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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Veröffentlicht in:	IEEE transactions on smart grid Jg. 7; H. 4; S. 2094 - 2107
Hauptverfasser:	Mhanna, Sleiman, Chapman, Archie C., Verbic, Gregor
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.07.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
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Abstract	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.
AbstractList	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.
Author	Chapman, Archie C. Verbic, Gregor Mhanna, Sleiman
Author_xml	– sequence: 1 givenname: Sleiman surname: Mhanna fullname: Mhanna, Sleiman email: sleiman.mhanna@sydney.edu.au organization: Univ. of Sydney, Sydney, NSW, Australia – sequence: 2 givenname: Archie C. surname: Chapman fullname: Chapman, Archie C. organization: Univ. of Sydney, Sydney, NSW, Australia – sequence: 3 givenname: Gregor surname: Verbic fullname: Verbic, Gregor organization: Univ. of Sydney, Sydney, NSW, Australia
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SubjectTerms	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
Title	A Fast Distributed Algorithm for Large-Scale Demand Response Aggregation
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