wiki.openmod-initiative.org - User contributions [en]

MicroGridsPy

2022-10-20T10:25:17Z

Gianluca Pellecchia:

{{Model
|Full_Model_Name=MicroGridsPy
|Acronym=MGpy
|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
|contact_persons=Nicolo' Stevanato
|contact_email=nicolo.stevanato@polimi.it
|website=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM
|source_download=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM.git
|logo=MGpy.png
|text_description=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.
|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
|Code documentation=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM/blob/MicroGridsPy-2.0/Code/_README.txt
|open_source_licensed=Yes
|license=European Union Public Licence Version 1.1 (EUPL-1.1)
|model_source_public=Yes
|Link to source=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM
|data_availability=all
|open_future=No
|modelling_software=Python (Pyomo)
|processing_software=Excel
|External optimizer=Gurobi, CPLEX, cbc, glpk
|GUI=No
|model_class=Energy Modeling Framework,
|sectors=Micro-grids design
|technologies=Renewables, Conventional Generation
|Demand sectors=Households, Industry, Commercial sector
|Energy carrier (Liquid)=Diesel
|Energy carriers (Renewable)=Sun, Wind
|Transfer (Electricity)=Distribution
|Storage (Electricity)=Battery
|Storage (Gas)=No
|Storage (Heat)=No
|User behaviour=Built-in load archetypes for rural users
|decisions=dispatch, investment
|Changes in efficiency=No
|georesolution=Village-scale
|timeresolution=Hour
|Observation period=More than one year
|Additional dimensions (Ecological)=Greenhouse gas emissions
|math_modeltype=Optimization
|math_modeltype_shortdesc=The model is based on two-stage stochastic optimisation and LP or MILP mathematical formulation
|math_objective=Single or multi objective optimization (NPC, operation costs, CO2 emissions)
|deterministic=Two-stage stochastic optimization
|is_suited_for_many_scenarios=Yes
|montecarlo=No
|computation_time_minutes=5-40
|citation_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,
|citation_doi=https://doi.org/10.1016/j.energy.2019.116073
|report_references=-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
|Specific properties=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.
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

MicroGridsPy

2022-10-20T10:22:58Z

Gianluca Pellecchia:

{{Model
|Full_Model_Name=MicroGridsPy
|Acronym=MGpy
|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
|contact_persons=Nicolo' Stevanato
|contact_email=nicolo.stevanato@polimi.it
|website=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM
|source_download=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM.git
|logo=MGpy.png
|text_description=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.
|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
|Code documentation=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM/blob/MicroGridsPy-2.0/Code/_README.txt
|open_source_licensed=Yes
|license=European Union Public Licence Version 1.1 (EUPL-1.1)
|model_source_public=Yes
|Link to source=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM
|data_availability=all
|open_future=No
|modelling_software=Python (Pyomo)
|processing_software=Excel
|External optimizer=Gurobi, CPLEX, cbc, glpk
|GUI=No
|model_class=Energy Modeling Framework,
|sectors=Micro-grids design
|technologies=Renewables, Conventional Generation
|Demand sectors=Households, Industry, Commercial sector
|Energy carrier (Liquid)=Diesel
|Energy carriers (Renewable)=Sun, Wind
|Transfer (Electricity)=Distribution
|Storage (Electricity)=Battery
|Storage (Gas)=No
|Storage (Heat)=No
|User behaviour=Built-in load archetypes for rural users
|decisions=dispatch, investment
|Changes in efficiency=No
|georesolution=Village-scale
|timeresolution=Hour
|Observation period=More than one year
|Additional dimensions (Ecological)=Greenhouse gas emissions
|math_modeltype=Optimization
|math_modeltype_shortdesc=The model is based on two-stage stochastic optimisation and LP or MILP mathematical formulation
|math_objective=Single or multi objective optimization (NPC, operation costs, CO2 emissions)
|deterministic=Two-stage stochastic optimization
|is_suited_for_many_scenarios=Yes
|montecarlo=No
|computation_time_minutes=5-40
|citation_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,
|citation_doi=https://doi.org/10.1016/j.energy.2019.116073
|report_references=-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
|Specific properties=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.
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

MicroGridsPy

2022-10-20T10:21:30Z

Gianluca Pellecchia:

{{Model
|Full_Model_Name=MicroGridsPy
|Acronym=MGpy
|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
|contact_persons=nicolo.stevanato@polimi.it
|contact_email=Nicolo' Stevanato
|website=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM
|source_download=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM.git
|logo=MGpy.png
|text_description=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.
|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
|Code documentation=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM/blob/MicroGridsPy-2.0/Code/_README.txt
|open_source_licensed=Yes
|license=European Union Public Licence Version 1.1 (EUPL-1.1)
|model_source_public=Yes
|Link to source=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM
|data_availability=all
|open_future=No
|modelling_software=Python (Pyomo)
|processing_software=Excel
|External optimizer=Gurobi, CPLEX, cbc, glpk
|GUI=No
|model_class=Energy Modeling Framework,
|sectors=Micro-grids design
|technologies=Renewables, Conventional Generation
|Demand sectors=Households, Industry, Commercial sector
|Energy carrier (Liquid)=Diesel
|Energy carriers (Renewable)=Sun, Wind
|Transfer (Electricity)=Distribution
|Storage (Electricity)=Battery
|Storage (Gas)=No
|Storage (Heat)=No
|User behaviour=Built-in load archetypes for rural users
|decisions=dispatch, investment
|Changes in efficiency=No
|georesolution=Village-scale
|timeresolution=Hour
|Observation period=More than one year
|Additional dimensions (Ecological)=Greenhouse gas emissions
|math_modeltype=Optimization
|math_modeltype_shortdesc=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.
|math_objective=Single or multi objective optimization (NPC, operation costs, CO2 emissions)
|deterministic=Two-stage stochastic optimization
|is_suited_for_many_scenarios=Yes
|montecarlo=No
|computation_time_minutes=5-40
|citation_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,
|citation_doi=https://doi.org/10.1016/j.energy.2019.116073
|report_references=-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
|Specific properties=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.
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

MicroGridsPy

2022-10-20T10:20:25Z

Gianluca Pellecchia:

{{Model
|Full_Model_Name=MicroGridsPy
|Acronym=MGpy
|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
|contact_persons=nicolo.stevanato@polimi.it
|contact_email=nicolo.stevanato@polimi.it
|website=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM
|source_download=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM.git
|logo=MGpy.png
|text_description=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.
|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
|Code documentation=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM/blob/MicroGridsPy-2.0/Code/_README.txt
|open_source_licensed=Yes
|license=European Union Public Licence Version 1.1 (EUPL-1.1)
|model_source_public=Yes
|Link to source=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM
|data_availability=all
|open_future=No
|modelling_software=Python (Pyomo)
|processing_software=Excel
|External optimizer=Gurobi, CPLEX, cbc, glpk
|GUI=No
|model_class=Energy Modeling Framework,
|sectors=Micro-grids design
|technologies=Renewables, Conventional Generation
|Demand sectors=Households, Industry, Commercial sector
|Energy carrier (Liquid)=Diesel
|Energy carriers (Renewable)=Sun, Wind
|Transfer (Electricity)=Distribution
|Storage (Electricity)=Battery
|Storage (Gas)=No
|Storage (Heat)=No
|User behaviour=Built-in load archetypes for rural users
|decisions=dispatch, investment
|Changes in efficiency=No
|georesolution=Village-scale
|timeresolution=Hour
|Observation period=More than one year
|Additional dimensions (Ecological)=Greenhouse gas emissions
|math_modeltype=Optimization
|math_modeltype_shortdesc=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.
|math_objective=Single or multi objective optimization (NPC, operation costs, CO2 emissions)
|deterministic=Two-stage stochastic optimization
|is_suited_for_many_scenarios=Yes
|montecarlo=No
|computation_time_minutes=5-40
|citation_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,
|citation_doi=https://doi.org/10.1016/j.energy.2019.116073
|report_references=-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
|Specific properties=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.
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

MicroGridsPy

2022-10-20T10:19:52Z

Gianluca Pellecchia:

{{Model
|Full_Model_Name=MicroGridsPy
|Acronym=MGpy
|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
|contact_persons=nicolo.stevanato@polimi.it
|contact_email=nicolo.stevanato@polimi.it
|website=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM
|source_download=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM.git
|logo=MGpy.png
|text_description=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.
|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
|Code documentation=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM/blob/MicroGridsPy-2.0/Code/_README.txt
|open_source_licensed=Yes
|license=European Union Public Licence Version 1.1 (EUPL-1.1)
|model_source_public=Yes
|Link to source=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM
|data_availability=all
|open_future=No
|modelling_software=Python (Pyomo)
|processing_software=Excel
|External optimizer=Gurobi, CPLEX, cbc, glpk
|GUI=No
|model_class=Energy Modeling Framework,
|sectors=Micro-grids design
|technologies=Renewables, Conventional Generation
|Demand sectors=Households, Industry, Commercial sector
|Energy carrier (Liquid)=Diesel
|Energy carriers (Renewable)=Sun, Wind
|Transfer (Electricity)=Distribution
|Storage (Electricity)=Battery
|Storage (Gas)=No
|Storage (Heat)=No
|User behaviour=Built-in load archetypes for rural users
|decisions=dispatch, investment
|Changes in efficiency=No
|georesolution=Village-scale
|timeresolution=Hour
|Observation period=More than one year
|Additional dimensions (Ecological)=Greenhouse gas emissions
|math_modeltype=Optimization
|math_modeltype_shortdesc=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.
|math_objective=Single or multi objective optimization (NPC, operation costs, CO2 emissions)
|deterministic=Two-stage stochastic optimization
|is_suited_for_many_scenarios=Yes
|montecarlo=No
|computation_time_minutes=5-40
|citation_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,
|citation_doi=https://doi.org/10.1016/j.energy.2019.116073
|report_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://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
|Specific properties=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.
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

MicroGridsPy

2022-10-20T10:19:10Z

Gianluca Pellecchia:

{{Model
|Full_Model_Name=MicroGridsPy
|Acronym=MGpy
|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
|contact_persons=gianluca.pellecchia@polimi.it
|contact_email=gianluca.pellecchia@polimi.it
|website=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM
|source_download=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM.git
|logo=MGpy.png
|text_description=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.
|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
|Code documentation=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM/blob/MicroGridsPy-2.0/Code/_README.txt
|open_source_licensed=Yes
|license=European Union Public Licence Version 1.1 (EUPL-1.1)
|model_source_public=Yes
|Link to source=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM
|data_availability=all
|open_future=No
|modelling_software=Python (Pyomo)
|processing_software=Excel
|External optimizer=Gurobi, CPLEX, cbc, glpk
|GUI=No
|model_class=Energy Modeling Framework,
|sectors=Micro-grids design
|technologies=Renewables, Conventional Generation
|Demand sectors=Households, Industry, Commercial sector
|Energy carrier (Liquid)=Diesel
|Energy carriers (Renewable)=Sun, Wind
|Transfer (Electricity)=Distribution
|Storage (Electricity)=Battery
|Storage (Gas)=No
|Storage (Heat)=No
|User behaviour=Built-in load archetypes for rural users
|decisions=dispatch, investment
|Changes in efficiency=No
|georesolution=Village-scale
|timeresolution=Hour
|Observation period=More than one year
|Additional dimensions (Ecological)=Greenhouse gas emissions
|math_modeltype=Optimization
|math_modeltype_shortdesc=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.
|math_objective=Single or multi objective optimization (NPC, operation costs, CO2 emissions)
|deterministic=Two-stage stochastic optimization
|is_suited_for_many_scenarios=Yes
|montecarlo=No
|computation_time_minutes=5-40
|citation_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,
|citation_doi=https://doi.org/10.1016/j.energy.2019.116073
|report_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://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
|Specific properties=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.
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

MicroGridsPy

2022-10-20T10:15:15Z

Gianluca Pellecchia:

{{Model
|Full_Model_Name=MicroGridsPy
|Acronym=MGpy
|author_institution=Université de Liège, 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_persons=gianluca.pellecchia@polimi.it
|contact_email=gianluca.pellecchia@polimi.it
|website=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM
|source_download=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM.git
|logo=MGpy.png
|text_description=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.
|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.
|User documentation=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM/blob/MicroGridsPy-2.0/README.md
|Code documentation=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM/blob/MicroGridsPy-2.0/Code/_README.txt
|open_source_licensed=Yes
|license=European Union Public Licence Version 1.1 (EUPL-1.1)
|model_source_public=Yes
|Link to source=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM
|data_availability=all
|open_future=No
|modelling_software=Python (Pyomo)
|processing_software=Excel
|External optimizer=Gurobi, CPLEX, cbc, glpk
|Primary purpose=Sizing and dispatch of energy in micro-grids in isolated places
|GUI=No
|model_class=Energy Modeling Framework,
|sectors=Micro-grids design
|technologies=Renewables, Conventional Generation
|Demand sectors=Households, Industry, Commercial sector
|Energy carrier (Liquid)=Diesel
|Energy carriers (Renewable)=Sun, Wind
|Transfer (Electricity)=Distribution
|Storage (Electricity)=Battery
|Storage (Gas)=No
|Storage (Heat)=No
|User behaviour=Built-in load archetypes for rural users
|decisions=dispatch, investment
|Changes in efficiency=No
|georesolution=Village-scale
|timeresolution=Hour
|Observation period=More than one year
|Additional dimensions (Ecological)=Greenhouse gas emissions
|math_modeltype=Optimization
|math_modeltype_shortdesc=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.
|math_objective=Single or multi objective optimization (NPC, operation costs, CO2 emissions)
|deterministic=Two-stage stochastic optimization
|is_suited_for_many_scenarios=Yes
|montecarlo=No
|computation_time_minutes=5-40
|citation_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,
|citation_doi=https://doi.org/10.1016/j.energy.2019.116073
|report_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://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
|Specific properties=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.
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

MicroGridsPy

2022-10-20T09:59:39Z

Gianluca Pellecchia: 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

{{Model
|Full_Model_Name=MicroGridsPy
|Acronym=MGpy
|author_institution=Université de Liège, 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_persons=gianluca.pellecchia@polimi.it
|contact_email=gianluca.pellecchia@polimi.it
|website=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM
|source_download=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM.git
|logo=MGpy.png
|text_description=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.
|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.
|User documentation=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM/blob/MicroGridsPy-2.0/README.md
|Code documentation=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM/blob/MicroGridsPy-2.0/Code/_README.txt
|open_source_licensed=Yes
|license=European Union Public Licence Version 1.1 (EUPL-1.1)
|model_source_public=Yes
|Link to source=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM
|data_availability=all
|open_future=No
|modelling_software=Python (Pyomo)
|processing_software=Excel
|External optimizer=Gurobi, CPLEX, cbc, glpk
|Primary purpose=Sizing and dispatch of energy in micro-grids in isolated places
|GUI=No
|model_class=Energy Modeling Framework, energy system optimization model
|sectors=Electric power, micro-grids design
|technologies=Renewables, Conventional Generation
|Demand sectors=Households, Industry, Commercial sector
|Energy carrier (Liquid)=Diesel
|Energy carriers (Renewable)=Sun, Wind
|Transfer (Electricity)=Distribution
|Storage (Electricity)=Battery
|Storage (Gas)=No
|Storage (Heat)=No
|User behaviour=Archetypes
|decisions=dispatch, investment
|Changes in efficiency=No
|georesolution=Village-scale
|timeresolution=Hour
|Observation period=More than one year
|Additional dimensions (Ecological)=Greenhouse gas emissions
|math_modeltype=Optimization
|math_modeltype_shortdesc=The model is based on two-stage stochastic optimisation, where the main
optimization variables are divided into first-stage variables (rated capacities of each
energy source) and second-stage variables (energy flows from the different
components), to deal with the parametric uncertainty, while LP or MILP
formulation can be used to tackle the structural uncertainty mainly related to the
modelling of non-linear behaviour. The optimization is performed in Python using
Pyomo Library. Energy balance, VRES generation constrains, Battery
charge/discharge constrains, Genset generation constrains are the main constrains
of the model while regarding the objective function, it’s possible switch between
NPC and Operation Cost minimization.
|math_objective=Single objective optimization: NPC or operation costs
|deterministic=Two-stage stochastic optimization
|is_suited_for_many_scenarios=Yes
|montecarlo=No
|citation_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,
|citation_doi=https://doi.org/10.1016/j.energy.2019.116073
|report_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://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
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

MicroGridsPy

2022-10-20T09:26:36Z

Gianluca Pellecchia: Created page with "{{Model |Full_Model_Name=MicroGridsPy |Acronym=MGpy |author_institution=Université de Liège, Politecnico di Milano |authors=Sergio Balderrama, Sylvain Quoilin, Francesco Lom..."

File:MGpy.png

2022-10-20T09:25:48Z

Gianluca Pellecchia: Logo

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