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Balancing energy demand and supply without forecasts: online approaches and algorithms
Giorgos Georgiadis
Overview
2
• 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
Overview (2)
3
• 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
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)
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)
Scheduling invisible house loadsDefinitions
6Barker et al (2012)
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…
Scheduling invisible house loadsSome results
8Barker et al (2012)
Overview
9
• 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
……
…
……
…
10
Lower the peaks!
………………
Both
electrical
and thermal
energy
Georgiadis, Papatriantafilou (2014)
Online load balancing with storage
• 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
12
Scheduling tasks to machines
Eliminate time parameter(for flexible tasks)
Incorporate storage
Identifying task types
Georgiadis, Papatriantafilou (2014)
Modeling energy dispatch
13
Simple: Assign incoming task to machine with min load-storage differenceEfficient: Within of the OPTlog 1n
Georgiadis, Papatriantafilou (2014)
Demand assignment algorithm
14
• By definition:• If then• Goal: prove
WiRi-1
Ri
Georgiadis, Papatriantafilou (2014)
Algorithm proof (core idea)
Overview
15
• 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
16
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
17
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
18
• Why ? %budget spend - 11bet e
Tinkering with AdwordsThe end result
19
• 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
20
Backup slides
What’s next?
21
• 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)
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)
22
Business-as-usual Moderate growth Smart house/neighborhood
Georgiadis, Papatriantafilou (2014)
Experimental results
23
Peaks: lowered!