A bi-level optimization framework for resilience enhancement of electricity and natural gas networks with participation of energy hubs. (November 2022)

• Record Type: Journal Article
• Authors: Alasvand Javadi, Ehsan; Joorabian, Mahmood; Barati, Hassan
• Is Part Of: International journal of electrical power & energy systems. Volume 142:Part A(2022)
• Publication Date: 2022-11
• Subjects: Integrated electricity and natural gas networks -- Energy hubs -- Resilience enhancement -- Distribution feeder reconfiguration -- Mobile distributed generators -- Mobile storage systems; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2022.108312
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Providing a bi-level framework for resilience enhancement of integrated energy systems. Reducing the deviation of the final schedule of hubs from their requested schedule. 52.71% reduction of FLS in the network by deploying MDGs and MESS. Reducing the FLS in the network through hardening and DFR. 408.22 kW reduction in FLS in industrial hubs through DLC program implementation. Abstract: This study develops a comprehensive model for enhancing the resilience of integrated electricity and natural gas networks in the presence of industrial, commercial and residential hubs. Distribution system hardening is used as a long-term plan to improve system resilience, while a bi-level operation model is proposed to enhance real-time resilience. In the proposed model, in the upper level, the hubs perform their optimal scheduling and send it to distribution system operator (DSO). Then, in the lower level, the DSO performs network scheduling with regard to hubs' schedules, and determines limitations and power/gas shortages during emergency conditions. It should be noted that DSO performs network scheduling with the possibility of distribution feeder reconfiguration (DFR) and deploying mobile distributed generations (MDGs) and mobile energy storage systems (MESSs). Finally, the limitations are sent to the upper level and the hubs perform their scheduling according to the new condition. The proposed model is implemented on a 69-bus electricity network integrated with a 14-node gas network, and the results show that the proposed model not only reduces the deviation of the final schedule of hubs from their requested schedule but also significantly reduces the amount of forced load shedding (FLS) in the network.