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Projected inflow compared to historic Capacity factors

Assessing uncertainty of climate change impacts on long-term hydropower generation using the CMIP5 ensemblePablo E. Carvajal11 UCL Energy Institute, University College London, [email protected]

INTRODUCTION• Hydropower is vulnerable to long-term climate change. The Coupled

Model Intercomparison Project (CMIP5) projects large uncertainty forprecipitation.

• Large hydro suffers of significant cost and time overruns.• Long-term energy system optimisation models usually consider constant

hydro-climatic conditions and only minimise cost, not risk.

CASE STUDY - ECUADOR• Hydro share in electricity generation was 75% in 2017 – 4368MW• Six large river basins with large hydro and still untapped potential.

RESULTS

CONCLUSIONS• Power expansion plans (and energy system optimisation models) should

consider minimum cost, minimum environmental impact and also minimum risk.

• The cheapest (minimal cost) portfolio might entail large uncertainties and price volatilities.

• There are still large discrepancies and uncertainties for future hydropower production under climate change.

REFERENCESAwerbuch (2006) Portfolio-based electricity generation planning: Policy

implications for renewables and energy security. Mitigation Adaption Strategyto Glob Chang 11:693–710.

Nijs & Poncelet (2016) Integrating recurring uncertainties in ETSAP energysystem models, VITO NV for ETSAP

METHOD INTEGRATED HYDRO - POWER – RISK MODEL

6

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Hydropower stations Gauging stations River network

Pacific Amazon

Esmeraldas Guayas Jubones Santiago Pastaza Napo

Andes Mountain Range

COLOMBIA

PERU

PACIFIC OCEAN

Toachi - A.J Pilatón

Toachi 205.4MW Pilatón 48.9MW

Mazar 170MW

Molino 1100MW

Sopladora 487MW

Coca Codo Sinclair 1500MW

Marcel Laniado 213MW

Minas San Francisco 275MW

Daule

Paute - D.J. Palmira

Jubones - D.J.San Francisco

San Francisco 230MW

Agoyán 156MW

Pastaza - Baños

Coca - San Rafael

kilometers

123456

Basins

Watersheds

0 520260130

Exploring hydropower’s largest sources of uncertainty: impacts of climate change and capital cost overruns, and how to assess them in long-term energy system models.

Runoff projections CMIP5 – RCP4.5* (2071-2100)• Large discrepancy among climate models for precipitation• Models reflect seasonal changes0

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Marcel Laniado (2)

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Minas San Francisco (3)

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Agoyán (5)

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Coca Codo Sinclair (6)

CMIP5 Ensemble mean Historic Individual GCMs

CMIP5 Ensemble mean ± SD

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Paute (4)

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−1SD

Toachi Pilatón (1)Marcel Laniado (2)Minas San Francisco (3)Paute (4)Agoyán (5)Coca Codo Sinclair (6)

HYDROLOGICAL MODEL• 40 GCMs RCP4.5 – 2001-2100 – monthly level – 0.5°grid res.• Precipitation -> Runoff -> Availability factor for hydro (%)

TIMES ENERGY SYSTEM MODELObjective function

Minimising costs and risks simultaneouslyDetail• Horizon 2060• 32 Time slices (12 months x 3 inter day periods)• Plant by plant (125 existing plants) and 15 new technologies.• Run of river and reservoir hydro by river basins and discrete sizes.

RISK MODEL Upper Absolute Deviation

𝑀𝑖𝑛(𝐸 𝑐𝑜𝑠𝑡 + 𝛾×𝑈𝑝𝐴𝑏𝑠𝐷𝑒𝑣(𝑐𝑜𝑠𝑡)𝑈𝑝𝐴𝑏𝑠𝐷𝑒𝑣 𝑐𝑜𝑠𝑡 =7 𝑝8× 𝑐𝑜𝑠𝑡8 − 𝐸 𝑐𝑜𝑠𝑡 :�

8• The UpAbsDev computes the average value of the positive total cost

deviations for all-states-of-the-world j (with probability pj).• Applied for fuel prices and capital cost of new technologies

*CMIP5 – Coupled Model Intercomparison Project IPCC AR5RCP 4.5 Representative Concentration Pathway

Available energy from hydropower system in different basins• Significant changes in expected hydropower production

Installed capacity (MW)

Observed generation (GWh/y)

Simulated annual generation (GWh/y)-1SD Ensemble mean +1SD

4368 20822 -55%↓ 6%↑ 39%↑

Next steps• Consider changes in long-

term hydro in TIMES for the whole Ecuadorian power system

• Include risk and derive a trade-off between system cost and risk

Cost – risk trade off curve (Pareto optimal)

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Agoyán (5)

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