This project has received funding from the European Union

A method for holistic energysystem designMODER event, Munich, March 7, 2018S. Thiem, V. Danov, M. Kautz, V. Chapotard, A. Zillich, J. Schaefer | CT REE ENS DEH-DE

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 680447

Unrestricted © Siemens AG 2018March 2018Page 2 CT REE ENS DEH-DE

Mobilization of innovative design tools for refurbishing of buildings at district level – Motivation for energy system design

Picture source: https://www.siemens.com/content/dam/internet/siemens-com/innovation/pictures-of-the-future/infrastructure-and-finance/other-assets/aspern-luftbild-a5-cschreinerkastler.jpg.adapt.916.high.jpg/1480604675037.jpg

(1) How should the optimal energy system design concept look like?

(2) How much total expenditures can be saved?

(3) How can carbon dioxide emissions cost-efficiently be reduced?

(4) What synergies can be achieved from considering electricity, thermal energy and water holistically?

[…]

Method for holistic energy system design


Agenda

• Method for holistic energy system design1

• Case study introduction: Suonenjoki, Finland2

• Key results3

• Conclusion & discussion4


Agenda


• Key results3




Holistic energy system design – Novel approach for the holistic optimization of the on-site energy supply system

• Optimization objective

Results (output data)

• Climate data

• Commodity prices

• Load profiles

Energy system designMandatory input data

• Technology selection

• Optimal capacities

• Optimal operation schedule

• Economical analysis

Optional input data

• Technologypre-selection

• Technology models and parameters

• Technology cost models

• Renewable generation profiles

$ CO2 PE • Mathematical optimization problem

• Find optimal combination within solution space


Agenda


• Key results3




© 2018 DigitalGlobe, Kartendaten, © 2018 Google

Modeling the energy system – Partition of the city into districts for considering district heating losses (multi-node approach)

North District (Sairaalapolku 4)

South District(Olavi Leskisen katu 10)

West District (Koulukatu 21)

LK14 (Rautalammintie 8)

Kuo(Kuopiontie 2)

LK25 (Kimpankatu 5)

LK15 (Koulukatu 23)

PN_LK25

PN_ND

PN_Kuo

PN_SD

Center District (Väinönkatu 7)

Suenonjoki, Finland• Population: 7366 [1]

• Area: 713.54 km2 [2]

Investigated area• Population: approximately 1500

• Area: 0.56 km2 [4]

[1] Population density by area 1.1.2016. Statistics Finland. Retrieved 12 February 2017..[2] Population according to language and the number of foreigners and land area km2 by area as of 31 December 2008". Statistics Finland's PX-

Web databases. Statistics Finland. Retrieved 29 March 2009. [4] Measured with Google Maps[5] Project communication with VTT and Sweco, 2017.

• LK14: LFO

• LK15: HFO

• LK25: Wood, peat (main); LFO (peak)

• Kuo: LPG

LFO: Light fuel oil; LPG: Liquid petroleum gas; HFO: Heavy fuel oil

[5]


Required input data for the energy system design study

Climate data

• Temperature• Global horizontal irradiance

Load profiles

• Electricity consumption• Heat consumption

• District heat• Other heat sources

Energy technologies

• Existing heat plants and boilers with different conventional fuels• Installable energy conversion units:

LPG-CHP (Residential + Utility), WP-CHP, GSHP, AWHP, EB• Installable storage units: HWS, LIB (Utility + Residential), RFB• Installable renewable: PV (Utility + Residential), ST

Commodity prices

• Electricity• CO2 emission price (carbon tax)• Heat generation fuels:

LPG, Wood, Peat, Light fuel oil (LFO), Heavy fuel oil (HFO), Heat oil

AWHP: Air-water heat pump, EB: Electric boiler, LIB: Lithium-ion battery, LPG: Liquid petroleum gas,LPG-CHP: Combined heat and power fired with liquid petroleum gas, GSHP: Ground-source heat pump,HWS: Hot water storage, PV: Photovoltaic, RFB . Redox-flow battery, ST: Solar thermal heat,WP-CHP: Combined heat and power fired with wood and peat

Multi-objective optimization considering total expenditures and carbon footprint(!) Social welfare optimum for entire city (residents + utility supplying district heat); assuming “on-site generation” is OK


Agenda




• Key results3


Aiming for lowest costs – How should Suonenjoki’s energy system look like?

CHP: Combined heat and power unit, DH: District heating pipeline, EB: Electric boiler, GSHP: Ground-source heat pump,HFO: Heavy fuel oil, HGP: Heat generation plant, HWS: hot water storage, LFO: Light fuel oil, LPG: Liquid petroleum gas,OB: Oil boiler, P: Peat, PG: Power grid, W: Wood, WB: Wood boiler, WP: Wood and peat

(1) Which technologies should be installed?

Peat-fired combined heat and power unit at LK14 and LK15, hot water storages



CHP: Combined heat and power unit, DH: District heating pipeline, EB: Electric boiler, GSHP: Ground-source heat pump,HFO: Heavy fuel oil, HGP: Heat generation plant, HWS: hot water storage, LFO: Light fuel oil, LPG: Liquid petroleum gas,OB: Oil boiler, P: Peat, PG: Power grid, W: Wood, WB: Wood boiler, WP: Wood and peat

(2) How large is the initial investment?

Roughly 1.5 Mio. € needed (district heating utility)

(3) What is the economic advantage of such system?

25.8% of total expenditures could be saved

✓ Significant cost saving possible



HFO: Heavy fuel oil, LFO: Light fuel oil, P: Peat, PG: Power grid, W: Wood

(4) But what about the carbon footprint?

Carbon footprint increased by 39.9%

X Significant increase of carbon footprint

Multi-objective energy system design aiming for both low costs and low carbon footprint necessary

? Is wood carbon neutral?(No carbon footprint?)


TOTE

X [k

€/a]

Car

bon

foot

prin

t [t/a

]

Car

bon

foot

prin

t [t/a

]

Multi-objective optimization of total expenditures and the carbon footprint – Wood carbon-neutral (one-node case)

Carbon footprintreduction withoutany significantcost increase…

Cost-optimizedcase

Cost and CO2improvement

…by firing woodinstead of peat

CHP: Combined heat and power unit, DH: District heating pipeline, EB: Electric boiler, GSHP: Ground-source heat pump,HFO: Heavy fuel oil, HGP: Heat generation plant, HWS: hot water storage, LFO: Light fuel oil, LPG: Liquid petroleum gas,OB: Oil boiler, P: Peat, PG: Power grid, RFB: Redox-flow battery, W: Wood, WB: Wood boiler, WP: Wood and peat

✓


TOTE

X [k

€/a]

Car

bon

foot

prin

t [t/a

]

Car

bon

foot

prin

t [t/a

]

Multi-objective optimization of total expenditures and the carbon footprint – Wood not carbon-neutral (one-node case)

CHP: Combined heat and power unit, DH: District heating pipeline, EB: Electric boiler, GSHP: Ground-source heat pump,HFO: Heavy fuel oil, HGP: Heat generation plant, HWS: hot water storage, LFO: Light fuel oil, LPG: Liquid petroleum gas,OB: Oil boiler, P: Peat, PG: Power grid, RFB: Redox-flow battery, W: Wood, WB: Wood boiler, WP: Wood and peat

[…]

?

Carbon footprintreduction muchmore costly…

Cost-optimizedcase

Cost and CO2improvement

…due to increasedpower grid usage andheat pumps

Note that peat replacementby wood cannotlower the carbon footprintto ~0 anymore

-43.9%


Carbon footprint [t/a]0 2000 4000 6000 8000 10000 12000 14000

-500

0

500

1000

1500

2000

2500

3000PV_ResPV_UtilST_UtilWP-HGP_Util (LK25)LFO-HGP_Util (LK25)LFO-HGP_Util (LK14)HFO-HGP_Util (LK15)LPG-HGP_Util (Kuo)EB_ResOB_ResWB_ResGSHP_ResGSHP_UtilAWHP_UtilWP-CHP_UtilHWS_UtilLIB_ResLIB_UtilRFB_UtilPGinWinPinPGout

Carbon footprint [t/a]0 1000 2000

TOTE

X [k

€/a]

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

Multi-objective optimization of total expenditures and the carbon footprint – Wood not carbon-neutral (one-node case)

AWHP: Air-water heat pump, CHP: Combined heat and power unit, DH: District heating pipeline, EB: Electric boiler,GSHP: Ground-source heat pump, HFO: Heavy fuel oil, HGP: Heat generation plant, HWS: hot water storage,LFO: Light fuel oil, LIB: Lithium-ion battery, LPG: Liquid petroleum gas, OB: Oil boiler, P: Peat, PG: Power grid,PV: Photovoltaic, RFB: Redox-flow battery, ST: Solar thermal heating, W: Wood, WB: Wood boiler, WP: Wood and peat

✓

(0) Cost-optimized case

(1) Replace peat by wood

(2) Increase utilization of power grid and decrease use of CHP

(3) Use electricity-driven heating technologies (in particular heat pumps) and hot water storages

(4) Install renewables(in particular photovoltaic)(5) Install batteries

(redox-flow and lithium-ion batteries)

-43.9%


Agenda



• Key results3



Conclusion – Concluding remarks concerning the energy system design study for Suonenjoki

• Holistic multi-modal energy system design methods developed within EU H2020 MODER project (WP4)

• Testing and validation by energy system design study for Suonenjoki, Finland (WP6)

• Assumption of “on-site energy system” use of combined heat and power attractive

• Multi-objective optimization Both costs and carbon footprint can be reduced simultaneously

• Is the thermal use of wood carbon-neutral?

• If not, follow this guideline to reduce carbon footprint most cost-efficiently:

(1) Replace peat by wood

(2) Increase utilization of power grid and decrease use of CHP

(3) Use electricity-driven heating technologies (in particular heat pumps) and hot water storages

(4) Install renewables (in particular photovoltaic)

(5) Install batteries (redox-flow and lithium-ion batteries)


Acknowledgements – We’d like to thank…

• … the European Commission for funding this Horizon 2020 project MODER:

• … MODER project partners for their support for and contribution to this case study:

• … other sources for making this case study possible:

Thank you for your attention! Do you have any questions?

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 680447

Documents

This project has received funding from the European Union