Multi-scale modelling of interdependent energy …...Workshop on Modelling of Integrated Multi...
Transcript of Multi-scale modelling of interdependent energy …...Workshop on Modelling of Integrated Multi...
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
[email protected] 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
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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
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Interactions between gas and electricity transmission system
Gas and electricity are connected through: Gas-fired generating units Electrically driven compressors Power-to-Gas technologies
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CGEN model: Structure
CGEN is a Combined Gas and Electricity Network operation model
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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
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• 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)?
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Decarbonisation of Neath Port Talbot
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Amongst UK’s most polluted cities
8th most populous local authority in Wales
17 areas within the top 10% most deprived in Wales
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
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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
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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
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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
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Community energy system - University of Warwick’s campus
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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.
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
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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
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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
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