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Abstract To achieve the EU climate and energy objectives, a transition towards a future sustainable energy system is needed. The integration of the huge potential for industrial waste heat recovery into smart energy system represents a main opportunity to accomplish these goals. To successfully implement this strategy, all the several stakeholders' conflicting objectives should be considered. In this paper an evolutionary multi-objective optimization model is developed to perform a sustainability evaluation of an energy system involving an industrial facility as the waste heat source and the neighbourhood as district heating network end users. An Italian case study of heat recovery from a steel casting facility shows how the model allows to properly select the district heating network set of users to fully exploit the available waste energy. Design directions such as the thermal energy storage capacity can be also provided. Moreover, the model enables the analysis of the trade-off between the stakeholders’ different perspectives, allowing to identify possible win-win solutions for both the industrial sector and the citizenship.

Changes in the main weather features are becoming perceivable in Italy. In the last years, the average temperature and the intensity of rainstorms are increasing. Such phenomena could lead to changes in the earth-atmosphere interaction. Accounting for the high vulnerability of the Italian territory, an obvious consequence would concern the effects on the hydro-geological hazards, including landslides and floods. As a matter of fact, the higher frequency of high magnitude flood and landslide events represent an alarm bell. It is necessary to explore with all available instruments the consequences of the potential climate changes. Since quantitative predictions are not possible, the only way to run is depicting rational scenarios for the most vulnerable contexts adopting a multidisciplinary approach. Based on projections of potential climate changes in the 21st century, the report examines their potential impact in the context of the Italian peninsula. After a general overview about typical slope responses to weather changes, some quantitative scenarios have been depicted for representative geomorphological contexts. The last part of the paper examines changes in the expected risk, based on the characteristics of the Italian territory and its vulnerability, looking at the initiatives to undertake for its mitigation.

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.

The crucial role evaluation can play in the co-development of project design and its implementation will be addressed through the analysis of a case study, the Green Communities (GC) project, funded by the Italian Ministry of Environment within the EU Interregional Operational Program (2007-2013) "Renewable Energy and Energy Efficiency". The project's broader goals included an attempt to trigger a change in Italian local development strategies, especially for mountain and inland areas, which would be tailored to the real needs of communities, and based on a sustainable exploitation and management of the territorial assets. The goal was not achieved, and this paper addresses the issues of how GC could have been more effective in fostering a vision of change, and which design adaptations and evaluation procedures would have allowed the project to better cope with the unexpected consequences and resistances it encountered. The conclusions drawn are that projects should be conceived, designed and carried out as dynamic systems, inclusive of a dynamic and engaged evaluation enabling the generation of feedbacks loops, iteratively interpreting the narratives and dynamics unfolding within the project, and actively monitoring the potential of various relationships among project participants for generating positive social change.

Abstract Economically harvesting energy from a microbial fuel cell (MFC), increasing its electrical power production, and developing its role as a practical energy supply, needs a low-cost and high-performance design of the MFC compartments. According to this strategy, a novel monolithic membrane electrode assembly (MEA) was fabricated and evaluated as an air–cathode in a single-chamber MFC (SCMFC). The MEA was made of bacterial cellulose (BC), conductive multi-walled carbon nanotubes (CNT), and nano-zycosil (NZ). BC, as a nano-celluloses with oxygen barrier property, can maintain anaerobic conditions for the anode compartment. Binder-less CNT coating on BC avoids costly binders such as poly-tetra fluoro ethylene (PTFE) and Nafion and decreases the MEA charge transfer resistance. NZ, as a very cheap modifier, not only prevents the anolyte leakage but also provides more MEA’s active sites for the oxygen reduction reaction (ORR). The electrochemical performance of the MEA was compared to a PTFE- based gas diffusion electrode (GDE) in the SCMFC. The MEA cell provided a pulse power density of 1790 mW/m2, roughly twice as high as the pulse power density of GDE (920 mW/m2). SCMFC’s internal resistance decreased from 1.84 KΩ (with GDE) to 0.8 KΩ (with MEA). Also, the cell’s columbic efficiency increased from 4.2% (with GDE) to11.7% (with MEA). Additionally, the capacitance of the MEA (65 mF) was much higher than the value for GDE (0.73 mF). Thus, the MEA compared to the GDE showed higher performance in the SCMFC for electricity generation and wastewater treatment at a lower cost.

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 Reaching universal access to electricity by 2030 requires a massive deployment of mini-grids in rural areas of developing countries. Among the many challenges hindering this process, there are the high uncertainties in assessing demand patterns in rural communities, the costs of field survey campaigns, and the absence of ample and reliable datasets coming from existing projects. This paper tries to address these issues by presenting and discussing a database of load profiles from sixty-one off-grid mini-grids from developing countries worldwide, gathered from the literature, private developers and fieldworks, and reported with technical, socio-economic and geographical characterization factors. A clustering procedure led to the identification of five archetypal load profile clusters, which are presented and analyzed together with their load duration curves. Subsequently, the distribution among the clusters of the various characterization factors selected is studied. The proposed approach allows to widen the range of load profiles usually considered, and to seek correlations between the load profile shapes, the peak power and average energy consumption per connection, the number of customers, the age of measurement, geographical position, operator model, type of tariff and generation technologies present. This work establishes a first step in the creation of a shared database for load profiles of rural mini-grids, helping to overcome the lack of available data and difficulties of demand assessment, proposing original insights for researchers to understand load patterns, and contributing to reduce risks and uncertainties for mini-grid developers.

Urea is the most common nitrogen (N) fertilizer in agriculture, due to its cheaper price and high N content. Although the reciprocal influence between NO3- and NH4+ nutrition are well known, urea (U) interactions with these N-inorganic forms are poorly studied. Here, the responses of two tomato genotypes to ammonium nitrate (AN), U alone or in combination were investigated. Significant differences in root and shoot biomass between genotypes were observed. Under AN+U supply, Linosa showed higher biomass compared to UC82, exhibiting also higher values for many root architectural traits. Linosa showed higher Nitrogen Uptake (NUpE) and Utilization Efficiency (NUtE) compared to UC82, under AN+U nutrition. Interestingly, Linosa exhibited also a significantly higher DUR3 transcript abundance. These results underline the beneficial effect of AN+U nutrition, highlighting new molecular and physiological strategies for selecting crops that can be used for more sustainable agriculture. The data suggest that translocation and utilization (NUtE) might be a more important component of NUE than uptake (NUpE) in tomato. Genetic variation could be a source for useful NUE traits in tomato; further experiments are needed to dissect the NUtE components that confer a higher ability to utilize N in Linosa.

Abstract A new generalized lamp model capable of simulating a lamp’s luminous flux in the time domain is presented. Reference is made to low power lamps (up to 25 W). The generalized lamp model consists of a model of the electrical circuit of the lamp and a model of the bulb itself and is based on the use of only three input parameters. The results of a preliminary experimental analysis are first presented with the aim of justifying the hypotheses of the model. Then, the model is described and used to analyze the frequency behavior of a lamp in the presence of voltage fluctuations; the model was experimentally validated for different kind of lamps. Finally, the results of a sensitivity analysis of the model versus its main parameters are presented.

Abstract Off-grid systems based on PV and batteries are becoming a solution of great interest for rural electrification. Nevertheless, sizing these systems is not straightforward since it means matching unpredictable energy sources with uncertain demands while providing the best reliability and costs. In our opinion, the effect of users’ energy consumptions uncertainty on the sizing of these systems has not been appropriately investigated. This paper addresses this issue and analyzes the effect of load profile uncertainty on the off-grid PV systems optimum design. Specifically a novel sizing methodology has been introduced based on: (i) an effective approach of modelling rural energy needs; (ii) an innovative stochastic method which formulates different possible realistic daily load profiles for un-electrified rural areas; (iii) a PV-battery techno-economic analysis via steady-state simulation; (iv) the evaluation of the optimum system sizing via a numerical method based on net present cost and loss of load probability. Finally, the proposed methodology has been applied to find the optimum size of an off-grid PV system for a peri-urban area of Uganda. The results show that the optimum system configurations are significantly affected by load profiles; consequently an approach to identify the robust solution with regards the assumed uncertainty is proposed.