A method for holistic energy system design

A method for holistic energy

system design

World Sustainable Energy Days (WSED) 2018, Wels / Austria S. Thiem, V. Danov, M. Kautz, V. Chapotard, A. Zillich, J. Schaefer | CT REE ENS DEH-DE | March 02, 2018

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under

grant agreement No 680447

Unrestricted © Siemens AG 2018

March 2018 Page 2 S. Thiem | 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 design 1

• Case study introduction: Suonenjoki, Finland 2

• Key results 3

• Conclusion & discussion 4



Agenda


• Key results 3





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 design Mandatory input data

• Technology

selection

• Optimal capacities

• Optimal operation

schedule

• Economical

analysis

Optional input data

• Technology

pre-selection

• Technology models

and parameters

• Technology

cost models

• Renewable

generation profiles

$ CO2 PE • Mathematical optimization problem

• Find optimal combination within solution space





Energy system design

Solution space (“superstructure”)

Inputs Energy conversion & storage Demands

Case-specific optimal system

Inputs Energy conversion & storage Demands

Existing

technologies

• What is the economic

advantage?

• What is the impact on the carbon

footprint and how can it be reduced?

• How future-proof / robust is

the optimal design?

Further questions to be answered:





Energy system design

“MM-ESD optimizes the design of an energy system

with respect to a certain objective under given constraints.”

• Optimization: Solving a mathematical optimization problem

• Design: Selection and capacities of energy conversion units, energy storages and energy transfer

• Energy system: Combination of energy conversion units, energy storages, energy transfer lines, loads,

renewables and others at multiple energy forms (multi-modal energy system)

• Objective: Achieve minimum total expenditures, CO2 emissions or primary energy consumption, or

combinations of these

• Constraints: Constraints to the optimization problem (e.g., power balances, part-load efficiencies, …)



Agenda


• Key results 3





© 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



Energy system design at different levels of detail


Suonenjoki

One-node model


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)

Multi-node model (district scale)

• Simplified model of the city with no transport processes

• Faster to solve

• Results easier to analyze and to communicate

• District heating system (and, e.g., losses) between districts can be

considered; electric grid considered as “copper plate” here

• Computationally much more intense to solve

• Results are difficult to analyze and quite complex to communicate



Energy system design for Suonenjoki – Scenario overview

One-node model Multi-node model (at district scale)

Op

tim

izati

on

su

pers

tru

ctu

re

Exis

tin

g s

yste

m

“Reference”

(optimized

operation of the

existing system)

“Optimized”

(optimized

operation of an

optimally

designed

system)

CHP: Combined heat and power unit, 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



Agenda




• Key results 3



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




look like?



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




look like?

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?)



Multi-objective optimization of total expenditures and the carbon

footprint – Wood carbon-neutral (one-node case)

Carbon footprint

reduction without

any significant

cost increase…

Cost-optimized

case

Cost and CO2

improvement

…by firing wood

instead of peat



OB: Oil boiler, P: Peat, PG: Power grid, RFB: Redox-flow battery, W: Wood, WB: Wood boiler, WP: Wood and peat

✓




footprint – Wood not carbon-neutral (one-node case)



OB: Oil boiler, P: Peat, PG: Power grid, RFB: Redox-flow battery, W: Wood, WB: Wood boiler, WP: Wood and peat

[…]

?

Carbon footprint

reduction much

more costly…

Cost-optimized

case

Cost and CO2

improvement

…due to increased

power grid usage and

heat pumps

Note that peat replacement

by wood cannot

lower the carbon footprint

to ~0 anymore

-43.9%




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 results 3




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

• Implications from one-node vs. multi-node model

• 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

Date post:	17-Oct-2021
Category:	Documents
Upload:	others
View:	1 times
Download:	0 times

A method for holistic energy system design

Documents