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

2. 2 Partners

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

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

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. 6 Agent based technology

7. 7 Multi Commodity Matcher HP electrical power bidHP thermal power bid heat price electr price aggr. electrical power bid aggr. thermal power bid heat price electr 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. 8 Business Concepts based on flexibility CaseBuyer of flexibilityObjective 1Prosumers (aggregated) reduce energy bill (buy at low prices) 5 Transmission System Operator (TSO) reduce imbalance on national level 2Energy retailer / BRP maximise the margin between purchases and sales of energy 3 Balancing Responsible Party (BRP) reduce imbalance in portfolio 4 Distribution System Operator (DSO) peak shaving (avoiding capacity problems)

9. 9 Case studies Tweewaters (BE) Supply: CHP (heat + electricity) + peak boilers (heat) + market (electricity) + DH Demand: residential consumers (heat + electricity) + market (electricity) Flexibility: CHP + smart appliances Houthaven (NL) Supply: HPs, PV, waste heat (incineration plant), ground source cold storage,…+ DHC Demand: low energy buildings - residential + commercial/ public buildings Potentially demand response (smart appliances, pumps) Bergamo (IT) Existing energy concept: DH + heat storage – shutdown of CHP Energy vision: different alternatives for heat production (centralized boiler, biomass..), PV (46 kW p ) Demand: Residential buildings + commercial/ public buildings Freiburg (GE) Supply: CHPs + boilers, centralized heat storage + DH Demand: residential buildings + commercial/ public buildings Dalian (CN) Supply: CHP + peak boiler (heat) + sewage source / seawater source HP (heat/cold) + solar collectors + DH Demand: residential consumers + industrial use (heat + electricity + cold)

10. 10 1.Reference or BAU scenario - conventional sources for energy supply - electricity from the public grid - heat produced by de-central gas fired boilers. 2.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) 3.Smart scenario or RES scenario with flexible energy demand and supply - renewable energy sources (as in 2 nd scenario) - demand-side flexibility - business objective: local balancing and national balancing Scenarios

11.  201.300 m 2 residential  13.900 m 2 commercial  14 aggregated buildings  16.8 km heat network  Copper plate grid  No cold network (electrical cooling)  Rooftop & District PV (4.5 kW p ) Example: district of Houthaven, Amsterdam

12. 12 Space heating– RES scenario

13. 13 Space heating– smart scenario

14. 14 Space cooling – RES scenario

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

16. 16 Results (preliminary)

17.  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 17 Conclusions