A novel microgrid support management system based on stochastic mixed-integer linear programming

Abstract

This paper focuses on a support management system for the management and operation planning of a microgrid by the new electricity market agent, the microgrid aggregator. The aggregator performs the management of microturbines, wind and photovoltaic systems, energy storage, electric vehicles, and usage of energy aiming at having the best participation in the market. Nowadays, the electricity market participation entails making decisions aided by a support and information system, which is an important part of a microgrid support management system. The microgrid support management system developed in this paper has a formulation based on a stochastic mixed-integer linear programming problem that depends on knowledge of the stochastic processes that describe the uncertain parameters. A set of plausible scenarios computed by Kernel Density Estimation sets the characterization of the random variables. But as commonly happen, a reduction of scenarios is necessary to avoid the need to have significant computational requirements due to the high degree of uncertainty. The reduction of scenarios carried out is a two-tier procedure, following a K-means clustering technique and a fast backward scenario reduction method. The case studies reveal the performance of the microgrid and validate the methodology basis conceived for the microgrid support management system.