Proactive day-ahead data center operation scheduling for energy efficiency: Solving a MIOCP using a multi-gene genetic algorithm

This paper addresses the problem of Data Centers (DC) energy efficiency by proposing a proactive optimization technique to schedule the day-ahead DC operation to minimize the operational cost. The proactive optimization technique is formalized as a Mixed Integer Optimal Control Problem, known to be NP-hard. Because the time needed for solving this problem by some of the gradient-based solvers depends on the input data, an evolutionary algorithm based solver that computes an approximate solution in a constant number of steps is proposed. The proactive DC optimization technique is implemented using the Lindo Lingo mathematical solver and using a genetic algorithm. Finally, the proposed solution is compared against a professional mathematical solver, Lindo Lingo, being able to compute an approximate solution in cases where the Lingo solver takes too long to determine the solution, and showing an overall cost improvement of the Data Center day-ahead operation of 5%, while the Lingo based solver achieves only 3.3% cost savings on the evaluated scenarios.

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