Smart control of multiple energy commodities on district scale

January 14, 2018 | Author: Anonymous | Category: Science, Health Science, Pediatrics
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Smart control of multiple energy commodities on district scale Frans Koene

Sustainable places, Nice, 1-3 Oct 2014

1

Partners

2

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 3

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

H E A T R E C O V E R Y S Y S T E M F O R S H O WE R T ype o f B uilding

D is t ric t Us a ge F a c t o r

H R E f f ic ie nc y

Inf ue nc e in C o ns um p.

Single Family Ho uses (SH)

10 0 .0 0 %

5 0 .0 0 %

8 5 .0 0 %

A partment B lo cks (A B )

10 0 .0 0 %

5 0 .0 0 %

8 5 .0 0 %

S pe c if ic A v e ra ge C o ns um pt io n

P e rc e nt a ge P ro f ile

D a y o f t he we e k SH

AB

SH

AB

1

0 .0 2 1 k Wh/ da y·m ²

0 .0 2 2 k Wh/ da y·m ²

P ro f ile N º1

P ro f ile N º1

2

0 .0 2 1 k Wh/ da y·m ²

0 .0 2 2 k Wh/ da y·m ²

P ro f ile N º1

P ro f ile N º1

3

0 .0 2 1 k Wh/ da y·m ²

0 .0 2 2 k Wh/ da y·m ²

P ro f ile N º1

P ro f ile N º1

4

0 .0 2 1 k Wh/ da y·m ²

0 .0 2 2 k Wh/ da y·m ²

P ro f ile N º1

P ro f ile N º1

5

0 .0 2 1 k Wh/ da y·m ²

0 .0 2 2 k Wh/ da y·m ²

P ro f ile N º1

P ro f ile N º1

6

0 .0 2 1 k Wh/ da y·m ²

0 .0 2 2 k Wh/ da y·m ²

P ro f ile N º1

P ro f ile N º1

7

0 .0 2 1 k Wh/ da y·m ²

0 .0 2 2 k Wh/ da y·m ²

P ro f ile N º1

P ro f ile N º1

4

Aggregated building model

=

F.G.H. Koene et al.: Simplified building model of districts, proceedings IBPSA BauSIM 22 -24 Sept 2014, Aachen, Germany

Inputs building model – Size, volume, windows, orientation – Thermal insulation – Thermal set points for heating & cooling – Internal heat generation – Parameters automatic solar shading

Agent based technology

10

[kW] power [kW] consumed consumed power

8 6 4 2 0

-2 0

5

10

15

20

-4 -6 -8 -10

electricity price [€ct/kWh]

6

Multi Commodity Matcher HP thermal power bid

electr price

electr price

HP electrical power bid

heat price

heat price aggr. thermal power bid

electr price

electr price

aggr. electrical power bid

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

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Business Concepts based on flexibility

Case Buyer of flexibility

Objective

1

Prosumers (aggregated)

reduce energy bill (buy at low prices)

2

Energy retailer / BRP

maximise the margin between purchases and sales of energy

3

Balancing Responsible Party reduce imbalance in portfolio (BRP)

4

Distribution System Operator peak shaving (avoiding capacity (DSO) problems)

5

Transmission System Operator (TSO)

reduce imbalance on national level 8

Case studies

Tweewaters (BE)

Houthaven (NL)

Bergamo (IT)

Freiburg (GE)

Dalian (CN)

• Supply: CHP (heat + electricity) + peak boilers (heat) + market (electricity) + DH • Demand: residential consumers (heat + electricity) + market (electricity) • Flexibility: CHP + smart appliances

• 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)

• Existing energy concept: DH + heat storage – shutdown of CHP • Energy vision: different alternatives for heat production (centralized boiler, biomass..), PV (46 kWp) • Demand: Residential buildings + commercial/ public buildings

• Supply: CHPs + boilers, centralized heat storage + DH • Demand: residential buildings + commercial/ public buildings

• Supply: CHP + peak boiler (heat) + sewage source / seawater source HP (heat/cold) + solar collectors + DH • Demand: residential consumers + industrial use (heat + electricity + cold)

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Scenarios 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 2nd scenario) - demand-side flexibility - business objective: local balancing and national balancing

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Example: district of Houthaven, Amsterdam  

201.300 m2 residential 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)

Virtual heat price

Space heating– RES scenario

Indoor temperature of building I4B1 in scenario 2 22 20 18 16 14 1 Jan

2 Jan

3 Jan

4 Jan Time

5 Jan

6 Jan

7 Jan

2

Power [ W/m ]

Consumed thermal power for heating for scenario 2 100 50 0 1 Jan

2 Jan

3 Jan

4 Jan Time

5 Jan

6 Jan

7 Jan

12

Virtual heat price

Space heating– smart scenario

Indoor temperature and flexibility boundaries of building I4B1 in scenario 3 25 20 15 10 1 Jan

2 Jan

3 Jan

4 Jan Time

5 Jan

6 Jan

7 Jan

2

Power [ W/m ]

Consumed thermal power for heating for scenario 3 40 20 0 1 Jan

2 Jan

3 Jan

4 Jan Time

5 Jan

6 Jan

7 Jan

13

Virtual electricity price

(Virtual) Electricity price as determined by the Multi Commodity Matcher in scenario 2 20

Power [ W/m ]

Space cooling – RES scenario

20

10

0 18 Jun

19 Jun

20 Jun

21 Jun Time

22 Jun

23 Jun

24 Jun

2

Consumed electrical power for cooling for scenario 2

10

0 18 Jun

19 Jun

20 Jun

21 Jun Time

22 Jun

23 Jun

24 Jun

14

Virtual electricity price

Space cooling – smart scenario

(Virtual) Electricity price as determined by the Multi Commodity Matcher in scenario 3 20 10

0 18 Jun

19 Jun

20 Jun

21 Jun Time

22 Jun

23 Jun

24 Jun

2

Power [ W/m ]

Consumed electrical power for cooling for scenario 3 20 10

0 18 Jun

19 Jun

20 Jun

21 Jun Time

22 Jun

23 Jun

24 Jun

Energy bill for cooling reduced by 36% 15

Results (preliminary) Tweewaters kWh/m2, %, kg CO2/m2, €/m2

50 45 40 35 30 25 20 15 10 5 0

BAU

100 90 80 70 60 50 40 30 20 10 0

BAU Green Smart

Electricity % electr demand by RES

Green kWh/m2, %, kg CO2/m2, €/m2

Electricity % electr demand by RES

Heat % heat by CO2 Electricity Heating Demand RES emissions bill bill

Dalian

Smart

Heat % heat by CO2 Electricity Heating Demand RES emissions bill bill

200 180 160 140 120 100 80 60 40 20 0

BAU Green Smart

Electricity % electr demand by RES

Heat % heat by CO2 Electricity Heating Demand RES emissions bill bill

Bergamo 250 kWh/m2, %, kg CO2/m2, €/m2

kWh/m2, %, kg CO2/m2, €/m2

Houthaven

200 150

BAU Green

100

Smart 50 0 Electricity % electr demand by RES

Heat % heat by CO2 Electricity Heating Demand RES emissions bill bill

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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 17

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