• Energy Research
• 2021-2025close
• CN close
• ES close
• CA close
• Polytechnic University of Milan close

Abstract Reliability analysis of IESs (Integrated Energy System) is complicated because of the complexity of system topology and dynamics and different kinds of uncertainties. Reliability is often calculated based on statistic methods, which always focus on historical performances and neglect the importance of their dynamics and structure. To overcome this problem, in this paper, a systematic framework for dynamically analysing the real-time reliability of IESs is proposed by integrating different machine learning methods and statistics. Firstly, the bootstrap-based Extreme Learning Machine is developed to forecast the conditional probability distributions of the productions of renewable energies and the energy consumptions. Then, the dynamic behaviour of IESs is simulated based on a stacked auto-encoder model, instead of using traditional mechanism-based simulation models, for improving computational efficiency. Besides, the variables representing the transient properties of natural gas pipeline networks, such as delivery pressures and flow rates, are taken as the indicators for quantifying the energy supply security in natural gas pipeline networks. The time-dependent relationships among these indicators and their statistic correlations are modelled for improving the effectiveness of the analysis results. Finally, the reliability assessment is performed by estimating the probability distribution of each functional state of the target IES. A case study of a realistic bi-directional IES is carried out to demonstrate the effectiveness of the proposed method. The results show that the method is able to effectively evaluate the reliability of IESs, which can provide useful information for system operation and management.

The annual increase in installation capacity and electrical production of renewable energy sources, primarily wind turbine generators (WTG), is shaping a renewable energy dominated power system. WTGs are susceptible to the temporary overvoltage caused by reactive power surplus following low-voltage ride through (LVRT). This can lead to the large-scale trip-off of WTGs and pose significant risks to the secure and stable operation of power systems. An insightful elaboration of the underlying mechanisms determining the occurrence of temporary overvoltage, and an analysis of influencing factors, is pivotal to ensure the reliable integration of WTGs. This paper investigates the temporary overvoltage in the AC systems integrated with multiple renewable energy stations. A temporary overvoltage model that accounts for various types of equipment has been derived. Resorting to the model, the influence of LVRT parameters of WTGs, SCR and IR of the AC system on the maximum terminal overvoltage has been quantitatively assessed. Simulations and semi-physical validations have been conducted to verify the effectiveness and accuracy of the theoretical analysis.

Abstract The degradation process of lithium-ion batteries has memory, i.e. it has long-range dependence (LRD). In this paper, an iterative model of the generalized Cauchy (GC) process with LRD characteristics is proposed for the remaining useful life (RUL) prediction of lithium-ion batteries. The GC process uses two independent parameters, fractal dimension and Hurst exponent, to measure the LRD of the degradation process. The diffusion term of the GC iterative model is replaced by the increment of the GC time sequences, constructed via the autocorrelation function (ACF) to describe uncertainty and the LRD characteristics of the lithium-ion batteries capacity degradation. Linear and nonlinear drift terms are used to explain the degradation trend of the lithium-ion batteries capacity. A comparison is made with fractional Brownian motion (FBM) and long-short-term memory (LSTM) network models to show how the GC iterative model has the best performance in RUL prediction of lithium-ion batteries.

Wind energy is gaining special interest worldwide due to the necessity of reducing pollutant emissions and employ renewable resources. Traditionally, horizontal axis wind turbines have been employed but certain situations require vertical axis wind turbines. With a view to improve the efficiency of a vertical axis wind turbine Savonius type, the present work proposes a bioinspired design blade profile relying on the Fibonacci spiral. This shape is repeatedly presented in nature and thus it leads to a bio-inspired blade profile. A numerical model was carried out and it was found that the Fibonacci shape improves the performance of the original Savonius shape, based on semicircular blade profiles. Particularly, the Fibonacci blade profile increases around 14% the power in comparison with the Savonius blade profile. Besides this comparison between Savonius and Fibonacci, a research study was carried out to improve the efficiency of the Fibonacci turbine. To this end, the effect of several parameters was analyzed: number of blades, aspect ratio, overlap, separation gap, and twist angle. Improvements on the average power greater than 30% were obtained.

The reliability prediction of gearbox is a complex and challenging topic. The purpose of this research is to propose a hybrid difference iterative forecasting model to forecast reliability of the gearbox. On this score, a hybrid model based on the fractional Lévy stable motion (fLsm), the Grey Model (GM) and the metabolism method is proposed. To solve the problem of insensitivity to weak faults inside the gearbox, we use feature extraction method to reveal the gearbox degradation. Then, the least square theory is used to separate the degradation sequence in the gearbox into a deterministic term with monotonicity and a stochastic term with Long-Range Dependence (LRD). Next, the fLsm with LRD and non-Gaussian is used to forecast the stochastic term, the deterministic term is simulated by the GM, and the hybrid forecasting model is used to modify the prediction results. The metabolism method is used to update the degradation sequence and to forecast longer-term trend. Finally, a case demonstrated that superiority and generality of the hybrid forecasting model.

Controlling building carbon emissions (CEs) is key to achieving the goal of carbon neutrality. Residential blocks are the main contributors of buildings’ carbon emissions and intensity, and thus can be manipulated to achieve carbon neutrality. This work aimed to evaluate the building carbon emissions intensity (CEI) levels of residential blocks using Rhino and Grasshopper and to quantify the relationship between the block form parameters and a building’s carbon emissions (CEs). Firstly, 48 cases were selected by stratified sampling, and they were classified by architectural typology. Secondly, the residential block morphological parameters and building carbon emissions were calculated. Thirdly, the relationship between the block form parameters and the building’s CE was quantified using statistical methods. Lastly, low-carbon planning strategies for residential blocks under the target of carbon neutrality were proposed. The findings showed that the influence of the block form parameters on a building’s CE was 31.66%. A building’s shape factor has a positive influence on its CE, and the floor area ratio, building volume–site area ratio, and building height have negative influences on its CE. A building’s shape factor, cover ratio, and surface–site area ratio synergistically impact its CE. The weight of a building’s shape factor on its carbon emissions was 3.84 times that of its cover ratio and 4.46 times that of its surface–site area ratio. The technology workflow proposed in this study can provide data in support of carbon emissions assessments and low-carbon planning strategies for urban blocks in other cities in China and worldwide.

We argue that environmental legislation and regulation of more developed countries reflects significantly their moral values, but in less developed countries it differs significantly from their moral values. We examined this topic by using the keywords “sustainability” and “sustainable development”, studying web pages and articles published between 1974 to 2018 in Web of Science, Scopus and Google. Australia, Zimbabwe, and Uganda were ranked as the top three countries in the number of Google searches for sustainability. The top five cities that appeared in sustainability searches through Google are all from Africa. In terms of academic publications, China, India, and Brazil record among the largest numbers of sustainability and sustainable development articles in Scopus. Six out of the ten top productive institutions publishing sustainable development articles indexed in Scopus were located in developing countries, indicating that developing countries are well aware of the issues surrounding sustainable development. Our results show that when environmental law reflects moral values for betterment, legal adoption is more likely to be successful, which usually happens in well-developed regions. In less-developed states, environmental law differs significantly from moral values, such that changes in moral values are necessary for successful legal implementation. Our study has important implications for the development of policies and cultures, together with the enforcement of environmental laws and regulations in all countries.