1,695 research outputs found
Incentive Design for Direct Load Control Programs
We study the problem of optimal incentive design for voluntary participation
of electricity customers in a Direct Load Scheduling (DLS) program, a new form
of Direct Load Control (DLC) based on a three way communication protocol
between customers, embedded controls in flexible appliances, and the central
entity in charge of the program. Participation decisions are made in real-time
on an event-based basis, with every customer that needs to use a flexible
appliance considering whether to join the program given current incentives.
Customers have different interpretations of the level of risk associated with
committing to pass over the control over the consumption schedule of their
devices to an operator, and these risk levels are only privately known. The
operator maximizes his expected profit of operating the DLS program by posting
the right participation incentives for different appliance types, in a publicly
available and dynamically updated table. Customers are then faced with the
dynamic decision making problem of whether to take the incentives and
participate or not. We define an optimization framework to determine the
profit-maximizing incentives for the operator. In doing so, we also investigate
the utility that the operator expects to gain from recruiting different types
of devices. These utilities also provide an upper-bound on the benefits that
can be attained from any type of demand response program.Comment: 51st Annual Allerton Conference on Communication, Control, and
Computing, 201
Achieving Reliable Coordination of Residential Plug-in Electric Vehicle Charging: A Pilot Study
Wide-scale electrification of the transportation sector will require careful
planning and coordination with the power grid. Left unmanaged, uncoordinated
charging of electric vehicles (EVs) at increased levels of penetration will
amplify existing peak loads, potentially outstripping the grid's capacity to
reliably meet demand. In this paper, we report findings from the OptimizEV
Project - a real-world pilot study in Upstate New York exploring a novel
approach to coordinated residential EV charging. The proposed coordination
mechanism seeks to harness the latent flexibility in EV charging by offering EV
owners monetary incentives to delay the time required to charge their EVs. Each
time an EV owner initiates a charging session, they specify how long they
intend to leave their vehicle plugged in by selecting from a menu of deadlines
that offers lower electricity prices the longer they're willing to delay the
time required to charge their EV. Given a collection of active charging
requests, a smart charging system dynamically optimizes the power being drawn
by each EV in real time to minimize strain on the grid, while ensuring that
each customer's car is fully charged by its deadline. Under the proposed
incentive mechanism, we find that customers are frequently willing to engage in
optimized charging sessions, allowing the system to delay the completion of
their charging requests by more than eight hours on average. Using the
flexibility provided by customers, the smart charging system was shown to be
highly effective in shifting the majority of EV charging loads off-peak to fill
the night-time valley of the aggregate load curve. Customer opt-in rates
remained stable over the span of the study, providing empirical evidence in
support of the proposed coordination mechanism as a potentially viable
"non-wires alternative" to meet the increased demand for electricity driven
growing EV adoption.Comment: 19 pages, 12 figure
Preemptive Scheduling of EV Charging for Providing Demand Response Services
We develop a new algorithm for scheduling the charging process of a large
number of electric vehicles (EVs) over a finite horizon. We assume that EVs
arrive at the charging stations with different charge levels and different
flexibility windows. The arrival process is assumed to have a known
distribution and that the charging process of EVs can be preemptive. We pose
the scheduling problem as a dynamic program with constraints. We show that the
resulting formulation leads to a monotone dynamic program with Lipschitz
continuous value functions that are robust against perturbation of system
parameters. We propose a simulation based fitted value iteration algorithm to
determine the value function approximately, and derive the sample complexity
for computing the approximately optimal solution.Comment: 21 pages, submitted to SEGA
The Critical Role of Public Charging Infrastructure
Editors: Peter Fox-Penner, PhD, Z. Justin Ren, PhD, David O. JermainA decade after the launch of the contemporary global electric vehicle (EV) market, most cities face a major challenge preparing for rising EV demand. Some cities, and the leaders who shape them, are meeting and even leading demand for EV infrastructure. This book aggregates deep, groundbreaking research in the areas of urban EV deployment for city managers, private developers, urban planners, and utilities who want to understand and lead change
- …