Multi-objective non-weighted optimization to explore new efficient design of electrical microgrids

Abstract

Centralized electrical networks induce a dependency of local territories for their power supply. However, thanks to microgrids, territories can increase their decision-making autonomy to design a network that matches their values. Technological and management choices are critical to minimize microgrids negative impacts on their environment. Influence of the latter on the design space is rarely discussed whereas extending the design space would help to find innovative microgrids. The purpose of this paper is to find several microgrids with various performances and parameters that are compromises between economic, technical and environmental objectives. The solutions’ variety therefore extends the decision-makers’ design space. A tool has been developed to answer this goal. Design parameters are both technological and management parameters. A physical modelling is implemented in a sequential simulation of the microgrid operation. The performance of the simulation allows to use genetic algorithms to perform multi-objective non-weighted optimizations. Two twoobjective optimizations are performed. Results show how the solutions’ diversity in terms of performances and parameters helps the user choosing innovative microgrids. Especially, it underlines the potential of this approach to find microgrids with close performances but different parameters.