COSIM Cockpit
Research-as-a-Service platform for studying the interaction between flexible energy assets, electricity markets, and distribution-grid operation.
Why Integrated Simulation Matters
Battery dispatch, electricity markets, and distribution-grid operation are mutually interdependent. No single-domain model captures their interaction.
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Market-optimal dispatch creates grid loading patterns invisible to pure price models
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Grid tariffs (TURPE7) distort market-optimal schedules in ways visible only under joint simulation
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Distribution congestion emerges from the interaction of PV, loads, and EMS decisions — not from any single signal
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Policy assessment requires co-simulation: a tariff reform propagates through market response → battery cycling → transformer loading → voltage profiles
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Bidirectional coupling is architecturally enforced: transformer loading feeds back into the EMS optimizer as a real-time congestion signal
What Is Simulated
Distribution Grid
PandaPower AC load flow with SimBench reference topologies. 15-min timesteps over a full calendar year.
Load & Generation Profiles
SimBench synthetic time series for residential, commercial, and industrial nodes. PV and wind generation included.
Battery Storage (BESS)
LP-based EMS with rolling 72-hour horizon, re-optimised every 24 hours. Gurobi or CBC/PuLP solver.
Electricity Markets
Day-ahead (DAA) and intraday (IDA) auction price time series. Directly integrated into EMS cost objectives.
Grid Tariffs (TURPE7)
French TURPE7 distribution tariff: volumetric AP and peak LP components. Configurable time-window YAML rules.
EMS Strategy Library
35 strategy variants across 8 families: SCO, arbitrage, peak shaving, TURPE7 tariff, FCR/aFRR, multi-use, degradation-aware, and co-located RES.
Analytical Workflow
CONFIGURATION
Scenario Config
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SimBench Profiles
Market Data (DAA/IDA)
Grid Tariffs (TURPE7)
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CO-SIMULATION (MOSAIK)
FCU / EMS Optimizer
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PandaPower Grid
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OUTPUT
DataCollector
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CSV Result Files
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ANALYSIS
Grid Analysis
Assets
Market–Grid
Comparison
Research & Application Focus
TURPE7 Tariff Impact
How AP + LP tariff structure alters BESS dispatch relative to pure wholesale arbitrage.
Congestion Management via Price Signals
Whether banded AP multipliers (1× → 10×) reduce transformer peak loading without explicit curtailment.
Market vs. Grid Signal Trade-offs
Conditions under which DAA/IDA prices dominate over regulatory grid tariff signals — and vice versa.
Degradation-Aware Dispatch
Whether aging cost (Xu-2017 / Sony LFP model) in the EMS objective extends BESS lifetime without sacrificing revenue.
Multi-Market Co-Optimisation
How to split BESS capacity between FCR reserve, arbitrage, self-consumption, and peak shaving simultaneously.
Example Research Questions
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Can a static LP peak tariff alone reduce transformer loading peaks in a rural MV grid?
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Does dynamic LP with seasonal time-of-use windows outperform static LP?
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How does a 3× AP weight multiplier affect annual cycling and grid peak loading?
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Do congestion-banded AP multipliers (70 / 80 / 90 % thresholds) defer transformer overloading in summer-peak scenarios?
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Which congestion path achieves better peak management: penalise both directions (Path A) or credit charging (Path B)?
Technical Specifications
Simulation resolution
15 minutes (900 s per step)
Simulation horizon
Full calendar year · 35,040 timesteps
EMS strategy variants
35 across 8 families
Optimisation solver
Gurobi (default) · CBC/PuLP · HiGHS
Parallel execution
joblib multi-process batch (20 scenarios validated)
Reference grid
SimBench 1-MV-rural--1-sw · 20 kV · ~110 buses
Reference BESS
10 MW / 20 MWh per scenario
Rolling EMS horizon
72 h · re-optimised every 24 h with perfect foresight
Extensibility
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New EMS strategy: one Python class in logic/, one entry in ems_config.yaml — no further changes required
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New tech model: implement a BatteryStorage / PVSystem / WindPower subclass in general/tech_models/ — composable with any optimizer
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New grid topology: supply a PandaPower JSON grid file or a SimBench grid code string
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New tariff scheme: write a YAML time-window file with tariff class rules — no EMS code changes needed
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New data interface: implement a VEDInterface subclass in vinterfaces/ — activated declaratively in ved_config JSON
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New analysis view: add a Reflex page and translation keys, register in cosim_cockpit.py
Platform Architecture
CONFIGURATION
Scenario Config
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ModuleRunner (Mosaik)
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CO-SIMULATION (MOSAIK)
SimBench Profiles
FCU / EMS
PandaPower
GridTariffs
DataCollector
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OUTPUT
CSV Result Files
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ANALYSIS
COSIM Cockpit
Grid Analysis
Flexibility Assets
Market–Grid
Comparison
Getting Started
1
Sign in with your credentials
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Select a simulation scenario from the Scenario Browser
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Open Grid Analysis to review voltage profiles and transformer loading
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Open Flexibility Assets to inspect BESS dispatch and EMS decisions
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Open Market–Grid Coupling to see cost breakdown and tariff impact
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Use Comparison to contrast two or more scenarios side by side
Research & Development
The COSIM Cockpit platform is developed within the collaborative research and development activities of the Institute for High Voltage Equipment and Grids, Digitalization and Energy Economics (IAEW) — Active Energy Distribution Grids group — at RWTH Aachen University and the Fraunhofer FIT research team Digitale Energie. The work combines academic expertise in active distribution networks, grid operation and planning, and flexibility integration with applied digital-energy system development and engineering-oriented analytical tooling.
Contact
Primary contact for this platform:
Steffen Kortmann
steffen.kortmann@fit.fraunhofer.de+49 241 80-92946Legal Notice
The COSIM Cockpit is a research and engineering platform developed at IAEW RWTH Aachen University and Fraunhofer FIT. Results are based on simulation models, modelling assumptions, and scenario-specific input data. Real-world system behaviour may differ from simulated outcomes. Analyses do not constitute operational guarantees or binding engineering assessments.
No warranty is provided regarding completeness, correctness, or fitness for a specific operational purpose. Users are responsible for independently validating conclusions before applying them to real-world decisions.
Research use only: This platform is designed exclusively for research and academic analysis. Results must not be used as the sole basis for investment, operational, or regulatory decisions without independent engineering validation.
Market data: Electricity price time series used in simulations (DAA/IDA from ENTSO-E Transparency Platform and EPEX Spot) are subject to the respective platform terms of use. Data are used solely for non-commercial academic research. Redistribution or commercial reuse requires separate authorisation from the original data provider.
Confidentiality: Simulation scenarios, configuration files, and result data may contain confidential project-specific information. Exported results and reports must not be shared beyond the intended audience without prior authorisation.
Technology & Open-Source Components
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Built on open-source software: Reflex (Apache 2.0), Plotly (MIT), pandapower (BSD 3-Clause), SimBench (Open Database License), pandas & NumPy (BSD 3-Clause).