Energy Infrastructure of the Factory as a Virtual Power Plant: Smart Energy Management

Smart energy factories are crucial for the development of upcoming energy markets in which emissions, energy use and network congestions are to be decreased. The virtual power plant (VPP) can be implemented in an industrial site with the aim of minimizing costs, emissions and total energy usage. A VPP considers the future situation forecasting and the situation of all energy assets, including renewable energy generation units and energy storage systems, to optimize the total cost of the plant, considering the possibility to trade with the energy market. For a VPP to be constructed, a proper communication system is essential. The energy management system (EMS) enables the monitoring, management and control of the different energy devices and permits the transference of the decisions made by the VPP to the different energy assets. VPP concept is explained together with the methods used for forecasting the future situation and the energy flow inside the facility. To reach its benefits, the optimization of the VPP is assessed. After that, the communication technologies that enable the VPP implementation are also introduced, and the advantages/disadvantages regarding their deployment are stated. With the tools introduced, the VPP can face the challenges of energy markets efficiently

Energy-Investment Decision-Making for Industry: Quantitative and Qualitative Risks Integrated Analysis

Industrial SMEs may take the decision to invest in energy efficient equipment to reduce energy costs by replacing or upgrading their obsolete equipment or due to external socio-political and legislative pressures. When upgrading their energy equipment, it may be beneficial to consider the adoption of new energy strategies rising from the ongoing energy transition to support green transformation and decarbonisation. To face this energy-investment decision-making problem, a set of different economic and environmental criteria have to be evaluated together with their associated risks. Although energy-investment problems have been treated in the literature, the incorporation of both quantitative and qualitative risks for decision-making in SMEs has not been studied yet. In this paper, this research gap is addressed, creating a framework that considers non-risk criteria and quantitative and qualitative risks into energy-investment decision-making problems. Both types of risks are evaluated according to their probability and impact on the company’s objectives and, additionally for qualitative risks, a fuzzy inference system is employed to account for judgmental subjectivity. All the criteria are incorporated into a single cost–benefit analysis function, which is optimised along the energy assets’ lifetime to reach the best long-term energy investment decisions. The proposed methodology is applied to a specific industrial SME as a case study, showing the benefits of considering these risks in the decision-making problem. Nonetheless, the methodology is expandable with minor changes to other entities facing the challenge to invest in energy equipment or, as well, other tangible assets.Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version

Quantitative and qualitative risk-informed energy investment for industrial companies

© 2023 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In the ongoing energy transition, small and medium-sized industrial companies are making energy equipment investments due to the obsolescence of their current equipment as well as social, political and market pressures. These firms typically choose investments with low risk exposure based on a combination of criteria that are not always quantifiable. However, published studies on energy investment to date have not been suitable for industrial SMEs because they do not assess the value of the investment over time, ignore the qualitative aspects of decision-making, and do not consider uncertainties. To fill this gap in the literature, this paper proposes a methodology that considers both quantitative and qualitative parameters and risks over time through an extended two-stage risk-informed approach. The proposed methodology includes fuzzy and statistical techniques for evaluating both qualitative and quantitative parameters, as well as their uncertainties, at the time of decision-making and over the investment lifetime. Fuzzy logic is used in the first stage of the optimisation process to measure qualitative parameters and their uncertainty, while quantitative parameters are expressed using probability density functions to account for their uncertainty and measure the quantitative risk assumed by the investor. This methodology is applied to a case study involving a real industrial SME, and the results show that considering both quantitative and qualitative parameters and uncertainties in the optimisation process leads to a more balanced consideration of economic, environmental and social criteria and reduces the variability of the outcome compared to economic-only approaches that do not account for risks. Specifically, the case study shows that considering these parameters and uncertainties resulted in a 15.7% reduction in the size of the cogeneration system due to its environmental and social impacts, and 4.2% reduction in the variability of the economic result.Peer ReviewedPostprint (published version

Semi-supervised transfer learning methodology for fault detection and diagnosis in air-handling units

Heating, ventilation and air-conditioning (HVAC) systems are the major energy consumers among buildings’ equipment. Reliable fault detection and diagnosis schemes can effectively reduce their energy consumption and maintenance costs. In this respect, data-driven approaches have shown impressive results, but their accuracy depends on the availability of representative data to train the models, which is not common in real applications. For this reason, transfer learning is attracting growing attention since it tackles the problem by leveraging the knowledge between datasets, increasing the representativeness of fault scenarios. However, to date, research on transfer learning for heating, ventilation and air-conditioning has mostly been focused on learning algorithmic, overlooking the importance of a proper domain similarity analysis over the available data. Thus, this study proposes the design of a transfer learning approach based on a specific data selection methodology to tackle dissimilarity issues. The procedure is supported by neural network models and the analysis of eventual prediction uncertainties resulting from the assessment of the target application samples. To verify the proposed methodology, it is applied to a semi-supervised transfer learning case study composed of two publicly available air-handling unit datasets containing some fault scenarios. Results emphasize the potential of the proposed domain dissimilarity analysis reaching a classification accuracy of 92% under a transfer learning framework, an increase of 37% in comparison to classical approaches.Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats SosteniblesObjectius de Desenvolupament Sostenible::12 - Producció i Consum ResponsablesPostprint (published version

Anomaly detection in electromechanical systems by means of deep-autoencoder

© 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 worksAnomaly detection in manufacturing processes is one of the main concerns in the new era of the Industry 4.0 framework. The detection of uncharacterized events represents a major challenge within the operation monitoring of electrical rotatory machinery. In this regard, although several machine learning techniques have been classically considered, the recent appearance of deep-learning approaches represents an opportunity in the field to increase the anomaly detection capabilities in front of complex electromechanical systems. However, each anomaly detection technique considers its own data interpretability and modelling strategy, which should be analyzed in front of the specificities of the data generated in an industrial environment and, specifically, by an electromechanical actuator. Thus, in this study, a comparison framework is considered including multiple fault scenarios in order to analyze the performance of four representative anomaly detection techniques, that is, one-class support vector machine, k-nearest neighbor, Gaussian mixture model and, finally, deep-autoencoder. The experimental results suggest that the use of the deep-autoencoder in the task of detecting anomalies of operation in electromechanical systems has a higher performance compared to the state of the art methods.Peer ReviewedPostprint (published version

Diagnosis electromechanical system by means CNN and SAE: an interpretable-learning study

© 2022 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.Cyber-physical systems are the response to the adaptability, scalability and accurate demands of the new era of manufacturing called Industry 4.0. They will become the core technology of control and monitoring in smart manufacturing processes. In this regard, the complexity of industrial systems implies a challenge for the implementation of monitoring and diagnosis schemes. Moreover, the challenges that is presented in technological aspects regarding connectivity, data management and computing are being resolved through different IT-OT (information technology and operational technology) convergence proposals. These solutions are making it possible to have large computing capacities and low response latency. However, regarding the logical part of information processing and analysis, this still requires additional studies to identify the options with a better complexity-performance trade-off. The emergence of techniques based on artificial intelligence, especially those based on deep-learning, has provided monitoring schemes with the capacity for characterization and recognition in front of complex electromechanical systems. However, most deep learning-based schemes suffer from critical lack of interpretability lying to low generalization capabilities and overfitted responses. This paper proposes a study of two of the main deep learning-based techniques applied to fault diagnosis in electromechanical systems. An analysis of the interpretability of the learning processes is carried out, and the approaches are evaluated under common performance metrics.Peer ReviewedPostprint (published version

Study for the computational resolution of conservation equations of mass, momentum and energy. Possible application to different aeronautical and industrial engineering problems: Case 5E

Aprofundir en la simulació de les equacions fonamentals de la dinàmica de fluids i transferència de calor i massa, així com la seva aplicació en algun cas d’interès per l’estudiant en el camp de l’enginyeria industrial i/o aeronàutica.   Primerament es proposen a l’estudiant uns casos bàsics d’integració de les equacions bàsiques de conservació de la massa, momentum i energia que li serviran per consolidar els seus coneixements en aquesta important temàtica (formulació matemàtica, tècniques computacionals, tècniques de programació, etc.). D’aquesta forma, l’estudiant crearà els seus propis codis de simulació. Tindrà suport tant per estructurar els programes com per la seva verificació. Es recomana el llenguatge de programació C o C++.   En base al treball de simulació desenvolupat, una segona part del TFG estarà dirigida a aplicacions específiques en el camp de l’optimització de sistemes i equips termo-fluídics i aeronàutics per tal d’aconseguir la seva optimització, i.e. màxima eficiència energètica amb el mínim cost i impacte ambiental. Aquí l’estudiant podrà proposar, d’acord amb els professors, aquelles situacions que consideri més adients als seus interessos. Com a possibles casos d’aplicació citarem:Study for the computational resolution of conservation equations of mass, momentum and energy. Application to aerodynamics of airfoils shaped bodies. Study for the computational resolution of conservation equations of mass, momentum and energy. Application to heating, ventilating, air conditioning and refrigeration (HVAC & R).  Study for the computational resolution of conservation equations of mass, momentum and energy. Application to combustion processes for aeronautic and aerospace applications.  Study for the computational resolution of conservation equations of mass, momentum and energy. Application to turbomachinery.  Study for the computational resolution of conservation equations of mass, momentum and energy. Application to solar thermal collectors for low and middle temperatures.  Study for the computational resolution of conservation equations of mass, momentum and energy. Application to thermal energy storage in industrial applications.  Study for the computational resolution of conservation equations of mass, momentum and energy. Application to analysis of the power block in thermoelectric plants.  Study for the computational resolution of conservation equations of mass, momentum and energy. Application to compact heat exchangers using micro-channels and fin-and-tube systems. Study for the computational resolution of conservation equations of mass, momentum and energy. Application to bioengineering applications.  Study for the computational resolution of conservation equations of mass, momentum and energy. Application to refrigeration of electric and electronic components.  Study for the computational resolution of conservation equations of mass, momentum and energy. Application to solid-fluid interaction.  El títol final del TFG dependrà de l’aplicació final escollida. El llistat anterior és representatiu de com podria quedar el títol final. Anàlisis previ d'antecedents i state-of-the-art. Plantejament del fenomen físic, formulació matemàtica i desenvolupament de les eines de simulació numèrica necessàries. Verificació dels codis i de les solucions numèriques. Obtenció i anàlisis de resultats. Aplicació al cas específic seleccionat i propostes d'optimització en el disseny. Sempre que sigui adient, es validaran els models desenvolupats i resultats obtinguts en base a la contrastació amb dades experimentals o de simulacions avançades obtingudes de la literatura tecnocientífica i/o d’estudis no publicats del CTTC (www.cttc.upc.edu). Conclusions

Optimization of industrial plants for exploiting energy assets and energy trading

The worldwide energy market is undergoing a transition that will lead to a greener and non-fossil fuel dependent situation in which demand side management and prosumers will play a key role. The digitalization of energetic industrial facilities to create a virtual power plant by forecasting future energy situation and modelling internal energy flow is performed for a specified case study. In this paper the proposal of creating a virtual power plant from an industrial plant is done to benefit from the opportunities raised by the energetic transition. A study of the market and exploitation approach is done. The feasibility of developing a virtual power plant considering future energy situation and internal energy assets is verified by optimizing its final cost in terms of performance against external markets. The results show that there are economic benefits for the owner of the facility while assuring the energy demand and the proper operation of the equipment.Peer ReviewedPostprint (published version