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Giorgos Georgiadis

Balancing energy demand and supply without forecasts: online approaches and algorithms. Giorgos Georgiadis. Overview. Papers Barker et al (2012), SmartCap: Flattening peak electricity demand in smart homes

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Giorgos Georgiadis

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  1. Balancing energy demand and supply without forecasts: online approaches and algorithms Giorgos Georgiadis

  2. Overview • Papers • Barker et al (2012), SmartCap: Flattening peak electricity demand in smart homes • Georgiadis, Papatriantafilou (2014), Dealing with storage without forecasts in Smart Grids: problem transformation and online scheduling algorithm • Georgiadis, Salem, Papatriantafilou (2015), Tailor your curves after your costume: Supply-following demand in Smart Grids through the Adwords problem • Focus • Online/offline approach • Modeling • Applicability 2

  3. Overview (2) • Barker et al (2012) • Premise: home, background loads, slack • Problem and algorithm • Georgiadis, Papatriantafilou (2014) • Premise: online, renewables, storage • Modeling • Greedy algorithm • Georgiadis, Salem, Papatriantafilou (2015) • Introduction • Online supply 3

  4. Scheduling invisible house loads Premise • Load management scheme for flattening household electricity usage or demand • Modifying background electrical loads that are completely transparent to home occupants and have no impact on their perceived comfort. • I.e. air conditioners (A/Cs), refrigerators, freezers, dehumidifiers, heaters • Online • Least Slack First (LSF) policy (inspired by the Earliest Deadline First algorithm) 4 Barker et al (2012)

  5. Scheduling invisible house loads Definitions • Slack: the remaining length of time the load can be off, i.e., disconnected from power, without assuring that it will violate its objective. • May change over time (online problem) 5 Barker et al (2012)

  6. Scheduling invisible house loads Definitions 6 Barker et al (2012)

  7. Scheduling invisible house loads Algorithm • Least Slack First (LSF) • supplies power to loads in ascending order of their current slack value. • ++ target capacity threshold • Once the sum of the background loads’ power usage reaches the capacity threshold, the scheduler stops powering additional background loads. • Concerns • Threshold too low: defers too many loads, resulting in their slack values approaching zero together… • Threshold too high: power too many background loads at a time. Spikes… 7 Barker et al (2012)

  8. Scheduling invisible house loads Some results 8 Barker et al (2012)

  9. Overview • Barker et al (2012) • Premise: home, background loads, slack • Problem and algorithm • Georgiadis, Papatriantafilou (2014) • Premise: online, renewables, storage • Modeling • Greedy algorithm • Georgiadis, Salem, Papatriantafilou (2015) • Introduction • Online supply 9

  10. Online load balancing with storage • … • … • … • … • … • … • … • … • … • … • … • … Lower the peaks! Both electrical and thermal energy Georgiadis, Papatriantafilou (2014)

  11. Definitions • Types of tasks • Elastic/inelastic • Electrical/thermal • Storage/simple • Simplifications and assumptions • No distinction of local/global storage • Diurnal pattern, hourly slots Georgiadis, Papatriantafilou (2014)

  12. Modeling energy dispatch Identifying task types Scheduling tasks to machines Eliminate time parameter (for flexible tasks) Incorporate storage Georgiadis, Papatriantafilou (2014)

  13. Demand assignment algorithm Simple: Assign incoming task to machine with min load-storage difference Efficient: Within of the OPT Georgiadis, Papatriantafilou (2014)

  14. Algorithm proof (core idea) Ri Ri-1 Wi • By definition: • If then • Goal: prove Georgiadis, Papatriantafilou (2014)

  15. Overview • Barker et al (2012) • Premise: home, background loads, slack • Problem and algorithm • Georgiadis, Papatriantafilou (2014) • Premise: online, renewables, storage • Modeling • Greedy algorithm • Georgiadis, Salem, Papatriantafilou (2015) • Introduction • Online supply 15

  16. Incorporation of online supply 0.357 SEK/KW ie 30 KW nordpool 0.231 SEK/KW Auction (a la Google Ads) budget price • How Google Ads work: • Advertisers come with their daily budgets • Query words appear in the stream and they bet on them • Google awards the word to the “highest” bidder according to the formula: 16

  17. Tinkering with Adwords Scheduling under constraints Svenska Kraftnat Nordpool 0.357 SEK/KW ie 30 KW 0.231 SEK/KW budget price A: The highest bidder  Q: A new task . Who is going to get it? 17

  18. Tinkering with Adwords The end result • Why ? 18

  19. Summary • Background loads, threshold, online-ness (forecasts?) • Online load balancing with storage • Energy dispatch: assignment/matching problem with guarantees • Transformation of time and unforecastability: resource allocation • High quality solution: analytical results and experiments based on real data • More: online load balancing using online supply • Using up available, online supply: dynamic Adwords • Rich problem, new way of thinking • Next: online demand? Think datacenters!

  20. Backup slides

  21. What’s next? ! • … • … ? • … • … • … • … • … • Mixed algorithms • Communication with global optimizer • Allow budget for scheduling over forecasted • Call optimizer when over budget • Strategic games • New modeling extensions/applications Georgiadis, Papatriantafilou (2014)

  22. Experimental setup • Two axis • 1) Demand mix • 2) Number and type of households • Comparison • Longest Processing Time (LPT): sorts tasks by decreasing processing time and then assigns each task to the machine that has the least load (breaking ties arbitrarily) Business-as-usual Moderate growthSmart house/neighborhood Georgiadis, Papatriantafilou (2014)

  23. Experimental results Peaks: lowered!

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