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Optimal wind farm energy and reserve scheduling incorporating wake interactions

Marin Mabboux-Fort, Majid Bastankhah, Peter C Matthews, Mokhtar Bozorg

TL;DR

This paper addresses the mismatch between wind-farm energy scheduling and market participation caused by wake interactions. It introduces a wake-aware, two-stage stochastic optimization that integrates a wake model via FLORIS to produce more realistic power estimates and enable wake steering for improved performance in energy and FRR markets. The approach combines scenario-generation for FR durations and wind conditions with scenario reduction (S=15) and solves a quadratic program (via IPOPT in Pyomo) to maximize expected profit while mitigating imbalances. A London Array case study in GB markets demonstrates that conventional power-curve-based estimates overstate production by 12-13% and incur penalties, whereas wake-aware scheduling reduces penalties and yields 1-2% higher income with wake-steering strategies; results highlight the economic and operational benefits of active wake management for higher wind penetration, with implications for grid stability and market profitability.

Abstract

This paper proposes a novel approach for optimal energy and reserve scheduling of wind farms by explicitly modelling wake interactions to enhance market participation and operational efficiency. Conventional methods often neglect wake effects, relying on power curve estimations that represent an upper limit and reduce market performance. To address this, a two-stage stochastic programming framework is developed, integrating a wake-aware power estimation model within the FLORIS simulation software. Wind and reserve uncertainties are addressed through scenario generation and reduction, enabling wind power producers to optimise participation in day-ahead energy and ancillary services markets, with particular focus on the Frequency Restoration Reserve (FRR). The wake-aware model provides more realistic power output predictions based on site-specific wind and atmospheric conditions, improving scheduling accuracy and reducing imbalance penalties. Wake steering is further employed to mitigate wake-induced losses and increase income through participation in ancillary services. The proposed approach is evaluated through a case study of the London Array offshore wind farm participating in the Great Britain (GB) electricity markets. Results show that conventional methods estimate production 12-13% higher, leading to imbalance penalties and 3% lower revenue compared with the wake-aware approach accounting for wake interactions. Moreover, the steering-enhanced approach yields an additional 1-2% increase in income relative to the wake-aware baseline. These findings underscore the value of accounting for wake interactions in wind farm scheduling and demonstrate the economic and operational benefits of active wake management, offering insights for improving grid stability and profitability as wind penetration continues to rise.

Optimal wind farm energy and reserve scheduling incorporating wake interactions

TL;DR

This paper addresses the mismatch between wind-farm energy scheduling and market participation caused by wake interactions. It introduces a wake-aware, two-stage stochastic optimization that integrates a wake model via FLORIS to produce more realistic power estimates and enable wake steering for improved performance in energy and FRR markets. The approach combines scenario-generation for FR durations and wind conditions with scenario reduction (S=15) and solves a quadratic program (via IPOPT in Pyomo) to maximize expected profit while mitigating imbalances. A London Array case study in GB markets demonstrates that conventional power-curve-based estimates overstate production by 12-13% and incur penalties, whereas wake-aware scheduling reduces penalties and yields 1-2% higher income with wake-steering strategies; results highlight the economic and operational benefits of active wake management for higher wind penetration, with implications for grid stability and market profitability.

Abstract

This paper proposes a novel approach for optimal energy and reserve scheduling of wind farms by explicitly modelling wake interactions to enhance market participation and operational efficiency. Conventional methods often neglect wake effects, relying on power curve estimations that represent an upper limit and reduce market performance. To address this, a two-stage stochastic programming framework is developed, integrating a wake-aware power estimation model within the FLORIS simulation software. Wind and reserve uncertainties are addressed through scenario generation and reduction, enabling wind power producers to optimise participation in day-ahead energy and ancillary services markets, with particular focus on the Frequency Restoration Reserve (FRR). The wake-aware model provides more realistic power output predictions based on site-specific wind and atmospheric conditions, improving scheduling accuracy and reducing imbalance penalties. Wake steering is further employed to mitigate wake-induced losses and increase income through participation in ancillary services. The proposed approach is evaluated through a case study of the London Array offshore wind farm participating in the Great Britain (GB) electricity markets. Results show that conventional methods estimate production 12-13% higher, leading to imbalance penalties and 3% lower revenue compared with the wake-aware approach accounting for wake interactions. Moreover, the steering-enhanced approach yields an additional 1-2% increase in income relative to the wake-aware baseline. These findings underscore the value of accounting for wake interactions in wind farm scheduling and demonstrate the economic and operational benefits of active wake management, offering insights for improving grid stability and profitability as wind penetration continues to rise.
Paper Structure (24 sections, 13 equations, 12 figures, 2 tables)

This paper contains 24 sections, 13 equations, 12 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Wind power control
  3. Electricity markets
  4. Research gap and paper contributions
  5. Methodology overview
  6. Scenario generation and reduction
  7. Scenario generation process
  8. FR duration scenario generation
  9. Wind condition scenario generation
  10. Number of scenarios
  11. Scenario reduction process
  12. Optimisation process
  13. Objective function
  14. Constraints
  15. Income
  16. ...and 9 more sections

Figures (12)

  • Figure 1: Proposed optimal energy and reserve scheduling strategy
  • Figure 2: The coefficient of variation of $E_{gen}$ as a function of the number of generated scenarios $S_g$
  • Figure 3: Average within-cluster sums of point-to-medoid distances
  • Figure 4: Wind rose for the dataset with normed (displayed in percent) results
  • Figure 5: Turbulence intensity as a function of wind speed for 10-minute intervals of all days in 2015
  • ...and 7 more figures