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Distributed power generation, using renewable energy, has been attracting attention to cope with global environment issues; a microgrid is a promising configuration for distributed power generation. To augment the stability and efficiency of the microgrid, an intelligent control, which considers the restrictions and characteristics of each unit, is indispensable. It can be achieved by constructing an efficient operation schedule for each power plant in the microgrid, depending on energy demand, and predicting passive power generation. The operation scheduling is regarded as a constrained optimization problem, which must have nonlinear characteristics in case of actual systems. Although several methods using metaheuristic optimization have been proposed, it would be trapped into a local minimum in some cases. In this paper, we statistically analyze operation schedules, computed for an actual power network of the demonstrative project. In addition, we conduct an investigation of the relationship between the input parameter space and the solution space, which can be exploited to obtain more appropriate initial solutions leading to better and faster converging solutions.