Combined Gas and Electricity Infrastructure

Planning

Meysam Qadrdan

Energy Infrastructure Symposium

Imperial College London

17th December 2014

Motivation

Gas-fired plants link power systems to gas networks. Availability and price of gas can affect expansion and

operation of the power system.

Emerging technologies (such as power-to-gas systems) make gas and electricity networks’ interactions stronger.

The increased interactions between these networks necessitates developing models to ‘unpack’ and make sense of the complex interactions.

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CGEN+

Determine where, when, what type and how much capacity need to be built, subject to: meeting energy demand, CO2 target (if set) and any other constraints.

Investigate impacts of a particular strategy on both networks (e.g. impact of GB shale gas exploitation on the gas import and generation mix).

CGEN+ is a Combined Gas and Electricity Network expansion planning model

CGEN+ uses cost minimisation approach to:

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CGEN+ (Cont.)

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CGEN+ model (Non Linear Mixed Integer Programming)

Objective function

Constraints

Emission and renewable targets (if set) Meeting gas and electricity demand (otherwise a

high shedding cost is incurred) Operation within technical capacity of infrastructure Maintaining a minimum level of capacity margins Resource availability: indigenous gas reserve, gas

and electricity imports, wind energy (spatial and seasonal capacity factor)

Inputs Regional and temporal demand data Capacity/location/ type of the existing infrastructure Capital and operating costs of infrastructure Fuel and carbon prices, discount rate Characteristics of infrastructure: efficiency, lifetime, emission intensity,…

Outputs Optimal capacity/location/type of the new infrastructure Optimal cost (investment and operation) of the system CO2 emission: total (tonne) and intensity (g/kWh)

min𝑍 = 1

1 + 𝑟 𝑡𝑡

𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑐𝑜𝑠𝑡 𝑜𝑓𝑛𝑒𝑤 𝑖𝑛𝑓𝑟𝑎𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒

+𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝑐𝑜𝑠𝑡𝑜𝑓 𝑡ℎ𝑒 𝑠𝑦𝑠𝑡𝑒𝑚𝑠

+𝑐𝑜𝑠𝑡 𝑜𝑓

𝑢𝑛𝑠𝑒𝑟𝑣𝑒𝑑 𝑒𝑛𝑒𝑟𝑔𝑦

+𝑐𝑜𝑠𝑡 𝑜𝑓

𝑐𝑎𝑟𝑏𝑜𝑛 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛

CGEN+: Geographical scale

Gas network for GB A simplified electricity network for GB

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CGEN+: Time steps granularity

2010 2020 2030 2040 2050

Planning time steps

off-peak (11 hours)

Intermediate (11 hours)

Peak

Demand profile for a representative day

Cold (181 days) Intermediate (92 days) Warm (92 days)

Different seasons in a year

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CGEN+ is flexible to take various time horizons and time steps: – Time horizon: between 10 years to few decades – Planning time step: between 1 year to 10 years

Case studies

CGEN+ was employed to:

Evaluate performance of a number of low carbon strategies (cost, emission and import dependency)

Investigate impacts of transition to a low carbon power system on the GB gas network

Analyse interactions between energy and water sector:

Regional water consumption for cooling thermal power plant was estimated.

Transport Energy Water

Central fuel

price, population and

GDP growth projected by

DECC

Minimal efficiency improvements and no DSR

Large increase in electric vehicle and heat pump

uptake; no energy conservation;

minimal efficiency improvement and solar installations; moderate

DSR

High CCS

High offshore generation

No technology is imposed

EHT-Nuclear

EHT-CCS

EHT-Offshore

GDP/population/ fuel price scenarios

Technological changes in demand sector

Generation technology

Strategies

MPI Yes

Yes

Yes

Yes

Carbon Price floor

High nuclear

In EHT-Nuclear, capacity of nuclear plants was assumed to reach 90 GW by 2050 (DECC 2050 pathway – level 3). In EHT-CCS, capacity of CCS-equipped coal and gas was assumed to reach 47 GW by 2050 (DECC 2050 pathway –

level 3). In EHT-Offshore, capacity of offshore wind was assumed to reach 100 GW by 2050 and capacity of tidal and wave

generation was assumed to reach 42 GW by 2050 (DECC 2050 pathway – level 3).

MPI: Minimal policy Intervention EHT: Electrification of Heat and Transport

Low carbon strategies for power sector

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Demand for gas and electricity

Annual and peak gas demand

Gas demand for power generation is excluded, as it is output of the model.

Electrification of heat sector significantly reduces the gas demand

Annual and peak electricity demand

Electrification of heat and transport sectors doubles the peak electricity demand

Demand data was produced by: P. Baruah and N. Eyre, Oxford University

Generation capacity mix

CCGTs play significant role in 2050 either to supply base load or act as peaking/backup generation technology.

Substantially large total generation capacity in EHT– Offshore (~2 x peak) due to variable wind power output.

Capacity factor for CCGTs

Capacity factor for CCGT plants drops to around 10% by 2050 in strategies which has large capacity of variable and inflexible generation technologies.

In EHT strategies, CCGT plants will operate mostly as back up for variable and inflexible generations.

Capacity payment is needed to encourage the investment

Import dependency increases in response to depletion of UKCS reserves.

LNG is expected to have the largest share in supplying gas by 2050.

Gas supply and import dependency

Strategy 2010 2050

MPI

55%

94%

EHT-Nuclear 84%

EHT-CCS 91%

EHT-Offshore 85%

Gas import capacity Import dependency

Annual gas supply

How can shale gas help?

74%

9%

56%

25%

Location of gas supply sources

Change in gas flow pattern

Gas network reinforcement in some part of the network

Source: National Grid

Cost and CO2 intensity

Considerably higher costs is expected when offshore generation (wind, wave and tidal) supply bulk of electricity demand, due to high capital and fixed O&M costs of offshore technologies.

Employing unabated CCGTs to provide backup for wind generation has an adverse impact on the CO2 intensity.

Cooling water for power generation/1

Regional cooling water demand was calculated for each generation strategy.

Large installation of CCS plants significantly increase abstraction of fresh water in some regions.

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Produced by: Colleagues from University of Newcastle and Oxford University

Cooling water for power generation/2

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Summary

CGEN+ provides insights into complex interdependent gas and electricity networks.

The model has continually evolved to also capture interactions with other sectors including water, transport and waste.

THANKS FOR YOUR ATTENTION!

Acknowledgement to: Prof. Nick Jenkins Cardiff University

Prof. Goran Strbac Imperial College London

Dr Modassar Chaudry Cardiff University

Contact: Meysam Qadrdan

m.qadrdan@imperial.ac.uk

qadrdanm@cardiff.ac.uk