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

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 scenario reduction is necessary to avoid the need to have significant computational requirements due to the high degree of uncertainty. The scenario reduction 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. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper presents a microgrid support management system based on a stochastic mixed-integer linear programming problem, managed and operated by a new electricity market agent, the microgrid aggregator. Plausible scenarios are computed using Kernel Density Estimation to characterize random variables, and a scenario reduction is carried out with a two-tier procedure involving K-means clustering technique and a fast backward scenario reduction method. Case studies demonstrate the performance of the microgrid and validate the methodology proposed for the microgrid support management system.