Title :
Precise Mass-Market Energy Demand Management Through Stochastic Distributed Computing
Author :
Papalexopoulos, Alex ; Beal, J. ; Florek, Steven
Author_Institution :
ECCO Int., San Francisco, CA, USA
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.
Keywords :
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; Biological system modeling; Color; Distributed computing; Home appliances; Load flow control; Pricing; Reliability; Demand response; distributed computing; locational marginal pricing; power market auctions; stochastic control;
Journal_Title :
Smart Grid, IEEE Transactions on
DOI :
10.1109/TSG.2013.2263396