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Regulated Expansion for Electricity Networks: Addressing Fluctuating Demand and Wind Power

This study explores the impact of fluctuating electricity demand and wind power generation on regulated expansion of transmission networks. By modeling various scenarios, it assesses the effectiveness of different regulatory regimes in optimizing network performance. The results show that a dynamic approach, such as the Hybrid Real Options Value (HRV), is more robust in handling demand and wind variations compared to static methods. The study also highlights the importance of considering realistic factors in network planning for future large-scale wind integration.

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Regulated Expansion for Electricity Networks: Addressing Fluctuating Demand and Wind Power

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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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