2017 Ninth Annual IEEE Green Technologies Conference
Electric utility residential demand response programs typically do not shape load using intraday, transactive, and local setpoint adjustments of storage-capable thermostatically controlled loads (TCL). In the future, it is envisioned that utilities will continually broadcast forecast dynamic prices of electricity to automatically shape residential load and create load elasticity by alternatively encouraging or discouraging electric energy use. In this research, a binary conditional algorithm was developed and applied to TCL appliance empirical time series data to estimate price-based instantaneous load add and shed opportunities. To overcome limitations of traditional stochastic methods in quantifying diverse, non-normal, non-stationary distributions, recent developments in spectral methods were applied to capture and simulate load in both the frequency and time domains. The performance of autoregressive and spectral reconstruction methods was compared, with phase reconstruction providing the best simulation ensembles. The terminal application of this work is simulating the monetary savings anticipated from wide-scale deployment of price-responsive model predictive control of air conditioning, domestic hot water (DHW) heating, and battery systems.
Cruickshank III, Robert F.; Henze, Gregor P.; Rajagopalan, Balaji; Hodge, Bri-Mathias S.; and Florita, Anthony R., "Quantifying the Opportunity Limits of Automatic Residential Electric Load Shaping" (2017). Civil Engineering Faculty Contributions. 17.