Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments

Abstract

Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing anoptimalandautomaticresidential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing theelectricity paymentand minimizing thewaiting timefor the operation of each appliance in household in presence of a real-time pricing tariffcombinedwithinclining block rates. Our design requires minimum effort from the users and is based on simplelinear programmingcomputations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requiresprice predictioncapabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficientweighted average price predictionfilter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of thecoefficientsfor each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resultingpeak-to-average ratioin load demand for various load scenarios. Therefore, the deployment of the proposed optimal energy consumption scheduling schemes is beneficial for both end users and utility companies.