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MicroGridsPy
by Politecnico di Milano
Authors:
Sergio Balderrama, Sylvain Quoilin, Francesco Lombardi, Giulia Guidicini, Lorenzo Rinaldi, Emanuela Colombo, Riccardo Mereu, Nicolò Stevanato, Ivan Sangiorgio, Gianluca Pellecchia
Contact:
nicolo.stevanato@polimi.it
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The MicroGridsPy model main objective is to provide an open-source alternative to the problem of sizing and dispatch of energy in micro-grids in isolated places. It’s written in python(pyomo) and use excel and text files as input and output data handling and visualisation.
Main features:
-Optimal sizing of PV panels, wind turbines, other renewable technologies, back-up genset and electrochemical storage system for least cost electricity supply in rural isolated areas.
-Optimal dispatch from the identified supply systems.
-Possibility to optimize on NPC or operation costs.
-LCOE evaluation for the identified system.
Possible features:
-Two-stage stochastic optimization.
-Multi-year evolving load demand and multi-step capacity expansion.
-Possibility of connecting to the national grid.
-Two-objective optimization (economic and environmental objective functions).
-Brownfield optimization.
-Built-in load archetypes for rural users.
-Endogenous calculation of renewable energy sources production.
Based on Python (Pyomo). Using Excel for data processing.
Website / Documentation
Download
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 Open Source
European Union Public Licence Version 1.1 (EUPL-1.1)
Directly downloadable
Input data shipped
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| Model Scope |
Model type and solution approach |
| Model class
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Energy Modeling Framework
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| Sectors
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Micro-grids design
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| Technologies
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Renewables, Conventional Generation
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| Decisions
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dispatch, investment
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| Regions
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| Geographic Resolution
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Village-scale
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| Time resolution
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Hour
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| Network coverage
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| Model type
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Optimization
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The model is based on two-stage stochastic optimisation to deal with the parametric uncertainty, while LP or MILP formulation can be used to tackle the structural uncertainty.
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| Variables
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| Computation time
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5-40"-40" is not declared as a valid unit of measurement for this property. minutes
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| Objective
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Single or multi objective optimization (NPC, operation costs, CO2 emissions)
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| Uncertainty modeling
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Two-stage stochastic optimization
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| Suited for many scenarios / monte-carlo
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Yes
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References
Scientific references
Sergio Balderrama, Francesco Lombardi, Fabio Riva, Walter Canedo, Emanuela Colombo, Sylvain Quoilin, A two-stage linear programming optimization framework for isolated hybrid microgrids in a rural context: The case study of the “El Espino” community, Energy (2019), 188,
https://dx.doi.org/https://doi.org/10.1016/j.energy.2019.116073
Reports produced using the model
-Sergio Balderrama, Francesco Lombardi, Fabio Riva, Walter Canedo, Emanuela Colombo, Sylvain Quoilin, A two-stage linear programming optimization framework for isolated hybrid microgrids in a rural context: The case study of the “El Espino” community, Energy (2019), 188, https://doi.org/10.1016/j.energy.2019.116073
-Nicolò Stevanato, Francesco Lombardi, Emanuela Colombo, Sergio Balderrama, Sylvain Quoilin, Two-Stage Stochastic Sizing of a Rural Micro-Grid Based on Stochastic Load Generation, 2019 IEEE Milan PowerTech, Milan, Italy, 2019, pp. 1-6. https://doi.org/10.1109/PTC.2019.8810571
-Nicolò Stevanato, Francesco Lombardi, Giulia Guidicini, Lorenzo Rinaldi, Sergio Balderrama, Matija Pavičević, Sylvain Quoilin, Emanuela Colombo, Long-term sizing of rural microgrids: Accounting for load evolution through multi-step investment plan and stochastic optimization, Energy for Sustainable Development (2020), 58, pp. 16-29, https://doi.org/10.1016/j.esd.2020.07.002
-Nicolò Stevanato, Lorenzo Rinaldi, Stefano Pistolese, Sergio Balderrama, Sylvain Quoilin, Emanuela Colombo, Modeling of a Village-Scale Multi-Energy System for the Integrated Supply of Electric and Thermal Energy, Applied Sciences (2020), https://doi.org/10.3390/app10217445
Example research questions
-Long-term sizing of rural microgrids
-Load evolution
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