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Controlling the energy production at home. Maurice Bosman PhD TW colloquium. 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. Energy market.
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Controlling the energy production at home Maurice Bosman PhD TW colloquium
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.
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
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
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)
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
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
If you live in Haaksbergen microCHP radiator grid 10
Problem setting • Use a microCHP in house • Apply this onto many houses • Electricity supplier offers global control of the appliances
Research goal • Study the consequences of introducing a fleet of microCHPs: • Controllability/scalability • Optimization heuristics
Controllability/scalability • Global Scheduler • Local Scheduler (Embedded Computer) • Hierarchical structure • Hard Constraints: • Household comfort • Limited communication • Real time decision making
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
Scheduling • Offline: use all available information • Online: receive a job and schedule immediately (example: earliest possible)
Our scheduling problem • Jobs : switched on microCHP appliances • Jobs have undetermined length! • Online problem; repetitive jobs
ILP formulation • Decision variable • ‘Accountancy’ equations
ILP formulation • Objective: minimize/maximize something • Heatstore; below LL: switch on • Heatstore; above UL: switch off
ILP formulation • Need to run minimum runtime MR • Stay switched off for minimum time MO • Fleet capacity restrictions
Scheduling problem • Optimal values (AIMMS)
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!