No abstract

Abstract-This paper proposes a combined distributed/centralized architecture for the control and monitoring of a hybrid DC/AC microgrid with energy storage capabilities. The monitoring system is based on an own developed C++ framework for the measurement and real-time state estimation of the microgrid. Calculations running at sampled values (SV) frequency (10kHz) are implemented into a set of distributed measurement and processing units which have a TCP/IP communication link with a central server running the powerflow (PF) algorithm. The architecture is fully scalable, with the only restrictions of the signal processing capabilities of the distributed units, LAN bandwidth and central server calculation capabilities. The proposed system includes the hardware and software architecture for the monitoring of the microgrid, the communications scheme, the implementation of real-time algorithms for grid state estimation, a graphical user interface including different visualization alternatives and data storing/retrieving capabilities.

Great concern regarding energy efficiency has led the research community to develop approaches which enhance the energy awareness by means of insightful representations. An example of intuitive energy representation is the partsbased representation provided by Non-Intrusive Load Monitoring (NILM) techniques which decompose non-measured individual loads from a single total measurement of the installation, resulting in more detailed information about how the energy is spent along the electrical system. Although there are previous works that have achieved important results on NILM, the majority of the NILM systems were only validated in residential buildings, leaving a niche for the study of energy disaggregation in nonresidential buildings, which present a specific behavior. In this paper, we suggest a novel fully-convolutional denoising autoencoder architecture (FCN-dAE) as a convenient NILM system for large non-residential buildings, and it is compared, in terms of particular aspects of large buildings, to previous denoising auto-encoder approaches (dAE) using real electrical consumption from a hospital facility. Furthermore, by means of three use cases, we show that our approach provides extra helpful funcionalities for energy management tasks in large buildings, such as meter replacement, gap filling or novelty detection.

No abstract

No abstract