At a historic time when the eco-sustainability of industrial manufacturing is considered one of the cornerstones of relations between people and the environment, the use of energy from Renewable Energy Sources (RES) has become a fundamental element of this new vision. After years of vain attempts to hammer out an agreement to significantly reduce CO2 emissions produced by the burning of fossil fuels, a binding global accord was finally reached (Paris December 2015-New York April 2016). As we know, however, some of the most commonly-used RES, such as solar or wind, present the problem of discontinuity in energy production due to the variability of weather and climatic conditions. For this reason, the authors thought it appropriate to study a new methodology capable of marrying industrial users' instantaneous need for energy with the production capacity of Renewable Energy Sources, supplemented, when necessary, by energy created through self-production and possibly acquired from third-party suppliers. All of this in order to minimize CO2 emissions and company energy costs. Given the massive presence of stochastic and sometimes aleatory elements, for the proposed energy management model we have used both Monte Carlo simulation and on-line real-time Discrete Event Simulation (DES), as well as appropriate predictive algorithms. A test conducted on a tannery located in southern Italy, equipped with a 700 KWp photovoltaic installation, showed extremely interesting results, both economically and environmentally. In particular the application of the model permitted an annual savings of several hundreds of thousands of euros in energy costs and a comparable parallel reduction of CO2 emissions. The systematic use of the proposed approach, gradually expanded to other manufacturing sectors, could result in very consistent benefits for the entire industrial system.

Combining technological solutions with investment profitability is a critical aspect in designing both traditional and innovative renewable power plants. Often, the introduction of new advanced-design solutions, although technically interesting, does not generate adequate revenue to justify their utilization. In this study, an innovative methodology is developed that aims to satisfy both targets. On the one hand, considering all of the feasible plant configurations, it allows the analysis of the investment in a stochastic regime using the Monte Carlo method. On the other hand, the impact of every technical solution on the economic performance indicators can be measured by using regression meta-models built according to the theory of Response Surface Methodology. This approach enables the design of a plant configuration that generates the best economic return over the entire life cycle of the plant. This paper illustrates an application of the proposed methodology to the evaluation of design solutions using an innovative linear Fresnel Concentrated Solar Power system

The idea of a methodology capable of determining in a precise and practical way the optimal sample size came from studying Monte Carlo simulation models concerning financial problems, risk analysis, and supply chain forecasting. In these cases the number of extractions from the frequency distributions characterizing the model is inadequate or limited to just one, so it is necessary to replicate simulation runs many times in order to obtain a complete statistical description of the model variables. Generally, as shown in the literature, the sample size is fixed by the experimenter based on empirical assumptions without considering the impact on result accuracy in terms of tolerance interval. In this paper, the authors propose a methodology by means of which it is possible to graphically highlight the evolution of experimental error variance as a function of the sample size. Therefore, the experimenter can choose the best ratio between the experimental cost and the expected results.

Abstract

The global financial crisis of 2007-2008 has highlighted the importance of a correct pricing of the so-called financial derivatives. Analyzing the methodology of pricing of non-listed derivatives by using the Monte 6168Ilaria Bendato et al.Carlo method, the Authors have realized that the determination of the sample size is not managed properly. This is because the research offices of banks rely on, as suggested by the literature of the field and technical manuals for practitioners, a standard number of simulation runs, by rules of thumb, between 1,000 and 10,000. The consequence is that financial institutions lead to financial statements fair values with no knowledge of its fluctuation band and the robustness of the result. Conscious of this practice, the Authors, dealing from a long time to the topic of output reliability in applications of discrete event simulation and Monte Carlo simulation, address the problem through the use of a methodology based on the control of Mean Pure Square Error (MSPE), already successfully tested in other contexts. Thanks to the proposed approach, applied for pricing complex derivatives, it is possible to determine the size of the experimental sample in order to ensure a pre-assigned degree of reliability of the output results.