Grimaldi Engineering Energy Intelligence Forschungsfront Energy Intelligence · Cluster 1 Forschungsfront Forschung überlebt nur, wenn sie ein Produkt verbessert, verteidigbare IP schafft oder glaubwürdige wissenschaftliche Ergebnisse liefert. Nichts heißt „State of the Art“ ohne veröffentlichten Benchmark gegen eine klassische Baseline.
Übersicht Repository-Register Entscheidungslog & Kill-Liste KPIs & CEO-Report Zielarchitektur Sicherheitsgrenze Agent-Tool-Verträge Kommerzieller Wedge Forschungsfront Externe Intelligenz Ein Benchmark-Eintrag enthält dataset baseline metric confidence interval hardware latency failure cases generalization ablation
Evidenzmatrix — Benchmark-Felder je Thema dataset baseline metric result CI hardware latency failures repro PINNs for AC/DC power flow PINNs for AC/DC power flow · dataset: IEEE 9-bus PINNs for AC/DC power flow · baseline: Newton-Raphson (pandapower) PINNs for AC/DC power flow · metric: Voltage-angle RMSE PINNs for AC/DC power flow · result: DC 0.0124° · AC 0.0035° (owner-stated) PINNs for AC/DC power flow · CI: not reported PINNs for AC/DC power flow · hardware: not reported PINNs for AC/DC power flow · latency: not reported PINNs for AC/DC power flow · failures: not reported PINNs for AC/DC power flow · repro: false MILP dispatch vs learned dispatch MILP dispatch vs learned dispatch · dataset: no benchmark MILP dispatch vs learned dispatch · baseline: no benchmark MILP dispatch vs learned dispatch · metric: no benchmark MILP dispatch vs learned dispatch · result: no benchmark MILP dispatch vs learned dispatch · CI: no benchmark MILP dispatch vs learned dispatch · hardware: no benchmark MILP dispatch vs learned dispatch · latency: no benchmark MILP dispatch vs learned dispatch · failures: no benchmark MILP dispatch vs learned dispatch · repro: no benchmark Probabilistic forecasting (load, PV, price) Probabilistic forecasting (load, PV, price) · dataset: no benchmark Probabilistic forecasting (load, PV, price) · baseline: no benchmark Probabilistic forecasting (load, PV, price) · metric: no benchmark Probabilistic forecasting (load, PV, price) · result: no benchmark Probabilistic forecasting (load, PV, price) · CI: no benchmark Probabilistic forecasting (load, PV, price) · hardware: no benchmark Probabilistic forecasting (load, PV, price) · latency: no benchmark Probabilistic forecasting (load, PV, price) · failures: no benchmark Probabilistic forecasting (load, PV, price) · repro: no benchmark Inertia / RoCoF with grid-forming inverters Inertia / RoCoF with grid-forming inverters · dataset: Book appendix figures (chapter 1) Inertia / RoCoF with grid-forming inverters · baseline: Swing equation Inertia / RoCoF with grid-forming inverters · metric: Test pass against stated figures Inertia / RoCoF with grid-forming inverters · result: 48 tests pass Inertia / RoCoF with grid-forming inverters · CI: n/a Inertia / RoCoF with grid-forming inverters · hardware: n/a Inertia / RoCoF with grid-forming inverters · latency: n/a Inertia / RoCoF with grid-forming inverters · failures: n/a Inertia / RoCoF with grid-forming inverters · repro: true Safe / constrained RL for dispatch Safe / constrained RL for dispatch · dataset: no benchmark Safe / constrained RL for dispatch · baseline: no benchmark Safe / constrained RL for dispatch · metric: no benchmark Safe / constrained RL for dispatch · result: no benchmark Safe / constrained RL for dispatch · CI: no benchmark Safe / constrained RL for dispatch · hardware: no benchmark Safe / constrained RL for dispatch · latency: no benchmark Safe / constrained RL for dispatch · failures: no benchmark Safe / constrained RL for dispatch · repro: no benchmark GNNs / neural operators for grid state GNNs / neural operators for grid state · dataset: no benchmark GNNs / neural operators for grid state · baseline: no benchmark GNNs / neural operators for grid state · metric: no benchmark GNNs / neural operators for grid state · result: no benchmark GNNs / neural operators for grid state · CI: no benchmark GNNs / neural operators for grid state · hardware: no benchmark GNNs / neural operators for grid state · latency: no benchmark GNNs / neural operators for grid state · failures: no benchmark GNNs / neural operators for grid state · repro: no benchmark Runtime assurance & agent verification Runtime assurance & agent verification · dataset: no benchmark Runtime assurance & agent verification · baseline: no benchmark Runtime assurance & agent verification · metric: no benchmark Runtime assurance & agent verification · result: no benchmark Runtime assurance & agent verification · CI: no benchmark Runtime assurance & agent verification · hardware: no benchmark Runtime assurance & agent verification · latency: no benchmark Runtime assurance & agent verification · failures: no benchmark Runtime assurance & agent verification · repro: no benchmark Uncertainty quantification & distribution s… Uncertainty quantification & distribution shift · dataset: no benchmark Uncertainty quantification & distribution shift · baseline: no benchmark Uncertainty quantification & distribution shift · metric: no benchmark Uncertainty quantification & distribution shift · result: no benchmark Uncertainty quantification & distribution shift · CI: no benchmark Uncertainty quantification & distribution shift · hardware: no benchmark Uncertainty quantification & distribution shift · latency: no benchmark Uncertainty quantification & distribution shift · failures: no benchmark Uncertainty quantification & distribution shift · repro: no benchmark
Eine volle Zeile ist die Bedingung für das Wort „State of the Art“. Heute ist keine Zeile voll.
Themen Thema Produktnutzen Modul Repository Zustand Nächster Schritt PINNs for AC/DC power flow Surrogate power flow for fast candidate screening physicsphysics-informed partial Publish the full record with CI, latency vs Newton, and behaviour on 39-bus / 118-bus (generalization). MILP dispatch vs learned dispatch Wedge A — BESS / community dispatch optimizeGridOS unbenchmarked Benchmark MILP against the customer's rule-based schedule on real prices; learned policies only after MILP is deployed. Probabilistic forecasting (load, PV, price) Forecast inputs to dispatch; forecast-error KPI forecastGridOS (MPC loop) unbenchmarked Persistence and seasonal-naive baselines first; pinball loss on open data (e.g. ENTSO-E Transparency, Open Power System Data). Inertia / RoCoF with grid-forming inverters Physics constraint checks; credibility physicsRenewables_Migration_*_Proof_Engine partial Separate book-consistency tests from physical validation against a public dynamics case. Safe / constrained RL for dispatch Not before MILP is in production optimize— watch Watch only. Revisit when there is customer data and a deployed baseline. GNNs / neural operators for grid state State estimation with sparse measurements twin— watch Watch only. Runtime assurance & agent verification NeuralBridge design neuralbridgeNeuralBridge (unlocated) · mcp-foundry unbenchmarked Specify the simplex-architecture style monitor; formalize the envelope check; property tests before any code. Uncertainty quantification & distribution shift Forecast intervals; refusal to act under shift forecast · planning— watch Conformal intervals on the forecasting module once it has a backtest.
Benchmark-Eintrag: PINNs for AC/DC power flow Datensatz Baseline Metrik Ergebnis KI Hardware Latenz Fehlerfälle reproduzierbar IEEE 9-bus Newton-Raphson (pandapower) Voltage-angle RMSE DC 0.0124° · AC 0.0035° (owner-stated) not reported not reported not reported not reported false
Benchmark-Eintrag: Inertia / RoCoF with grid-forming inverters Datensatz Baseline Metrik Ergebnis KI Hardware Latenz Fehlerfälle reproduzierbar Book appendix figures (chapter 1) Swing equation Test pass against stated figures 48 tests pass n/a n/a n/a n/a true
Beobachtete Front OPF AC/DC power flow MPC GNNs neural operators PINNs differentiable simulation safe RL constrained RL uncertainty quantification distribution shift causal inference probabilistic forecasting digital twins multi-agent optimization formal methods runtime assurance agent verification
Stand 2026-09-05 Maschinenlesbar: /api/energy/research Alle Ressourcen: /api/energy/index
Verfasst und gepflegt von Vincenzo Ceccarelli Grimaldi, Frankfurt am Main.