Addressing demand uncertainty in long-term planning models

Addressing demand uncertainty in long-term planning models

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  Addressing demand uncertaintyin long-term planning models Tim Mertens ETSAP workshop 17/06/2018
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  229/06/2018 Demand uncertainty -Why could it be relevant? Unexpected demand growth cannot be met by sudden capacity changes. • 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. • OPEX estimation depends on the demand (screening curves). Risk of ending up with a suboptimal capacity mix to cover electricity demand. • How can we deal with demand uncertainty in long- term planning models?
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  329/06/2018 Planning reserve margins Safetymargin that ensures an amount of reserve capacity to be in place. • Planning reserve margin forces the total installed capacity to exceed the perceived peak demand. • Hedge against being inadequate and the deployment of emergency measures. Drawback: • 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 𝑝𝑒𝑎𝑘 (1 + 𝑃𝑅𝑀) ∙ 𝐷 𝑝𝑒𝑎𝑘 Nuclear Coal CCGT OCGT RES Load
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  429/06/2018 Methodology Two planning modelcomparisons: 1. Effect of capturing expected OPEX 2. Effect of capturing expected OPEX + endogenous RES capacity credit assessment
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  529/06/2018 Deterministic model withplanning reserve constraint 𝑀𝑖𝑛 𝑔∈𝐺 𝑓𝑔 ∙ 𝐾𝑔 + 𝑡∈𝑇 𝑔∈𝐺 𝑐 𝑔 ∙ 𝑄 𝑔,𝑡 + 𝑉𝑂𝐿𝐿 ∙ 𝐸𝑁𝑆𝑡 s.t.: • 𝑔∈𝐺 𝑄 𝑔,𝑡 = 𝐷𝑡 − 𝐸𝑁𝑆𝑡 ∀𝑡 ∈ 𝑇 • 𝑄 𝑔,𝑡 ≤ 𝐾𝑔 ∀𝑔 ∈ 𝐺 𝐷𝑖𝑠𝑝, ∀𝑡 ∈ 𝑇 • 𝑄 𝑔,𝑡 ≤ 𝑅𝑃𝑔,𝑡 ∙ 𝐾𝑔 ∀𝑔 ∈ 𝐺 𝑅𝐸𝑆, ∀𝑡 ∈ 𝑇 • 𝑔∈𝐺 𝐷𝑖𝑠𝑝 𝐾𝑔 + 𝑔∈𝐺 𝑅𝐸𝑆 𝑅𝑃𝑔,𝑡 ∙ 𝐾𝑔 ≥ (1 + 𝑃𝑅𝑀) ∙ 𝐷𝑡 ∀𝑡 ∈ 𝑇 Operational cost estimation based on single demand scenario. PRC ensures sufficient capacity to cover demand. • No load shedding in optimal solution. • RES capacity credit depends on production profile in peak residual demand timestep Fixed costs Operational costs
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  629/06/2018 A stochastic planningmodel 𝑀𝑖𝑛 𝑔∈𝐺 𝑓𝑔 ∙ 𝐾𝑔 + 𝜔∈Ω 𝑝 𝜔 ∙ 𝑡∈𝑇 𝑔∈𝐺 𝑐 𝑔 ∙ 𝑄 𝑔,𝑡,𝜔 + 𝑉𝑂𝐿𝐿 ∙ 𝐸𝑁𝑆𝑡,𝜔 s.t.: • 𝑔∈𝐺 𝑄 𝑔,𝑡,𝜔 = 𝐷𝑡,𝜔 − 𝐸𝑁𝑆𝑡,𝜔 ∀𝑡 ∈ 𝑇, ∀𝜔 ∈ Ω • 𝑄 𝑔,𝑡,𝜔 ≤ 𝐾𝑔 ∀𝑔 ∈ 𝐺 𝐷𝑖𝑠𝑝, ∀𝑡 ∈ 𝑇, ∀𝜔 ∈ Ω • 𝑄 𝑔,𝑡,𝜔 ≤ 𝑅𝑃𝑔,𝑡 ∙ 𝐾𝑔 ∀𝑔 ∈ 𝐺 𝑅𝐸𝑆, ∀𝑡 ∈ 𝑇, ∀𝜔 ∈ Ω Fixed costs Expected operational costs 1. No load shedding: • RES capacity credit depends on production profile in peak residual demand timestep 2. Load shedding • Endogenous assessment of capacity credit for RES over multiple timesteps • Depends on value of lost load (VOLL)
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  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 𝑈𝑅 ∙ 𝐷𝑡 𝑫 𝒕 𝑃𝑅𝑀 ∙ 𝐷𝑡
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  829/06/2018 Deterministic with PRC + Aplanning reserve constraint favors the technology with the lowest investment cost. • The cost of possibly operating these OCGTs is not considered • Only peak technology contributes to planning reserve margin
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  929/06/2018 Stochastic – Noload shedding S-NoENS-low S-NoENS-high
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  1029/06/2018 Performance of deterministiccapacity mix Residual load duration curves…
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  1129/06/2018 Stochastic – loadshedding Tradeoff between load shedding and OCGT investments -> depends on VOLL Two effects combined: • Expected operational costs • Endogenous assessment of RES capacity credit S-ENS-low S-ENS-high
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  1229/06/2018 Wrap up Planning reserveconstraints 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. • Capturing multiple scenarios changes the perception of the expected load duration. • Planning reserve constraints do not capture this effect. Capacity credit assessment of RES is important for model result interpretation. • Planning reserve constraints fix RES capacity credit on single timestep, which might lead to significant technology biases (especially for high RES penetration). • Endogenous assessment of RES capacity credit might provide different results.
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  CONNECTING TECHNOLOGICAL INNOVATIONTO DECISION MAKING FOR SUSTAINABILITY SEE YOU IN BRUSSELS!! November 28-30