Two-Stage Optimal Operation of Smart Homes Participating in Competitive Electricity Markets

• Autor(a) principal: Silva, P.
• Data de Publicação: 2021
• Outros Autores: Osório, G. J., Gough, M., Santos, S. F., Home-Ortiz, J. M. [UNESP], Shafie-Khah, M., Catalão, J. P.S.
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/EEEIC/ICPSEurope51590.2021.9584775; http://hdl.handle.net/11449/223658
• topic: demand response; energy management system; energy storage system; internet of things; smart grid; smart home; stochastic programming

End users have become active participants in local electricity market transactions because of the growth of the smart grid concept and energy storage systems (ESS). This participation is optimized in this article using a stochastic two-stage model considering the day-ahead and real-time electricity market data. This model optimally schedules the operation of a Smart Home (SH) to meet its energy demand. In addition, the uncertainty of wind and photovoltaic (PV) generation is considered along with different appliances. In this paper, the participation of an EV (electric vehicle), together with the battery energy storage systems, which allow for the increase in bidirectional energy transactions are considered. Demand Response (DR) programs are also incorporated which consider market prices in real-time and impact the scheduling process. A comparative analysis of the performance of a smart home participating in the electricity market is carried out to determine an optimal DR schedule for the smart homeowner. The results show that the SH's participation in a real-time pricing scheme not only reduces the operating costs but also leads to better than expected profits. Moreover, total, day-ahead and real-time expected profits are better in comparison with existing literature. The objective of this paper is to analyze the SH performance within the electrical market context so as to increase the system's flexibility whilst optimizing DR schedules that can mitigate the variability of end-users generation and load demand.