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The adaptation of energy production to demand has been traditionally very important for utilities in order to optimize resource consumption. This is especially true also in microgrids where many intelligent elements have to adapt their behaviour depending on the future generation and consumption conditions. However, traditional forecasting has been performed only for extremely large areas, such as nations and regions. This work aims at presenting a solution for short-term load forecasting (STLF) in microgrids, based on a three-stage architecture which starts with pattern recognition by a self-organizing map (SOM), a clustering of the previous partition via k-means algorithm, and finally demand forecasting for each cluster with a multilayer perceptron. Model validation was performed with data from a microgrid-sized environment provided by the Spanish company Iberdrola. (C) 2014 Elsevier Ltd. All rights reserved.

Abstract The Brazilian electricity system is an important example of a large country relying on a high renewable energy matrix with a major focus on hydropower, which has historically allowed for low carbon electricity production. However, the increase in the demand and climate change impacts on the availability of these renewable resources represent important challenges for long-term power planning. The contribution of this paper is twofold: Firstly, a first attempt to use the EnergyPLAN model for the analysis of the Brazilian electricity sector and in particular to study future scenarios is presented. Secondly, the possibility of achieving a 100% RES system is also addressed. The 100% RES scenario is found to be theoretically possible but a substantial increase in the overall installed capacity would be required, to support the grid mainly during the spring and summer season. The results underline the importance of seasonal complementarity of hydro and wind power and reveal how an increase in RES would add exportation potential, reducing also the Brazilian external energy dependency. The study identifies risk factors for these high RES scenarios and outlines several avenues for future research to address cost, environmental and technical uncertainties of the system.

Abstract Energy harvesting from vibration for low-power electronics has been investigated intensively in recent years, but rotational energy harvesting is less investigated and still has some challenges. In this paper, a methodology for low-speed rotational energy harvesting using piezoelectric transduction and frequency up-conversion is analysed. The system consists of a piezoelectric cantilever beam with a tip magnet and a rotating magnet on a revolving host. The angular kinetic energy of the host is transferred to the vibration energy of the piezoelectric beam via magnetic coupling between the magnets. Frequency up-conversion is achieved by magnetic plucking, converting low frequency rotation into high frequency vibration of the piezoelectric beam. A distributed-parameter theoretical model is presented to analyse the electromechanical behaviour of the rotational energy harvester. Different configurations and design parameters were investigated to improve the output power of the device. Experimental studies were conducted to validate the theoretical estimation. The results illustrate that the proposed method is a feasible solution to collecting low-speed rotational energy from ambient hosts, such as vehicle tires, micro-turbines and wristwatches.

Abstract Radiant floor systems enhanced with Phase Change Materials (PCMs) could achieve significant energy savings while improving the thermal comfort of occupants in lightweight buildings. Effective integration of PCMs typically requires customised solutions based on a comprehensive analysis due to their complex nature. The objective of the present study is the experimental and numerical investigation of a hydronic radiant floor heating system integrated with macroencapsulated PCM. Experimental tests were carried out on a laboratory-scale by the University of Ferrara, Italy, within the H2020 European project IDEAS. A 2D model was then implemented in COMSOL Multiphysics and calibrated in steady as well as in transient state according to the experimental tests. The behaviour of the system, including temperature distribution and heat flux, were analysed under different conditions. The impact of using dry and wet sand, as well as the effect of the position of PCM – above or under heating pipes – on thermal performance, were investigated. Results showed that the use of high thermal conduction in mortar increases much faster the overall performance of the PCM integrated underfloor heating system. Furthermore, the coupling technology with PCM containers installed under piping significantly enhances the positive effect of wet sand.

Abstract An alternative way has been proposed for the PVC-containing medical wastes valorization by co-hydrothermal carbonization (HTC) with lignocellulosic biomass. The organic-Cl in PVC can be converted to the inorganic-Cl via hydrolysis, defunctionalization, recondensation, and aromatization in the HTC process. Followed by the washing process with the condensed water, the inorganic-Cl with high water-solubility could be removed from the solid products (i.e. hydrochar). Lignin as a biomass component can significantly improve the dechlorination efficiency of PVC in the HTC process. Here, the dechlorination performance of lignocellulosic components is given as the following order: lignin > cellulose > hemicellulose. In addition, lignin can adjust the particle sizes of solid products by inhibiting the agglomeration in the order of lignin > hemicellulose > cellulose. In the pilot-scale HTC process, the addition of woodchips improves the dechlorination efficiency of hospital wastes (HW). The hydrochar particles with low-chlorine content and higher heating value could be used as a clean coal-alternative fuel.

This paper proposes a model for short-term electricity load with differentiated temperature and daylight effects by time of day, which are determined by variations in intraday economic activity. The relationship between electricity load and economic activity implies that the electricity demand response to changes in exogenous variables like temperature is non-linear as well as non-homogeneous along the day. The proposed framework, a smooth transition regression model with double threshold (LSTR2), models the observed intraday patterns in load curves to explicitly capture the effect of the circadian rest-activity cycle on the distinct responses of electricity demand to temperature and daylight variations throughout the day. The model shows that the sensitivity of demand to low temperatures is significantly larger in the “active” compared to the “rest” state. If temperatures decrease from 10 °C to 0 °C, electricity demand in the “active” state increases by 960.5 MW h per 1 °C decrease, but by only 26.6 MW h per 1 °C decrease in the “rest” state. When temperatures are higher, in the “rest state” demand decreases by 602.9 MW h per 1 °C if temperature falls from 26 °C to 21 °C, while in the “active” state demand only decreases by 323.6 MW h per 1 °C variation

A new torrefaction model was proposed for predicting solid mass loss in torrefaction as a function of biomass main macromolecular composition and type, as well as on the operating conditions. To do this, solid degradation kinetics were modelled following a 2-successive reaction scheme for each macro-compound and the additive modelling approach through biomass macromolecular component behavior in torrefaction proposed by Nocquet et al. (2014). The use of extracted fractions from different woody and agricultural biomass species (ash-wood, beech, miscanthus, pine and wheat straw) instead of commercial compounds increased the accuracy of the prediction of solid kinetics in biomass torrefaction. The validation of the proposed model with 9 raw biomasses in torrefaction showed an accurate prediction for woods, while the prediction for agricultural biomasses was acceptable.

Abstract This paper is about development and use of a research based stereo vision system for vibration and operational modal analysis on a parked, 1-kW, 3-bladed vertical axis wind turbine (VAWT), tested in a wind tunnel at high wind. Vibrations were explored experimentally by tracking small deflections of the markers on the structure with two cameras, and also numerically, to study structural vibrations in an overall objective to investigate challenges and to prove the capability of using stereo vision. Two high speed cameras provided displacement measurements at no wind speed interference. The displacement time series were obtained using a robust image processing algorithm and analyzed with data-driven stochastic subspace identification (DD-SSI) method. In addition of exploring structural behaviour, the VAWT testing gave us the possibility to study aerodynamic effects at Reynolds number of approximately 2 × 105. VAWT dynamics were simulated using HAWC2. The stereo vision results and HAWC2 simulations agree within 4% except for mode 3 and 4. The high aerodynamic damping of one of the blades, in flatwise motion, would explain the gap between those two modes from simulation and stereo vision. A set of conventional sensors, such as accelerometers and strain gauges, are also measuring rotor vibration during the experiment. The spectral analysis of the output signals of the conventional sensors agrees the stereo vision results within 4% except for mode 4 which is due to the inaccuracy of spectral analysis in picking very closely spaced modes. Finally, the uncertainty of the 3D displacement measurement was evaluated by applying a generalized method based on the law of error propagation, for a linear camera model of the stereo vision system.