Need for integrated simulations: Integration of electricity supply and demand

Need for integrated simulations:

Integration of electricity supply and demand

Kenneth Bruninxwith Erik Delarue and William D’haeseleer

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Basic principles of electricity generation

Electric power• Travels at speed of light• Is difficult to store

→ Supply must meet demand instantaneously • Network required for transport

• High voltage – Transmission• Low voltage – Distribution

Time [h]D

eman

d [M

W]

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Basic principles of electricity generation

Different technologies used and scheduled to meet demand

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TechnologiesElectricity generation system is mix of • Dispatchable units• Non-dispatchable units

o Often intermittent• Storage

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TechnologiesDispatchable units: output can be actively controlled• Nuclear, coal, lignite

o Rankine steam cycleo Large units ~1000 MWo Continuous flat operation (?)

• Gas Combined Cycle o Brayton gas cycle + Rankine steam cycleo η ≈ 50%-55%o Typical size: ~400 MWo Flexible operation

• Gas turbineo Peak units, very flexible

• Renewable Energy Sources (RES) o Biomass, hydro (basin)

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TechnologiesNon-dispatchable units: output cannot (or only limitedly) be controlled• Intermittent units

– Variable output– Output is predictable only to limited extent– Wind and solar photovoltaics (PV)

• Zero marginal cost

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Technologies

Variable output & limited predictability

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Wind power production & forecasts (ELIA, BE)

ForecastProduction

Pow

er [M

W]

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Technologies• Storage

o Pumped hydro storageo Compressed Air Energy Storage

(CAES)o Flywheelso Batterieso H2, CH4o …

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Power plant schedulingGiven portfolio of power plants,

how to meet certain electricity demand?o At lowest variable costo Significant amount can be non-dispatchable generationo Taking into account technical constraints of power plantso Taking into account safety marginso Dealing with uncertaintieso Network restrictions, import/export

This optimization problem is known as the unit commitment problemo Difficult to solve because of on/off nature of decision variables

• Required to represent start-up costs and start-up behavior, minimum operating point, minimum up & down times

Power plant scheduling

Example of simplified unit commitment• Given set of power plants

o Capacity, minimum operating point, efficiency, fuel price, start-up cost, minimum up & down time

• Meet certain demand profile• Minimize fuel and start-up costs• Fuel cost dependent on load level• Technical constraints

o Respect minimum operating point if ono Respect minimum up & down time

Optimized with Mixed Integer Linear Programming (MILP)(Operations Research technique)

Power plant scheduling

Example of simplified unit commitment• Given set of power plants

o Capacity, minimum operating point, efficiency, fuel price, start-up cost, minimum up & down time

• Meet certain demand profile• Minimize fuel and start-up costs• Fuel cost dependent on load level• Technical constraints

o Respect minimum operating point if ono Respect minimum up & down time

Optimized with Mixed Integer Linear Programming (MILP)(Operations Research technique)

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Power plant scheduling

• Consider power plants i

• Consider time periods jo E.g., one week, hourly time steps: j = 1 … 168

Unit Capacity [MW] Full load efficiency [%]

Nuclear 1000 33%

Coal 800 40%

Gas CCGT 400 50%

Gas GT 60 35%

Oil turbojet 20 30%

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Power plant scheduling• How to meet demand at lowest cost?• Minimize

• Supply = Demand

With gi,j electricity generation of plant i in period j [MW] and dj electricity demand in period j [MW]

𝑐𝑜𝑠𝑡=∑𝑖 , 𝑗

𝑓𝑢𝑒𝑙𝑐𝑜𝑠𝑡 𝑖 , 𝑗+∑𝑖 , 𝑗𝑠𝑡𝑎𝑟𝑡𝑢𝑝𝑐𝑜𝑠𝑡 𝑖 , 𝑗

∑𝑖𝑔𝑖 , 𝑗=𝑑 𝑗

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Power plant scheduling

Unit Capacity (Pmax) [MW]

Full load efficiency

[%]

Minimum operating

point (Pmin)[MW]

Efficiency at Pmin,

relative to full load

efficiency [%]

Startup cost S [€]

Fuel price [€/MWhp]

Nuclear 1000 33% 600 90% 60000 3.3

Coal 800 40% 240 90% 30000 12

Gas CCGT

400 50% 150 80% 20000 30

Gas GT 60 35% 15 60% 1000 30

Oil turbojet

20 30% 5 60% 0 40

Power plant scheduling

Fuel costs• Assume linear cost behavior between Pmin and Pmax• Other function possible, e.g., quadratic

𝑓𝑢𝑒𝑙𝑐𝑜𝑠𝑡 𝑖 , 𝑗=𝑐𝑖 ∙ 𝑧𝑖 , 𝑗+𝑏𝑖 ∙ δ𝑖 , 𝑗

Fuel

cos

t [€/

h]

Output, g [MW]

~ η

c

b

Linear :

Pmin Pmax

δ 𝑖 , 𝑗≤(𝑃𝑚𝑎𝑥 ¿¿ 𝑖−𝑃𝑚𝑖𝑛𝑖) ∙𝑧 𝑖 , 𝑗¿𝑔𝑖 , 𝑗=𝑃𝑚𝑖𝑛𝑖 ∙𝑧 𝑖 , 𝑗+δ𝑖 , 𝑗

δ 𝑖 , 𝑗≥0𝑧𝑖 , 𝑗=0𝑜𝑟 1δ

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Power plant scheduling

• Deriving c and b from data previous table yields

Unit Capacity [MW]

Minimum operating

point [MW]

c [€/h] b [€/MWh]

Nuclear 1000 600 6667 8.3Coal 800 240 8000 28.6Gas CCGT 400 150 11250 51.0Gas GT 60 15 2143 66.7Oil turbojet 20 5 111 103.7

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Power plant scheduling

Startup costs• Bringing power online (from zero to 1) incurs a cost• E.g., amount of heat required to bring steam to appropriate

temperature and pressure

𝑠𝑡𝑎𝑟𝑡𝑢𝑝𝑐𝑜𝑠𝑡𝑖 , 𝑗≥ 0

𝑠𝑡𝑎𝑟𝑡𝑢𝑝𝑐𝑜𝑠𝑡𝑖 , 𝑗≥𝑆𝑖 ∙[𝑧𝑖 , 𝑗−𝑧 𝑖 , 𝑗−1]Unit Capacity

[MW]Startup cost

S [€]

Nuclear 1000 60000Coal 800 30000Gas CCGT 400 20000Gas GT 60 1000Oil turbojet 20 0

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Power plant scheduling: an example

Total installed capacity: 6520 MW

One week period (168 h)o Peak demand = 5244 MW (95% of dispatchable capacity)o Given certain intermittent profile

Unit Capacity [MW] # units [-]

Nuclear 1000 2

Coal 800 2

Gas CCGT 400 4

Gas GT 60 4

Oil GT 20 4

RES intermittent 2000

Power plant scheduling

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Power plant scheduling

Results

Unit Total generation

[MWh]

Load factor

[%]

Operational cost [k€]

Average cost

[€/MWh]

CO2 emissions

[ton]

Average CO2 emissions [kg/MWh]

Nuclear 333 963 99% 3 343 10.0 0 0

Coal 226 141 84% 6 858 30.3 194 599 861

Gas CCGT 79 558 30% 5 156 64.8 33 490 421

Gas GT 2 311 6% 222 96.2 1 398 605

Oil GT 215 2% 31 145.1 210 980

RES intermittent 95 011 28% 0 0 0 0

Challenges and issues:

Challenges and issues: a paradigm shiftResidual load in Germany in 2050

Challenges and issues: a paradigm shiftYearly load duration curves of supluses due to fluctuating electricity supply

Challenges and issues

Flexibility will be required• Generation side• Storage• Interconnections• Curtailment• Demand side activation

o Through smart gridso Demand side response

Challenges and issues

Activation of demand side• Modeling of demand response

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hour [h]

pow

er [M

W]

unit commitment, without demand shift

windoilnatural gascoal

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hour [h]

pow

er [M

W]

unit commitment, with demand shift

windnatural gascoalnuclear

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Challenges and issuesModeling of demand responseo Cost based models

• Centrally planned• Incentive payment to consumers (function of amount shifted)• Explicit modeling of flexibility

• E.g., heating/cooling systems, transport (including storage)

o Price based models• Time-of-use pricing (e.g., two tariff system), critical peak pricing,

real time pricing • Demand elasticities

• Own and cross price elasticities

• Maximizing overall social welfare• Quadratically constrained programming or iterative piecewise linear

optimization

Challenges and issues

Source: De Jonghe, Delarue, D’haeseleer, Belmans, 2011, PSCC

Fixed demand

-0.2 own-price elasticity

Challenges and issues

Source: De Jonghe, Delarue, D’haeseleer, Belmans, 2011, PSCC

Electricity price

-0.2 own-price elasticity

Challenges and issues

Source: EEX Spot prices

Challenges and issues

• Old situation:o Load drives generation

• New paradigm:o Generation drives load

o Consume when & where there is electricity generation

Integrated modelling: a case study‘Impact of intelligent thermal systems on electricity generation – a systems approach’• Research questions:

o Effect of DS flexibility on electricity generation • Cost• Carbon intensity• RES utilization

o Quantify the usefulness of DS flexibility• Motivation: in literature

o OR focus on DS: technology-based analysiso OR focus on SS: economics-based analysis (see example above)Here: integrated approach, where the (economic) flexibility stems from a technology-based model.

• Ongoing research

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Integrated modelling: a case studyModelling approach: cost-based• Minimize

• Supply = Demand

With gi,j electricity generation of plant i in period j [MW] and dj electricity demand in period j [MW]

𝑐𝑜𝑠𝑡=∑𝑖 , 𝑗

𝑓𝑢𝑒𝑙𝑐𝑜𝑠𝑡 𝑖 , 𝑗+∑𝑖 , 𝑗𝑠𝑡𝑎𝑟𝑡𝑢𝑝𝑐𝑜𝑠𝑡 𝑖 , 𝑗

∑𝑖𝑔𝑖 , 𝑗=𝑑 𝑗 , 𝑓𝑖𝑥𝑒𝑑+𝑑 𝑗 ,𝑣𝑎𝑟

Optimization variable

Integrated modelling: a case studyModelling approach: cost-based• Variable demand is sum of demand of all flexible devices

of all consumer groups• Constraints

o Technology: peak power, stored heato Comfort level

Integrated modelling: a case studyNo flexibility 20% flexible demand

Integrated modelling: a case studyChallenges in modelling• Consumer behaviour

o Attendance pattern determines optimal solutiono Limited number of consumer groups currently

considered (25)• Correct representation of the building stock and its energy

storage potential• Problem size

o computational effort rises as more flexible device

Conclusions

• Forms of RES are non-dispatchableo Intermittent: variability & limited predictability

• Impact on dispatchable generation systemo Increases flexibility requirements

• Can be absorbed by flexible generation if limited• At higher penetration additional flexibility required

• Can lead to negative instantaneous residual demand• Storage, interconnections, curtailment, flexible demand• New paradigm: generation drives load

• Also other technical, economic and regulatory challenges