On the definition of part-load operation strategies in a complex trigeneration system with hourly-seasonal demands: Exergoeconomics and optimization

Abstract

A prominent oil & gas research institute initially met all energy demands (electricity, heat, and refrigeration) with two mechanical vapor compression chillers and a gas boiler, with high electrical demand from the grid and two standby diesel generators (for emergencies). Then new technologies were added, completing the installed capacity of the trigeneration system by including two natural gas cogeneration modules, three different absorption chillers, two gas microturbines and one fuel cell. An R&D study employed exergoeconomics to subsidize decision-making regarding the viability of: i) the trigeneration system meeting all energy demands; ii) definition of the operational strategy; iii) adjust the electrical demand from the grid, and iv) definition of maintenance strategy. Although the objectives were achieved, the method employed did not enable an automatic process of definition of operation strategies, relying on the simulation and exergoeconomic skills of the operator. This limitation was overcome using Excel-based optimization. This study presents, discusses, and compares the procedures and results of the operation strategies for the trigeneration system, using both exergoeconomics and optimization. Optimization used the generalized reduced gradient within MS Excel. Although the results obtained from the exergoeconomic and optimization approaches were very close, the operation strategies defined by optimization resulted in lower operational costs in almost all cases. The slight differences can be due to premature convergence of optimization or imprecision in the operator’s judgment and decision. Both methods showed that the research center should adjust its electricity contract with the grid, reducing the contracted power by more than 55%. This leads to savings of more than USD 21,000/month. Although both methods (exergoeconomics and optimization) were helpful to draw the main conclusions, the optimization method allows for an automatic procedure in a user-friendly tool (MS Excel), which is less dependent on the analyst’s expertise.