Real Data · Full-Year 2024 · Annualised

Backtest Results: Real Market Data

Deterministic DP · DAMagedOpt vs. price-following baseline · Full calendar year 2024 (annualised)

+10.76%

Net value uplift vs baseline

€6,78M

Net gain / year (300 MW)

+11.13%

Gross revenue uplift

300 MW

Reference turbine

€23k/MW·yr

Uplift per MW

4 seasonal weeks × 13.07

Sampling method

Four Representative Seasonal Weeks

The annualised result is derived from 672 sampled hours spanning all four seasons of 2024, scaled by ×13.07 to a full leap year.

Winter (Jan)

2024-01-15 to 2024-01-22

Spring (Apr)

2024-04-15 to 2024-04-22

Summer (Jul)

2024-07-15 to 2024-07-22

Autumn (Oct)

2024-10-14 to 2024-10-21

Deterministic DP, SMARD DE-LU real day-ahead prices, spec-estimated hillchart

Methodology

Price source
SMARD DE real data (German DE-LU day-ahead, 2024)
Backtest period
Full calendar year 2024 · 672h sampled · ×13.0714 annualised
Solver
Deterministic Dynamic Programming (dp_deterministic)
Baseline
baseline_price_following
Reference turbine
Representative Francis Unit 300MW

Hillchart estimated from nameplate specs only — no measured, model-test, CFD, or SCADA data provided

Results are for a 300 MW Francis reference turbine under 2024 DE-LU market conditions. Hillchart estimated from nameplate specs only — no measured, model-test, CFD, or SCADA data. Your actual uplift depends on your turbine specs, hillchart quality, reservoir configuration, and local market prices. Past performance does not guarantee future results.

Paid Report · Instant PDF Download

What's inside the €49 report

  • Dispatch methodology: Dynamic Programming model, 36h rolling horizon, SMARD DE-LU day-ahead prices
  • 2024 DE-LU headline results: gross revenue uplift +8.03% (+€5.23M), net uplift +7.82% (+€5.05M) — annualised from 4 representative seasonal weeks
  • Month-by-month breakdown: optimised vs. baseline net value for Oct, Nov, and Dec 2024
  • Wear cost analysis: quantified avoided turbine wear cost per dispatch decision
  • Inflow model: Open-Meteo meteorological features → ML inflow forecaster (Grande Dixence proxy)
  • Caveats & limitations: data sources, model assumptions, and real-world applicability notes

Full methodology + results + caveats · instant PDF download · no sales call

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