Introduction

Note

Continuous Development: MicroGridsPy is under continuous development, with new versions being released regularly. We strive to enhance the model by fixing bugs, making small edits, and implementing new features. Therefore, it is advisable to ensure that you have the latest version of the folder downloaded from the repository.

MicroGridsPy is a bottom-up, open-source optimization model, running on Pyomo, a Python library used to model optimisation problems, whose primary goal is to offer an open-source approach to the issue of energy scaling and dispatch in micro grids in remote locations. It was firstly developed in 2016 by University of Liege and the code is freely available on GitHub [40]. The model enables the optimization of micro-grid size and its dispatch strategy, typically at 1-hour temporal resolution, returning also as output the fixed and variable costs associated with each technology and the Levelized Cost of Energy (LCOE) of the system. It is based on Linear Programming, and it enables the choice of the installed capacities of batteries, generators, and renewable energy sources that results in the lowest Net Present Cost (NPC) or lowest Operation and Maintenance expenses (O&M) during the project’s lifespan while achieving the system limitations.

Visualization of the model conceptual structure

The model requires time series of load demand and RES production (both at 1-hour resolution for a year or more), and techno-economic parameters of technologies and project parameters. It addresses long-term load evolution, integrating tools for generating stochastic load profiles, aiding in robust investment decisions under various scenarios.

In the latest version of MicroGridsPy, these advanced features have been implemented:

• Multi-Year Formulation: Adapts to changes in energy demand over time, analyzing various patterns like population growth, network connections, and shifts in daily energy usage.

• Capacity Expansion: Strategically plans infrastructure enhancements to meet rising demand, including new generation facilities and renewable energy sources.

• Multi-objective Optimization: Balances costs and emissions to model scenarios with different priorities, such as environmental impact.

• Mixed-Integer Linear Programming (MILP): Integrates a unit commitment approach for optimal power generation scheduling, balancing operational costs and non-linear behavior.

• Generator Partial Load Effect Modeling: Addresses the efficiency and operational aspects of generators under partial load conditions.

• Endogenous Load Curve Estimation: Uses archetypes specific to rural villages in Sub-Saharan Africa to estimate load curves.

• Endogenous RES Production Time Series Calculation: Leverages the NASA POWER platform for accurate solar and wind energy predictions.

• Brownfield Feature: Optimizes microgrids considering pre-existing technologies in the field.

• Main Grid Connectivity Simulation: Details electricity flows to and from an existing main grid, including grid availability during blackouts.

Note

This project is under active development!

Table of Contents

User Guide

• Introduction
  • Table of Contents
  • MicroGridsPy in academic literature
• Energy Optimization Models
  • Challenges in isolated Microgrids Modeling
  • Why MicroGridsPy is developed?
  • License
  • References
• Download and Installation
  • Recommended Installation Method
  • Solvers
  • Environment Overview
• Building and Running a Model
  • Energy System
  • Data Input Interface and Parameters
  • Time Series Data
• User Interface Walkthrough
  • Model configuration
  • Advanced features
  • Resource Assessment
  • Load Demand Assessment
  • Renewables Characterization
  • Storage System Characterization
  • Backup System Characterization
  • Running the Model
  • Obtaining the Results
• Analysing the Results
  • Results Spreadsheets
  • Plots
• Advanced Features
  • Capacity Expansion
  • Weighted Average Cost of Capital
  • Multi-Objective Optimization
  • Multi-Scenario Optimization
  • RES Time Series Estimation
  • Load Demand Estimation
  • MILP Formulation
  • Generator Partial Load Effect
  • Variable Fuel Cost
  • National Grid Connection
  • Brownfield
  • Battery Bank Degradation
  • References
• Example Gallery
  • Case Study
  • Default Scenario
  • Capacity Expansion and Increasing Fuel Cost
  • Capacity Expansion and Grid Connection
  • Multi-Objective Optimization
  • Solvers Comparison: GLPK and Gurobi
• Contributors

Developers Guide

• General Overview
  • Terminology
  • Inputs
• Model Structure
  • Model Creation Module
  • Initialization Module
  • Demand Module
  • RES Time Series Estimation Module
• Mathematical Formulation
  • Problem Statement
  • Objective function
  • Cost
  • Energy
  • Emissions
• Model Resolution and Results
  • Overview
  • Initialization and Parameter Parsing
  • Constraint Definition
  • Capacity Expansion and Renewable Integration
  • Solver Configuration and Execution
  • Multi-Objective Resolution

API Reference

• API

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MicroGridsPy in academic literature

• 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, 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, pp. 1-6

• Nicolò Stevanato, Francesco Lombardi, Giulia Guidicini, Lorenzo Rinaldi, Sergio L. 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

• Nicolò Stevanato, Gianluca Pellecchia, Ivan Sangiorgio, Diana Shendrikova, Castro Antonio Soares, Riccardo Mereu, Emanuela Colombo, “Planning third generation minigrids: Multi-objective optimization and brownfield investment approaches in modelling village-scale on-grid and off-grid energy systems”, Renewable and Sustainable Energy Transition 2023, 3, 100053

• Giacomo Crevani, Castro Soares, Emanuela Colombo, “Modelling Financing Schemes for Energy System Planning: A Mini-Grid Case Study”, ECOS 2023, pp. 1958-1969

• N. Stevanato, I. Sangiorgio, R. Mereu and E. Colombo, “Archetypes of Rural Users in Sub-Saharan Africa for Load Demand Estimation”, 2023 IEEE PES/IAS PowerAfrica, Marrakech, Morocco, 2023, pp. 1-5, doi: 10.1109/PowerAfrica57932.2023.10363287.