Are open source energy system optimization tools mature enough for serious use?

Abstract Historically, energy system tools were predominantly proprietary and not shared with others. In recent years, there has been an increase in developing open source tools by international research and development organizations. More than half of the open energy modeling (openmod) initiative listed tools are based on the freely available scripting language Python. Previous comparisons of energy and power system modeling tools focused on comparisons such as which tool category (e.g. optimization, simulation) or energy demand (e.g. electricity, cooling, and heating) can be considered. Until now, the assessment of incorporated functions such as unit commitment (UC) or optimum power flow (OPF) has been ignored. Therefore, this work assesses 31 mostly open source tools based on 81 functions for their maturity. The result shows that available open source tools such as Switch, TEMOA, OSeMOSYS, and pyPSA are mature enough based on a function comparison with commercial or proprietary tools for serious use. Nevertheless, future commercial, as well as open source energy system analysis tools, have to consider more functions such as the impact of ambient air conditions and part-load behavior to allow better assessments of including high shares or renewable energy sources and other flexibility measures in existing and new energy systems.

[1]  Christos N. Markides,et al.  Modelling and optimising the marginal expansion of an existing district heating network , 2017 .

[2]  Russell Bent,et al.  Security-Constrained Design of Isolated Multi-Energy Microgrids , 2018, IEEE Transactions on Power Systems.

[3]  Andrew Rice,et al.  Open source software and crowdsourcing for energy analysis , 2012 .

[4]  Riccardo Manzini,et al.  The scheduling of maintenance. A resource-constraints mixed integer linear programming model , 2015, Comput. Ind. Eng..

[5]  Wang Jun,et al.  Optimal operation for integrated energy system considering thermal inertia of district heating network and buildings , 2017 .

[6]  P. Hamard,et al.  Increasing the total productivity of a land by combining mobile photovoltaic panels and food crops , 2017 .

[7]  Hermann Hofbauer,et al.  Development and Application of a Simulation Tool for Biomass Gasification Based Processes , 2008 .

[8]  Adisa Azapagic,et al.  An integrated life cycle sustainability assessment of electricity generation in Turkey , 2016 .

[9]  Adam Hawkes,et al.  Energy systems modeling for twenty-first century energy challenges , 2014 .

[10]  Mahmoud M. El-Halwagi,et al.  An MINLP model for the simultaneous integration of energy, mass and properties in water networks , 2014, Comput. Chem. Eng..

[11]  Fariborz Haghighat,et al.  Optimization approaches in district heating and cooling thermal network , 2017 .

[12]  Joao P. S. Catalao,et al.  Multi-Period Integrated Framework of Generation, Transmission, and Natural Gas Grid Expansion Planning for Large-Scale Systems , 2015, IEEE Transactions on Power Systems.

[13]  Andres Ramos,et al.  Tight and compact MIP formulation of configuration-based combined-cycle units , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[14]  Marc C. Steinbach,et al.  Computational optimization of gas compressor stations: MINLP models versus continuous reformulations , 2016, Mathematical Methods of Operations Research.

[15]  Tengfei Ma,et al.  Energy flow modeling and optimal operation analysis of the micro energy grid based on energy hub , 2017 .

[16]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.

[17]  Javier M. Moguerza,et al.  A strategic optimization model for energy systems planning , 2014 .

[18]  Christos T. Maravelias,et al.  Simultaneous Utility and Heat Exchanger Area Targeting for Integrated Process Synthesis and Heat Integration , 2017 .

[19]  Viktor Dorer,et al.  Optimisation of a district energy system with a low temperature network , 2017 .

[20]  Robert J. Thomas,et al.  Secure Planning and Operations of Systems With Stochastic Sources, Energy Storage, and Active Demand , 2013, IEEE Transactions on Smart Grid.

[21]  Jonas Allegrini,et al.  A review of modelling approaches and tools for the simulation of district-scale energy systems , 2015 .

[22]  Michael C. Georgiadis,et al.  Optimal energy planning and scheduling of microgrids , 2017 .

[23]  Matthias Fripp,et al.  Switch: a planning tool for power systems with large shares of intermittent renewable energy. , 2012, Environmental science & technology.

[24]  Enrico Carpaneto,et al.  Cogeneration planning under uncertainty. Part II: Decision theory-based assessment of planning alternatives , 2011 .

[25]  Efstratios N. Pistikopoulos,et al.  A spatial multi-period long-term energy planning model: A case study of the Greek power system , 2014 .

[26]  Andrew Crossland,et al.  A Linear Programming Approach for Battery Degradation Analysis and Optimization in Offgrid Power Systems with Solar Energy Integration , 2017 .

[27]  Efstratios N. Pistikopoulos,et al.  A multiperiod MINLP model for the synthesis of flexible heat and mass exchange networks , 1994 .

[28]  Omar J. Guerra,et al.  An optimization framework for the integrated planning of generation and transmission expansion in interconnected power systems , 2016 .

[29]  Nilay Shah,et al.  A MINLP multi-objective optimization model for operational planning of a case study CCHP system in urban China , 2018 .

[30]  Christos S. Ioakimidis,et al.  On the planning and analysis of Integrated Community Energy Systems: A review and survey of available tools , 2011 .

[31]  Mathias SIRVENT,et al.  Linearized model for optimization of coupled electricity and natural gas systems , 2017 .

[32]  Eric S. Fraga,et al.  A multi-objective framework for cost-unavailability optimisation of residential distributed energy system design , 2017 .

[33]  Jan Carmeliet,et al.  Optimization framework for distributed energy systems with integrated electrical grid constraints , 2016 .

[34]  Daniele Vigo,et al.  An optimization approach for district heating strategic network design , 2016, Eur. J. Oper. Res..

[35]  Mohammad Reza Mohammadi,et al.  Energy hub: From a model to a concept – A review , 2017 .

[36]  M. Stadler,et al.  A mixed integer linear programming approach for optimal DER portfolio, sizing, and placement in multi-energy microgrids , 2017 .

[37]  André Bardow,et al.  Automated superstructure-based synthesis and optimization of distributed energy supply systems , 2013 .

[38]  Michael Baldea,et al.  A multi-scale framework for simultaneous optimization of the design and operating strategy of residential CHP systems , 2017 .

[39]  S. Pfenninger.  Energy scientists must show their workings , 2017, Nature.

[40]  Ange-Lionel Toba,et al.  Modeling Social, Economic, Technical & Environmental Components in an Energy System , 2016 .

[41]  Joseph F. DeCarolis,et al.  Modeling for insight using Tools for Energy Model Optimization and Analysis (Temoa) , 2013 .

[42]  Nouredine Hadjsaid,et al.  Modelling the impacts of variable renewable sources on the power sector: Reconsidering the typology of energy modelling tools , 2015 .

[43]  Philip Voll,et al.  TRusT: A Two-stage Robustness Trade-off approach for the design of decentralized energy supply systems , 2017 .

[44]  Iain Staffell,et al.  The importance of open data and software: Is energy research lagging behind? , 2017 .

[45]  Christos T. Maravelias,et al.  A superstructure-based framework for simultaneous process synthesis, heat integration, and utility plant design , 2016, Comput. Chem. Eng..

[46]  Iván Palomares,et al.  Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support , 2017 .

[47]  P. Fleming,et al.  Generation expansion planning optimisation with renewable energy integration: A review , 2017 .

[48]  Alastair R. Buckley,et al.  A review of energy systems models in the UK: Prevalent usage and categorisation , 2016 .

[49]  Christodoulos A. Floudas,et al.  Mixed Integer Linear Programming in Process Scheduling: Modeling, Algorithms, and Applications , 2005, Ann. Oper. Res..