Profitably scheduling the energy hub of inhabitable houses considering electric vehicles, storage systems, revival provenances and demand side management through a modified particle swarm optimization. (May 2023)

• Record Type: Journal Article
• Authors: Cheng, Yanhui; Zheng, Haiyan; Juanatas, Ronaldo A.; Golkar, Mohammad Javad
• Is Part Of: Sustainable cities and society. Volume 92(2023)
• Publication Date: 2023-05
• Subjects: Inhabitable energy hub -- Particle swarm optimization -- Power-using hybrid wheels -- Saving approach; EH Power Heart -- REH hub of inhabitable power -- CHP thermal and energy combination -- MCHP thermal and energy combination -- PV Photovoltaic -- MCS resemblance of Monte Carlo -- EES electrical power saving -- DG distributed production -- GA genetic algorithm -- PHEV plug-in hybrid wheels -- HSS thermal saving system -- G2V network to wheels -- V2G wheels to network -- OF identical performance -- PDF presumption compression performance -- CDF accumulative compression performance -- V2H car to house -- DT check-out moment -- AT check-in moment -- SOC charge state -- SCC short orbit flow -- OCV voltage of the open orbit -- WG/WS1/WS2 Possible states for PV modules -- ψWG/ψWG1/ψWG2 Corresponding probability of PV; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2023.104487
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: Presenting a comprehensive model for residential energy hub management. Considering the uncertainty of renewable resources and electricity market price. Consider economic and stochastic scenarios-based objective function. Providing a new optimization method based on the particle swarm optimization. Considering the energy storage system, electric vehicles and demand response. Abstract: To increase power proficiency, reliability, and productive gain, hubs of energy are widely used as poly-transfer approaches. Therefore, the schedule can technically and commercially improve the inhabitable department. As one of the most prominent issues, system uncertainties can negatively affect energy hubs and inhabitable energy hubs. The random modality of power delivery over the provenance of revival power has been perceived as more of a supply-side ambiguity in inhabitable energy hubs than in modeling and demand-side studies, such as hybrid electric vehicles (PHEV). Accordingly, in terms of the demand-side management schedule by the energy storage system, this paper introduces the Energy Hub model for the indeterminacy of revival resources and electricity rates. The production and reduction scenarios were applied to model the indeterminacy of revival provenances. Subsequently, the proposed model would become an optimization problem and would benefit from the improved particle swarm algorithm with local and global operators. Finally, the proposed approach and model were discussed in a study system in different scenarios. The results showed that PHEV and thermal storage systems can act as suitable solutions to reduce utilization expenses of the inhabitable energy hub. Hence, implementing a demand-side management schedule could shift loads from peak hours to other times and avoid higher operational costs.