Empirical analysis of real time pricing mechanisms for demand side management: Contemporary review

The smart grid promises a myriad of benefits for both the consumer and energy service providers. However, realising its potential is subject to solving a number of complex issues. One of the major directions of smart grid research is demand response modelling that aimed at reducing the peak demand and billing by introducing appropriate real time pricing. The main difficulty is in managing the optimum pricing on a real time basis. In this paper, we will provide a state of the art review of an existing approach that models demand response in real time and also the underlying model that is tested with real data by using a stochastic iterative process with simultaneous perturbation stochastic approximation algorithm. Our model shows that real time price is better than a flat rate price. We will provide a brief outlook to the future where we propose a model that takes complex customers' behaviour into consideration. The proposed model includes a real time Price Suggestion Unit (PSU) that assists users to further reduce their electricity price while reducing the aggregate load for the energy providers.