This paper is the result of a test case that evaluated various options to obtain economical solutions for the daily operation planing for the Enagas pipeline network in Spain. The exercise involved the building of a set of spreadsheet-based models, the automation, and the evaluation of more advanced techniques in search for optimal solutions.
The constant increase in energy demands has led to problems of saturation on pipeline capacities in the Spaniard market. Enagas has made significant reinforcements to their gas transmission system giving a complex dynamic to its network. In light of these infrastructural developments, it is felt that some decision making tools could be implemented to support Enagas planning team to get their objectives minimizing risks. Whereas the physical elements of any hydraulic system are largely automated, the planning and operational tasks are still essentially relying on human experience and expertise. At the same time, management science has been developing solutions to models not covered yet by classical solving algorithms. The present work is part of a study about the feasibility of implementing decision making tools to support the daily operational activities of the gas pipeline company. Enagas team has already made an effort building a model for their planning and management tasks. As a result, a set of spreadsheets is used to model the daily gas volumes for a period of thirty days. The model covers hydraulic system boundaries and contractual restrictions as well. The Enagas operation team has to solve the model on a daily basis in order to find the best feasible solution to the daily changing restrictions. This manual search has had some success over the years and has encouraged Enagas to look for some automation in this field. The present work analyzes some possible solving methods for the Enagas spreadsheet-based model. To explore possibilities some prototypes were built accordingly to the algorithms proposed. The original spreadsheet-based model is analyzed in a progressive way from its simplest form and adding more complex restrictions/boundaries to make it more realistic. The testing process in a simplified controlled environment had a very positive outcome as explained in this work. Additionally some other models were tested in order to attempt a more complete approach. This way, machinery performance and efficiency are also considered when calculating operational costs; moreover, some clues can be obtained after a systematic analysis of the working method in order to improve the pipeline operation in term of monetary savings. The success of these procedures should not mask the principal difficulty that the decision-making team will encounter in the presence of a concrete optimization problem: the choice of an "efficient" method capable of producing an acceptable "quality" solution at the cost of a "reasonable" computing time.
Everyday, the decision makers are confronted with problems of growing complexity in many technical fields, e.g. in operations research, the planning activities behind the operation of pipeline companies, the location of the best