[Colloquium] Reminder - Peter Lin MS Presentation/Dec 17, 2021

[meganwoodward at uchicago.edu]

This is an announcement of Peter Lin's MS Presentation
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Candidate: Peter Lin

Date: Friday, December 17, 2021

Time:  2 pm CST

Remote Location: https://uchicago.zoom.us/s/2674223059?pwd=VUVzZmdrV3NXWndKbFBTRzlDT2wzQT09 Meeting ID: 267 422 3059 Passcode: 465902

Location: JCL 298

M.S. Paper Title: How to Effectively Couple Datacenters as Flexible Loads to the Power Grid for Carbon Reduction

Abstract: The rapid growth of datacenter (DC) loads can be leveraged to help meet renewable portfolio standard (RPS, renewable fraction) targets in power grids. The ability to manipulate DC loads over time (temporal shifting) can facilitate the absorption of renewables and grid decarbonization. We study DC-grid coupling models, characterizing their impact on grid dynamics (e.g. power price, carbon emissions) and DC behaviors, with the goal of finding how to effectively couple dynamic DC loads to the power grid for carbon reduction.

With a detailed grid model, we consider a spectrum of coupling models varied in who controls the dynamic load and what DC load information is shared with the grid (e.g. next hour or day's load). Results show that understanding the effects of dynamic DC load management requires modeling the dynamics of both load and power grid.  Dynamic DC-grid coupling can produce large improvements: (1) reduce grid dispatch cost (-3%), (2) increase grid renewable fraction (+1.58%), and (3) reduce DC power cost (-16.9%).  However, it also has negative effects: (1) increase cost for both DCs and non-DC customers, (2) differentially increase prices for non-DC customers---fairness issue, and (3) create large power-level changes that may harm DC productivity. Such negative effects are more evident when uncoordinated DC-local control overshifts the load. Addressing these, we find that by utilizing hourly price information, constraining capacity variation, and sharing such 24-hour dynamic load with the power grid, DC-local control can approximate the best performance of global grid-controlled optimization with lower DC capacity variation.

Advisors: Andrew Chien

Committee Members: John Birge, Andrew Chien, and Sanjay Krishnan