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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.

Power-to-methane (PtM) is a prospective solution to the mismatching between the supply and consumption of renewable energy resources (RES) by converting renewable power into methane. However, the continuous fluctuation of RES causes the PtM system to deviate from the design condition in the vast majority of cases, and thus it is significantly vital to study the operating characteristics of the PtM system under off-design conditions. This paper proposes a comprehensive investigation framework from design to off-design steps for the performance improvement of a PtM system combining solid oxide electrolysis cell with methanation reactor, and solar energy is selected as renewable energy input. Firstly, the system with the total exergy efficiency (ηEX,tot) of 11.83% and levelized cost of exergy (LCOE) of 150.76 $/MWh is selected as the optimal design condition based on the homogeneous assessment from both thermodynamic and economic aspects, by means of Non-dominated sorting genetic algorithm-II. Then, based on the optimal design point, the off-design performances are quantitatively investigated under varying solar radiation and key operating parameters, in terms of synthetic natural gas (SNG) yield and ηEX,tot. The results indicate that with the increment in solar radiation, the SNG yield rises, while the ηEX,tot increases first and then decreases. Finally, the multi-objective optimization based on the Artificial Neural Network models is implemented for the system under off-design conditions to acquire the best trade-off between hourly SNG yield and ηEX,tot. The off-design optimization solutions reveal that the hourly optimal SNG yield is located in the range of 275.06–946.53 kW, achieving a total annual SNG yield of 1697 MWh/y, and the hourly optimal ηEX,tot mainly varies in the range of 10.40–11.40%.

Abstract With drastic fossil fuel depletion and environmental deterioration concerns, a move towards a more sustainable bioenergy-based economy is essential. Lately, the application of microwave (MW) irradiation for waste processing has been attracting interest globally. MW-assisted heating possesses several advantages such as the provision of high microwave energy into dielectric materials with deeper penetration for internal heat generation, showing beneficial features in improving the heating rate and reducing the reaction time. Consequently, the most recent literature regarding the applications of MW-assisted heating for biomass pretreatment as well as biofuel and bioenergy production was reviewed and consolidated in this study. An impressive increase in the product yield and improvement of the product properties are reported, with the use of MW-assisted heating in several conversion routes to produce biofuels. Despite being a promising technology for biofuel production, some major fundamental data of MW-assisted heating have not been comprehensively identified. Therefore, the feasibility of this technology for large-scale implementation is still subpar. Understanding the interaction between the feedstock and the microwave electromagnetic field, and the optimization of several operational and mechanical parameters are the two main keystones that would propel the industrialization of MW heating in the near future. This provides key insights leading to increased feasibility and more advanced application of MW heating.

This study evaluates quality function deployment-based innovation pathways for renewable energy projects. For this purpose, a 2-stage novel model is suggested. Firstly, the weights of the factors are calculated by considering Pythagorean fuzzy decision-making trial and evaluation laboratory methodology based on 2-tuple linguistic values. With the help of the impact-relation map, the activities, and immediate predecessors are identified. Within this context, five different paths are determined for this situation. Secondly, the duration pathways and innovation costs are calculated for renewable energy projects. The findings indicate that the activities A (customer expectations) and E (production requirement) play the most critical role because they take place in all different paths. Therefore, it is strongly recommended that renewable energy investment companies should take necessary actions to satisfy customer expectations, such as offering affordable products and providing necessary information for the use of these projects.

Due to increasing volume of measurements in smart grids, surrogate based learning approaches for modeling the power grids are becoming popular. This paper uses regression based models to find the unknown state variables on power systems. Generally, to determine these states, nonlinear systems of power flow equations are solved iteratively. This study considers that the power flow problem can be modeled as an data driven type of a model. Then, the state variables, i.e., voltage magnitudes and phase angles are obtained using machine learning based approaches, namely, Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), and Support Vector Regression (SVR). Several simulations are performed on the IEEE 14 and 30-Bus test systems to validate surrogate based learning based models. Moreover, input data was modified with noise to simulate measurement errors. Numerical results showed that all three models can find state variables reasonably well even with measurement noise.

Abstract The role of BaO in the glassy structured Na2Si3O7 was investigated in the context of gamma radiations shielding parameters in the study. The mass attenuation coefficient, half layer value, and mean free path of the Na2Si3O7/BaO composites were calculated experimentally for the photons with the energies of 81 keV and 356 kev emitted from 133Ba point radioactive source. The same parameters were also calculated by Monte Carlo N-particle simulation (MCNP5) for the gamma photons which are emitted from 133Ba, 241Am, 99mTc, 177Lu, 192Ir, and 137Cs radioactive sources. The effective atomic number and effective electron density were determined by WinXCom software. Additionally, the scattered gamma photon intensity of the composites was realized for the energy of 364 keV and compared with the most utilized radiation shielding material lead. It was concluded that the composite having the highest BaO additive exhibits the best gamma photon absorption ability at all energies investigated.

Abstract The framework of this study is to weight 8 selected determinants and rank energy alternatives for hydrogen investments. For this purpose, different criteria that are based on two dimensions are identified with supported literature and interval type-2 (IT2) fuzzy decision-making trial and evaluation laboratory (DEMATEL) with alpha cuts is considered to measure the significant criteria. Additionally, renewable and non-renewable energy alternatives are ranked regarding the appropriateness for hydrogen energy investments with the help of IT2 fuzzy technique for order preference by similarity to ideal solution (TOPSIS) and IT2 fuzzy Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) with alpha cuts. It is concluded that the weights of the criteria are quite similar for different alpha cuts. Also, ranking results of different energy alternatives are almost the same for both IT2 fuzzy TOPSIS and IT2 fuzzy VIKOR. Thus, it can be concluded that the analysis results are reliable and coherent. The principal results indicate that cost-efficiency and reserve adequacy play a key role in hydrogen investments since they have the highest weight (0.129). Moreover, it is also found that technological capacity also plays a critical role in this regard with the weight of 0.127. However, legal regulation has the lowest importance weight (0.121) in comparison with other factors. Additionally, the weights of personnel competency (criterion 3) and storage (criterion 4) are also low (0.122). The major conclusion show that renewable energy alternatives are more suitable to generate hydrogen energy in comparison with non-renewable ones. Within this context, it is identified that solar and geothermal energies are more appropriate alternatives for hydrogen production whereas coal and nuclear are on the last rank. Hence, the main strategy should be lowering the costs by following up-to-date technological developments. Another important issue is that it becomes more logical to produce hydrogen in renewable energy sources that will not be consumed forever so that sustainable production of the hydrogen can be provided.

Abstract To provide a tool for performance evaluation of IEEE 802.15.4 with sleep mode enabled, a novel model based on real time queueing analysis is proposed in this paper. A low-rate wireless personal area network (LR-WPAN), composed of multiple nodes which send packets to the coordinator, is considered. The queueing behaviour of IEEE 802.15.4 node with sleep mode enabled differs from others because the packet arrivals in sleep period accumulate at the beginning of the active period, which makes a heavier load in the beginning than at any other time. This model analyses this behaviour by dividing the active portion of the superframe into backoff slots and then using an embedded discrete-time Markov chain model. The concept of virtual service time is introduced into this model which makes the proposed queueing model novel and different from typical ones. The accuracy of the proposed model is validated by Monte Carlo simulations in existing typical application scenarios, which indicates that the proposed queueing model can accurately evaluate the performance of IEEE 802.15.4 in the context of the application scenarios described in the simulations.