Preferences in constraint problems are common but significant in many real world applications. In this paper, we extend our conditional and composite CSP (CCCSP) framework, managing CSPs in a dynamic environment, in order to handle preferences. Unlike the existing CSP models managing one form of preferences, ours supports four types, namely : variable value and constraint preferences, composite preferences and conditional preferences. This offers more expressive power in representing a wide variety of constraint problems. The preferences are considered here as a set of soft constraints using a c-semiring structure with combination and projection operators. Solving constraint problems with preferences consists of finding a solution satisfying all the constraints while optimizing the preference values. This is handled by a variant of the branch and bound algorithm, we propose in this paper, and where constraint propagation is used to improve the time efficiency. Experimental tests, we c...