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Stochastic modelling of intermittent renewables in TIMES models.

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Stochastic modelling of intermittent renewables in TIMES models.

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Stochastic modelling of intermittent renewables in TIMES models.

  1. 1. Stochastic modelling of intermittent renewables in TIMES models Lisa Kvalbein, Pernille Seljom Renewables energy systems, IFE 02.07.2020 ZOOM 77th Semi-annual ETSAP workshop
  2. 2. • Assessment of the Value of Flexibility Services from the Norwegian Energy System (ASSETS) • Important to model intermittent renewables in a proper manner to provide good model insights on the low carbon transition • This work investigates how to model wind and PV generation that provides reasonable results • Demonstrated in IFE-TIMES-Norway Motivation 2
  3. 3. 3 Wind PV (real production) Intermittent renewable has a high short- term uncertainty. In Norway the weather changes rapidly throughout the days. For a good representation of wind and PV in the model, uncertainty is needed to be taken into account.
  4. 4. • Stochastic programming (SP) is a mathematical framework to consider uncertainty & value flexibility in optimisation models • Ignoring uncertainty can give misleading decision support from TIMES models • Depending on analysis • SP can be used to explicit model uncertainty and value flexibility Stochastic programming of short-term uncertainty 4
  5. 5. • Two-stage stochastic model • Scenarios represent realisation of uncertain parameters • Scenario independent investment decisions & scenario dependent operational decisions • Minimise: Investment costs + expected operational costs Short-term uncertainty 5 2015 2020 2030 2040 s1 s2 s3 p1 p2 p3 Investments Operation
  6. 6. • Scenario is a discrete representation of uncertainty with a given probability to occur • Number of scenarios effect the computational effort • Poor scenarios can give misleading results and insights • Important to evaluate the quality of the stochastic scenarios and model solution Stochastic scenario generation • Example: 21 stochastic PV production scenarios Objectives: - How many scenarios is needed to provide reasonable model insights? - What is a good method to generate stochastic scenarios? 6
  7. 7. • Stochastic scenarios considerations • Temporal and regional correlations • Statistical properties/moments • Scenario generation methods • Random sampling (2 seconds per season) • Statistical methods: Moment matching • Repetitive sampling (10 min per season) • Optimization (Time limit of 6h per season) • Solution evaluation methods • In-sample and out-of-sample stability tests Stochastic scenario generation 7
  8. 8. • Input – 19 years of hourly resolution capacity factors for solar and wind in Norway • Total of 65 sets of scenarios created • M = 3, 9, 15, 21, 30 • Optimizing – 1 set for each M • Random Sampling – 10 set for each M • Repetitive Sampling – 10 set for each M • Total 21 set of scenarios created for each M • Also created deterministic scenario of median values Stochastic scenarios 8
  9. 9. • Is the scenario generator creating scenarios resulting in similar optimal values? Model result: In-sample stability 9 • The energy system cost depends on how PV and wind is modelled • More scenarios gives less variations among sampling methods
  10. 10. • 101 = 10 min • 102 = 100 min ~ 1,5 hours • 103 = 1000 min ~ 16,5 hours • 104 = 10 000 min ~ 167 hours ~7 days and nights Computational time 10 Scenario creation time not included The solution time depends highly on number of stochastic scenarios
  11. 11. Model results: Wind and PV in 2040 11 PV capacity depends highly on model representation of intermittent generation
  12. 12. 12 Conclusion and further work • Stochastic modelling of short-term uncertainty in TIMES models gives more realistic representation than a deterministic approach • Stochastic scenarios, number of scenarios and methods used, affect the solution quality • PV is influenced the most in our analysis • Further analysis and quality tests on in-sample and out-of-sample stability will give insights on • Required number of scenarios • Suitable methods for scenario generation
  13. 13. Questions? Lisa Kvalbein Master of Science IFE

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