MicroGrid Operation and Design Optimization With Synthetic Wins and Solar Resources

Microgrids have been significantly developed, enhanced by concerns about climate change and energy security, their decreasing costs and the development of renewable energy sources. However, an important concern is the limited information available to estimate these renewable resources. We develop an optimization model with cost and reliability objective functions for the design and operation of micro-networks using a nested strategy and limited resource information. Design optimization utilizes Genetic Algorithms and 2 objective functions: Expected Energy Not Supplied EENS and Levelized Cost of Energy. In addition, Green House Gas (GHG) emissions are estimated. Operational optimization utilizes Generating Sets Search Algorithm. We include models for wind turbines, solar panels, fuel cells, diesel generators, gas turbines, and battery banks. We address the limited data available for these applications by synthesizing series of wind and solar radiation with basic statistical parameters. Pareto-Optimal trade-off curves between cost and reliability are presented here for an example network.