http://caes.ewi.utwente.nl

Computer Architecture for Embedded Systems (CAES) groupFaculty of Electrical Engineering, Mathematics and Computer Science

University of TwenteEnschede, The Netherlands

November 13, 2008

Controlling the energy production at home

Maurice BosmanPhD TW colloquium

November 13, 2008 2

The year is AD 2008…

And electricity is entirely produced by large power plants.

Well not entirely! There is a strong

trend towards a distributed elec-tricity production.

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Energy market

Liberalised on July 1, 2004 Competition! Electricity producers vs electricity suppliers

meet on electricity market (APX) Grid operators are obliged to allow all

suppliers in their region

Entrance possibility: distributed production

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Distributed electricity production

Less transport losses Higher efficiency of production at home Use of renewable sources CO2 reduction Relief of loads of electricity grid

Production capacity limited Demand/supply matching

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Electricity production at home

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Heat production at home

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MicroCHP

Micro Combined Heat and Power Input: gas Output: electricity and heat

Electricity consumed at home or delivered to the grid

Heat consumed at home (no concrete plans to share heat within a neighbourhood)

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Heat demand in a house

Central heating, tap water Immediate supply by household device

(unless you live in Enschede Zuid) Heat buffer necessary for scheduling

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Electricity demand in a house

Fridge, tv, coffee machine, … Supply is no issue (unless you live in

Haaksbergen) Electricity pricing Electricity buffer possible, but not

necessary

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radi

ator

If you live in Haaksbergen

grid

mic

roC

HP

10

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Problem setting

Use a microCHP in house Apply this onto many houses Electricity supplier offers global control of

the appliances

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Research goal

Study the consequences of introducing a fleet of microCHPs: Controllability/scalability Optimization heuristics

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Controllability/scalability Global Scheduler Local Scheduler (Embedded Computer) Hierarchical structure Hard Constraints:

Household comfort Limited communication Real time decision making

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Optimization heuristics

Several objectives Minimize total electricity costs Minimize total energy costs Maximize total electricity revenue Minimize transportational losses Minimize peak loads at transformers

Make use of electricity market (APX) Make use of electricity and heat profiles

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APX prices

0,00

20,00

40,00

60,00

80,00

100,00

120,00

140,00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

time (h)

pric

e (

eu

ro

/MW

h)

7-11-2008

8-11-2008

9-11-2008

10-11-2008

11-11-2008

12-11-2008

13-11-2008

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Scheduling

Offline: use all available information Online: receive a job and schedule

immediately (example: earliest possible)

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Our scheduling problem

Jobs : switched on microCHP appliances Jobs have undetermined length! Online problem; repetitive jobs

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ILP formulation

Decision variable ‘Accountancy’ equations

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ILP formulation

Objective: minimize/maximize something

Heatstore; below LL: switch on

Heatstore; above UL: switch off

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ILP formulation

Need to run minimum runtime MR

Stay switched off for minimum time MO

Fleet capacity restrictions

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Scheduling problem

Optimal values (AIMMS)

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Scheduling problem

When is it good to use longer jobs? Divide jobs into classes

Heatstore information Runtime information Consumption prediction

Make decisions that balance classes! Make switch off decisions!

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Questions

?