Abstract
As smart load adoption grows on the electric power system, potential for losing load diversity increases, possibly in ways that impact system stability. Cloud computing resources are able to coordinate large amounts of behind-the-meter loads and resources. Inadvertent or malicious actions could potentially result in gigawatts of load, distributed across large regions, acting nearly simultaneously. We study the resulting impacts of such a perturbation, which were previously recognized, with improved fidelity and granularity using a physically-based power system and demand model. The ResStock tool was used to calculate residential air conditioning load at more than 3,000 locations across the Western Interconnection, corresponding in time to heavy summer and light spring loading. Under an assumption that one cloud platform managed smart thermostats controlling 10%, 15%, or 20% of residential air-conditioning, calculated load steps could be injected into Positive Sequence Load Flow dynamic simulations. These load-driven effects were coupled with two classes of distributed generation ride-through to evaluate the potential for cascading outages. We found frequency deviations in the spring case far exceed the credible contingency event, leading to widespread distributed generation loss, while voltage depressions during the summer loading lead to widespread distributed generation loss and system separation.
Highlights
T HE RAPID adoption of connected end-use loads presents an opportunity to reach previously unachievable levels of energy efficiency and dynamic demand integration with grid operations
A total of 38 (6 control schemes x 3 controllable fractions x 2 ride-through criteria + 2 Palo Verde contingencies) Positive Sequence Load Flow (PSLF) simulations were run.A time series of the system frequency is presented as the primary result, with all six relevant traces on each plot
The Western Wind and Solar Integration Study (WWSIS) ride-through results are shown with solid traces, and the PG&E ride-through with dashed traces
Summary
T HE RAPID adoption of connected end-use loads presents an opportunity to reach previously unachievable levels of energy efficiency and dynamic demand integration with grid operations. As markets adapt to Federal Energy Regulatory Commission rulings 745 [1], 841 [2], and 2222 [3], grid-interactive loads will become more valuable, further accelerating their adoption. Accompanying the benefits, such devices could have potential vulnerabilities which are exploited to impact electric power system operations. There are more than 17 million connected thermostats deployed in North America today (and this is expected to grow to at least 60 million by 2027 [14]) Each of these operates a major load, the RAC, which is the category that dominates utility peak load planning. Of the 118 million households in the United States [15], 90% have air conditioning [16], which means roughly 15% of households (and presumably a significantly higher fraction of RAC demand, roughly 25%) is currently served by connected thermostats
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