1. Scheduling deferrable electric appliances in smart homes: a bi-objective stochastic optimization approach
- Author
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Jamal Toutouh, Segio Nesmachnow, Diego Gabriel Rossit, Inmabb Uns-Conicet, Bahía Blanca, Argentina, and Francisco Luna
- Subjects
Mathematical optimization ,SMART HOMES ,smart cities ,Computer science ,bi-objective optimization ,Scheduling (production processes) ,HOUSEHOLD ENERGY PLANNING ,household energy planning ,MONTE CARLO SIMULATION ,Electricity ,Bi objective ,QA1-939 ,Heuristics ,Humans ,SMART CITIES ,Cities ,purl.org/becyt/ford/2.11 [https] ,mixed-integer programming ,Applied Mathematics ,urban data analysis ,URBAN DATA ANALYSIS ,monte carlo simulation ,General Medicine ,stochastic optimization ,greedy heuristic ,Computational Mathematics ,BI-OBJECTIVE OPTIMIZATION ,purl.org/becyt/ford/2 [https] ,smart homes ,Modeling and Simulation ,GREEDY HEURISTIC ,Stochastic optimization ,MIXED-INTEGER PROGRAMMING ,General Agricultural and Biological Sciences ,Algorithms ,STOCHASTIC OPTIMIZATION ,TP248.13-248.65 ,Mathematics ,Biotechnology - 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. Fil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina Fil: Nesmachnow, Sergio. Universidad de la República; Uruguay Fil: Toutouh, Jamal. Universidad de Málaga. Departamento de Lenguajes y Ciencias de la Computación; España Fil: Luna, Francisco. Universidad de Málaga. Departamento de Lenguajes y Ciencias de la Computación; España
- Published
- 2022