A systematic data-driven Demand Side Management method for smart natural gas supply systems. (1st April 2019)

• Record Type: Journal Article
• Authors: Su, Huai; Zio, Enrico; Zhang, Jinjun; Chi, Lixun; Li, Xueyi; Zhang, Zongjie
• Is Part Of: Energy conversion and management. Volume 185(2019)
• Page Start: 368
• Page End: 383
• Publication Date: 2019-04-01
• Subjects: Natural gas supply system -- Demand Side Management -- Data-driven -- Deep learning -- Multi-objective optimization; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2019.01.114
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: A data-driven DSM method is developed for complex natural gas networks. Multiple smart technologies are introduced into natural gas supply systems. The proposed method can improve the reliability and efficiency of gas grids. The effectiveness of the demand response method is tested. Abstract: Advanced sensor and communication technologies can make natural gas supply systems smarter than ever before, in both system management and operation. This paper presents the development of a novel data-driven Demand Side Management, whose framework includes demand forecasting, customer response analysis, prediction of dynamic condition of the gas network, quick supply reliability evaluation, multi-objective optimization and decision-making. The aims of this DSM method are to smooth load profiles, improve company profit and enhance system reliability, by means of a dynamic pricing strategy. To verify the effectiveness of the developed framework, a case study is considered, concerning the management of a relatively complex gas supply system, wherein four different pricing periods are introduced for comprehensively testing. The results in the case study show that the DSM framework is able to effectively achieve the targets of peak shaving and valley filling. Besides, it can significantly and stably improve the system efficiency and reliability, for different pricing periods. Finally, pricing period determination is discussed in relation to the features of performance.