Application of genetic algorithms and the cross-entropy method in practical home energy management systems

• Published in: IET renewable power generation 2019-07, Vol.13 (9), p.1474-1483
• Main Authors: Abreu, Cláudia, Soares, Inês, Oliveira, Leonel, Rua, David, Machado, Paulo, Carvalho, Leonel, Peças Lopes, Joao A
• Format: Article
• Language: English
• Subjects: acceptable execution time; air temperature comfort configurations; cross‐entropy method; energy management systems; entropy; generation equipment; genetic algorithms; integer programming; linear programming; low‐power HEMS; mixed‐integer linear programming formulation; nondeterministic optimisation methods; optimisation problem; practical applications; practical home energy management systems; Research Article; total energy cost; traditional deterministic solvers; user preferences
• ISSN: 1752-1416; 1752-1424; 1752-1424
• DOI: 10.1049/iet-rpg.2018.6022

Home energy management systems (HEMSs) are important platforms to allow consumers the use of flexibility in their consumption to optimise the total energy cost. The optimisation procedure embedded in these systems takes advantage of the nature of the existing loads and the generation equipment while complying with user preferences such as air temperature comfort configurations. The complexity in finding the best schedule for the appliances within an acceptable execution time for practical applications is leading not only to the development of different formulations for this optimisation problem, but also to the exploitation of non-deterministic optimisation methods as an alternative to traditional deterministic solvers. This study proposes the use of genetic algorithms (GAs) and the cross-entropy method (CEM) in low-power HEMS to solve a conventional mixed-integer linear programming formulation to optimise the total energy cost. Different scenarios for different countries are considered as well as different types of devices to assess the HEMS operation performance, namely, in terms of outputting fast and feasible schedules for the existing devices and systems. Simulation results in low-power HEMS show that GAs and the CEM can produce comparable solutions with the traditional deterministic solver requiring considerably less execution time.