Sustainable Microgrids: Modeling, Optimization, and Cost Ana

In this session, participants will explore the core principles of sustainable energy systems, including energy sources, conversion processes, and system boundaries. By the end of the session, learners will be able to identify key components of microgrid architectures, distinguish between conventional and renewable energy pathways, and understand the role of sustainability metrics such as efficiency, emissions, and resource use. This foundational knowledge sets the stage for advanced modeling and optimization techniques introduced in later modules.

In this session, participants will learn how to optimize energy systems by balancing key performance indicators such as cost, emissions, and reliability. Through practical examples and modeling techniques, learners will explore how to apply multi-objective optimization tools to compare technologies, assess trade-offs, and identify Pareto-efficient solutions. By the end of the session, participants will be able to interpret optimization results, evaluate system configurations, and make informed decisions that align with both technical and sustainability goals.

In this session, participants will dive into the environmental evaluation of energy systems using Life Cycle Assessment (LCA) and exergy analysis. Learners will understand how to quantify emissions, resource depletion, and thermodynamic efficiency across all life stages—from raw material extraction to end-of-life. By the end of the session, participants will be able to interpret sustainability indicators, compare technologies based on environmental impact, and integrate LCA and exergy metrics into energy system design and optimization. This layer adds critical depth to decision-making and ensures long-term viability of microgrid solutions.

In this final session, participants will engage with a real-world case study centered on the design and optimization of a hybrid microgrid for a small island in the Comoros. The session explores the technical, environmental, and economic challenges of deploying solar, biogas, and battery systems in remote settings. Learners will examine system configurations, apply multi-criteria optimization techniques, and interpret sustainability metrics using life cycle assessment (LCA) and exergy analysis. By the end of the session, participants will be equipped to evaluate trade-offs, simulate hybrid energy flows, and propose context-sensitive solutions aligned with local constraints and development goals.

This session introduces key constants and parameters used in hybrid energy system modeling. Participants will understand CAPEX, OPEX, efficiency metrics, SOC constraints, and emissions factors.

This session demonstrates how to convert photovoltaic (PV) capacity factors into actual hourly generation values based on installed capacity. Participants will learn to implement this conversion in Python, visualize PV output over a 24-hour period, and prepare generation profiles for integration into energy system optimization models.

This session guides participants through the simulation of battery state of charge (SOC) dynamics over a 24-hour period using Python. Learners will model charging and discharging behavior based on PV generation and load demand, apply efficiency constraints, and visualize SOC evolution to assess battery performance in microgrid scenarios.

This session teaches participants how to simulate full-day energy dispatch using Python, coordinating PV, battery, and biogas systems. Learners will track SOC, compute served/unserved energy, and apply operational constraints to evaluate microgrid reliability.

This session introduces multi-objective sampling for energy system design. Participants will generate candidate configurations and evaluate each one based on cost and emissions proxies using Python.