Title
Precise Mass-Market Energy Demand Management Through Stochastic Distributed Computing
Abstract
Even though demand response (DR) participation has substantial benefits to the market as a whole, current DR programs suffer from a collection of market, regulatory, infrastructure and technology problems, such as lack of scalability, lack of privacy, imprecision, and nonacceptance by customers. This paper describes how a fundamentally different DR approach, based on service priority tiers for appliances and on stochastic distributed computing, can overcome these problems and be integrated with energy markets. Our approach takes advantage of inexpensive communications technology to estimate the state of home and small-business major electrical appliances and have those appliances respond to power grid state signals within a few seconds. Organizing appliances into service priority tiers allows retail customer power demand to be de-commoditized, making these DR resources a potent force for improving the efficiency of energy markets. This paper describes the proposed methodology, examines how it can be integrated into energy markets, and presents results from mathematical analysis and from simulation of 100 000 devices.
Year
DOI
Venue
2013
10.1109/TSG.2013.2263396
Smart Grid, IEEE Transactions
Keywords
Field
DocType
demand side management,domestic appliances,energy management systems,mathematical analysis,power markets,stochastic processes,DR participation,energy markets,home electrical appliances,inexpensive communication technology,mathematical analysis,power grid state signals,power market design,precise mass-market energy demand management,retail customer power demand,service priority tiers,small-business major electrical appliances,stochastic distributed computing,Demand response,distributed computing,locational marginal pricing,power market auctions,stochastic control
Economics,Energy demand management,Demand response,Stochastic process,Power grid,Power demand,Information and Communications Technology,Mass market,Distributed computing,Scalability
Journal
Volume
Issue
ISSN
4
4
1949-3053
Citations 
PageRank 
References 
2
0.38
3
Authors
3
Name
Order
Citations
PageRank
ALEX PAPALEXOPOULOS172.40
Jacob Beal2474.39
Steven Florek320.38