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Abstract Fuel cell based trigeneration plants, that utilize absorption chillers to convert waste heat into cooling energy, are a promising technology to satisfy heat, power, and cooling demand in warm climates. Polymer electrolyte membrane fuel cells, that operate at low temperature ( 100 ° C), are the most technologically mature among the several types of fuel cells. Thermally activated cooling technologies are widely utilized in trigeneration plants to improve their efficiency. However, absorption chillers require relatively high grade thermal energy and their coupling with low temperature fuel cells is relatively untapped. Herein, we perform a techno-economic analysis of a trigeneration plant based on low temperature polymer electrolyte membrane fuel cells and half-effect absorption chillers. A thermo-chemical model is developed to estimate the performance of a cogeneration plant based on low temperature fuel cells and of the half-effect absorption chiller. The behavior of such combined heat, cooling, and power plant is also analyzed within real energy management scenarios, considering different energy demands, climatic conditions, energy costs, and plant layouts. The control strategy of the power plant is optimized through a graph-based methodology previously developed and validated by the authors. Total energy cost and CO2 emissions are then compared to those of a reference scenario where electricity is acquired from the distribution grid, thermal energy is produced through a natural gas boiler, and a mechanical chiller is used for cooling. The results show that the utilization of half-effect absorption chillers boosts the environmental and economic benefits for all the considered scenarios. We also demonstrate that the utilization of the absorption chiller reduces the imbalance between the results obtained for the different scenarios (i.e. climates), although economic and environmental benefits associated to distributed generation are strongly influenced by the energy context.

Cogeneration power plants based on fuel cells are a promising technology to produce electric and thermal energy with reduced costs and environmental impact. The most mature fuel cell technology for this kind of applications are polymer electrolyte membrane fuel cells, which require high-purity hydrogen. The most common and least expensive way to produce hydrogen within today's energy infrastructure is steam reforming of natural gas. Such a process produces a syngas rich in hydrogen that has to be purified to be properly used in low temperature fuel cells. However, the hydrogen production and purification processes strongly affect the performance, the cost, and the complexity of the energy system. Purification is usually performed through pressure swing adsorption, which is a semi- batch process that increases the plant complexity and incorporates a substantial effi- ciency penalty. A promising alternative option for hydrogen purification is the use of se- lective metal membranes that can be integrated in the reactors of the fuel processing plant. Such a membrane separation may improve the thermo-chemical performance of the en- ergy system, while reducing the power plant complexity, and potentially its cost. Herein, we perform a technical analysis, through thermo-chemical models, to evaluate the inte- gration of Pd-based H2-selective membranes in different sections of the fuel processing plant: (i) steam reforming reactor, (ii) water gas shift reactor, (iii) at the outlet of the fuel processor as a separator device. The results show that a drastic fuel processing plant simplification is achievable by integrating the Pd-membranes in the water gas shift and reforming reactors. Moreover, the natural gas reforming membrane reactor yields signifi- cant efficiency improvements. This project has received funding from the Fuel Cells and Hydrogen Joint Undertaking under grant agreement No. 671396. This Joint Undertaking receives support from the Eu- ropean Unions Horizon 2020 research and innovation pro- gram and United Kingdom, Germany, Greece, Croatia, Italy, Switzerland, Norway. Swiss partners are funded by the State Secretariat for Education, Research and Innovation of the Swiss Confederation

Proton exchange membrane fuel cells are a promising and mature technology for combined heat and power plants. High efficiency (in particular for small size devices), practically zero pollutant emissions, noiseless operation and fast response to transient demand make these energy systems excellent prime movers for residential and commercial application. Nevertheless, due to large capital costs, their utilization and commercialization are still limited to demonstrative projects. In this scenario we are working on a research project, called AutoRe, which utilizes an automotive derivative fuel cell for a cogeneration plant to create a synergy between two non competitive industries (automotive and stationary plants) and to realize a significant economy of scale that will drastically cut the costs of fuel cell based cogenerative plants. In this paper we perform a thorough techno-economic analysis of the AutoRe (AUTomotive deRivative Energy system) power plant. A number of realistic energy management scenarios are constructed by varying the energy demand, the climatic condition, the energy cost, and the efficiency of the surrounding energy system. The control strategy is determined on an hourly basis, by minimizing the cost or the primary energy consumption through a graph based methodology. The resulting global parameters are compared to a reference scenario where electricity is acquired from the grid and heat is locally produced through a natural gas boiler. We consider 5 different building types (Office, Apartment district, Clinic, Hotel, Supermarket), 5 different climatic conditions (Hot, Cooling Based, Moderate, Heating based, Cold), and 2 differ- ent surrounding energy systems (USA and Europe). The results show that overall the proposed plant is economically sustainable and effective in reducing the energy costs and the primary energy consumption. Nevertheless, the building type and the energy prices impact on the return on investment, while the climatic condition affects the relative cost and energy variations. In the US scenario the management based on cost and primary energy minimization exhibits similar patterns. On the contrary, in Europe cost minimization might increase the primary energy consumption with respect to the reference scenario. This project has received funding from the Fuel Cells and Hydrogen Joint Undertaking under grant agreement N◦ 671396. This Joint Undertaking receives support from the European U- nion’s Horizon 2020 research and innovation programme and United Kingdom, Germany, Greece, Croatia, Italy, Switzerland, Norway. Swiss partners are funded by the State Secretariat for Education, Research and Innovation of the Swiss Confederation.

Abstract Distributed generation and, in particular, cogeneration and trigeneration are generally considered viable solutions to reduce energy consumption and mitigate the environmental impact of developed economies. Nonetheless, such systems need to be carefully designed and managed to effectively meet all the economic and environmental expectations. The design of a distributed generation plant and the choice of its proper management policy are complex tasks that require effective support methodologies and tools. In this paper, we develop a methodology to determine the optimal control strategy for a trigeneration plant. The model enforces mass end energy balances and accounts for the nonlinear and the basic dynamic behavior of each energy converter, for the time varying energy prices and environmental conditions, for maintenance and cold start costs, and for the possibility to store energy. We built on a methodology previously developed and we dramatically broaden its field of application to complex smart grids with a very high temporal detail, by cutting down its computational costs. To this aim, we implement an heuristic procedure that reduces the computational complexity of the non linear optimization problem. The total cash flow, the primary energy consumption, the plant efficiency, and the CO 2 emissions, besides the instantaneous set-point of the plant, are among the most relevant results of the model. The model is first validated through 11 test-cases specifically designed to stress the possible weaknesses of the heuristic procedure. The validation evidences that the proposed procedure does not introduce further approximations to the mathematical model. The global optimum is retrieved for all the considered cases. Afterwards, we apply the proposed methodology to a realistic energy management scenario: the assessment of a fuel cell based trigeneration plant for a civil building for a whole year. The discussion highlights the effectiveness of the proposed method for different applications including the optimization of the control strategy for existing plants, the design of new distributed generation systems, the assessment of innovative energy conversion technologies, and the evaluation of national energy policies.

Abstract This paper presents an application of Airborne Laser Scanning (ALS) data in conjunction with an IRS LISS-III image for mapping forest fuel types. For two study areas of 165 km 2 and 487 km 2 in Sicily (Italy), 16,761 plots of size 30-m × 30-m were distributed using a tessellation-based stratified sampling scheme. ALS metrics and spectral signatures from IRS extracted for each plot were used as predictors to classify forest fuel types observed and identified by photointerpretation and fieldwork. Following use of traditional parametric methods that produced unsatisfactory results, three non-parametric classification approaches were tested: (i) classification and regression tree (CART), (ii) the CART bagging method called Random Forests, and (iii) the CART bagging/boosting stochastic gradient boosting (SGB) approach. This contribution summarizes previous experiences using ALS data for estimating forest variables useful for fire management in general and for fuel type mapping, in particular. It summarizes characteristics of classification and regression trees, presents the pre-processing operation, the classification algorithms, and the achieved results. The results demonstrated superiority of the SGB method with overall accuracy of 84%. The most relevant ALS metric was canopy cover, defined as the percent of non-ground returns. Other relevant metrics included the spectral information from IRS and several other ALS metrics such as percentiles of the height distribution, the mean height of all returns, and the number of returns.

Repeated burning in a short return period (< 3–5 years) may significantly contribute to land degradation in Mediterranean countries. We apply logistic regression analysis to model spatial relationships between reburning and site (topographic) and landscape-level factors (land use pattern); forest areas burned twice at least between 2006 and 2009 in the Italian peninsula are used as a case study. Few landscape factors prove to be correlated with the probability of fire recurrence, and the overall logistic regression model explains 24% of the variation in the fire recurrence. This fact is not surprising since human factors are known to be the biggest determinant of wildfire events in Mediterranean countries such as Italy. Nonetheless, findings from this exploratory study prove to be qualitatively helpful to identify at least landscape drivers, easily quantifiable and available on a national scale, significantly affecting short-term fire recurrence (i.e. slope roughness, exposure, distance from the nearest water body, pre-fire dominant forest type). L'articolo è disponibile sul sito dell'editore http://www.tandfonline.com

Wildfires are the most common disturbances in Mediterranean forest ecosystems that cause significant emissions of greenhouse gases as a result of biomass burning. Despite this, there is reasonably high uncertainty regarding the actual fraction of burnt biomass and the related CO2 and non-CO2 gas emissions released during forest fires. The aim of this paper is to compare existing methodologies adopted in the National Greenhouse Gas Inventory reports of five of the most fire-affected countries of southern Europe (Italy, Spain, Greece, Portugal, France) with those proposed in the literature, to operationally estimate forest fire emissions, and to discuss current perspectives on reducing uncertainties in reporting activities for the Land Use, Land Use Change and Forestry sector under the United Nations Framework Convention on Climate Change and the Kyoto Protocol. Five selected approaches have been experimentally applied for the estimation of burnt biomass in forest fire events that occurred in Italy in the period 2008–2010. Approaches based on nominal rates of biomass loss can lead to an overly conservative value or, conversely, to underestimation of the fraction of burnt biomass. Uncertainties can be greatly reduced by an operational method able to assess inter-annual and local variability of fire effects on fire-affected forest types. L'articolo è disponibile sul sito dell'editore http://www.publish.csiro.au