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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 A series of Ultraviolet-B broadband solar irradiance, 280–315 nm, measured during the period 2002–2011 in Valladolid (Spain) is analysed. UV-B daily values follow the pattern of the solar elevation angle. Daily maximum value occurred in June, 50.29 kJ m −2 , and minimum, 0.88 kJ m −2 , in December. The total accumulated UV-B irradiation along a mean year reached 7.1 MJ m −2 . The elemental statistical characteristics of hourly and daily irradiation show that the inter-quartile range is small in winter and increases in spring; maximum stability in UV-B takes place at solar noon and around summer; it can be concluded that this maximum may be considered representative of the UV-B irradiance values. The monthly-integrated UV-B irradiation values show a large annual cycle with a maximum in July, when the influence of the annual ozone column diminishes and summer solstice occurs. The results show that the harmonic analysis permits constructing long-term monthly UV-B values under clear and all sky conditions and from low number of parameters. The UV-B percentage attenuated by the atmosphere increases from winter to summer, a maximum value of 8% is obtained in summer and a minimum of 2% in winter. The cloudiness effects on the surface solar UV-B radiation are strong during the winter months, being the cloud transmittance 0.7 in December and 0.94 in July.

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.

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.

Some simplified correlations of mean hourly global and diffuse luminous efficacy on the horizontal plane for all sky conditions have been obtained for Arcavacata di Rende (Italy) and have been compared with other literature models and other experimental data measured in Geneva (Switzerland), Vaulx-en-Velin (France), Bratislava (Slovakia) and Osaka (Japan). The comparisons show that, for global efficacy, the differences among the various models are not significant, and the use of a model with a constant value of efficacy gives good predictions of global illuminance. For the prediction of diffuse illuminance the differences among the models are larger, but the use of a constant diffuse efficacy provides a good first estimate of diffuse illuminance.

Abstract The authors show an application of the multicriteria decision-making methodology used to assess an action plan for the diffusion of renewable energy technologies at regional scale. This methodological tool gives the decision-maker considerable help in the selection of the most suitable innovative technologies in the energy sector, according to preliminary fixed objectives. In this paper, a case study is carried out for the island of Sardinia. This region presents, on one hand, a high potential for energy resources exploitation, but on the other hand, it represents a specific case among other Italian regions, because of its socio-economic status and history. Three decision scenarios have been supposed, each one representing a coherent set of actions, on the basis of which strategies of diffusion are developed.

Abstract This paper proposes a new methodology for determining the geometrical parameters and the optimal distribution of small-scale linear Fresnel reflectors on flat roofs of urban buildings. This engineering problem is highly complex as it involves 21 variables; in contrast to present-day studies available in the literature, our method provides a global solution. The algorithm involves several stages, in which it uses multiple objective functionals: maximization of the used area of the roof, minimization of the cost, minimization of losses. The solution found by this method combines all the relevant aspects from the technical and/or economic point of views and obtains, as the final objective, the maximization of the absorbed annual energy for the stated problem.