Demand side management given distributed generation and storage: A comparison for different pricing and regulation scenarios

With the advent of demand side management (DSM) in smart grid environment, energy can be operated more effectively from the consumers' side. In this paper firstly a day-ahead DSM optimization problem is formulated, which includes demand, generation, storage and cost models. Then a scenario based study for Australian residential households is conducted to reveal the effect of the pricing and regulation on the DSM's performance. Three typical scenarios of pricing and regulation suitable for Australia are studied, i.e. “Real time pricing scheduling”, “ToU with FiT”, “Real time pricing plus FiT”. A distributed algorithm is used to optimize DSM problem, which can preserve the user's privacy and is also scalable in both time domain and sample size. Through this study, we show the differences and insights of the impact of pricing and regulations on DSM's performance, which can provide useful information for utilities to design proper schemes in the future.