A systematic data-driven Demand Side Management method for smart natural gas supply systems

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.