This paper proposes an individual-based model (IBM) for the modeling of large-scale heterogenous complex systems. The IBM decouples the complex systems into independent individuals based on physical characteristics. In order to formulate the actions of individuals and interactions between individuals, the IBM is formalized with a quintuple parameter set comprised of input, knowledge, state, function, and output sets. Accordingly, individuals can make decisions independently applying accurate evolutionary mechanisms described in the function set. Additionally, the individuals can interact with others by input and output sets in a uniform manner. Consequently, a complex system can be modelled by independent individuals whose internal characteristics are fully specified and hidden from the external environment. The IBM is applied to model heterogenous integrated energy systems (IES) and simulate the multi-time scale dynamics of the IES under conditions of disturbances and faults. The results show that the IBM are capable of simulating quantified system states and unquantified rules of the IES dynamics simultaneously. Furthermore, the IBM can significantly improve the computation efficiency in terms of computation time and interaction, compared with the traditional equal-step method. Therefore, it is verified that the proposed IBM is an efficient method for complex systems.