Coordination of optimal sizing of energy storage systems and production buffer stocks in a net zero energy factory. (15th March 2019)

• Record Type: Journal Article
• Authors: Caro-Ruiz, C.; Lombardi, P.; Richter, M.; Pelzer, A.; Komarnicki, P.; Pavas, A.; Mojica-Nava, E.
• Is Part Of: Applied energy. Volume 238(2019)
• Page Start: 851
• Page End: 862
• Publication Date: 2019-03-15
• Subjects: Net zero energy factories -- Flexibility options -- Energy storage systems -- Optimal manufacturing scheduling -- Production buffer stock -- Renewable energy sources; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.01.125
• Languages: English
• ISSNs: 0306-2619

Abstract

Graphical abstract: Highlights: Evaluating the degree of flexibility of a manufacturing process. Developing optimization controlling algorithms for matching PV generation. Sizing of the electric energy storage system and of production buffer stock. Evaluating the dependence of the size of added flexibility options on the NZEF system. Abstract: Green smart factories will be one of the key elements of the future smart grids. They will play an active role in managing the volatility of power generated by renewable energy sources (RES). In order to do that, the green smart factories must set up energy management systems to control the production processes according to both the on-site power generation by RES and external signals sent by the system operators. The aim of the study is to size energy storage systems and production buffer stocks as the flexibility options, allowing the highest integration of power generated by volatile energy within a manufacturing system. The concept of the net zero energy factory is considered. Algorithms for coordinating the flexibility options optimally have been developed. These algorithms are based on mixed-integer linear programming and aim to maximize the matching between RES power generation and flexible demand. A case study is used to verify the framework results. Key performance indices, such as the system self-sufficiency index and the system self-consumption index have been considered for evaluating the solutions developed. Finally, sensitivity analysis has been performed to assess the robustness of the solutions found.