balancing energy demand and supply without forecasts: online approaches and algorithms giorgos...
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Balancing energy demand and supply without forecasts: online approaches and algorithms
Giorgos Georgiadis
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Overview
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• 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
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Overview (2)
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• 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
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Scheduling invisible house loadsPremise
4Barker et al (2012)
• 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)
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Scheduling invisible house loadsDefinitions
5Barker et al (2012)
• 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)
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Scheduling invisible house loadsDefinitions
6Barker et al (2012)
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Scheduling invisible house loadsAlgorithm
7Barker et al (2012)
• 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…
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Scheduling invisible house loadsSome results
8Barker et al (2012)
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Overview
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• 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
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……
…
……
…
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Lower the peaks!
………………
Both
electrical
and thermal
energy
Georgiadis, Papatriantafilou (2014)
Online load balancing with storage
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• Types of tasks• Elastic/inelastic• Electrical/thermal• Storage/simple
• Simplifications and assumptions• No distinction of local/global storage• Diurnal pattern, hourly slots
11Georgiadis, Papatriantafilou (2014)
Definitions
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Scheduling tasks to machines
Eliminate time parameter(for flexible tasks)
Incorporate storage
Identifying task types
Georgiadis, Papatriantafilou (2014)
Modeling energy dispatch
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Simple: Assign incoming task to machine with min load-storage differenceEfficient: Within of the OPTlog 1n
Georgiadis, Papatriantafilou (2014)
Demand assignment algorithm
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• By definition:• If then• Goal: prove
WiRi-1
Ri
Georgiadis, Papatriantafilou (2014)
Algorithm proof (core idea)
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Overview
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• 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
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Auction(a la Google Ads)
nord
pool
budget price
ie 30 KW
0.357 SEK/KW
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:
%budget spend - 11bet e
0.231 SEK/KW
Incorporation of online supply
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Q: A new task . Who is going to get it?
Nordpool
budget price
ie 30 KW
0.357 SEK/KW
0.231 SEK/KW
A: The highest bidder 1
1l s
budgetloade
price
SvenskaKraftnat
Tinkering with AdwordsScheduling under constraints
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• Why ? %budget spend - 11bet e
Tinkering with AdwordsThe end result
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• 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!
Summary
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Backup slides
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What’s next?
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• 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)
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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)
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Business-as-usual Moderate growth Smart house/neighborhood
Georgiadis, Papatriantafilou (2014)
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Experimental results
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Peaks: lowered!