Energy optimization of smart home in electrical microgrids considering economic and technical multi-objective functions and demand response programs

• This paper provides an analysis of methods and strategies aimed at optimizing energy use in smart homes in SMG, which are conceptualized as decentralized within the broader framework of power distribution networks. • It also incorporates the utilization of distributed energy resources and electric vehicles that serve as adaptable storage solutions, all managed by a meticulously organized and coordinated scheduling system intended to enhance efficiency and sustainability in energy management. • A stochastic problem is recognized by examining the uncertainties linked to load demand, renewable energy generation, and electricity pricing. • The DRP is executed to attain optimal energy consumption by factoring in electricity pricing and curtailing peak demand. This study proposes a framework for optimizing energy consumption in smart homes, conceptualized as power dispatch entities within power distribution networks, through the integrated management of distributed energy resources and electric vehicles acting as adaptive storage. The defined approach is governed by a carefully coordinated scheduling mechanism designed to enhance efficiency and sustainability in energy management, formulating a stochastic problem that explicitly considers uncertainties in load demand, renewable energy generation, and dynamic electricity pricing. A Demand Response Program (DRP) is implemented to facilitate optimal energy consumption, specifically targeting peak demand reduction and cost efficiency based on real-time pricing signals, while multi-objective optimization aims to simultaneously maximize consumer comfort (CC), minimize the peak-to-average ratio (PAR), and reduce operational costs through the optimal coordination of smart appliances and available resources. Numerical simulations, conducted using MATLAB software across two distinct case studies, provide empirical validation for the proposed strategies. Ultimately, the results highlight the optimal achievement of these objectives and underscore the critical importance of consumer participation, as demonstrated through a comparative analysis across various scenarios. Finally, the findings confirm that an intelligently DRP-based cooperative stochastic scheduling strategy, complemented by adaptive storage management and multi-objective optimization, offers significant improvements in both the economic viability and technical performance of smart grid deployments.

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