Computer Architecture for Embedded Systems (CAES) group Faculty of Electrical Engineering, Mathematics and Computer Science University of Twente Enschede, The Netherlands November 13, 2008 Controlling the energy production at home Maurice Bosman PhD TW colloquium

November 13, 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.

November 13, 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

November 13, Distributed electricity production  Less transport losses  Higher efficiency of production at home  Use of renewable sources  CO 2 reduction  Relief of loads of electricity grid  Production capacity limited  Demand/supply matching

November 13, Electricity production at home

November 13, Heat production at home

November 13, 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)

November 13, 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

November 13, 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

November 13, radiator If you live in Haaksbergen grid microCHP 10

November 13, Problem setting  Use a microCHP in house  Apply this onto many houses  Electricity supplier offers global control of the appliances

November 13, Research goal  Study the consequences of introducing a fleet of microCHPs:  Controllability/scalability  Optimization heuristics

November 13, Controllability/scalability  Global Scheduler  Local Scheduler (Embedded Computer)  Hierarchical structure  Hard Constraints:  Household comfort  Limited communication  Real time decision making

November 13, 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

November 13, APX prices

November 13, Scheduling  Offline: use all available information  Online: receive a job and schedule immediately (example: earliest possible)

November 13, Our scheduling problem  Jobs : switched on microCHP appliances  Jobs have undetermined length!  Online problem; repetitive jobs

November 13, ILP formulation  Decision variable  ‘Accountancy’ equations

November 13, ILP formulation  Objective: minimize/maximize something  Heatstore; below LL: switch on  Heatstore; above UL: switch off

November 13, ILP formulation  Need to run minimum runtime MR  Stay switched off for minimum time MO  Fleet capacity restrictions

November 13, Scheduling problem  Optimal values (AIMMS)

November 13, 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!

November 13, Questions ?