@Article{cmes.2023.029453,
AUTHOR = {Ali S. Alghamdi, Mohana Alanazi, Abdulaziz Alanazi, Yazeed Qasaymeh, Muhammad Zubair, Ahmed Bilal Awan, M. G. B. Ashiq},
TITLE = {Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {3},
PAGES = {2163--2192},
URL = {http://www.techscience.com/CMES/v137n3/53725},
ISSN = {1526-1506},
ABSTRACT = {To maximize energy profit with the participation of electricity, natural gas, and district heating networks in the
day-ahead market, stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and
wind resources, has been carried out. This has been done using a new meta-heuristic algorithm, improved artificial
rabbits optimization (IARO). In this study, the uncertainty of solar and wind energy sources is modeled using
Hang’s two-point estimating method (TPEM). The IARO algorithm is applied to calculate the best capacity of hub
energy equipment, such as solar and wind renewable energy sources, combined heat and power (CHP) systems,
steam boilers, energy storage, and electric cars in the day-ahead market. The standard ARO algorithm is developed
to mimic the foraging behavior of rabbits, and in this work, the algorithm’s effectiveness in avoiding premature
convergence is improved by using the dystudynamic inertia weight technique. The proposed IARO-based scheduling framework’s performance is evaluated against that of traditional ARO, particle swarm optimization (PSO),
and salp swarm algorithm (SSA). The findings show that, in comparison to previous approaches, the suggested
meta-heuristic scheduling framework based on the IARO has increased energy profit in day-ahead electricity, gas,
and heating markets by satisfying the operational and energy hub limitations. Additionally, the results show that
TPEM approach dependability consideration decreased hub energy’s profit by 8.995% as compared to deterministic
planning.},
DOI = {10.32604/cmes.2023.029453}
}