Simulation and optimization toolchain

SimSES allows to conduct time-series simulations for energy storage systems in an application-specific context. The focus lies on detailed modeling of all relevant components of a storage system to monitor the state evolution (e.g. state of charge, state of health) in a predefined applicational context. Through a series of integrated technical and economic analysis features, the user may derive and observe system parameters and key performance indicators, e.g. storage degradation (aging), dissipation losses (efficiency), net present value generation, etc. SimSES can work stand-alone or can be coupled to external programs defining the dispatch strategy for the storage system, e.g. the optimization tools presented on this page.   

The optimization tools lp_opt3D_milp, and mu_opt have been developed to optimally control the energy management system (EMS) of lithium-ion battery storage in either singular or multiple use cases to be served simultaneously. More details about their specific features and aims can be found in the specific enlargement tabs.

Tools

Simulation of stationary energy storage systems (SimSES)

Highlights & Features:

  • Time series analysis tool for detailed technical and economical evaluation of stationary storage systems
  • Degradation of stationery and vehicle storage system
  • Modelling of batteries, redox-flow systems and PTG systems
  • Modelling of DC/DC and AC/DC converters
  • Thermal Modelling

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Linear programming optimization tool for energy storage systems (lp_opt)

Highlights & Features:

  • Co-optimization of stationery and vehicle storage's operation strategy
  • Multi-storage optimization for residential power flow model
  • Minimization of operating expenses in form of electricity costs
  • Maximization of self-consumption and self-sufficiency
  • Considering various technical components of residential entity
  • Uni-directional and bi-directional charging schemes of electric vehicle
  • Considering varying vehicle usage patterns

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Multi-use optimization tool for energy storage systems (mu_opt)

Highlights & Features:

  • Dynamic allocation of energy and power of energy storage system
  • Segmentation into virtual energy and power partitions
  • Behind-the-meter and in-front-the-meter applications
  • Mixed-integer linear programming + rolling-horizon optimization
  • Linearization of non-linear internal processes
  • Degradation model for well-established cell chemistry
  • Consideration of market and regulatory constraints

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Three dimensional optimiziation for arbitrage application (3D-milp)

The tool was created by Volkan Kumtepeli at the Energy Research Institute at Nanyang Technological University in collaboration with Institute for Electrical Energy Storage Technology at the Technical University of Munich.

Highlights & Features:

  • A battery dispatch optimization tool for arbitrage market operations
  • Detailed semi-empirical battery aging model taking cell power, state of charge, and temperature into account
  • Mixed-integer linear programming approach for piecewise affine (PWA) approximation in three-dimensional input space (i.e., cell power, state of charge, and temperature)
  • Optimally-placed breakpoints at each axis for PWA approximation
  • Easy integration and scalability via MATLAB, YALMIP toolbox, and Gurobi Optimizer

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open_BEA Tool

Highlights & Features:

  • Coupling tool for SimSES and eDisGo:
  • Integration of SimSES time series analysis tool for detailed technical evaluation
  • Power-flow analysis with eDisGo
  • Positioning of stationary battery storage systems in distribution grids
  • Potential of Vehicle-2-Grid in distribution grids
  • Minimization of grid reinforcement costs

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