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Addressing demand uncertainty in long-term planning
models
Tim Mertens
ETSAP workshop
17/06/2018
229/06/2018
Demand uncertainty - Why could it be relevant?
Unexpected demand growth cannot be met by sudden
capacity chang...
329/06/2018
Planning reserve margins
Safety margin that ensures an amount of reserve
capacity to be in place.
β€’ Planning r...
429/06/2018
Methodology
Two planning model comparisons:
1. Effect of capturing expected OPEX
2. Effect of capturing expect...
529/06/2018
Deterministic model with planning reserve constraint
𝑀𝑖𝑛
π‘”βˆˆπΊ
𝑓𝑔 βˆ™ 𝐾𝑔 +
π‘‘βˆˆπ‘‡ π‘”βˆˆπΊ
𝑐 𝑔 βˆ™ 𝑄 𝑔,𝑑 + 𝑉𝑂𝐿𝐿 βˆ™ 𝐸𝑁𝑆𝑑
s.t.:...
629/06/2018
A stochastic planning model
𝑀𝑖𝑛
π‘”βˆˆπΊ
𝑓𝑔 βˆ™ 𝐾𝑔 +
πœ”βˆˆΞ©
𝑝 πœ” βˆ™
π‘‘βˆˆπ‘‡ π‘”βˆˆπΊ
𝑐 𝑔 βˆ™ 𝑄 𝑔,𝑑,πœ” + 𝑉𝑂𝐿𝐿 βˆ™ 𝐸𝑁𝑆𝑑,πœ”
s.t.:
β€’ π‘”βˆˆπΊ 𝑄 𝑔,...
729/06/2018
Case study overview
Deterministic – PRC
(reference)
Stochastic - no ENS Stochastic - ENS
Low
dispatchable
CAPE...
829/06/2018
Deterministic with PRC
+
A planning reserve constraint favors the technology with the lowest investment cost.
...
929/06/2018
Stochastic – No load shedding
S-NoENS-low S-NoENS-high
1029/06/2018
Performance of deterministic capacity mix
Residual load duration curves…
1129/06/2018
Stochastic – load shedding
Tradeoff between load shedding and OCGT investments -> depends on VOLL
Two effects...
1229/06/2018
Wrap up
Planning reserve constraints favor the technology with the lowest investment costs, since
operational...
CONNECTING TECHNOLOGICAL INNOVATION TO
DECISION MAKING FOR SUSTAINABILITY
SEE YOU IN BRUSSELS!!
November 28-30
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Addressing demand uncertainty in long-term planning models

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Addressing demand uncertainty in long-term planning models

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Addressing demand uncertainty in long-term planning models

  1. 1. Addressing demand uncertainty in long-term planning models Tim Mertens ETSAP workshop 17/06/2018
  2. 2. 229/06/2018 Demand uncertainty - Why could it be relevant? Unexpected demand growth cannot be met by sudden capacity changes. β€’ Technology lead times make that investment decisions are made years before the moment of operation. Optimal investments decisions are based on future operational cost (OPEX) estimation. β€’ OPEX estimation depends on the demand (screening curves). Risk of ending up with a suboptimal capacity mix to cover electricity demand. β€’ How can we deal with demand uncertainty in long- term planning models?
  3. 3. 329/06/2018 Planning reserve margins Safety margin that ensures an amount of reserve capacity to be in place. β€’ Planning reserve margin forces the total installed capacity to exceed the perceived peak demand. β€’ Hedge against being inadequate and the deployment of emergency measures. Drawback: β€’ No estimation of the expected OPEX associated with an uncertain demand is taken into account. How important is it to capture this expected OPEX? 𝐷 π‘π‘’π‘Žπ‘˜ (1 + 𝑃𝑅𝑀) βˆ™ 𝐷 π‘π‘’π‘Žπ‘˜ Nuclear Coal CCGT OCGT RES Load
  4. 4. 429/06/2018 Methodology Two planning model comparisons: 1. Effect of capturing expected OPEX 2. Effect of capturing expected OPEX + endogenous RES capacity credit assessment
  5. 5. 529/06/2018 Deterministic model with planning reserve constraint 𝑀𝑖𝑛 π‘”βˆˆπΊ 𝑓𝑔 βˆ™ 𝐾𝑔 + π‘‘βˆˆπ‘‡ π‘”βˆˆπΊ 𝑐 𝑔 βˆ™ 𝑄 𝑔,𝑑 + 𝑉𝑂𝐿𝐿 βˆ™ 𝐸𝑁𝑆𝑑 s.t.: β€’ π‘”βˆˆπΊ 𝑄 𝑔,𝑑 = 𝐷𝑑 βˆ’ 𝐸𝑁𝑆𝑑 βˆ€π‘‘ ∈ 𝑇 β€’ 𝑄 𝑔,𝑑 ≀ 𝐾𝑔 βˆ€π‘” ∈ 𝐺 𝐷𝑖𝑠𝑝, βˆ€π‘‘ ∈ 𝑇 β€’ 𝑄 𝑔,𝑑 ≀ 𝑅𝑃𝑔,𝑑 βˆ™ 𝐾𝑔 βˆ€π‘” ∈ 𝐺 𝑅𝐸𝑆, βˆ€π‘‘ ∈ 𝑇 β€’ π‘”βˆˆπΊ 𝐷𝑖𝑠𝑝 𝐾𝑔 + π‘”βˆˆπΊ 𝑅𝐸𝑆 𝑅𝑃𝑔,𝑑 βˆ™ 𝐾𝑔 β‰₯ (1 + 𝑃𝑅𝑀) βˆ™ 𝐷𝑑 βˆ€π‘‘ ∈ 𝑇 Operational cost estimation based on single demand scenario. PRC ensures sufficient capacity to cover demand. β€’ No load shedding in optimal solution. β€’ RES capacity credit depends on production profile in peak residual demand timestep Fixed costs Operational costs
  6. 6. 629/06/2018 A stochastic planning model 𝑀𝑖𝑛 π‘”βˆˆπΊ 𝑓𝑔 βˆ™ 𝐾𝑔 + πœ”βˆˆΞ© 𝑝 πœ” βˆ™ π‘‘βˆˆπ‘‡ π‘”βˆˆπΊ 𝑐 𝑔 βˆ™ 𝑄 𝑔,𝑑,πœ” + 𝑉𝑂𝐿𝐿 βˆ™ 𝐸𝑁𝑆𝑑,πœ” s.t.: β€’ π‘”βˆˆπΊ 𝑄 𝑔,𝑑,πœ” = 𝐷𝑑,πœ” βˆ’ 𝐸𝑁𝑆𝑑,πœ” βˆ€π‘‘ ∈ 𝑇, βˆ€πœ” ∈ Ξ© β€’ 𝑄 𝑔,𝑑,πœ” ≀ 𝐾𝑔 βˆ€π‘” ∈ 𝐺 𝐷𝑖𝑠𝑝, βˆ€π‘‘ ∈ 𝑇, βˆ€πœ” ∈ Ξ© β€’ 𝑄 𝑔,𝑑,πœ” ≀ 𝑅𝑃𝑔,𝑑 βˆ™ 𝐾𝑔 βˆ€π‘” ∈ 𝐺 𝑅𝐸𝑆, βˆ€π‘‘ ∈ 𝑇, βˆ€πœ” ∈ Ξ© Fixed costs Expected operational costs 1. No load shedding: β€’ RES capacity credit depends on production profile in peak residual demand timestep 2. Load shedding β€’ Endogenous assessment of capacity credit for RES over multiple timesteps β€’ Depends on value of lost load (VOLL)
  7. 7. 729/06/2018 Case study overview Deterministic – PRC (reference) Stochastic - no ENS Stochastic - ENS Low dispatchable CAPEX D-low S-NoENS-low S-ENS-low High dispatchable CAPEX D-high S-NoENS-high S-ENS-high 𝑫 𝒕 π‘ˆπ‘… βˆ™ 𝐷𝑑 𝑫 𝒕 𝑃𝑅𝑀 βˆ™ 𝐷𝑑
  8. 8. 829/06/2018 Deterministic with PRC + A planning reserve constraint favors the technology with the lowest investment cost. β€’ The cost of possibly operating these OCGTs is not considered β€’ Only peak technology contributes to planning reserve margin
  9. 9. 929/06/2018 Stochastic – No load shedding S-NoENS-low S-NoENS-high
  10. 10. 1029/06/2018 Performance of deterministic capacity mix Residual load duration curves…
  11. 11. 1129/06/2018 Stochastic – load shedding Tradeoff between load shedding and OCGT investments -> depends on VOLL Two effects combined: β€’ Expected operational costs β€’ Endogenous assessment of RES capacity credit S-ENS-low S-ENS-high
  12. 12. 1229/06/2018 Wrap up Planning reserve constraints favor the technology with the lowest investment costs, since operational costs of reserve capacity are not included in the optimization. Demand uncertainty affects technology choices in long-term planning models. β€’ Capturing multiple scenarios changes the perception of the expected load duration. β€’ Planning reserve constraints do not capture this effect. Capacity credit assessment of RES is important for model result interpretation. β€’ Planning reserve constraints fix RES capacity credit on single timestep, which might lead to significant technology biases (especially for high RES penetration). β€’ Endogenous assessment of RES capacity credit might provide different results.
  13. 13. CONNECTING TECHNOLOGICAL INNOVATION TO DECISION MAKING FOR SUSTAINABILITY SEE YOU IN BRUSSELS!! November 28-30

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