Important

Work in progress: The repository is under active development and interfaces may change without notice. Please use GitHub issues for raising problems, questions, comments and feedback.

What is MESMO?#

MESMO stand for “Multi-Energy System Modeling and Optimization” and is an open-source Python tool for the modeling, simulation and optimization of multi-scale electric and thermal distribution systems along with distributed energy resources (DERs), such as flexible building loads, electric vehicle (EV) chargers, distributed generators (DGs) and energy storage systems (ESS).

Features#

MESMO implements 1) non-linear models for simulation-based analysis and 2) convex models for optimization-based analysis of electric grids, thermal grids and DERs. Through high-level interfaces, MESMO enables modeling operation problems for both traditional scenario-based simulation as well as optimization-based decision support. An emphasis of MESMO is on the modeling of multi-energy systems, i.e. the coupling of multi-commodity and multi-scale energy systems.

  1. Electric grid modeling

    • Simulation: Non-linear modeling of steady-state nodal voltage / branch flows / losses, for multi-phase / unbalanced AC networks.

    • Optimization: Linear approximate modeling via global or local approximation, for multi-phase / unbalanced AC networks.

  2. Thermal grid modeling

    • Simulation: Non-linear modeling of steady-state nodal pressure head / branch flow / pump losses, for radial district heating / cooling systems.

    • Optimization: Linear approximate modeling via global or local approximation, for radial district heating / cooling systems.

  3. Distributed energy resource (DER) modeling

    • Simulation & optimization: Time series models for non-dispatchable / fixed DERs.

    • Optimization: Linear state-space models for dispatchable / flexible DERs.

    • Currently implemented DER models: Conventional fixed loads, generic flexible loads, flexible thermal building loads, non-dispatchable generators, controllable electric / thermal generators, electric / thermal energy storage systems, combined heat-and-power plants.

  4. Solution interfaces

    • Simulation: Solution of non-linear power flow problems for electric / thermal grids.

    • Optimization: Solution of convex optimization problems for electric / thermal grids and DERs, through third-party numerical optimization solvers.

    • Generic optimization problem interface: Supports defining custom constraints and objective terms to augment the built-in models. Enables retrieving duals / DLMPs for the study of decentralized / distributed control architectures for energy systems.

    • High-level problem interfaces: Nominal operation problem for simulation-based studies; Optimal operation problem for optimization-based studies.

Use cases#

District-scale energy systems are evolving from unidirectional, top-down structures into bidirectional, distributed and multi-commodity systems. Furthermore, the increased coupling of electric, thermal and other energy systems in terms of multi-energy systems enables additional flexibility across conventional system boundaries. MESMO is intended to support the various studies that are motivated by these recent developments for district-scale energy systems, such as the examples highlighted below:

  1. Emerging energy demands: What is the impact of EV charging deployment on distribution system operation?

  2. Distribution automation: How can the distribution system operator address imminent operational issues?

  3. Peak load management: Are transactive energy mechanisms suited to increase participation of DERs in maintaining distribution system reliability?

  4. Distributed generation and storage: Can energy storage systems help mitigate adverse impacts of large-scale solar PV deployment?

  5. Expansion planning: What are optimal investment decisions for distribution system upgrades?

Acknowledgements#

  • MESMO is developed in collaboration between TUMCREATE, the Institute for High Performance Computing, A*STAR and the Chair of Renewable and Sustainable Energy Systems, TUM.

  • Sebastian Troitzsch implemented the initial version of MESMO and maintains this repository.

  • Sarmad Hanif and Kai Zhang developed the underlying electric grid modeling, fixed-point power flow solution and electric grid approximation methodologies.

  • Arif Ahmed implemented the implicit Z-bus power flow solution method & overhead line type definitions.

  • Mischa Grussmann developed the thermal grid modeling and approximation methodologies.

  • Verena Kleinschmidt implemented several multi-energy DER models, such as the heating plant and CHP plant models.

  • Sebastian Troitzsch and Tom Schelo implemented the optimization problem class.

  • This work was financially supported by the Singapore National Research Foundation under its Campus for Research Excellence And Technological Enterprise (CREATE) programme.