MicroGridsPy - Revision history

Gianluca Pellecchia at 10:25, 20 October 2022

2022-10-20T10:25:17Z

Gianluca Pellecchia at 10:22, 20 October 2022

2022-10-20T10:22:58Z

Gianluca Pellecchia at 10:21, 20 October 2022

2022-10-20T10:21:30Z

Gianluca Pellecchia at 10:20, 20 October 2022

2022-10-20T10:20:25Z

Gianluca Pellecchia at 10:19, 20 October 2022

2022-10-20T10:19:52Z

Gianluca Pellecchia at 10:19, 20 October 2022

2022-10-20T10:19:10Z

Gianluca Pellecchia at 10:15, 20 October 2022

2022-10-20T10:15:15Z

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

2022-10-20T09:59:39Z

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

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..."

2022-10-20T09:26:36Z

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..."

New page

{{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
|contact_email=gianluca.pellecchia@
|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.
|open_source_licensed=No
|model_source_public=No
|open_future=No
|GUI=No
|Storage (Gas)=No
|Storage (Heat)=No
|is_suited_for_many_scenarios=No
|montecarlo=No
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

@@ Line 13: / Line 13: @@
 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 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.
+-Optimal dispatch from the identified supply systems;
 -LCOE evaluation for the identified system.
 Possible features:
--Two-stage stochastic optimization.
+-Two-stage stochastic optimization;
--Multi-year evolving load demand and multi-step capacity expansion.
--Possibility of connecting to the national grid.
+-Multi-year evolving load demand and multi-step capacity expansion;
--Two-objective optimization (economic and environmental objective functions).
--Brownfield optimization.
+-Possibility of connecting to the national grid;
--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.
@@ Line 58: / Line 68: @@
 |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

@@ Line 4: / Line 4: @@
 |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_persons=Nicolo' Stevanato
-|contact_email=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
@@ Line 58: / Line 58: @@
 |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_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

@@ Line 5: / Line 5: @@
 |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
+|contact_email=Nicolo' Stevanato
 |website=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM
 |source_download=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM.git

@@ Line 66: / Line 66: @@
 |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
+|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, 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

@@ Line 4: / Line 4: @@
 |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_persons=nicolo.stevanato@polimi.it
-|contact_email=gianluca.pellecchia@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

@@ Line 2: / Line 2: @@
 |Full_Model_Name=MicroGridsPy
 |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
 |contact_persons=gianluca.pellecchia@polimi.it
@@ Line 27: / Line 27: @@
 -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
@@ Line 40: / Line 39: @@
 |processing_software=Excel
 |External optimizer=Gurobi, CPLEX, cbc, glpk
 |GUI=No
 |model_class=Energy Modeling Framework,
 |sectors=Micro-grids design
 |technologies=Renewables, Conventional Generation

@@ Line 10: / Line 10: @@
 |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.
@@ Line 25: / Line 42: @@
 |Primary purpose=Sizing and dispatch of energy in micro-grids in isolated places
 |GUI=No
-|model_class=Energy Modeling Framework, energy system optimization model
+|model_class=Energy Modeling Framework,
-|sectors=Electric power, micro-grids design
+|sectors=Micro-grids design
 |technologies=Renewables, Conventional Generation
 |Demand sectors=Households, Industry, Commercial sector
@@ Line 35: / Line 52: @@
 |Storage (Gas)=No
 |Storage (Heat)=No
-|User behaviour=Archetypes
+|User behaviour=Built-in load archetypes for rural users
 |decisions=dispatch, investment
 |Changes in efficiency=No
@@ Line 43: / Line 60: @@
 |Additional dimensions (Ecological)=Greenhouse gas emissions
 |math_modeltype=Optimization
-|math_modeltype_shortdesc=The model is based on two-stage stochastic optimisation, where the main
+|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.
-optimization variables are divided into first-stage variables (rated capacities of each
+|math_objective=Single or multi objective optimization (NPC, operation costs, CO2 emissions)
-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
 |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
 }}