Day-Ahead Optimal Power Flow for Efficient Energy Management of Urban Microgrid

This study deals with the urban microgrid energy management that is dedicated to individual and collective self-consumption by providing flexibility to the distribution grid (DG). The proposed urban community microgrid is interconnected to a DG and it consists of an association of centralized storage units (called community energy storage system), community intermittent renewable generation, and intelligent energy management system (EMS). One of the main advantages of urban microgrid is that, in case of faults in the DG, it can cut existing interconnections and continue to supply the responsible community in the island mode. In this study, the developed urban microgrid EMS is based on the predictive control management through the day-ahead optimal power flow (DA-OPF) strategy. The main contributions of this work can be defined by two points. The first point is related to a development of the DA-OPF strategy for the urban microgrid based on the intelligent deep learning data forecasting and the mixed-integer nonlinear programming optimization methods. The second point concerns a development of an optimization function integrating the concept of ancillary services of DG flexibility. Experimental results and economical evaluation are presented in this article. By using the proposed strategies, it results in an important electricity price reduction for the considered urban microgrid, compared to a conventional distribution system and basic operation schemes.

Automated summary

The study focuses on urban microgrid energy management for individual and collective self-consumption, providing flexibility to the distribution grid. The proposed urban community microgrid incorporates centralized storage units, community renewable generation, and intelligent energy management system, with the ability to continue supplying power to the community in island mode during DG faults. The developed EMS for urban microgrid is based on predictive control through DA-OPF strategy, integrating deep learning data forecasting and optimization methods for electricity price reduction.