3,267 research outputs found
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
Multiple Timescale Dispatch and Scheduling for Stochastic Reliability in Smart Grids with Wind Generation Integration
Integrating volatile renewable energy resources into the bulk power grid is
challenging, due to the reliability requirement that at each instant the load
and generation in the system remain balanced. In this study, we tackle this
challenge for smart grid with integrated wind generation, by leveraging
multi-timescale dispatch and scheduling. Specifically, we consider smart grids
with two classes of energy users - traditional energy users and opportunistic
energy users (e.g., smart meters or smart appliances), and investigate pricing
and dispatch at two timescales, via day-ahead scheduling and realtime
scheduling. In day-ahead scheduling, with the statistical information on wind
generation and energy demands, we characterize the optimal procurement of the
energy supply and the day-ahead retail price for the traditional energy users;
in realtime scheduling, with the realization of wind generation and the load of
traditional energy users, we optimize real-time prices to manage the
opportunistic energy users so as to achieve systemwide reliability. More
specifically, when the opportunistic users are non-persistent, i.e., a subset
of them leave the power market when the real-time price is not acceptable, we
obtain closedform solutions to the two-level scheduling problem. For the
persistent case, we treat the scheduling problem as a multitimescale Markov
decision process. We show that it can be recast, explicitly, as a classic
Markov decision process with continuous state and action spaces, the solution
to which can be found via standard techniques. We conclude that the proposed
multi-scale dispatch and scheduling with real-time pricing can effectively
address the volatility and uncertainty of wind generation and energy demand,
and has the potential to improve the penetration of renewable energy into smart
grids.Comment: Submitted to IEEE Infocom 2011. Contains 10 pages and 4 figures.
Replaces the previous arXiv submission (dated Aug-23-2010) with the same
titl
A generic method for energy-efficient and energy-cost-effective production at the unit process level
Optimised Residential Loads Scheduling Based on Dynamic Pricing of Electricity : A Simulation Study
This paper presents a simulation study which addresses Demand Side Management (DSM) via scheduling and optimization of a set of residential smart appliances under day-ahead variable pricing with the aim of minimizing the customerâs energy bill. The appliancesâ operation and the overall model are subject to the manufacturer and user specific constraints formulated as a constrained linear programming problem. The overall model is simulated using MATLAB and SIMULINK / SimPowerSystems basic blocks. The results comparing Real Time Pricing (RTP) and the Fixed Time Tariff (FTT) demonstrate that optimal scheduling of the residential smart appliances can potentially result in energy cost savings. The extension of the model to incorporate renewable energy resources and storage system is also discussedNon peer reviewedFinal Accepted Versio
Autonomic Cloud Computing: Open Challenges and Architectural Elements
As Clouds are complex, large-scale, and heterogeneous distributed systems,
management of their resources is a challenging task. They need automated and
integrated intelligent strategies for provisioning of resources to offer
services that are secure, reliable, and cost-efficient. Hence, effective
management of services becomes fundamental in software platforms that
constitute the fabric of computing Clouds. In this direction, this paper
identifies open issues in autonomic resource provisioning and presents
innovative management techniques for supporting SaaS applications hosted on
Clouds. We present a conceptual architecture and early results evidencing the
benefits of autonomic management of Clouds.Comment: 8 pages, 6 figures, conference keynote pape
Demand Response Driven Load Scheduling in Formal Smart Grid Framework
In this technical report, we present the current state of the research conducted during the first part of the PhD project named âDemand Response Driven Load Scheduling in Formal Smart Grid Frameworkâ. The PhD project focuses on smart grids which employ information and communication technologies to assist the electricity production, distribution, and consumption. Designing smart grid applications is a novel challenging task that requires modeling, integrating, and validating different grid aspects in an efficient way. The project tackles such challenges by proposing an effective framework to formally describe smart grid elements along with their interactions. To validate this framework, the report concentrates on deploying efficiency in managing the electricity consumption in households which requires focusing on different impacts of demand response programs running in the smart grid to engage consumers to participate. A demand response system is considered which is connected to all households and utilizes their information to determine an effective load management strategy taking into account the grid constraints imposed by distribution system operators. The main responsibility of the demand response system is scheduling the operation of appliances of a large number of consumers in order to achieve a network-wide optimized performance. Finally, the PhD report demonstrates the simulation results, publications, courses, and dissemination activities done during this period. They are followed by envisaging future plans that will lead to completion of the PhD study
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