Data forecasting for Optimized Urban Microgrid Energy Management

This paper deals with energy management in the urban microgrid dedicated to individual and collective self-consumption. This microgrid is connected to the national grid, with a possibility of bidirectional power flow. The studied microgrid consists of some building integrated photovoltaic systems, a community photovoltaic field and a community storage unit. The provided household devices and public services can be classified in three categories, namely as adjustable, schedulable, and critical loads. In this paper, a concept of self-consumption in urban areas is studied and the decision support laws in the management of energy flow are developed and proposed. The paper addresses aspects related to the overall supervision of the system, whose performance depends on the quality of the means of real time communication and information exchange. The study is performed according to a methodology that is based on a load forecasting methodology to develop the Energy Flow Management algorithm, which is validated using an experimental test bench. As contribution, this paper proposes a development of an optimized energy management strategy in an urban self-consumption microgrid based on an intelligent load forecasting method. The results are presented and analysed in this paper.