A Fast Distributed Algorithm for Large-Scale Demand Response Aggregation

A major challenge to implementing residential demand response is that of aligning the objectives of many households, each of which aims to minimize its payments and maximize its comfort level, while balancing this with the objectives of an aggregator that aims to minimize the cost of electricity pur...

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Bibliographic Details
Published in:IEEE transactions on smart grid Vol. 7; no. 4; pp. 2094 - 2107
Main Authors: Mhanna, Sleiman, Chapman, Archie C., Verbic, Gregor
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.07.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
accelerated gradient methods
Algorithm design and analysis
Algorithms
Couplings
demand response aggregation
Devices
Direct reduction
Distributed algorithms
Dual decomposition
Electric utilities
energy management
Energy resources
Energy states
Households
Load management
Markets
Mathematical models
mixed-integer variables
Residential energy
smart grid
smoothing techniques
Temperature distribution
Tuning
Dual decomposition
smart grid
accelerated gradient methods
smoothing techniques
mixed-integer variables
energy management
demand response aggregation
ISSN:1949-3053, 1949-3061
Online Access:Get full text
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Summary:A major challenge to implementing residential demand response is that of aligning the objectives of many households, each of which aims to minimize its payments and maximize its comfort level, while balancing this with the objectives of an aggregator that aims to minimize the cost of electricity purchased in a pooled wholesale market. This paper presents a fast distributed algorithm for aggregating a large number of households with a mixture of discrete and continuous energy levels. A distinctive feature of the method in this paper is that the nonconvex demand response (DR) problem is decomposed in terms of households as opposed to devices, which allows incorporating more intricate couplings between energy storage devices, appliances, and distributed energy resources. The proposed method is a fast distributed algorithm applied to the double smoothed dual function of the adopted DR model. The method is tested on systems with up to 2560 households, each with 10 devices on average. The proposed algorithm is designed to terminate in 60 iterations irrespective of system size, which can be ideal for an on-line version of this problem. Moreover, numerical results show that with minimal parameter tuning, the algorithm exhibits a very similar convergence behavior throughout the studied systems and converges to near-optimal solutions, which corroborates its scalability.
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ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2016.2536740