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|Title:||Day-ahead scheduling of distribution level integrated electricity and natural gas system based on fast-ADMM with restart algorithm||Authors:||Chen, Jian
|Issue Date:||2018||Source:||Chen, J., Zhang, W., Zhang, Y., & Bao, G. (2018). Day-ahead scheduling of distribution level integrated electricity and natural gas system based on fast-ADMM with restart algorithm. IEEE Access, 6, 17557-17569.||Series/Report no.:||IEEE Access||Abstract:||Power generated by the natural gas (NG) is a promising option for solving the restrictions on the development of the power industry. Consequently, the high interdependence between NG network and electricity network should be considered in this integration. In this paper, a day-ahead scheduling framework of integrated electricity and NG system (IENG) is proposed at a distribution level based on the fast alternating direction multiplier method with restart algorithm considering demand side response and uncertainties. Within the proposed framework, the detailed model of the IENG system at a distribution level is established, where the NG flow equation is processed by incremental linearization method to improve the computational efficiency. The objective is to minimize the operation costs of the entire system. With consideration of the uncertainties of distributed generation and electricity load as well as the uncertainties from the NG load, a two-stage robust optimization model is introduced to obtain the worst case within the uncertainty set, which is solved by column and constraints generation algorithm. In addition, the demand-side response (DSR) model including the decentralized air conditioning (AC) load model and the centralized ice-storage AC load model is integrated into the scheduling framework. Finally, the proposed day-ahead scheduling framework is verified by numerical studies where the optimal scheduling schemes are obtained in different cases, both the effects of the uncertainties and the performance with introducing DSR to the system operation are analyzed.||URI:||https://hdl.handle.net/10356/87724
|DOI:||http://dx.doi.org/10.1109/ACCESS.2018.2818756||Rights:||© 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
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