Energy crisis in Europe: the European Union’s objectives and countries’ policy trends—new transition paths?

Amidst the ongoing European energy crisis, the EU has proposed a legislative package to enhance gas independence from Russia, diversify energy supplies, and increase renewable energy targets. However, the urgency for energy security has led some countries to prioritise gas independence over decarbonisation, potentially sacrificing or delaying EU targets. Considering this framework, this article contributes to the body of knowledge by examining the electricity mix of the six most significant EU countries in terms of generation capacity, considers their alignment with 2025 energy transition goals, and analyses the latest legislative trends to evaluate their compatibility with EU objectives. The findings from these analyses indicate that EU members are currently prioritising gas independence, which has led to re-starting or extending the lifespan of coal-fired power plants and an increasing interest in nuclear energy as a low-carbon alternative. These findings have significant implications as they reveal how countries are being steered away from their pre-crisis energy transition paths, resulting in the formation of new perspectives for both the short and long term.This research has been funded by the European Social Fund and the Secretariat of Universities and Research of Catalonia.Peer ReviewedPostprint (published version

Activity-aware HVAC power demand forecasting

The forecasting of the thermal power demand is essential to support the development of advanced strategies for the management of local resources on the consumer side, such as heating ventilation and air conditioning (HVAC) equipment in buildings. In this paper, a novel hybrid methodology is presented for the short-term load forecasting of HVAC thermal power demand in smart buildings based on a data-driven approach. The methodology implements an estimation of the building's activity in order to improve the dynamics responsiveness and context awareness of the demand prediction system, thus improving its accuracy by taking into account the usage pattern of the building. A dedicated activity prediction model supported by a recurrent neural network is built considering this specific indicator, which is then integrated with a power demand model built with an adaptive neuro-fuzzy inference system. Since the power demand is not directly available, an estimation method is proposed, which permits the indirect monitoring of the aggregated power consumption of the terminal units. The presented methodology is validated experimentally in terms of accuracy and performance using real data from a research building, showing that the accuracy of the power prediction can be improved when using a specialized modeling structure to estimate the building's activity.Peer ReviewedPostprint (author's final draft

A methodology for energy prediction and optimization of a system based on the Energy Hub Concept using Particle Swarms

In this paper, a methodology for the energy prediction for the different consumptions of a system based in the Energy Hub concept is presented. The methodology that has been used for the energy prediction is based on an Adaptive Neuro-Fuzzy Inference System. An optimization method based on Particle Swarms has been used to minimize the energy cost of a system with multiple sources such as, photovoltaic, electrical grid and natural gas.Peer ReviewedPostprint (author's final draft

New mathematical model of an inverter-based generator for stability studies of microgrid Systems

A new mathematical model of a renewable generator, with a DC-AC interface, based on the concept of electrostatic machine is presented. This new model has a direct relationship between the DC and AC side. Moreover, it can be used for stability studies, taking into account the dynamics of the DC link and to find saturations and limits on the control signals.Peer ReviewedPostprint (author’s final draft

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

Uncertainty analysis for industries investing in energy equipment and renewable energy sources

This paper studies the optimal design and operation of new energy equipment including renewable energy sources for prosumer industries. In order to augment the interest of industries in performing energy actions, the economic parameters of the investment are analysed and the risk related to it considering the uncertainty in energy markets is evaluated. A two-stage optimization approach is proposed considering the whole lifetime of the energy equipment and an uncertainty analysis performed through the evaluation of the deterministic model under Latin Hypercube Samples of uncertain parameters. A case study based on a real industry is presented, whose results expose the robustness of the optimization methodology and the acceptable risk of investing in renewable energy sources and energy equipment for prosumer purposes.Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPostprint (published version

Study of large-signal stability of an inverter-based generator using a Lyapunov function

This document analyses the large-signal stability for an inverter-based generator such as photovoltaic and wind power sources. The objective of this study is to determine the stability region taking into account the electrical and control signal of the generator. The generator uses the concept of the electrostatic machine for the model of the generator. Finally, the applied procedure to find the Lyapunov's function is the Popov method, which not only permits to generate a valid function but also to determine the stability region of the system.Postprint (author's final draft

Estudio de microgrids con interfases de convertidores de DC/AC

Este artículo presenta un estudio de Microgrids con interfases de convertidores DC/AC. El artículo muestra los modos de operación de las Microgrids, además implementa un modelo de pequeña señal para el modo de operación aislado que permite estudiar la estabilidad del sistema. Finalmente se muestra el funcionamiento de la Microgrid en ambos modos de operación.Peer ReviewedPostprint (published version