Balancing energy demand and supply without forecasts: online approaches and algorithms

Giorgos Georgiadis

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

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

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

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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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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

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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)

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

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)

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)

Experimental results

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Peaks: lowered!