Scheduling deferrable electric appliances in smart homes: a bi-objective stochastic optimization approach

Diego G. Rossit; Segio Nesmachnow; Jamal Toutouh; Francisco Luna; Diego G. Rossit; Segio Nesmachnow; Jamal Toutouh; Francisco Luna

doi:10.3934/mbe.2022002

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 1: 34-65. doi: 10.3934/mbe.2022002

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Scheduling deferrable electric appliances in smart homes: a bi-objective stochastic optimization approach

1.
Department of Engineering, Universidad Nacional del Sur, Bahía Blanca, Argentina
2.
INMABB UNS-CONICET, Bahía Blanca, Argentina
3.
Universidad de la República, Montevideo, Uruguay
4.
Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Málaga, España

Academic Editor:Wai Lok Woo

Received: 21 August 2021 Accepted: 28 October 2021 Published: 08 November 2021

In the last decades, cities have increased the number of activities and services that depends on an efficient and reliable electricity service. In particular, households have had a sustained increase of electricity consumption to perform many residential activities. Thus, providing efficient methods to enhance the decision making processes in demand-side management is crucial for achieving a more sustainable usage of the available resources. In this line of work, this article presents an optimization model to schedule deferrable appliances in households, which simultaneously optimize two conflicting objectives: the minimization of the cost of electricity bill and the maximization of users satisfaction with the consumed energy. Since users satisfaction is based on human preferences, it is subjected to a great variability and, thus, stochastic resolution methods have to be applied to solve the proposed model. In turn, a maximum allowable power consumption value is included as constraint, to account for the maximum power contracted for each household or building. Two different algorithms are proposed: a simulation-optimization approach and a greedy heuristic. Both methods are evaluated over problem instances based on real-world data, accounting for different household types. The obtained results show the competitiveness of the proposed approach, which are able to compute different compromising solutions accounting for the trade-off between these two conflicting optimization criteria in reasonable computing times. The simulation-optimization obtains better solutions, outperforming and dominating the greedy heuristic in all considered scenarios.
- smart cities,
- smart homes,
- urban data analysis,
- household energy planning,
- stochastic optimization,
- mixed-integer programming,
- Monte Carlo simulation,
- bi-objective optimization,
- greedy heuristic
Citation: Diego G. Rossit, Segio Nesmachnow, Jamal Toutouh, Francisco Luna. Scheduling deferrable electric appliances in smart homes: a bi-objective stochastic optimization approach[J]. Mathematical Biosciences and Engineering, 2022, 19(1): 34-65. doi: 10.3934/mbe.2022002

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Abstract

In the last decades, cities have increased the number of activities and services that depends on an efficient and reliable electricity service. In particular, households have had a sustained increase of electricity consumption to perform many residential activities. Thus, providing efficient methods to enhance the decision making processes in demand-side management is crucial for achieving a more sustainable usage of the available resources. In this line of work, this article presents an optimization model to schedule deferrable appliances in households, which simultaneously optimize two conflicting objectives: the minimization of the cost of electricity bill and the maximization of users satisfaction with the consumed energy. Since users satisfaction is based on human preferences, it is subjected to a great variability and, thus, stochastic resolution methods have to be applied to solve the proposed model. In turn, a maximum allowable power consumption value is included as constraint, to account for the maximum power contracted for each household or building. Two different algorithms are proposed: a simulation-optimization approach and a greedy heuristic. Both methods are evaluated over problem instances based on real-world data, accounting for different household types. The obtained results show the competitiveness of the proposed approach, which are able to compute different compromising solutions accounting for the trade-off between these two conflicting optimization criteria in reasonable computing times. The simulation-optimization obtains better solutions, outperforming and dominating the greedy heuristic in all considered scenarios.

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