Effects of power plants mothballing on electricity markets Ahmed - - PowerPoint PPT Presentation

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Effects of power plants mothballing on electricity markets Ahmed - - PowerPoint PPT Presentation

Nov 06, 2023 499 likes β€’709 views

Effects of power plants mothballing on electricity markets Ahmed Ousman Abani *,+ , Marcelo Saguan x , Vincent Rious x , Nicolas Hary *,+ * Mines ParisTech/PSL Research University, France + Microeconomix (Deloitte France), France x Florence School

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SLIDE 1

Effects of power plants mothballing

  • n electricity markets

Ahmed Ousman Abani*,+, Marcelo Saguanx, Vincent Riousx, Nicolas Hary*,+

* Mines ParisTech/PSL Research University, France + Microeconomix (Deloitte France), France x Florence School of Regulation, Italy

15th IAEE European Conference, 6 Sept. 2017

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SLIDE 2

Outline

  • Motivation and research question
  • Methodology
  • Simulations and results
  • Concluding remarks

2

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SLIDE 3

Motivation and research question

  • Until recently, mothballing decisions have been overlooked in

dynamic simulation models used for generation adequacy assessment

  • This paper aims at:
  • Proposing a methodology for the integration of mothballing decisions in

dynamic simulation models

  • Assess the consequences of such decisions in the case of an energy-only

market in terms of:

  • Investments
  • Shutdowns

3

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SLIDE 4

Outline

  • Motivation and research question
  • Methodology
  • Simulations and results
  • Concluding remarks

4

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SLIDE 5

Methodology (1/7)

General l fu funct ctionin ing of

  • f th

the model

  • Main features and assumptions of the model
  • System dynamics approach
  • Representative agent
  • Energy-only market (for now)
  • Several generation technologies (Nuclear, Coal, gas-fired CCGT, oil-fired CT)
  • Simple dispatch module (for now)
  • Uncertain electricity demand
  • Yearly time step for investments/mothballings/shutdowns

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SLIDE 6

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Dispatch module Long-term decisions module Actual system Forecast module

  • Final/Intermediate

decisions

  • Investments
  • Mothballings
  • Shutdowns
  • Actual capacity
  • Actual load
  • Forecast installed capacity
  • Forecast load
  • Final decisions
  • Investments
  • Mothballings
  • Shutdowns
  • Actual revenues
  • Actual generation

Actual shortages

  • Etc.
  • Forecast

revenues

  • Actual capacity
  • Actual load

Methodology (2/7)

General l fu funct ctionin ing of

  • f th

the model

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SLIDE 7

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Dispatch module Long-term decisions module Actual system Forecast module

  • Final/Intermediate

decisions

  • Investments
  • Mothballings
  • Shutdowns
  • Actual capacity
  • Actual load
  • Forecast installed capacity
  • Forecast load
  • Final decisions
  • Investments
  • Mothballings
  • Shutdowns
  • Actual revenues
  • Actual generation

Actual shortages

  • Etc.
  • Forecast

revenues

  • Actual capacity
  • Actual load

Methodology (3/7)

General l fu funct ctionin ing of

  • f th

the model

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SLIDE 8

Methodology (4/7)

In Investment decis cisions

  • Investment decisions are based on the results
  • f the forecast module
  • The attractiveness of an investment is

assessed through the profitability index (NPV divided by investment cost)

  • Agents select the one with the highest

profitability index first

  • They add capacity until new investments are

no longer profitable

Compute the profitability index (PI) of 1 unit of investment for each technology (based on the results of the forecast module) Step 1 Select the technology with the highest PI Step 2 𝑁𝑏𝑦𝑗𝑛𝑣𝑛 𝑄𝐽 ≀ 0 Step 3 Invest 1 unit of the selected technology Step 4 Update the generation fleet Step 5 Stop Yes No 8

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SLIDE 9

Methodology (5/7)

Sim imple shutdown decis cisions (wit ithout t mot

  • thballi

ling)

  • Shutdown decisions are based on the expected profitability of operating the plant over the

forecast horizon

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y+2 y+1 y+3 y+4 y+5 Positive operating cash flow Negative operating cash flow Case 1: the plant is kept online y+2 y+1 y+3 y+4 y+5 Positive operating cash flow Negative operating cash flow Case 2: the plant is shut down

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SLIDE 10

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y+2 y+1 y+3 y+4 y+5 Positive operating cash flow Negative operating cash flow Case 1: the plant is kept online Mothballing cost Restart cost y+2 y+1 y+3 y+4 y+5 Positive operating cash flow Negative operating cash flow Case 2: the plant is mothballed Mothballing cost Restart cost y+2 y+1 y+3 y+4 y+5 Positive operating cash flow Negative operating cash flow Case 3: the plant is shut down Mothballing cost Restart cost

Methodology (6/7)

Shutdown and moth thballin ing decis ecisions – Example e for

  • r an activ

ctive pla lant

When mothballing is considered, the decision process is more complex but the general logic presented before remains

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SLIDE 11

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y+2 y+1 y+3 y+4 y+5 Positive operating cash flow Negative operating cash flow Case 1: the plant is restarted Mothballing cost Restart cost y+2 y+1 y+3 y+4 y+5 Positive operating cash flow Negative operating cash flow Case 2: the plant is kept mothballed Mothballing cost Restart cost y+2 y+1 y+3 y+4 y+5 Positive operating cash flow Negative operating cash flow Case 3: the plant is shut down Mothballing cost Restart cost

Methodology (7/7)

Shutdown and moth thballin ing decis ecisions – Example e for

  • r a mothball

lled pla lant

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SLIDE 12

Outline

  • Motivation and research question
  • Methodology
  • Simulations and results
  • Concluding remarks

12

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SLIDE 13

Simulations and results (1/4)

Sim imulations setu tup

  • Comparison between two settings using a Monte Carlo simulation (200 runs) over

a 20-year horizon

  • A setting in with no possibility to mothball plants  Setting 1
  • A setting in which mothballing is allowed  Setting 2
  • We use data from the literature (IEA 2015, Petitet 2016) for plants parameters
  • Mothballing and restart costs are modelled as a % (25%) of annual O&M costs

based on Frontier Economics (2015)

  • The model is initialized with an optimal generation mix (based on the French load

duration curve for 2015)

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SLIDE 14

Simulations and results (2/4)

Im Impact of

  • f mothball

llin ing on

  • n shutdown le

levels ls (M (Monte Ca Carlo)

  • There seems to be no significant effect on the overall level of shutdowns on average
  • However mothballing tends to delay shutdowns (not visible on this figure)

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SLIDE 15

Simulations and results (3/4)

Im Impact of

  • f mothball

llin ing on

  • n in

investment t le levels ls (M (Monte Ca Carlo)

  • Investment levels are reduced (on average) when mothballing is introduced
  • This effect is different depending on the technologies (see next slide)

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SLIDE 16

Simulations and results (4/4)

Im Impact of

  • f mothball

llin ing on

  • n in

investment t le levels ls (M (Monte Ca Carlo)

16

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SLIDE 17

Outline

  • Motivation and research question
  • Methodology
  • Simulations and results
  • Concluding remarks

17

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SLIDE 18

Concluding remarks

  • Our method primarily choses the least cost strategy between mothballing and staying online (or

restarting and staying mothballed)

  • It also ensures that the selected strategy is profitable ultimately (given agents’ expectations)
  • Shutdown is only considered in last resort
  • In an energy-only market, our simulations suggest that recurrent mothballings lead to lower levels
  • f investments (particularly in CT)
  • Shutdowns are delayed due to mothballings but there seems to be no significant effect on their

level in the long run

  • Further work include
  • Adding some technical constraints in the dispatch module to represent flexibility (min load,

ramp-up/down, etc.)

  • Modelling other types market designs (e.g., capacity mechanisms)
  • Finding more information on mothballing/restart costs

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SLIDE 19

Thank you !

Feel free to send me your comments at:

ahmed.ousmanabani@mines-paristech.fr aousmanabani@deloitte.fr

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