A new stochastic optimal smart residential energy hub management system for desert environment

The main purpose of this study is to develop a sustainable smart energy management system for the desert climate, which aims to reduce energy expenses, energy consumption, and greenhouse gas (GHG) emissions via finding optimum power output and smart scheduling while considering users' uncertain behaviors. Moreover, the effectiveness of five metaheuristic optimization algorithms is analyzed and reviewed for presented system, which is modeled as a multiobjective function and contains over 1000 variables. The case study is city of Kashan located at the desert area of Iran with hot and dry climate. Presented system is established based on smart residential energy hub and home energy management system. Residential loads for a modern household are appropriately categorized and modelled. Ten different uncertain scenarios for users' energy consumption are simulated within the algorithm with considering users' comfort level simultaneously. Both energy cost and users' comfort deviation for studied multi-energy system are formulated as a multiobjective function with two weighting factors. Our results present a comparison between different cases studied and the effects of uncertain power consumption on energy cost, comfort level, and computing time. Our findings indicate that the presented system with specified weighting factors is able to reduce energy expenses around 50% in different cases. Accordingly, due to a noticeable decrease in energy consumption, GHG emissions from fossil fuels are reduced remarkably considering the fact that only 1% of Iran's power supply is provided by clean energies. Results also illustrate that considering uncertainty has more effect on users' comfort level than energy cost.

Automated summary

The study aims to develop a sustainable smart energy management system for desert climate, analyzing the effectiveness of five metaheuristic optimization algorithms. The presented system is able to reduce energy expenses by around 50% in different cases, considering uncertain user behaviors and utilizing various weighting factors in a multiobjective function.