Energy Resources Intelligent Management using on line real-time simulation: A decision support tool for sustainable manufacturing. (15th March 2017)

• Record Type: Journal Article
• Authors: Cassettari, Lucia; Bendato, Ilaria; Mosca, Marco; Mosca, Roberto
• Is Part Of: Applied energy. Volume 190(2017)
• Page Start: 841
• Page End: 851
• Publication Date: 2017-03-15
• Subjects: Sustainable manufacturing -- Renewable energy -- On-line real-time discrete event simulation -- Energy management -- Energy saving -- CO2 reduction; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.01.009
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A new way to manage the self production of energy from RES in manufacturing industry. Optimization in terms of both energy costs and environmental impact (CO2 emissions). The use of on-line real-time Discrete Event Simulation to manage the stochasticity. The energy production plan is dynamically suited to weather and manufacturing needs. The test case presented highlights the effectiveness of the proposed methodology. Abstract: 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.