A hybrid algorithm for mixed integer nonlinear programming in residential energy management

Demand response plays an important role in helping us achieve clean energy and climate goals. It offers a variety of environmental benefits, such as reducing energy usage, offsetting the need for fossil-fueled power plants, and helping to manage system challenges from increased wind and solar energy...

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Bibliographic Details
Published in:Journal of cleaner production Vol. 226; pp. 940 - 948
Main Authors: Huang, Yantai, Wang, Wanliang, Hou, Beiping
Format: Journal Article
Language:English
Published: Elsevier Ltd 20.07.2019
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ISSN:0959-6526, 1879-1786
Online Access:Get full text
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Summary:Demand response plays an important role in helping us achieve clean energy and climate goals. It offers a variety of environmental benefits, such as reducing energy usage, offsetting the need for fossil-fueled power plants, and helping to manage system challenges from increased wind and solar energy. However, DR suffers from difficulties associated with optimization model complexity. Especially, when the DR problem is formulated as a mixed integer nonlinear programming (MINLP) problem, which is NP-hard problem. In order to solve this problem, a global optimization approach that combines a particle swarm optimization (PSO) algorithm with a sequential quadratic programming (SQP) local optimizer is proposed in this paper. This allows us to incorporate the global search characteristics of PSO algorithms with the local search capabilities of SQP algorithms. This unique combination increase the possibility of a successful global solution by balancing the exploration-exploitation trade-off and is particularly effective atsolving MINLP problems. Application of the approach in residential energy management indicates that the proposed algorithm is effective atresolving the MINLP DR problem. •A complex mixed nonlinear integer models is proposed and applied to a residential scenario.•A hybrid algorithm was implemented to solve the non-convex MINLP, and can obtain near optimal solution.•The performance of proposed algorithm is better for solving demand response related MINLP than commercial solver Knitro.
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ISSN:0959-6526
1879-1786
DOI:10.1016/j.jclepro.2019.04.062