Economic and environmental assessment of interconnected hub systems in the presence of thermal, water and hydrogen storage systems. (15th November 2022)

• Record Type: Journal Article
• Authors: Guo, Xinghua; Guo, Qun; Qiannan, Tian; Nojavan, Sayyad
• Is Part Of: Journal of energy storage. Volume 55:Part B(2022)
• Publication Date: 2022-11-15
• Subjects: Interconnected hub systems -- Managing freshwater extraction -- Adjustable robust optimization approach -- Risk management and analysis; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2022.105531
• Languages: English
• ISSNs: 2352-152X

Abstract

Abstract: Simultaneous management of freshwater consumption and energy expenses in an uncertain environment is one of the most challenging issues. So, in this paper, a model comprising three interconnected hub systems is studied in an uncertain environment considering two financial and environmental targets. Minimizing energy expenses is the financial target, and freshwater extraction is regarded as an environmental target. The uncertain nature of pool market prices and electrical, thermal and water demands and the risk linked with uncertain parameters are modelled and managed using an adjustable robust optimization approach. Furthermore, two incompatible objective functions are handled simultaneously using the ɛ-constraint method. The studied model is simulated in eleven different robustness levels and four different levels of water extraction, which makes effective strategies that can be selected and used based on the operator's priorities. According to the results, with an approximate 13.56 % increase in operation costs, freshwater extraction can decrease from the highest to the lowest level. Besides, the studied hub systems can reach the highest resistance level with nearly 56.09 % increases in operating expenses. Highlights: Risk-oriented energy management of interconnected hub systems is studied. Economic and environmental functions are solved using the ɛ-constraint method. Adjustable robust optimization approach is proposed to model the uncertain parameters. The studied model is simulated in eleven different robustness levels.