Generate a test protocol for validating machine-learning energy-optimization models in a combined-cycle power-plant digital-twin proof-of-concept

Plant Performance Dataset *

Energy Market Context * Select market context Deregulated competitive market with LMP pricing Long-term PPA with fixed pricing structure Capacity market with grid reliability constraints Merchant plant exposed to spot market volatility Utility owned integrated resource planning Microgrid with islanding requirements Industrial co-generation with thermal host Combined heat and power district energy system

Regulatory Framework * Choose regulatory context EPA Clean Air Act with MATS compliance State renewable portfolio standards integration FERC Order 2222 distributed energy resources rules NERC Critical Infrastructure Protection standards International ISO 50001 energy management EU Industrial Emissions Directive compliance Regional greenhouse gas initiative (RGGI) requirements North American Reliability Standards planning criteria

Stakeholder Validation Requirements * Choose primary stakeholders Independent power producer asset managers Utility regulatory commission staff Energy trader and portfolio optimizer Grid operator balancing authority EPA compliance officer Commercial lender financing team Insurance underwriter risk team Independent engineering consultant Corporate sustainability officer Operations and maintenance crew

Model Performance Criteria *

Performance Boundaries * Select constraint types Steam turbine stress limitations Combustion turbine temperature limits NOx emissions cap constraints Water consumption restrictions Startup/shutdown cycle limitations Auxiliary power load maximums Fuel quality variation handling Ambient temperature operating range Grid frequency response requirements

Uncertainty Quantification Scope * Choose analysis methodology Deterministic single-point optimization Monte Carlo simulation with 90% confidence bounds Bayesian network with prior incorporation Ensemble modeling with model disagreement metrics Probabilistic load forecast integration Weather derivative uncertainty propagation Equipment degradation scenario modeling Fuel price volatility impact assessment Grid intermittency risk quantification

Validation Test Duration * Choose validation period Single peak day validation test Weekly operational cycling analysis Monthly maintenance window integration Full seasonal operational cycle Annual dispatch optimization verification Multi-year degradation trend assessment Equipment lifecycle cost analysis LTSA contract term optimization

Output Documentation Standards * Choose report format Executive dashboard with KPI summary Detailed technical appendix for engineers Financial pro forma for investment committee Peer-review journal publication format ASME performance test code compliance NERC CIP security documentation EPA compliance certification package Auditor-ready control documentation

Additional Technical Specifications