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Integrated Models and Tools for Microgrid Planning and Designs
This white paper focuses on tools that support design, planning and operation of microgrids (or aggregations of microgrids) for multiple needs and stakeholders (e.g., utilities, developers,
Data-driven Microgrid Operation Towards Optimized Battery Energy
This paper proposes a new data-driven approach for two-stage operation of a microgrid (MG) towards optimized battery energy storage (BES) lifetime degradation. At the first stage (day-ahead), the BES
Data-driven modeling of solar-powered urban microgrids
We propose a microgrid model and study its citywide implementation, identifying the self-sufficiency and temporal properties of micro-grids. Using a simple optimization scheme, we find microgrid
A data-driven framework for microgrid design integrating machine
To achieve these objectives, we developed a data-driven model that combines Homer-Pro with a custom Python tool integrating extreme gradient boosting (XGBoost) machine learning algorithm and thirteen
Optimizing Microgrid Composition for Sustainable Data Centers
Based on the co-simulator Vessim (Wiesner et al., 2024), we perform a black-box optimization to identify promising microgrid compositions for data centers.
Advanced AI approaches for the modeling and optimization of
These AI models maximize the use of renewable energy, reduce wastage, and improve microgrid resilience and responsiveness to supply and demand fluctuations. Experiments
A data-driven framework for microgrid design integrating machine
The integration of hydrogen (H2) into renewable energy-based microgrids enables long-term energy storage, prolongs battery (BT) life, minimizes energy costs, and improves microgrid...
Behavioral Modeling for Microgrid Simulation
Comparisons to eld data collected from a microgrid on-board a 270-foot (82 meter) US Coast Guard medium-endurance cutter demonstrate the utility of this tool and approach. INDEX TERMS Load
A novel data-driven NLMPC strategy for techno-economic microgrid
As renewable energy sources become more widespread and energy consumption continues to grow, there is an urgent requirement for smarter, more flexible control methods to
Data-Driven Voltage Control in Isolated AC Microgrids Subject to
This paper proposes a distributed data-driven adaptive iterative learning control (DAILC) method to address the challenges of modeling and voltage regulation in isolated AC microgrids
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