|
|
| (6 intermediate revisions by one user not shown) |
| 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=Nicolo' Stevanato |
| − | |contact_email=gianluca.pellecchia@polimi.it | + | |contact_email=nicolo.stevanato@polimi.it |
| | |website=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM | | |website=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM |
| | |source_download=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM.git | | |source_download=https://github.com/SESAM-Polimi/MicroGridsPy-SESAM.git |
| | |logo=MGpy.png | | |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. | | |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
| + | |
| | + | 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. | | |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 23: |
Line 49: |
| | |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, energy system optimization model | + | |model_class=Energy Modeling Framework, |
| − | |sectors=Electric power, micro-grids design | + | |sectors=Micro-grids design |
| | |technologies=Renewables, Conventional Generation | | |technologies=Renewables, Conventional Generation |
| | |Demand sectors=Households, Industry, Commercial sector | | |Demand sectors=Households, Industry, Commercial sector |
| Line 35: |
Line 60: |
| | |Storage (Gas)=No | | |Storage (Gas)=No |
| | |Storage (Heat)=No | | |Storage (Heat)=No |
| − | |User behaviour=Archetypes | + | |User behaviour=Built-in load archetypes for rural users |
| | |decisions=dispatch, investment | | |decisions=dispatch, investment |
| | |Changes in efficiency=No | | |Changes in efficiency=No |
| Line 43: |
Line 68: |
| | |Additional dimensions (Ecological)=Greenhouse gas emissions | | |Additional dimensions (Ecological)=Greenhouse gas emissions |
| | |math_modeltype=Optimization | | |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 and LP or MILP mathematical formulation |
| − | 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 | | |deterministic=Two-stage stochastic optimization |
| | |is_suited_for_many_scenarios=Yes | | |is_suited_for_many_scenarios=Yes |
| | |montecarlo=No | | |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_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 | | |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 | | -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
| |
| − |
| |
| | | | |
| | + | -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 | | |example_research_questions=-Long-term sizing of rural microgrids |
| | + | |
| | -Load evolution | | -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 input file format=No |
| | |Model file format=No | | |Model file format=No |
| | |Model output file format=No | | |Model output file format=No |
| | }} | | }} |
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
-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
