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Abstract Microgrids integrating local renewable energy sources at low-voltage level show promising potentials in realizing a reliable, efficient, and clean supply of electricity. Further improvements are expected when such a microgrid is operated based on direct current (dc) instead of alternating current (ac) infrastructure for power distribution commonly in use today. Our study aims to systemically quantify the gap between environmental impacts of microgrids at building level using the case study of power distribution within office buildings. For this purpose, a scalable comparative life cycle assessment (LCA) is conducted based on a technical bottom-up analysis of differences between ac and dc microgrids. Particularly, our approach combines the micro-level assessment of required power electronic components with the macro-level requirements for daily operation derived from a generic grid model. The results indicate that the environmental impacts of employed power electronics are substantially reduced by operating a microgrid based on dc power distribution infrastructure. Our sensitivity analyses show that efficient dc microgrids particularly lead to savings in climate change impact emissions. In addition, our study shows that the state-of-the-art scaling rules of power electronics currently used in LCAs leads to inaccurate results. In contrast, the proposed methodology applies a more technical approach, which enables a detailed analysis of the environmental impacts of power electronic components at system level. Thus, it provides the foundation for an evaluation criterion for a comprehensive assessment of technological changes within the framework of energy policy objectives.

Abstract Public participation is often part of planning and decision-making processes relating to the German energy transformation (Energiewende). Factors influencing the active involvement of individuals have not been fully investigated, although these factors may impact the outcome of participatory decision making. However, a few concepts are discussed relating to what kind of people participate in governance processes: political efficacy, place attachment, value orientation, and sociodemographic characteristics. We further assumed that the aspects of attitudes toward renewable energy technologies, general knowledge about environment and energy, specific knowledge about electricity-generating technologies, personality strength, and living situation might influence people's participation in planning and decision making related to energy issues. In this study, we examine the relevance of these concepts based on a survey for which (n=) 2400 respondents were recruited from an access panel to build up a quota sample on the three crossed characteristics: gender, age, and school education. Many of the respondents are aware of participation options but very few become actively engaged in participation processes. The multivariate analyses conducted showed that attitudes towards renewable energy technologies, value orientation towards nature, political efficacy, personality strength, and individuals' specific knowledge have a strong influence on whether someone becomes actively involved or not.

Synopsis Classic debates in community ecology focused on the complexities of considering an ecosystem as a super-organ or organism. New consideration of such perspectives could clarify mechanisms underlying the dynamics of forest carbon dioxide (CO2) uptake and water vapor loss, important for predicting and managing the future of Earth’s ecosystems and climate system. Here, we provide a rubric for considering ecosystem traits as aggregated, systemic, or emergent, i.e., representing the ecosystem as an aggregate of its individuals or as a metaphorical or literal super-organ or organism. We review recent approaches to scaling-up plant water relations (hydraulics) concepts developed for organs and organisms to enable and interpret measurements at ecosystem-level. We focus on three community-scale versions of water relations traits that have potential to provide mechanistic insight into climate change responses of forest CO2 and H2O gas exchange and productivity: leaf water potential (Ψcanopy), pressure volume curves (eco-PV), and hydraulic conductance (Keco). These analyses can reveal additional ecosystem-scale parameters analogous to those typically quantified for leaves or plants (e.g., wilting point and hydraulic vulnerability) that may act as thresholds in forest responses to drought, including growth cessation, mortality, and flammability. We unite these concepts in a novel framework to predict Ψcanopy and its approaching of critical thresholds during drought, using measurements of Keco and eco-PV curves. We thus delineate how the extension of water relations concepts from organ- and organism-scales can reveal the hydraulic constraints on the interaction of vegetation and climate and provide new mechanistic understanding and prediction of forest water use and productivity.

This paper proposes a technique for the probabilistic wind power forecasting (WPF) of a newly built wind farm (NWF) using a limited amount of historical data. First, the state-of-the-art Transformer network is employed to capture the power generation pattern of different wind farms (WFs) based on abundant historical training samples. Then, the Bayesian averaging regression method is applied to transfer the learned power generation pattern to the NWF by assigning proper weights to the WPF results of different WFs. This enables the proposed method to yield accurate NWF power predictions utilizing a limited amount of historical data. The Bayesian characteristics further enable the quantification of multiple uncertainties in forecasting results that may be essential for the NWF operator when the input is uncertain. Comprehensive tests were also performed by employing other deterministic and probabilistic WPF methods using field data. By comparing the results, the proposed method is demonstrated to produce accurate forecasting results with sparse historical data. Moreover, the uncertainties of outcomes are quantified, and acceptable performance is achieved.

AbstractSustainable biorefineries have a critical role to play in our common future. The need to provide more goods using renewable resources, combined with advances in science and technology, has provided a receptive environment for biorefinery systems development. Biorefineries offer the promise of using fewer non‐renewable resources, reducing CO2 emissions, creating new employment, and spurring innovation using clean and efficient technologies. Lessons are being learned from the establishment of first‐generation biofuel operations. The factors that are key to answering the question of biorefinery sustainability include: the type of feedstock, the conversion technologies and their respective conversion and energy efficiencies, the types of products (including coproducts) that are manufactured, and what products are substituted by the bioproducts. The BIOPOL review of eight existing biorefineries indicates that new efficient biorefineries can revitalize existing industries and promote regional development, especially in the R&D area. Establishment can be facilitated if existing facilities are used, if there is at least one product which is immediately marketable, and if supportive policies are in place. Economic, environmental, and social dimensions need to be evaluated in an integrated sustainability assessment. Sustainability principles, criteria, and indicators are emerging for bioenergy, biofuels, and bioproducts. Practical assessment methodologies, including data systems, are critical for both sustainable design and to assure consumers, investors, and governments that they are doing the ‘right thing’ by purchasing a certain bioproduct. If designed using lifecycle thinking, biorefineries can be profitable, socially responsible, and produce goods with less environmental impact than conventional products … and potentially even be restorative!. Copyright © 2010 Society of Chemical Industry and John Wiley & Sons, Ltd

This study assesses the socio-economic impacts in terms of value added, imports and employment of sugarcane-derived bioethanol production in Northeast (NE) Brazil. An extended inter-regional Input–Output (IO) model has been developed and is used to analyse three scenarios, all projected for 2020: a business-as-usual scenario (BaU) which projects current practices, and two scenarios that consider more efficient agricultural practices and processing efficiency (scenario A) and in addition an expansion of the sector into new areas (scenario B). By 2020 in all scenarios, value added and imports increase compared to the current situation. The value added by the sugarcane–ethanol sector in the NE region is 2.8 billion US$ in the BaU scenario, almost 4 billion US$ in scenario A, and 9.4 billion US$ in scenario B. The imports in the region will grow with 4% (BaU scenario), 38% (scenario A) and 262% (scenario B). This study shows that the large reduction of employment (114,000 jobs) due to the replacement of manual harvesting by mechanical harvesting can be offset by additional production and indirect effects. The total employment in the region by 2020 grows with 10% in scenario A (around 12,500 jobs) and 126% in scenario B (around 160,000 jobs). The indirect effects of sugarcane production in the NE are large in the rest of Brazil due to the import of inputs from these regions. The use of an extended inter-regional IO model can quantify direct and indirect socio-economic effects at regional level and can provide insight in the linkages between regions. The application of the model to NE Brazil has demonstrated significant positive socio-economic impacts that can be achieved when developing and expanding the sugarcane–ethanol sector in the region under the conditions studied here, not only for the NE region itself but also for the economy of the rest of Brazil.

From the British debate on the depletion of coal in 1865 to the First World Power Conference held in London in 1924, scientists, engineers, industrialists, and politicians produced new interpretations of the past, present, and future in terms of the mobilisation of energy resources. This thesis identifies an emerging ‘energy developmentalism’, which called for maximising energy use to maintain or improve a nation’s place in international competition. Energy developmentalism was not a marginal worldview confined to ‘energeticists’, but a coherent set of claims, measurements, and arguments that informed energy governance on an international scale. Rather than focusing on a single resource, energy developmentalism applied a unified schema to all energy sources, including those like solar and tidal energy that were still mostly theoretical. Drawing on sources from across Europe, while staying grounded in political changes in Britain and France, makes it possible to understand how a general formula for transforming raw materials with maximum efficiency was applied differently depending on specific political contexts. This period saw the articulation of problems like the depletion of resources, the difference between renewable and nonrenewable energy, the intermittency of renewables, the overreliance on a single source of energy, and the centrality of energy to modern economies – problems that are often associated with later periods. Scientific measurements of efficiency, horsepower, and kilowatts became operators in political debates centred on questions of national standing and progress. Even as oil became increasingly important in the world economy, the delegates at the First World Power Conference transformed a vision of a renewable energy future into one of a general expansion of energy consumption as the basis of progress. In so doing, they downplayed the continued importance of fossil fuels and equated ‘conservation’ with the fullest possible use of all energy sources, renewable or not.

Abstract Level(s) is a common European Union framework of core sustainability indicators for measuring the performance of buildings along their life cycle, enabling emissions reductions and circular resource flows. A fundamental tool for the development of European policies to boost the market for sustainable, resilient and climate change adapted buildings. The objective of this study is to contribute to the existing body of knowledge in the field of sustainable building research, through the definition of strategies to adopt Level(s) for bringing buildings into the Circular Economy. For this reason, a triple SWOT-Analytical Hierarchy Process (AHP)-TOWS analysis was applied. The strengths, weaknesses, opportunities and threats (SWOT) of the Level(s) have been identified in relation to the availability of resources, product quality, internal and market structure, consumer perception, among others. The results obtained are conclusive in terms of the experts’ positive assessment of the tool; highlighting factors such as its response to the need to adapt buildings to climate change, its a standard reference language, and its use in multiple situations. However, several barriers have also been identified, which may affect its development, including its complexity of use, its lack of self-sufficiency, and its dependence the criteria used in each evaluation. Finally, the key strategies to be carried out for the implementation of the Levels have been established.

Electricity load forecasting is a key aspect for power producers to maximize their economic efficiency in deregulated markets. So far, many solutions have been employed to forecast the consumption load in power grids. However, most of these methods have suffered in modeling the time-series state of data and removing noise from real-world data. Thus, the forecasting results in most cases did not have acceptable accuracy due to the mentioned problems. In this paper, in order to short-term electricity load forecast in Tabriz, Iran, a hybrid technique based on deep learning applications called Variational Autoencoder Bidirectional Long Short-Term Memory (VAEBiLSTM) is presented. Pre-processing, noise cancellation, and time-series state modeling of the data are prominent features of the developed load forecasting model. In addition, in order to prevent overfitting problems in the process of training large amounts of data, the training process is developed in the form of batch training. Load forecasting is done using meteorological and environmental data of Tabriz city as well as historical information and days of the week as input variables. In the hybrid method structure, the Variational Autoencoders are applied to the data for data preprocessing and reconstruction. Then, the normalized, noise-free data is utilized as a dataset for training the Bidirectional Long Short-Term Memory (BiLSTM) network. The proposed training method for BiLSTM is based on batch training. To present the effectiveness of the proposed technique in a comparative approach, the conventional LSTM and Support Vector Regression (SVR) algorithms are also applied to the data. Each network is trained with input data related to the years of 2017 and 2018 to predict the electricity load of the Tabriz city separately for each of the four seasons of the 2019 year. The forecasting results obtained from each method are evaluated by different statistical performance indicators. It can be seen that the proposed model forecasts the load with the correlation coefficients (R) of 99.78%, 99.57%, 99.33%, and 99.76% for spring, summer, autumn, and winter, respectively. The presented results show that the proposed VAEBiLSTM method with the highest R values and minimum forecasting errors compared to the LSTM and SVR methods has high effectiveness and performance.