722 research outputs found
Capturing Aggregate Flexibility in Demand Response
Flexibility in electric power consumption can be leveraged by Demand Response
(DR) programs. The goal of this paper is to systematically capture the inherent
aggregate flexibility of a population of appliances. We do so by clustering
individual loads based on their characteristics and service constraints. We
highlight the challenges associated with learning the customer response to
economic incentives while applying demand side management to heterogeneous
appliances. We also develop a framework to quantify customer privacy in direct
load scheduling programs.Comment: Submitted to IEEE CDC 201
From Packet to Power Switching: Digital Direct Load Scheduling
At present, the power grid has tight control over its dispatchable generation
capacity but a very coarse control on the demand. Energy consumers are shielded
from making price-aware decisions, which degrades the efficiency of the market.
This state of affairs tends to favor fossil fuel generation over renewable
sources. Because of the technological difficulties of storing electric energy,
the quest for mechanisms that would make the demand for electricity
controllable on a day-to-day basis is gaining prominence. The goal of this
paper is to provide one such mechanisms, which we call Digital Direct Load
Scheduling (DDLS). DDLS is a direct load control mechanism in which we unbundle
individual requests for energy and digitize them so that they can be
automatically scheduled in a cellular architecture. Specifically, rather than
storing energy or interrupting the job of appliances, we choose to hold
requests for energy in queues and optimize the service time of individual
appliances belonging to a broad class which we refer to as "deferrable loads".
The function of each neighborhood scheduler is to optimize the time at which
these appliances start to function. This process is intended to shape the
aggregate load profile of the neighborhood so as to optimize an objective
function which incorporates the spot price of energy, and also allows
distributed energy resources to supply part of the generation dynamically.Comment: Accepted by the IEEE journal of Selected Areas in Communications
(JSAC): Smart Grid Communications series, to appea
Battery Capacity of Deferrable Energy Demand
We investigate the ability of a homogeneous collection of deferrable energy
loads to behave as a battery; that is, to absorb and release energy in a
controllable fashion up to fixed and predetermined limits on volume, charge
rate and discharge rate. We derive bounds on the battery capacity that can be
realized and show that there are fundamental trade-offs between battery
parameters. By characterizing the state trajectories under scheduling policies
that emulate two illustrative batteries, we show that the trade-offs occur
because the states that allow the loads to absorb and release energy at high
aggregate rates are conflicting
Distributional Analysis for Model Predictive Deferrable Load Control
Deferrable load control is essential for handling the uncertainties
associated with the increasing penetration of renewable generation. Model
predictive control has emerged as an effective approach for deferrable load
control, and has received considerable attention. In particular, previous work
has analyzed the average-case performance of model predictive deferrable load
control. However, to this point, distributional analysis of model predictive
deferrable load control has been elusive. In this paper, we prove strong
concentration results on the distribution of the load variance obtained by
model predictive deferrable load control. These concentration results highlight
that the typical performance of model predictive deferrable load control is
tightly concentrated around the average-case performance.Comment: 12 pages, technical report for CDC 201
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
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