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SUMMARY:Competitive Online Peak-Demand Minimization using Energy Storage -
  Minghua Chen\, City University of Hong Kong
DTSTART:20230428T120000Z
DTEND:20230428T130000Z
UID:TALK200203@talks.cam.ac.uk
CONTACT:Srinivasan Keshav
DESCRIPTION:We consider an increasingly popular demand-response scenario w
 here large-load customers\, e.g.\, datacenters\, utilize energy storage to
  reduce the peak procurement from the grid\, which accounts for up to 80% 
 of their electric bills. We focus on minimizing the peak-demand charge usi
 ng energy storage under the online setting\, where the loads and renewable
  generations are revealed sequentially in time but we have to make irrevoc
 able decisions at current epoch with little or no future information. Such
  an online problem is uniquely challenging due to (i) the coupling of irre
 vocable decisions across time imposed by the inventory constraints and (ii
 ) the noncumulative nature of the peak procurement. We tackle this issue b
 y developing an optimal online algorithm for the problem that attains the 
 best possible competitive ratio (CR) among all deterministic and randomize
 d algorithms. We show that the optimal CR can be computed in polynomial ti
 me\, by solving a linear number of linear-fractional problems. More import
 antly\, we generalize our approach to develop an anytime-optimal online al
 gorithm that achieves the best possible CR at any epoch\, given the inputs
  and online decisions so far. The algorithm retains the optimal worst-case
  performance and achieves adaptive average-case performance. Simulation re
 sults based on real-world traces show that\, under typical settings\, our 
 algorithms improve peak reduction by over 19% as compared to baseline alte
 rnatives.\nThis is a joint work with Yanfang Mo\, Qiulin Lin\, and Joe Qin
 \, all from City University of Hong Kong. \n\nBio:\n\nMinghua Chen is a Pr
 ofessor at School of Data Science\, City University of Hong Kong. His rese
 arch interest is in online optimization\, machine learning\, energy system
 s\, transportation\, and networked systems.\nhttps://www.mhchen.com
LOCATION:FW 11\, Willam Gates Hall. Zoom link: https://cl-cam-ac-uk.zoom.u
 s/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&from=addon 
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