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Smart control of multiple energy commodities on district scale Frans Koene

Smart control of multiple energy commodities on district scale Frans Koene. Sustainable places, Nice, 1-3 Oct 2014. Partners. Challenge. Facilitate the implementation of large shares of renewables in energy supply systems. Daily mismatch. Annual mismatch.

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Smart control of multiple energy commodities on district scale Frans Koene

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  1. Smart control of multiple energy commodities on district scale Frans Koene Sustainable places, Nice, 1-3 Oct 2014

  2. Partners

  3. Challenge Facilitate the implementation of large shares of renewables in energy supply systems Daily mismatch Annual mismatch • How can we match energy supply and demand? • Energy storage • Smart control of appliances→ time shift of demand

  4. Simulation environment Models of components Control algorithm to match supply & demand of heat and electricity Dynamic aggregated model of buildings in the district boiler PV CHP storage Simulation Engine GUI Electricity and DHW profiles Business models based on flexibility of demand

  5. Aggregated building model = Inputs building model • Size, volume, windows, orientation • Thermal insulation • Thermal set points for heating & cooling • Internal heat generation • Parameters automatic solar shading F.G.H. Koene et al.: Simplified building model of districts, proceedings IBPSA BauSIM 22 -24 Sept 2014, Aachen, Germany

  6. Agent based technology

  7. Multi Commodity Matcher HP electrical power bid HP thermal power bid electr price electr price heat price heat price aggr. thermal power bid aggr. electrical power bid electr price electr price heat price heat price P. Booij et al.: Multi-agent control for integrated heat and electricity management in residential districts , proceedings of AAMAS - ATES conference, 6-10 May 2013, USA

  8. Business Concepts based on flexibility

  9. Case studies

  10. Scenarios Reference or BAU scenario- conventional sources for energy supply- electricity from the public grid- heat produced by de-central gas fired boilers. RES (Renewable Energy Sources) or green scenario with fixed energy demand- heat and electricity are (partly) produced with renewables (PV, biomas CHP)- no demand-side flexibility (i.e. no smart appliances) Smartscenario or RES scenario with flexible energy demand and supply- renewable energy sources (as in 2nd scenario)- demand-side flexibility - business objective: local balancing and national balancing

  11. Example: district of Houthaven, Amsterdam • 201.300 m2residential • 13.900 m2 commercial • 14 aggregated buildings • 16.8 km heat network • Copper plate grid • No cold network (electrical cooling) • Rooftop & District PV (4.5 kWp)

  12. Space heating– RES scenario

  13. Space heating– smart scenario

  14. Space cooling – RES scenario

  15. Space cooling – smart scenario Energy bill for cooling reduced by 36%

  16. Results (preliminary)

  17. Conclusions • Results are incomplete and preliminary • Net energy demand does not vary much between 3 scenarios • Increase of %RES in smart scenario depends on amount of flexibility • Depending on business case, benefits from smart scenario may be lower energy bill, peak shaving etc. Future work using the simulation platform: • Effect of smart (predictive) agents • Use of electrical storage, i.e. electric vehicles

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