|
|
| Line 2: |
Line 2: |
| | |Full_Model_Name=MicroGridsPy | | |Full_Model_Name=MicroGridsPy |
| | |Acronym=MGpy | | |Acronym=MGpy |
| − | |author_institution=Université de Liège, Politecnico di Milano | + | |author_institution=Politecnico di Milano |
| | |authors=Sergio Balderrama, Sylvain Quoilin, Francesco Lombardi, Giulia Guidicini, Lorenzo Rinaldi, Emanuela Colombo, Riccardo Mereu, Nicolò Stevanato, Ivan Sangiorgio, Gianluca Pellecchia | | |authors=Sergio Balderrama, Sylvain Quoilin, Francesco Lombardi, Giulia Guidicini, Lorenzo Rinaldi, Emanuela Colombo, Riccardo Mereu, Nicolò Stevanato, Ivan Sangiorgio, Gianluca Pellecchia |
| | |contact_persons=gianluca.pellecchia@polimi.it | | |contact_persons=gianluca.pellecchia@polimi.it |
| Line 27: |
Line 27: |
| | -Built-in load archetypes for rural users. | | -Built-in load archetypes for rural users. |
| | -Endogenous calculation of renewable energy sources production. | | -Endogenous calculation of renewable energy sources production. |
| − | |Primary purpose=Sizing and dispatch of energy in micro-grids in isolated places
| |
| | |Primary outputs=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. | | |Primary outputs=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. |
| | |User documentation=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM/blob/MicroGridsPy-2.0/README.md | | |User documentation=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM/blob/MicroGridsPy-2.0/README.md |
| Line 40: |
Line 39: |
| | |processing_software=Excel | | |processing_software=Excel |
| | |External optimizer=Gurobi, CPLEX, cbc, glpk | | |External optimizer=Gurobi, CPLEX, cbc, glpk |
| − | |Primary purpose=Sizing and dispatch of energy in micro-grids in isolated places
| |
| | |GUI=No | | |GUI=No |
| − | |model_class=Energy Modeling Framework, | + | |model_class=Energy Modeling Framework, |
| | |sectors=Micro-grids design | | |sectors=Micro-grids design |
| | |technologies=Renewables, Conventional Generation | | |technologies=Renewables, Conventional Generation |
Revision as of 10:19, 20 October 2022
|
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:
gianluca.pellecchia@polimi.it
|
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
|
 Open Source
European Union Public Licence Version 1.1 (EUPL-1.1)
Directly downloadable
Input data shipped
|
| Model Scope |
Model type and solution approach |
| Model class
|
Energy Modeling Framework
|
| Sectors
|
Micro-grids design
|
| Technologies
|
Renewables, Conventional Generation
|
| Decisions
|
dispatch, investment
|
| Regions
|
|
| Geographic Resolution
|
Village-scale
|
| Time resolution
|
Hour
|
| Network coverage
|
|
|
| Model type
|
Optimization
|
|
|
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.
|
| Variables
|
|
| Computation time
|
5-40"-40" is not declared as a valid unit of measurement for this property. minutes
|
| Objective
|
Single or multi objective optimization (NPC, operation costs, CO2 emissions)
|
| Uncertainty modeling
|
Two-stage stochastic optimization
|
| Suited for many scenarios / monte-carlo
|
Yes
|
|
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
◀ back to model list
