Sustainable Microgrids: Modeling, Optimization, and Cost Ana

Learn to design, optimize and evaluate microgrids with exergy, LCA, and cost analysis”

What you'll learn
- Model and simulate hybrid microgrids integrating PV, battery, diesel, and biogas systems
- Apply multi-objective optimization techniques (GA, Pareto front) to energy systems
- Evaluate system performance using LCOE, CAPEX/OPEX, and CO₂ emissions metrics
- Analyze exergy efficiency and perform Life Cycle Assessment (LCA) for sustainability
- Build and interpret dispatch strategies using Python and MATLAB
- Translate technical results into actionable insights for energy planning and policy
- Learn to translate technical modeling results into actionable insights for energy planning and policy decisions.

Requirements
- No prior experience in optimization, LCA, or exergy required. Basic familiarity with MATLAB or Python is helpful, but all key concepts and tools are introduced progressively
- Basic understanding of energy systems is helpful, but not required. You’ll be guided step by step through all technical concepts and modeling tools.

Description
This course provides a comprehensive and hands-on introduction tosustainable microgrid design and optimization, bringing together environmental assessment, thermodynamic rigor, and economic evaluation in a unique and practical way. Rather than focusing on theory alone, it equips you with the analytical and computational tools needed tomodel, simulate, and optimize microgrid systemsunder real-world conditions.

You will explore how to applyLife Cycle Assessment (LCA)to quantify environmental impacts, how to useexergy analysisto measure energy efficiency at the system level, and how to employmulti-objective optimization toolsto balance trade-offs between cost, performance, and sustainability. Through guided exercises inMATLABandPython, combined with real datasets, you will learn how to simulate energy flows, compare technology pathways, and critically assess design options forbiogas, solar, hybrid, and other renewable energy systems.

Beyond the technical dimension, the course emphasizes theeconomic and strategic aspectsof microgrids. You will gain practical skills to calculateCAPEX, OPEX, and LCOE, conduct sensitivity analyses, and benchmark performance across scenarios—skills that are directly applicable to project planning, investment appraisal, and policy support.

Designed forengineers, consultants, researchers, and institutional decision-makers, this modular and bilingual program offers flexibility and immediate applicability. Each module integratescase studies, quizzes, and downloadable resourcesto reinforce learning and support independent practice.

By the end of the course, you will be able not only to design and optimize microgrids but also to build your ownsimulation engine, communicate results effectively, and provide evidence-based insights that guideenergy policy, business strategy, and technical innovation. This training will sharpen yoursystem-level thinking, preparing you to address the urgent energy challenges of bothemerging economies and advanced contextswith confidence, precision, and impact.

Who this course is for:
- This course is designed for engineers, energy consultants, and advanced students interested in modeling and optimizing sustainable microgrids. It’s ideal for those working in renewable energy, rural electrification, or energy planning—especially in island or off-grid contexts. Whether you're a technical decision-maker, a researcher, or a practitioner looking to apply Python/MATLAB to real-world energy systems, this course will give you the tools to simulate, evaluate, and improve hybrid microgrid performance.
- Engineers, energy consultants, advanced students, and technical decision-makers seeking to master modeling, optimization, and environmental evaluation of sustainable microgrids using MATLAB and Python.
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