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Schill, Rosellón, Egerer

Regulated Expansion of Electricity Transmission Networks: the effects of Fluctuating Demand and Wind Generation. Schill, Rosellón, Egerer. Outline. Motivation The model Model application Results Conclusions and challenges. Motivation.

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Schill, Rosellón, Egerer

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  1. Regulated Expansion of Electricity Transmission Networks: the effects of Fluctuating Demand and Wind Generation Schill, Rosellón, Egerer Juan Rosellon, CIDE and DIW Berlin

  2. Outline • Motivation • The model • Model application • Results • Conclusions and challenges

  3. Motivation • Starting point: Hogan, Rosellón and Vogelsang (2010) - “HRV“ • Rosellón and Weigt (2011), and Rosellón, Myslikóvá and Zenón (2011): “Seminal, but simplified” • Challenges: • Demand and prices vary considerably over a day / over a year • Increasing importance of fluctuating wind power • Comparison to other regulatory regimes • Our approach: • Include hourly time resolution and appropriate data • Implement additional regulatory regimes  How will the HRV model perform?

  4. The model • MPEC approach (Rosellón and Weigt, 2011) • Dispatch problem (lower level):

  5. Maximization Transco Problem (upper level)

  6. Additional equations • HRV cap on fix part:

  7. Model application • Implementation in GAMS • Elmod framework for load flows • Stylized central European network Table 1: Variable generation costs and available capacity

  8. Different cases Hourly reference demand at different nodes Figure 2: Hourly nodal reference demand in DRes and WindRes

  9. Hourly reference prices Figure 3: Hourly nodal reference prices in DRes and WindRes Hourly overall demand and wind pattern Figure 4: Wind generation and overall reference demand in WindRes

  10. Results: Static  Network extension: HRV closest to WF-max

  11. Line expansion (Static) Figure 6: Time path of overall extension in the Static case Extension: Germany-Netherlands and France-Belgium

  12. Results: Static vs. DRes  Demand fluctuations increase extension in wf-max and HRV  Opposite effect in noreg and costreg cases!

  13. Price convergence in DRes Figure 18: Convergence of hourly nodal prices under different regulatory approaches in DRes

  14. WindRes Table 5: Welfare results DRes: Differences to baseline without extension in bn € Table 6: Welfare results WindRes: Differences to baseline without extension in bn €  HRV again closer to wf-max than noreg and costreg

  15. Comparison of welfare and extension results Figure 17: Social welfare gain of extension compared to WFMax for different model runs  Fluctuating demand and wind power both increase the gap between wf-max and the regulatory cases  HRV much closer to wf-optimum in all cases  robust!

  16. Conclusions • Details matter in electricity market modelling: • Demand: simplified, static approach systematically underestimates the need for transmission upgrades • Fluctuating wind: further increases expansion requirements • HRV is robust against demand and wind fluctuations • WF: HRV closest to wf-max • Extension: HRV also leads to second-highest outcomes • Performance of HRV relative to alternatives increases with more realistic setting! • HRV has favourable characteristics for future large-scale wind integration (high extension)  further research necessary

  17. Challenges • Computationally very intensive • Data: • Better reference demands and prices • More realistic wind power fluctuations • Strong assumptions: • Perfect competition in generation • A single Transco

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