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It is generally known that addition of conducting or insulating particles to mineral transformer oil, lowers its breakdown strength, Ed. However, if the particulates are of molecular dimensions, or nanoparticles, (NPs), as they are called, the breakdown strength is seen to increase considerably. Recent experiments by the authors on oil cooled power equipment such as transformers showed that, nanofluids comprising NPs of selected oxides of iron, such as Fe 3 O 4 , called magnetite, added to transformer oil increased the breakdown voltage of the virgin oil and more importantly a remarkable enhancement in the thermal conductivity and the viscosity and hence an increased loadability of the transformer for a given top oil temperature (TOT).

The utilization of demand response flexibility has become a significant method to cope with the intermittence of renewable energy sources in distributed systems. This paper proposed a new pricing method for demand response resources managed by a distribution system aggregator, which is deduced from analyzing the operating revenue within the timescale from hours to years. In the proposed model, the hourly decision-making of an aggregator is formulated as a newsvendor model and uncertainties in the long-term decisions are modelled by a backward valuation process. It maximizes the benefit of an aggregator by considering the price and quantity uncertainties of distributed load/generation in day-ahead and real-time wholesale electricity markets. Meanwhile, the coexistence of controllable and uncontrollable loads is also considered, where the former refers to electricity consumption from end-users who are equipped with smart devices for energy management, and the latter load demand of passive end-users who have no willingness or capability to participate in the demand response schemes. Finally, numerical studies are carried out to demonstrate the feasibility and effectiveness of the developed model and methods, and the impacts of active end-user percentage on the aggregator operation under the proposed pricing method are also compared and illustrated.

The article contains laboratory data comparing the rate of gasification of five types of woody plants—beech, oak, willow, poplar and rose. The gasification rate was determined thermogravimetrically. Carbon dioxide and steam were used as gasification gases. Willow wood was the most gasifiable, whereas rose wood the least.

Abstract Time series data is a sequence of values recorded systematically over a period which are mostly used for prediction, clustering, and analysis. The two essential features of a time series data are trend and seasonality. Preprocessing of the time series data is necessary for performing prediction tasks. In most of the cases, the trend and the seasonality are removed before applying the regression algorithms. The accuracy of such algorithms depends upon the functions used for the removal of trend and seasonality. Clustering of an unlabeled time series data with the presence of trend and seasonality is challenging. In this paper, we propose a probabilistic representational learning method for grouping the time series data. We introduce five terminologies in our method of clustering namely the trendlets, uplets, downlets, equalets and trendlet string. These elements are the representational building blocks of our proposed method. Experiments on the proposed algorithm are performed with the renewable energy data on the electricity supply system of continental Europe which includes the demand and inflow of renewable energy for the term 2012 to 2014 and UCR-2018 time series archive containing 128 datasets. We compared our proposed representational method with various clustering algorithms using the silhouette score. Mini-batch k-means and agglomerative hierarchical clustering algorithms show better performance in terms of quality, logical accordance with data and time taken for clustering.

Abstract. Wake steering is a technique that optimizes the energy production of a wind farm by employing yaw control to misalign upstream turbines with the incoming wind direction. This work highlights the important dependence between wind direction variations and wake steering optimization. The problem is formalized over time as the succession of multiple steady-state yaw control problems interconnected by the rotational constraints of the turbines and the evolution of the wind. Then, this work proposes a reformulation of the yaw optimization problem of each time step by augmenting the objective function by a new heuristic based on a wind prediction. The heuristic acts as a penalization for the optimization, encouraging solutions that will guarantee future energy production. Finally, a synthetic sensitivity analysis of the wind direction variations and wake steering optimization is conducted. Because of the rotational constraints of the turbines, as the magnitude of the wind direction fluctuations increases, the importance of considering wind prediction in a steady-state optimization is empirically demonstrated. The heuristic proposed in this work greatly improves the performance of controllers and significantly reduces the complexity of the original sequential decision problem by decreasing the number of decision variables.

The study is on the variation of pressure in different faces of rectangular plan-shaped tall buildings with interference conditions due to the presence of another building with the same shape and dimension connected through a multipurpose bridge. The analysis is done on computational fluid dynamics (namely ANSYS CFX) for 0° to 90° wind incidence angle (WIA). The buildings are spaced 60 m from each other. Different cases have been taken and analyzed based on the different positions of the bridge (i.e., 0.33H, 0.5H, and 0.67H) and compared with the isolated case (i.e., buildings without the bridge). A comparison study has been done in the form of interference factor (IF), so we can clearly understand the effect of the bridge at different heights of the building and isolated conditions on rectangular plan-shaped buildings. The study shows that when the bridge is at 0.5H and 0.33H, the force coefficient along X and Y produces similar results to an isolated condition for WIA of 60° and 30°, respectively.

The development of a concrete mixture design process for high-quality concrete production with sustainable values is a complex process because of the multiple required properties at the green/hardened state of concrete and the interdependency of concrete mixture parameters. A new multicriteria decision making (MCDM) technique based on Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) methodology is applied to a fuzzy setting for the selection of concrete mix factors and concrete mixture design methods with the aim towards sustainable concrete quality management. Three objective properties for sustainable quality concrete are adopted as criteria in the proposed MCDM model. The seven most dominant concrete mixture parameters with consideration to sustainable concrete quality issues, i.e., environmental (density, durability) and socioeconomic criteria (cost, optimum mixture ingredients ratios), are proposed as sub-criteria. Three mixture design techniques that have potentiality to include sustainable aspects in their design procedure, two advanced and one conventional concrete mixture design method, are taken as alternatives in the MCDM model. The proposed selection support framework may be utilized in updating concrete design methods for sustainability and in deciding the most dominant concrete mix factors that can provide sustainable quality management in concrete production as well as in concrete construction. The concrete mix factors found to be most influential to produce sustainable concrete quality include the water/cement ratio and density. The outcomes of the proposed MCDM model of fuzzy TOPSIS are consistent with the published literature and theory. The DOE method was found to be more suitable in sustainable concrete quality management considering its applicable objective quality properties and concrete mix factors.