## 5 Conclusions and Future Works  
Energy consumption is one of the criteria for determining the level of development and quality of life in a country. The continuity of energy supply and ensuring long-term access to resources have required a comprehensive energy plan. Energy carriers are one of the critical topics in the field of energy operation. Thus, this paper presents a methodology called Energy Commitment (EC) for the optimized operation of energy carriers using the Following Optimization Algorithm (FOA).  
The goal in EC is determination of the most appropriate pattern of using energy carriers to supply energy demand considering technical and economic issues. EC is a constrained optimization problem that can be solved using various methods, especially the optimization algorithms. In this paper, the FOA algorithm has been applied to solve the EC problem.  
Figure 2: Convergence curve of Following Optimization Algorithm (FOA).  
EC has been implemented on an energy grid with 10 power plants and including different consumption sectors of transportation, agriculture, industrial, residential, commercial, and general. Important outputs and information have been obtained by the implementation of an EC study on this energy network. This information and output signify the need for different energy carriers to meet the energy demand, the optimal combination and production of units (UC and EC outputs), and the amount of imports and exports of energy carriers.  
Various combinations of power plants are available to supply the electricity demand for every hour of operation. According to the different fuel inputs to each power plant, different combinations of energy carriers are obtained. The appropriate combination of power plants and proper energy combination carriers to supply the energy consumption has been determined using the FOA. The FOA has a good performance in solving the EC problem by presenting a smooth convergent curve and achieving the optimal solution in the appropriate number of iterations.  
The simulation results of the proposed study on the energy grid show that the EC analyzes the energy grid more efficiently and accurately than the UC. UC only studies the electrical energy network, while the EC operates energy carriers in an integrated energy network with energy demand for different energy carriers. Therefore, UC is one of the important outputs of the EC study and in fact, UC is a subset of the EC study.  
The authors suggest implementing EC on various energy networks and scenarios, as well as using other optimization techniques for future studies. Ideas for further studies and future research include the introduction of new objective functions with new constraints such as the impact of CO\({}_{2}\) and EC solving considering the response time required by each energy storage/carrier.  
Conceptualization, M.D., M.M., R.A.R.-M., R.M.-M., A.A. and J.M.G.; methodology, M.D.; software, M.D.; validation, J.M.G., J.M., R.A.R.-M., R.M.-M., A.A. and O.P.M.; formal analysis, R.M.-M., O.P.M.; investigation, M.D. and O.P.M.; resources, J.M.G.; data curation, A.A.; writing--original draft preparation, M.D. and M.M.; writing--review and editing, O.P.M., R.A.R.-M., R.M.-M., A.A. and J.M.G.; visualization, M.D.; supervision, M.M.; project administration, M.D. and M.M.; funding acquisition, R.A.R.-M., R.M.-M. All authors have read and agreed to the published version of the manuscript.  
The current project was funded by Tecnologico de Monterrey and FEMSA Foundation (grant CAMPUSCITY project).  
The authors declare no conflict of interest. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.