Abstract
Electric utility residential demand response programs typically reduce load a few times a year during periods of peak energy use. In the future, utilities and consumers may monetarily and environmentally benefit from continuously shaping load by alternatively encouraging or discouraging the use of electricity. One way to shape load and introduce elasticity is to broadcast forecasts of dynamic electricity prices that orchestrate electricity supply and demand in order to maximize the efficiency of conventional generation and the use of renewable resources including wind and solar energy. A binary control algorithm that influences the on and off states of end uses was developed and applied to empirical time series data to estimate price-based instantaneous opportunities for shedding and adding electric load. To overcome the limitations of traditional stochastic methods in quantifying diverse, non-Gaussian, non-stationary distributions of observed appliance behaviour, recent developments in wavelet-based analysis were applied to capture and simulate time-frequency domain behaviour. The performance of autoregressive and spectral reconstruction methods was compared, with phase reconstruction providing the best simulation ensembles. Results show spatiotemporal differences in the amount of load that can be shed and added, which suggest further investigation is warranted in estimating the benefits anticipated from the wide-scale deployment of continuous automatic residential load shaping. Empirical data and documented software code are included to assist in reproducing and extending this work.
Highlights
Residential demand response programs typically are implemented as utility-initiated, infrequent, short-duration deferrals of peak electricity usage through direct load control
It was assumed that utilities would evolve beyond direct load control and use automated residential load shaping (ARLS) to create elasticity in demand by continually broadcasting a forecast dynamic price of electricity to Internet of Things (IoT) appliances in order to maximize generation efficiency and minimize the cost of electric power production and consumption
Changes in pricing resulted in instantaneous load shed and add opportunities which were quantified for single-family homes in the Northwest United States
Summary
Residential demand response programs typically are implemented as utility-initiated, infrequent, short-duration deferrals of peak electricity usage through direct load control. Customers allow their utility to remotely turn off appliances such as air conditioning and water heating—a few times a year—for a credit on their electric bill [1]. Despite the promise of load management through supervisory control of the Internet of Things (IoT), direct load control has remained the primary form of residential demand response for several decades [1,2]. Direct load control was never designed to shed and add load to accommodate the ebb and flow of wind and solar energy. As an alternative to direct load control, automatic residential load shaping (ARLS). Is explored as a load elasticity solution for maximizing the system-wide efficiency of electric power generation via intraday control of IoT devices [3] while meeting the needs and comfort preferences of consumers.
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