Workshop on Modelling of Integrated Multi-Energy Networks - current practices and innovation gaps12th April 2019 | London

Multi-scale modelling of interdependent energy networks

Meysam QadrdanCardiff University

qadrdanm@Cardiff.ac.ukCIREGS Research Team:

Prof. Nick Jenkins, Prof. Jianzhong Wu Dr Modassar Chaudry, Dr Muditha Abetsekera Mr Lahiru Jaysuriya, Mr Alex Canet, Ms Sathsara Aneysingh

Outline

Background

Example case studiesNational gas & electricity system – Local energy system

Research challenges

Slide 2 of 24

Whole System thinking: A new Norm

Increased penetration of distributed generation Uptake of electric vehicles and demand response

Slide 3 of 15

National energy system Consumers

Information

Energy

Optimisation and Analysis of GB Combined Ele./Gas Networks (CGEN model)

Simulation and Analysis of Community-Level Multi-Vector Energy Networks

Modelling of European Energy Systems

Multi-scale energy systems

Simulation and Analysis of Building-Level Multi-Vector Energy Systems

Slide 4 of 24

Simulation and Analysis of GB Combined Ele./Gas Networks (CGEN model)

Simulation and Analysis of Community-Level Multi-Vector Energy Networks

Modelling of European Energy Systems

Multi-scale energy systems

Simulation and Analysis of Building-Level Multi-Vector Energy Systems

Slide 5 of 24

Interactions between gas and electricity transmission system

Gas and electricity are connected through: Gas-fired generating units Electrically driven compressors Power-to-Gas technologies

Slide 6 of 24

CGEN model: Structure

CGEN is a Combined Gas and Electricity Network operation model

7

CGEN model Objective function

Constraints Operating limits of technologies Meeting gas and electricity demand

Inputs Outputs

min𝑍𝑍 = �𝑡𝑡

�𝑖𝑖

𝐶𝐶𝑖𝑖𝑓𝑓 + 𝐶𝐶𝑖𝑖var 𝑃𝑃𝑖𝑖,𝑡𝑡 + �

𝑘𝑘

𝐶𝐶𝑘𝑘,𝑡𝑡𝑠𝑠𝑠𝑠 + �

𝑘𝑘

𝐶𝐶𝑘𝑘,𝑡𝑡𝑠𝑠𝑠𝑠

𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝑪 𝑪𝑪𝒐𝒐 𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑪𝑪𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑪𝑪𝑬𝑬 𝑮𝑮𝑬𝑬𝑮𝑮𝑬𝑬𝑬𝑬𝑮𝑮𝑪𝑪𝑬𝑬𝑪𝑪𝑮𝑮

+ ��g

𝐶𝐶g𝑔𝑔𝑔𝑔𝑠𝑠 𝑄𝑄g,𝑡𝑡 + �

s

(𝐶𝐶𝑠𝑠𝑡𝑡 𝑄𝑄𝑠𝑠,𝑡𝑡

𝑡𝑡 + 𝐶𝐶𝑠𝑠𝜔𝜔 𝑄𝑄𝑠𝑠,𝑡𝑡𝜔𝜔

𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝑪 𝑪𝑪𝒐𝒐 𝒈𝒈𝑮𝑮𝑪𝑪 𝑪𝑪𝒔𝒔𝒔𝒔𝒔𝒔𝑬𝑬𝑬𝑬

+�𝑏𝑏

𝐶𝐶𝑔𝑔𝑠𝑠𝑢𝑢 𝑃𝑃𝑏𝑏,𝑡𝑡𝑠𝑠𝑢𝑢 + �

𝑛𝑛

𝐶𝐶𝑠𝑠𝑔𝑔 𝑄𝑄𝑛𝑛,𝑡𝑡𝑠𝑠𝑔𝑔

𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝑪 𝑪𝑪𝒐𝒐 𝑼𝑼𝑮𝑮𝑪𝑪𝑬𝑬𝑬𝑬𝑼𝑼𝑬𝑬𝑼𝑼 𝑬𝑬𝑮𝑮𝑬𝑬𝑬𝑬𝒈𝒈𝑬𝑬

Spatial and temporal demand

Gas and electricity networks

Fuel and O&M costs

Operating limits of components

Optimal operation cost of the system

Optimal power dispatch

Optimal linepackmanagement

Optimal gas dispatch

CGEN model: Geographical and temporal scale

8

• Gas networks • A simplified electricity network for GB

16 busbars15 transmission circuits120 thermal plants

Depends on the purpose of themodelling, more detailed/simplified

networks can be used.

Impacts of wind variability on the operation of gas network – flexibility from linepack

Large increase in wind generation capacity beyond 2020

Gas-fired generators are potential candidates to compensate for wind variability

Wind variability leads to comparable changes in gas demand

Questions: Is the gas network capable of meeting abrupt

increase in the gas demand (due to drop in wind generation)?

What are the impacts on the operation of gas network (pressure and linepack)?

9

Historical swings in linepack (Source: National Grid)

Slide 10 of 24

Energy hub representation of distribution systems

Slide 11 of 24

Slide 12 of 15

Decarbonisation of heat –Local potential vs National policy

Decarbonisation of Neath Port Talbot

Slide 13 of 15

Amongst UK’s most polluted cities

8th most populous local authority in Wales

17 areas within the top 10% most deprived in Wales

Annual energy flows

14

Heat supply options

~34,000 local areas (LSOAs) in England and Wales

Heat demand and energy infrastructure differ significantly across LSOAs

No single solution can be applied to all LSOAs

Slide 15 of 24

A bi-level optimisation approach for heat decarbonisation

Taking into account objectives of key players

Bilevel problem

Electricity sector problem

(supplying electricity with lowest cost)

Heat sector problem (supplying heat with lowest

cost)

Electricity priceElectricity demand

for heating

Inputs

• Heat/electricity technology cost

• Heat demand

• Electricity demand (excl. heat)

• Carbon cost

Outputs

• Electricity generation mix for 2050

• Heating supply mix for 2050

Slide 16 of 24

GHG emissions for different carbon prices

0

20

40

60

80

100

120

140

160

GHG

emiss

ion

(Mill

ion

tonn

e of

CO

2)

Carbon price (£/tonne CO2)

Emission from heat supply

Emission from electricitysupply

Slide 17 of 24

Simulation and Analysis of GB Combined Ele./Gas Networks (CGEN model)

Simulation and Analysis of Community-Level Multi-Vector Energy Networks

Modelling of European Energy Systems

Multi-scale energy systems

Simulation and Analysis of Building-Level Multi-Vector Energy Systems

Slide 18 of 24

Community energy system - University of Warwick’s campus

19

A 24/7 community with a population of 25000

290 hectares ----------------------------------------- 7000 student rooms More than 150 academic

buildings 3 conference centres 2 sport centres Retail/ cafes/ restaurants Arts Centre Offices & teaching

buildings Industrial & research

buildings

Energy Centre 1

CHP - 3 x 1.4MWe Aux Boiler – 2 x 4.85 MWth

Energy Centre 2

CHP – 2 x 2MWeAux Boiler – 1 x 5 MWth

650kWth Maths &Stat -Abs. chiller

400kWth Physics Abs. chiller

220kWth WBS Abs. chiller

3 distributed cooling networks with 3 absorption chillers + electric chillers

Energy loadsElectricity demand ~ 10MWe (peak)Heat demand ~ 15MWth(peak)

2013/14

Electricity consumption: 60 GWh (50% self generated) ~15000 homes eq. Gas consumption : 150 GWh ~10,000 homes eq.

Slide 20 of 15CHP Gas boiler

Absorption chiller

Elec

tric

ityHe

atin

g

Ref: Abeysekera M. Canet, A. et al. , Co-ordination of integrated energy systems for optimal energy supply, Poster Presented at International Conference of Energy Systems Integration, NREL, USA – December 17’

Monthly electricity and heat demand and supply

21

Note- Half hourly electricity and heat demand data for 2016 was used for the analysis

Optimal operation strategies for different combinations of electricity and heat load

Optimal integration across various energy vectors (i.e. electricity, cooling/heating) and scales (i.e. buildings and networks) allows: (a) cost-effective design and operation of district energy systems and (b) maximum use of local low-carbon energy resources.

CHP

Electric Chiller

Gas Boiler

Absorption chiller

Electricity import

Slide 22 of 24

Summary & research challenges

Integration across scales enables: Take advantage from economy of scale Exploit local resources and potentials

Further research to Understand interactions between scales and cascading

effects Address complexities Computational complexities Understand and model objectives of key players

Slide 23 of 15

Acknowledgement

Supergen

UKERC

ITRC MISTRAL

UKRI Innovation Fellowship

Slide 24 of 15