An energy management system design using fuzzy logic control: smoothing the grid power profile of a residential electro-thermal microgrid

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This work deals with the design of a Fuzzy Logic Control (FLC) based Energy Management System (EMS) for smoothing the grid power profile of a grid-connected electro-thermal microgrid. The case study aims to design an Energy Management System (EMS) to reduce the impact on the grid power when renewable energy sources are incorporated to pre-existing grid-connected household appliances. The scenario considers a residential microgrid comprising photovoltaic and wind generators, flat-plate collectors, electric and thermal loads and electrical and thermal energy storage systems and assumes that neither renewable generation nor the electrical and thermal load demands are controllable. The EMS is built through two low-complexity FLC blocks of only 25 rules each. The first one is in charge of smoothing the power profile exchanged with the grid, whereas the second FLC block drives the power of the Electrical Water Heater (EWH). The EMS uses the forecast of the electrical and thermal power balance between generation and consumption to predict the microgrid behavior, for each 15-minute interval, over the next 12 hours. Simulations results, using real one-year measured data show that the proposed EMS design achieves 11.4% reduction of the maximum power absorbed from the grid and an outstanding reduction of the grid power profile ramp-rates when compared with other state-of-the-art studies.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version

Convex Programming and Bootstrap Sensitivity for Optimized Electricity Bill in Healthcare Buildings under a Time-Of-Use Pricing Scheme

Efficient energy management is strongly dependent on determining the adequate power contracts among the ones offered by different electricity suppliers. This topic takes special relevance in healthcare buildings, where noticeable amounts of energy are required to generate an adequate health environment for patients and staff. In this paper, a convex optimization method is scrutinized to give a straightforward analysis of the optimal power levels to be contracted while minimizing the electricity bill cost in a time-of-use pricing scheme. In addition, a sensitivity analysis is carried out on the constraints in the optimization problems, which are analyzed in terms of both their empirical distribution and their bootstrap-estimated statistical distributions to create a simple-to-use tool for this purpose, the so-called mosaic-distribution. The evaluation of the proposed method was carried out with five-year consumption data on two different kinds of healthcare buildings, a large one given by Hospital Universitario de Fuenlabrada, and a primary care center, Centro de Especialidades el Arroyo, both located at Fuenlabrada (Madrid, Spain). The analysis of the resulting optimization shows that the annual savings achieved vary moderately, ranging from −0.22 % to +27.39%, depending on the analyzed year profile and the healthcare building type. The analysis introducing mosaic-distribution to represent the sensitivity score also provides operative information to evaluate the convenience of implementing energy saving measures. All this information is useful for managers to determine the appropriate power levels for next year contract renewal and to consider whether to implement demand response mechanisms in healthcare buildings

Volcanic Micro-Earthquake Classification With Spectral Manifolds in Low-Dimensional Latent Spaces

Micro-earthquakes are frequently associated with volcanic activity and are vital indicators of volcanic processes. These minor seismic events occur within or near volcanic systems, yielding valuable insights into subsurface activities. Geologists meticulously record and analyze these events to monitor volcanoes and forecast eruptions. While recent years have seen several studies proposing automated detection and classification systems of seismic events, approaches based on Manifold Learning techniques could be beneficial in terms of information interpretability and transfer learning to other Machine Learning tasks. This paper presents a novel approach employing audio features and psychoacoustic scales to represent micro-earthquakes at Cotopaxi and Llaima Volcanoes, and these representations are then transformed into low-dimensional latent spaces. We implemented a multi-class classification system for events generated by these volcanoes, incorporating feature selection techniques based on audio-inspired features. This approach enhances the detection of volcanic phenomena triggering eruptions and improves interpretability. Our results indicated high accuracy, with rates of 94.44% for Llaima Volcano and 95.45% for Cotopaxi Volcano when utilizing mutual information to select the most relevant features. Spectral Roll-off Point and Spectral Flux dominate in classifying events for both volcanoes. These findings suggest that low-dimensional latent spaces, particularly when utilizing spectral features, can be a promising foundation for developing transfer learning schemes in general, and new multi-class classification systems in particular, for detecting volcanic micro-earthquakes

Analyzing and Forecasting Electrical Load Consumption in Healthcare Buildings

Healthcare buildings exhibit a different electrical load predictability depending on their size and nature. Large hospitals behave similarly to small cities, whereas primary care centers are expected to have different consumption dynamics. In this work, we jointly analyze the electrical load predictability of a large hospital and that of its associated primary care center. An unsupervised load forecasting scheme using combined classic methods of principal component analysis (PCA) and autoregressive (AR) modeling, as well as a supervised scheme using orthonormal partial least squares (OPLS), are proposed. Both methods reduce the dimensionality of the data to create an efficient and low-complexity data representation and eliminate noise subspaces. Because the former method tended to underestimate the load and the latter tended to overestimate it in the large hospital, we also propose a convex combination of both to further reduce the forecasting error. The analysis of data from 7 years in the hospital and 3 years in the primary care center shows that the proposed low-complexity dynamic models are flexible enough to predict both types of consumption at practical accuracy levels

Fuzzy-based energy management of a residential electro-thermal microgrid based on power forecasting

In this paper, an energy management strategy based on microgrid power forecasting is applied to a residential grid-connected electro-thermal microgrid with the aim of smoothing the power profile exchanged with the grid. The microgrid architecture under study considers electrical and thermal renewable generation, energy storage system (ESS), and loads. The proposed strategy manages the energy stored in the ESS to cover part of the energy required by the thermal generation system for supplying domestic hot water to the residence. The simulation results using real data and the comparison with previous strategy have demonstrated the effectiveness of the proposed strategy.Peer ReviewedPostprint (published version

Fuzzy-based energy management of a residential electro-thermal microgrid based on power forecasting

In this paper, an energy management strategy based on microgrid power forecasting is applied to a residential grid-connected electro-thermal microgrid with the aim of smoothing the power profile exchanged with the grid. The microgrid architecture under study considers electrical and thermal renewable generation, energy storage system (ESS), and loads. The proposed strategy manages the energy stored in the ESS to cover part of the energy required by the thermal generation system for supplying domestic hot water to the residence. The simulation results using real data and the comparison with previous strategy have demonstrated the effectiveness of the proposed strategy.Peer Reviewe