Scenario-based real-time demand response considering wind power and price uncertainty

Real-time pricing can potentially lead to economic advantages for consumers in the environment of smart grid. Compared with flat rates, dynamic pricing allows consumers more engagement through measures of demand response (DR). This paper investigated the optimal hourly electricity consumption scheduling problem of a given consumer responding real-time price. The objective of the proposed model is to maximize the surplus of a consumer that is equipped with wind power and storage devices. Hourly utility curve is considered as a function of power consumption. Bidirectional communication between the consumer and the supplier allows for interval price updates, so the consumer can flexibly adjust hourly demand. Key sources influencing final performance are price uncertainty and renewable power generation uncertainty. Uncertainties are modelled via scenario-based stochastic optimization, where its feasibility is illustrated in numerical simulations.