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[EN] Renewable energies are a central element in the search for energy sustainability, so they are becoming a substantial component of the energy scenario of every country, both as systems connected to the grid or in stand-alone applications. Feasibility of these renewable energy systems could be necessary not only in their application in isolated areas, but also in systems connected to the grid, in this last case when their contribution reaches a substantial fraction of the total electricity demand. To overcome this reliability problem, hybrid renewable systems could become essential and activities to optimize their design should be addressed, both in the simulation and in the experimental areas. In this paper, a laboratory to simulate and verify the reliability of hybrid renewable systems is presented and its application to the feasibility analysis of multicomponent systems including photovoltaic panels, wind generator and biomass gasification plant, plus energy storage in a battery bank, are described.

Abstract Breastshot water wheels are gravity hydraulic machines employed in low head sites. The scope of this work is to test the performance of a breastshot water wheel with two geometric inflow configurations: a sluice gate at different openings and two vertical overflow weirs. With the sluice gate, the maximum efficiency of the plant is 75%, constant over a wide range of flow rates, while the efficiency with the weir is increasing in the same flow rate range. Therefore, the wheel with the weir can exploit higher water volumes, and also it performs better at high power input. In practical applications, the inflow configuration can be effectively controlled to optimize the operative working conditions of breastshot water wheels, depending on the external hydraulic ones. The experimental results are also discussed in dimensionless terms, in order to support engineers in the design of similar breastshot water wheels.

Abstract Probability density functions (PDFs) are normally used to describe wind speed distribution for the proper selection of wind turbines in a given location. The identification of a suitable PDF is fundamental for accurately assessing the wind energy potential and designing the wind farms. To achieve this objective, the use of a mixture of two truncated normal distributions (MTTND), defined for v ≥ 0 and obtained by linearly combining two normal distributions with different means and variances, is proposed in this work for the representation of the wind speed PDF. The distribution is a function of five parameters, does not require a high computational burden and allows the representation of wind calm hours (v = 0). The use of the MTTND allows an accurate estimation to be obtained of the experimental discrete distribution of the probability density and cumulative probability, and the characteristic statistical quantities used to estimate the available energy and the performance indicators in the selection of both the site and wind turbine. The validity of the use of the MTTND was verified by comparison with the most widespread PDFs in the scientific literature: Weibull, Rayleigh, lognormal, gamma, inverse Gaussian and Burr. This comparison was developed using experimental wind speed data relating to five Italian locations and a location in Colorado (USA) belonging to the National Renewable Energy Laboratory. For each location, the parameters of each PDF were obtained with the least squares non-linear regression method. The results of the comparisons, in terms of the coefficient of determination R2 and root mean square error (RMSE) for goodness of fit and in terms of relative error in the calculation of the statistical quantities, show that the use of the MTTND gives rise to greater accuracy than a conventional wind speed PDF.

Abstract The Ria de Muros is a large coastal embayment on the north-western coast of Spain in which the peak tidal currents exceed 2 m/s. It is therefore a promising site for tidal stream power. The key point when assessing this resource is the accurate estimation of the tidal currents. In this work a finite difference numerical model is used for this purpose. The model solves the vertically integrated Navier–Stokes hydrodynamics and transport equations. It is validated with in situ velocity measurements performed by means of an acoustic Doppler current profiler (ADCP). Thereafter the tidal flow velocities and the corresponding power densities are computed. The largest values are found in a section of the inner ria, in which two points are selected for a detailed assessment of the resource. The model is then run to compute the tidal stream and the corresponding power density at these points during a 14-day period, so as to cover the spring–neap cycle. The power density curve thus obtained is numerically integrated to compute the annual energy output that can be obtained by a tidal stream power plant at each location.

Abstract Solar energy, as main energy supply that sustains life on Earth, is also an unavoidable component of the complex strategy in achieving a clean and fair energy transition and goals for sustainable development by 2030. The present work studies the potential of installing Photovoltaic Distributed Generation at Universidad Politecnica de Madrid – Ciudad Universitaria campus. To this end, the study focuses on the electricity generation, carbon reduction and economic feasibility of solar photovoltaic systems installation using and comparing two different approaches based on data input with different time resolution, simulation software and level of details. Results show that the optimal photovoltaic power that maximizes emissions savings also ensures the best economic return, and in addition coincides with the maximum solar potential of the Campus, which is about 3.3 MW. At campus level, approximately 77% of the photovoltaic electricity production would be consumed locally, which would suppose a coverage of about 40% of the total electricity consumption. Emissions savings could reach 30% and an in-depth economic analysis indicates that the project is highly profitable. These results and methodology could be used to assess the feasibility of photovoltaic systems at other universities and help entities study the solar potential of their buildings.

This work proposes a new empirical direct methodology to estimate both the solar flux distribution and intensity on the surface of central receivers. In solar power tower plants with deteriorated heliostats, the numerical simulations to estimate the incident solar flux are not precise. Hence the thermal behaviour of the receivers cannot be determined. In those cases, direct measurement or semi-empirical methodologies are required to characterize the radiant power on the receiver. The new methodology proposed, named "Superposition method", consists in the hourly characterization of the reflected solar beam of each individual heliostat by means of a pyrheliometer, a passive screen, a flux sensor, a camera and digital image analysis. According to the aiming strategy used during receiver operation, each individual solar flux distribution and intensity can be gathered to obtain the total incident radiant power on the solar receiver. This non-real-time method has the advantage of reproducing any solar flux distribution on the receiver at present and past time.

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.