• Energy Research
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As a relatively mature technology, biomass molded fuel (BMF) is widely used in distributed and centralized heating in China and has received considerable government attention. Although many BFM incentive policies have been developed, decreased domestic traditional fuel prices in China have caused BMF to lose its economic viability and new policy recommendations are needed to stimulate this industry. The present study built a regionalized net present value (NPV) model based on real production process simulation to test the impacts of each policy factor. The calculations showed that BMF production costs vary remarkably between regions, with the cost of agricultural briquette fuel (ABF) ranging from 86 US dollar per metric ton (USD/t) to 110 (USD/t), while that of woody pellet fuel (WPF) varies from 122 USD/t to 154 USD/t. The largest part of BMF’s cost composition is feedstock, which accounts for up 50%–60% of the total; accordingly a feedstock subsidy is the most effective policy factor, but in consideration of policy implementation, it would be better to use a production subsidy. For ABF, the optimal product subsidy varies from 26 USD/t to 57 USD/t among different regions of China, while for WPF, the range is 36 USD/t to 75 USD/t. Based on the data, a regional BMF development strategy is also proposed in this study.

The prevailing need for a more sustainable management of natural resources depends not only on the decisions made by governments and the will of the population, but also on the knowledge of the role of energy in our society and the relevance of preserving natural resources. In this sense, critical work is being done to instill key concepts—such as the circular economy and sustainable energy—in higher education institutions. In this way, it is expected that future professionals and managers will be aware of the importance of energy optimization, and will learn a series of computational methods that can support the decision-making process. In the context of higher education, this paper reviews the main trends and challenges related to the concepts of circular economy and sustainable energy. Besides, we analyze the role of simulation and serious games as a learning tool for the aforementioned concepts. Finally, the paper provides insights and discusses open research opportunities regarding the use of these computational tools to incorporate circular economy concepts in higher education degrees. Our findings show that, while efforts are being made to include these concepts in current programs, there is still much work to be done, especially from the point of view of university management. In addition, the analysis of the teaching methodologies analyzed shows that, although their implementation has been successful in favoring the active learning of students, their use (especially that of serious games) is not yet widespread.

It is imperative to increase the connectable capacity (i.e., hosting capacity) of distributed generation in order to decarbonise electricity distribution networks. Hybrid generation that exploits complementarity in resource characteristics among different renewable types potentially provides value for minimising technical constraints and increasing the effective use of the network. Tidal, wave and wind energy are prominent offshore renewable energy sources. It is of importance to explore their potential complementarity for increasing network integration. In this work, the novel introduction of these distinct offshore renewable resources into hosting capacity evaluation enables the quantification of the benefits of various resource combinations. A scenario reduction technique is adapted to effectively consider variation of these renewables in an AC optimal power flow-based nonlinear optimisation model. Moreover, the beneficial impact of active network management (ANM) on enhancing the renewable complementarity is also investigated. The combination of complementary hybrid generation and ANM, specifically where the maxima of the generation profiles rarely co-occur with each other and with the demand minimum, is found to make the best use of the network components.

Daily operations and planning in a smart grid require a day-ahead load forecasting of its customers. The accuracy of day-ahead load-forecasting models has a significant impact on many decisions such as scheduling of fuel purchases, system security assessment, economic scheduling of generating capacity, and planning for energy transactions. However, day-ahead load forecasting is a challenging task due to its dependence on external factors such as meteorological and exogenous variables. Furthermore, the existing day-ahead load-forecasting models enhance forecast accuracy by paying the cost of increased execution time. Aiming at improving the forecast accuracy while not paying the increased executions time cost, a hybrid artificial neural network-based day-ahead load-forecasting model for smart grids is proposed in this paper. The proposed forecasting model comprises three modules: (i) a pre-processing module; (ii) a forecast module; and (iii) an optimization module. In the first module, correlated lagged load data along with influential meteorological and exogenous variables are fed as inputs to a feature selection technique which removes irrelevant and/or redundant samples from the inputs. In the second module, a sigmoid function (activation) and a multivariate auto regressive algorithm (training) in the artificial neural network are used. The third module uses a heuristics-based optimization technique to minimize the forecast error. In the third module, our modified version of an enhanced differential evolution algorithm is used. The proposed method is validated via simulations where it is tested on the datasets of DAYTOWN (Ohio, USA) and EKPC (Kentucky, USA). In comparison to two existing day-ahead load-forecasting models, results show improved performance of the proposed model in terms of accuracy, execution time, and scalability.

This paper presents an energy management strategy for secondary energy sources and their optimization in the context of the tax gap on mineral oils represented by the tax gap on petrol and diesel. Energy companies face drastic economic and environmental challenges; therefore, this area necessarily requires the setting up of transparent economic instruments and, of course, production. The tax gap in VAT collection represents the gap between how much the state could potentially collect from VAT in accordance with the law and how much VAT really comes into the state coffers. The loss of tax revenues is caused by unpaid and undeclared tax liability. The Estimation of the Gap on Corporate Tax in Slovakia is a pilot project of the International Monetary Fund (IMF) in cooperation with the Institute for Financial Policy (IFP). The results present the estimation of the tax gap within a two-year delay. It is necessary to consider the estimation of the tax gap in the last two years as preliminary due to possible revisions of the national accounts data. Estimations of the tax gap from 2010 to 2017 indicate a decreasing trend. The significant part of the tax gap decrease can be observed from 2014. The main factor, which determines this decrease, is the improving condition of the Slovak economy. From the point of view of the tax gap, the volume of losses is decreasing and the volume of profit, from which the tax is paid, is increasing.

The development of new climate change policies has increased the motivation to reduce energy use in buildings, as reflected by a stringent regulatory landscape. The construction industry is expected to adopt new methods and strategies to address such requirements, focusing primarily on reducing energy demand, improving process efficiency and reducing carbon emissions. However, the realisation of these emerging requirements has been constrained by the highly fragmented nature of the industry, which is often portrayed as involving a culture of adversarial relationships and risk avoidance, which is exacerbated by a linear workflow. Recurring problems include low process efficiency, delays and construction waste. Building information modelling (BIM) provides a unique opportunity to enhance building energy efficiency (EE) and to open new pathways towards a more digitalised industry and society. BIM has the potential to reduce (a) waste and carbon emissions, (b) the endemic performance gap, (c) in-use energy and (d) the total lifecycle impact. BIM also targets to improve the whole supply chain related to the design, construction as well as the management and use of the facility. However, the construction workforce is required to upgrade their skills and competencies to satisfy new requirements for delivering BIM for EE. Currently, there is a real gap between the industry expectations for employees and current training and educational programmes. There is also a set of new requirements and expectations that the construction industry needs to identify and address in order to deliver more informed BIM for EE practices. This paper provides an in-depth analysis and gap identification pertaining to the skills and competencies involved in BIM training for EE. Consultations and interviews have been used as a method to collect requirements, and a portfolio of use cases have been created and analysed to better understand existing BIM practices and to determine current limitations and gaps in BIM training. The results show that BIM can contribute to the digitalisation of the construction industry in Europe with adapted BIM training and educational programmes to deliver more informed and adapted energy strategies.

Economic development is mainly dependent on fossil fuels. The massive use of fossil fuels has led to changes in the climate environment, in which the deterioration of air quality has affected people’s daily lives. This paper introduces the green growth level as a control variable to explore the connection between carbon dioxide emissions and the level of economic growth. It uses the EKC algorithm and VEC model to analyze Nanjing city’s data from 1993 to 2018. Given the data availability, the ARIMA algorithm was used to project carbon emissions for 2019–2025. It is found that the EKC curve of Nanjing City shows an N-shape, and the growth of economic level will cause the enhancement of carbon dioxide emissions. Carbon emissions will reach 7,592,140 tons in 2025. At present, we are in an essential stage of transition from N-shape to inverted U-shape, and this paper makes several recommendations based on the findings.