Residential demand response under uncertainty

Paul Scott; Sylvie Thiébaux; Menkes Van Den Briel; Pascal Van Hentenryck

doi:10.1007/978-3-642-40627-0_48

Residential demand response under uncertainty

Paul Scott, Sylvie Thiébaux, Menkes Van Den Briel, Pascal Van Hentenryck

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

50 Citations (Scopus)

Abstract

This paper considers a residential market with real-time electricity pricing and flexible electricity consumption profiles for customers. Such a market raises an optimisation problem for home automation systems where they need to schedule consumption activities to reduce costs, whilst maintaining a base level of comfort and convenience. This optimisation problem faces uncertainty in real-time prices, weather conditions, and occupant behaviour. The paper presents two online stochastic combinatorial optimisation algorithms that produce fast, high-quality solutions to this problem. These algorithms are compared with reactive control strategies and a clairvoyant controller. Our results demonstrate the value of stochastic information and online stochastic optimisation in residential demand response.

Original language	English
Title of host publication	Principles and Practice of Constraint Programming - 19th International Conference, CP 2013, Proceedings
Pages	645-660
Number of pages	16
DOIs	https://doi.org/10.1007/978-3-642-40627-0_48
Publication status	Published - 2013
Event	19th International Conference on Principles and Practice of Constraint Programming, CP 2013 - Uppsala, Sweden Duration: 16 Sept 2013 → 20 Sept 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8124 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Principles and Practice of Constraint Programming, CP 2013
Country/Territory	Sweden
City	Uppsala
Period	16/09/13 → 20/09/13

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10.1007/978-3-642-40627-0_48

Cite this

Scott, P., Thiébaux, S., Van Den Briel, M., & Van Hentenryck, P. (2013). Residential demand response under uncertainty. In Principles and Practice of Constraint Programming - 19th International Conference, CP 2013, Proceedings (pp. 645-660). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8124 LNCS). https://doi.org/10.1007/978-3-642-40627-0_48

Scott, Paul ; Thiébaux, Sylvie ; Van Den Briel, Menkes et al. / Residential demand response under uncertainty. Principles and Practice of Constraint Programming - 19th International Conference, CP 2013, Proceedings. 2013. pp. 645-660 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Residential demand response under uncertainty",

abstract = "This paper considers a residential market with real-time electricity pricing and flexible electricity consumption profiles for customers. Such a market raises an optimisation problem for home automation systems where they need to schedule consumption activities to reduce costs, whilst maintaining a base level of comfort and convenience. This optimisation problem faces uncertainty in real-time prices, weather conditions, and occupant behaviour. The paper presents two online stochastic combinatorial optimisation algorithms that produce fast, high-quality solutions to this problem. These algorithms are compared with reactive control strategies and a clairvoyant controller. Our results demonstrate the value of stochastic information and online stochastic optimisation in residential demand response.",

author = "Paul Scott and Sylvie Thi{\'e}baux and {Van Den Briel}, Menkes and {Van Hentenryck}, Pascal",

year = "2013",

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Scott, P, Thiébaux, S, Van Den Briel, M & Van Hentenryck, P 2013, Residential demand response under uncertainty. in Principles and Practice of Constraint Programming - 19th International Conference, CP 2013, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8124 LNCS, pp. 645-660, 19th International Conference on Principles and Practice of Constraint Programming, CP 2013, Uppsala, Sweden, 16/09/13. https://doi.org/10.1007/978-3-642-40627-0_48

Residential demand response under uncertainty. / Scott, Paul; Thiébaux, Sylvie; Van Den Briel, Menkes et al.
Principles and Practice of Constraint Programming - 19th International Conference, CP 2013, Proceedings. 2013. p. 645-660 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8124 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - This paper considers a residential market with real-time electricity pricing and flexible electricity consumption profiles for customers. Such a market raises an optimisation problem for home automation systems where they need to schedule consumption activities to reduce costs, whilst maintaining a base level of comfort and convenience. This optimisation problem faces uncertainty in real-time prices, weather conditions, and occupant behaviour. The paper presents two online stochastic combinatorial optimisation algorithms that produce fast, high-quality solutions to this problem. These algorithms are compared with reactive control strategies and a clairvoyant controller. Our results demonstrate the value of stochastic information and online stochastic optimisation in residential demand response.

AB - This paper considers a residential market with real-time electricity pricing and flexible electricity consumption profiles for customers. Such a market raises an optimisation problem for home automation systems where they need to schedule consumption activities to reduce costs, whilst maintaining a base level of comfort and convenience. This optimisation problem faces uncertainty in real-time prices, weather conditions, and occupant behaviour. The paper presents two online stochastic combinatorial optimisation algorithms that produce fast, high-quality solutions to this problem. These algorithms are compared with reactive control strategies and a clairvoyant controller. Our results demonstrate the value of stochastic information and online stochastic optimisation in residential demand response.

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Scott P, Thiébaux S, Van Den Briel M, Van Hentenryck P. Residential demand response under uncertainty. In Principles and Practice of Constraint Programming - 19th International Conference, CP 2013, Proceedings. 2013. p. 645-660. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-40627-0_48

Residential demand response under uncertainty

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