Ghada Abdulnasser , Essam E.M. Mohamed , Mostafa F. Shaaban , Abdelfatah Ali
{"title":"A multi-objective strategic planning of smart energy hubs and hydrogen refueling stations toward net-zero emissions","authors":"Ghada Abdulnasser , Essam E.M. Mohamed , Mostafa F. Shaaban , Abdelfatah Ali","doi":"10.1016/j.segan.2025.101690","DOIUrl":null,"url":null,"abstract":"<div><div>Hydrogen-based generation and storage technologies have been increasingly emerging as an appealing candidate for decarbonizing different sectors, including microgrids and transportation. Practically, energy hubs (EH) and Hydrogen refueling stations (HRS) could provide an ideal environment for integrating such technologies. However, the management process involves several conflicting objectives that must be met to a satisfactory extent. In this regard, this paper proposes a stochastic bi-level tri-objective optimization framework for the planning and operation of EHs and on-site green/blue HRSs. The multiple objectives involve total cost (i.e., capital, operation and maintenance, Hydrogen, and emissions), load profile deviation resulting from engaging the demand response program (DRP), and the dissatisfaction of fuel cell electric vehicles (FCVs) owners. The proposed model forms a bi-level optimization strategy. The upper-level optimization (i.e., planning level) optimizes the sizes and locations of renewable energy sources (RESs) along with the capacities, rates, and locations of the other resources (i.e., photovoltaic, wind turbine, Hydrogen storage system, thermal storage system) integrated into both EHs and HRSs incorporated into the IEEE-69 system. On the other hand, the lower level (i.e., operation level) precisely optimizes the charging and discharging profiles of the different resources incorporated in EHs and HRSs along with FCVs. The Pareto optimal solution is employed to find the best-compromised solution among the conflicting tri-objective solutions. The simulation results demonstrate that the green-controlled approach has validated its superiority for net-zero emissions transition with a 10.74 % reduction in emissions costs at almost the same total cost compared to the other approaches (i.e., blue-uncontrolled, blue-controlled, green-uncontrolled).</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101690"},"PeriodicalIF":4.8000,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Energy Grids & Networks","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352467725000724","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
Hydrogen-based generation and storage technologies have been increasingly emerging as an appealing candidate for decarbonizing different sectors, including microgrids and transportation. Practically, energy hubs (EH) and Hydrogen refueling stations (HRS) could provide an ideal environment for integrating such technologies. However, the management process involves several conflicting objectives that must be met to a satisfactory extent. In this regard, this paper proposes a stochastic bi-level tri-objective optimization framework for the planning and operation of EHs and on-site green/blue HRSs. The multiple objectives involve total cost (i.e., capital, operation and maintenance, Hydrogen, and emissions), load profile deviation resulting from engaging the demand response program (DRP), and the dissatisfaction of fuel cell electric vehicles (FCVs) owners. The proposed model forms a bi-level optimization strategy. The upper-level optimization (i.e., planning level) optimizes the sizes and locations of renewable energy sources (RESs) along with the capacities, rates, and locations of the other resources (i.e., photovoltaic, wind turbine, Hydrogen storage system, thermal storage system) integrated into both EHs and HRSs incorporated into the IEEE-69 system. On the other hand, the lower level (i.e., operation level) precisely optimizes the charging and discharging profiles of the different resources incorporated in EHs and HRSs along with FCVs. The Pareto optimal solution is employed to find the best-compromised solution among the conflicting tri-objective solutions. The simulation results demonstrate that the green-controlled approach has validated its superiority for net-zero emissions transition with a 10.74 % reduction in emissions costs at almost the same total cost compared to the other approaches (i.e., blue-uncontrolled, blue-controlled, green-uncontrolled).
期刊介绍:
Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.