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The demand for eco-products due to the exigency of friendly and environmental production could affect the employment of the firms. This paper tests whether product and process eco-innovations boost employment in Spain differentiating by the environmental goals (material and energy efficiency and environment responsiveness), by the level of qualification of the workers, and by the dirtiness of the industries. We apply a Green Harrison model, using the Technological Innovation Panel (PITEC) for Spain from 2008 to 2016. Results show a positive relationship between all types of product eco-innovations and employment, while the influence of process eco-innovations on employment depends on the environmental goals, the level of skills, and the industry. Specifically, distinguishing by industry there is a labour-saving effect in clean industries and a labour-friendly effect for low-skilled employment in dirty industries. [ABSTRACT FROM AUTHOR]

Energy management systems allow the Smart Grids industry to track, improve, and regulate energy use. Particularly, demand-side management is regarded as a crucial component of the entire Smart Grids system. Therefore, by aligning utility offers with customer demand, anticipating future energy demands is essential for regulating consumption. An updated examination of several forecasting techniques for projecting energy short-term load forecasts is provided in this article. Each class of algorithms, including statistical techniques, Machine Learning, Deep Learning, and hybrid combinations, are comparatively evaluated and critically analyzed, based on three real consumption datasets from Spain, Germany, and the United States of America. To increase the size of tiny training datasets, this paper also proposes a data augmentation technique based on Generative Adversarial Networks. The results show that the Deep Learning-hybrid model is more accurate than traditional statistical methods and basic Machine Learning procedures. In the same direction, it is demonstrated that more comprehensive datasets assisted by complementary data, such as energy generation and weather, may significantly boost the accuracy of the models. Additionally, it is also demonstrated that Generative Adversarial Networks-based data augmentation may greatly improve algorithm accuracy. [ABSTRACT FROM AUTHOR]

Combined Heat and Power (CHP) has been identified by the EU as a powerful resource capable of making substantial contributions to energy savings and reducing GHG emissions. Spain's effort to promote CHP has been prolific since the 1980s. In this regard, there have been various Laws, Royal Decrees (RDs) and European Union (EU) Directives addressed to reach the national objectives set for the CHP sector. Despite these attempts, the evolution and growth of installed CHP capacity has been irregular, compared to other technologies. Likewise, the academic treatment of the Spanish CHP evolution has not deserved the same attention as other technologies such as wind, photovoltaic and thermal solar systems. As a result, this article is aimed at providing a comprehensive overview of the regulatory frameworks applied to the Spanish CHP sector and analysing the reasons behind the variable evolution of the installed CHP capacity. The study covers the legislative context from 1980 to 2020, describing the evolution during both the pre-liberalization and liberalization periods, highlighting the modifications in economic policies that affected self-producers and the so-called Special Regime (SR) for CHP, and examining the challenges faced during the cost containment measures that followed. The manuscript finds and explains the connection between the regulatory framework and the evolution of installed CHP capacity in Spain. Likewise, the connection between the industrial situation and the promotion of CHP, as well as the influence of the Spanish Electricity Sector (SES)'s liberalization on the CHP sector are also pointed out. The paper intends to provide valuable insights for CHP experts and policymakers by showcasing the importance of aligning regulatory measures with the objectives of energy efficiency. It also serves as a reference for countries in various stages of promoting CHP, and provides evidence for the importance of stable energy-policy control mechanisms. [ABSTRACT FROM AUTHOR]

Effectively utilizing low-grade thermal energy is a promising approach to mitigating greenhouse gas emissions while reducing the burden on centralized power grids. Current thermoacoustic heat pumps and power generators face challenges such as high onset temperature differentials and low performance. This paper addresses these challenges by introducing a gas-liquid resonator into a thermoacoustic combined heat and power systems to recover low-grade thermal energy in residential buildings. Through Sage modeling and calculations, the internal characteristics of the proposed system and its output performance under different operating conditions are explored. At a heating temperature of 350 °C, the system can generate 6.4 kW of output thermal power, 0.9 kW of electricity, and overall exergy efficiency is 79.3 %. Combining neural network models with case studies conducted in Spain and Finland, the system can annually save 5.6 MWh and 20.7 MWh in fuel energy, reduce emissions of 1374 kg and 5180 kg of carbon dioxide, and save a total cost of €611 and €2324, respectively. Furthermore, comparisons with other emerging micro-CHP systems highlight the efficiency of the proposed system. These results indicate the high potential of thermoacoustic combined heat and power systems in recovering low-grade thermal energy and achieving energy savings and emission reductions. • Introducing an innovative thermoacoustic micro-CHP system. • Achieving an exergy efficiency of 79.3 % under standard operating conditions. • One system can annually reduce household CO 2 emissions by 5180 kg. • Ideal for households, balancing lower output levels for both heat and electricity. [ABSTRACT FROM AUTHOR]

The operation of solar photovoltaic installations for many years may lead to problems such as reduced energy efficiency and aging of components. Under this framework, replacing certain components in the facility to extend the service life of the power plant is viewed, in some cases, as a feasible strategy. However, despite its enormous strategic interest for the solar PV industry, we find that revamping PV installations has been the focus of very few scientific publications, and even less so when it comes to analyzing real facilities. Thus, this paper performs a techno-economic analysis of three revamping strategies at a real PV power plant located in the south-east of Spain. The strategies consist of substituting, in each case, the PV modules, the inverter, and both components. The simulated results show that the highest production value by installed power is obtained when both the modules and the inverter are replaced. In the same vein, the economic analysis reveals this scenario as the most favorable in terms of profitability. Based on the results obtained, an interesting future line of research would be focused on the extension of the analysis at country level to study the joint impact of numerous revamping actions. • Techno-economic analysis of revamping projects of a real solar PV power plant. • Replacing modules and inverter emerges as the most feasible strategy. • Study's applicability extends to other revamping initiatives and geographical regions. • Economic outcomes allow the most cost-effective alternatives to be determined. • This research can contribute to efficient and sustainable energy planning. [ABSTRACT FROM AUTHOR]

• We present a novel integration for reducing CO 2 emissions in blast furnaces. • It combines top gas recycling, oxy-fuel regime, power to gas and biomass pyrolysis. • The integration was modelled using the extended operating line methodology. • The best performance was found for pyrolysis at 700 °C. • The CO 2 emissions were reduced by 58% (9.8 MJ/kg CO2 energy consumption) The paper introduces a novel approach for mitigating CO 2 emissions in blast furnaces by integrating top gas recycling, an oxy-fuel regime, power to gas, and biomass pyrolysis. Various case studies were conducted, involving the adjustment of pyrolysis temperatures (300 °C, 500 °C, 700 °C, and 900 °C) and varying the quantity of blast furnace gas directed to methanation for carbon recycling. Pinus radiata, abundant and cost-effective in Chile and Spain, was chosen as the biomass source. The integration was modeled using the extended operating line methodology and evaluated through 12 key performance indicators, such as flame temperature, coke consumption, CO 2 emissions, and specific primary energy consumption per unit of CO 2 avoided. Optimal performance was observed with pyrolysis at 700 °C and no blast furnace gas recycled through methanation. This configuration achieved a 58 % reduction in CO 2 emissions, with an energy consumption of 9.8 MJ/kg CO2 , and obviated the need for geological storage. Comparing this innovative proposal with other oxygen blast furnace approaches from the literature revealed a 13 percentage point improvement in CO 2 reduction over the second-best alternative. Additionally, the required electrolysis capacity, influencing capital expenditure, was 57 % lower, and energy consumption was reduced by 44 %. [ABSTRACT FROM AUTHOR]

This paper presents a model for optimizing the life cycle economic and environmental impacts of a hybrid renewable energy and battery storage system – as energy supply technologies (EST) for off-grid farms. Micro-hydropower and photovoltaics represent the primary renewable energy (RE) sources, with batteries and a diesel generator considered as possible back-up systems. A modified Non-dominated Sorting Genetic Algorithm II (NSGA-II) was developed and applied to a case study farm in Southern Spain and provided different EST solutions to optimize a design based on economic (life cycle costing or LCC) and environmental (life cycle assessment or LCA) constraints. The results highlighted that cases with a strong dependence on batteries could achieve minimum costs and lower impacts for climate change and energy resources (non-renewable fuels). In contrast, the optimal system for minimizing the consumption of material resources (metals/minerals) resulted in higher fuel use, which was up to 25% more expensive. The optimal EST solutions used up to 37% of the total energy generated; however, the results were sensitive to energy production-demand distributions. This methodology can help identify the most cost-effective solutions with the lower environmental impacts for decarbonizing the agricultural sector. [Display omitted] [ABSTRACT FROM AUTHOR]