanding of how methods applied at different levels and geared
towards different resource types interact. Specifically, the impact of mobility
and collaboration schemes requiring incentives are expected to be different in
edge architectures compared to the classic cloud solutions. Finally, we find
that fewer works are dedicated to the study of non-functional properties or to
quantifying the footprint of resource management techniques, including
edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless
Communications and Mobile Computing journa
Foresighted Demand Side Management
We consider a smart grid with an independent system operator (ISO), and
distributed aggregators who have energy storage and purchase energy from the
ISO to serve its customers. All the entities in the system are foresighted:
each aggregator seeks to minimize its own long-term payments for energy
purchase and operational costs of energy storage by deciding how much energy to
buy from the ISO, and the ISO seeks to minimize the long-term total cost of the
system (e.g. energy generation costs and the aggregators' costs) by dispatching
the energy production among the generators. The decision making of the entities
is complicated for two reasons. First, the information is decentralized: the
ISO does not know the aggregators' states (i.e. their energy consumption
requests from customers and the amount of energy in their storage), and each
aggregator does not know the other aggregators' states or the ISO's state (i.e.
the energy generation costs and the status of the transmission lines). Second,
the coupling among the aggregators is unknown to them. Specifically, each
aggregator's energy purchase affects the price, and hence the payments of the
other aggregators. However, none of them knows how its decision influences the
price because the price is determined by the ISO based on its state. We propose
a design framework in which the ISO provides each aggregator with a conjectured
future price, and each aggregator distributively minimizes its own long-term
cost based on its conjectured price as well as its local information. The
proposed framework can achieve the social optimum despite being decentralized
and involving complex coupling among the various entities
A Three-Step Methodology to Improve Domestic Energy Efficiency
Increasing energy prices and the greenhouse effect lead to more awareness of energy efficiency of electricity supply. During the last years, a lot of technologies have been developed to improve this efficiency. Next to large scale technologies such as windturbine parks, domestic technologies are developed. These domestic technologies can be divided in 1) Distributed Generation (DG), 2) Energy Storage and 3) Demand Side Load Management. Control algorithms optimizing a combination of these techniques can raise the energy reduction potential of the individual techniques. In this paper an overview of current research is given and a general concept is deducted. Based on this concept, a three-step optimization methodology is proposed using 1) offline local prediction, 2) offline global planning and 3) online local scheduling. The paper ends with results of simulations and field tests showing that the methodology is promising.\u
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