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| | |processing_software=PostgreSQL, Ethereum | | |processing_software=PostgreSQL, Ethereum |
| | |External optimizer=Gurobi, CPLEX, GLPK | | |External optimizer=Gurobi, CPLEX, GLPK |
| − | |Additional software=PostgreSQL, Ethereum | + | |Additional software=PyCharm, PostgreSQL, Ethereum |
| | |GUI=No | | |GUI=No |
| | |model_class=agent-based simulation | | |model_class=agent-based simulation |
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| | |is_suited_for_many_scenarios=No | | |is_suited_for_many_scenarios=No |
| | |montecarlo=Yes | | |montecarlo=Yes |
| | + | |computation_time_minutes=20 |
| | + | |computation_time_hardware=CPU: Intel Core i7-8550U, SSD: Samsung 3000MB/s read, 1800 MB/s write |
| | + | |computation_time_comments=50 prosumers, one day |
| | |Model input file format=No | | |Model input file format=No |
| | |Model file format=No | | |Model file format=No |
| | |Model output file format=No | | |Model output file format=No |
| | }} | | }} |
| Model Scope |
Model type and solution approach |
| Model class
|
agent-based simulation
|
| Sectors
|
local energy markets
|
| Technologies
|
Renewables, Conventional Generation, CHP
|
| Decisions
|
|
| Regions
|
|
| Geographic Resolution
|
|
| Time resolution
|
|
| Network coverage
|
|
|
| Model type
|
Simulation, Agent-based
|
|
|
Agents: intertemporal convex optimization
Markets: (iterative) double-sided auctions, p2p clearing
Forecasting: naive, deterministic forecasting, neural networks
|
| Variables
|
|
| Computation time
|
20 minutes (50 prosumers, one day)
|
| Objective
|
|
| Uncertainty modeling
|
perfect forecast, deterministic, stochastic
|
| Suited for many scenarios / monte-carlo
|
No
|
|
