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This paper discusses optimal operation strategies of a cluster of microturbines (MTs) for electrical load-following applications. Cluster operation ensures higher operational flexibility, but raises the issue of taking into account the partial-load MT characteristics, in terms of energy efficiency and pollutant emissions. In particular, from experimental results the NOx and CO emissions exhibit nonlinear and to some extent complementary trends at different partial-load levels. Hence, individual optimizations of fuel consumption and emission reduction are first carried out in this paper to show the conflicting nature of such objectives. Then, multi-objective optimization is performed to directly determine the best-known Pareto front. For this purpose, a procedure based on evolutionary programming is illustrated and applied to a practical case study. The results point out the degree of trade-off that can be sought when minimizing the local environmental impact of such distributed energy systems.