# CoSimulating Communication Networks and Electrical System for Performance Evaluation in Smart Grid
## 1 Introduction  
The smart grid is a new kind of electrical grid where the power system and the IT system are tightly coupled with each other. In the smart grid, various elements of the power system are managed by remote controls, which are performed based on situational awareness [1]. Moreover, the deployment of eco-friendly elements such as renewable energy generators (solar generators and wind generators) and PHEV (Plug-in Hybrid Electric Vehicle) requires a lot of information exchange and processing. The information exchange and processing can contribute to not only providing numerous electricity services, but also making power grid more stable, efficient and eco-friendly. Therefore, it is exceedingly important to investigate how we should apply the advanced computing and networking technologies to the power system in the smart grid. There are many studies aimed to enhance the performance of the smart grid, especially the networking system. [PERSON] et al. [2] addressed the data communication scheme for Advanced Metering Infrastructure (AMI) considering the reduction of network delay and the privacy of the smart grid users. [PERSON] et al. [3] proposed a novel voucher scheme for the secure trading between the customersof the smart grid. Furthermore, [PERSON] et al. [4] designed a wireless communication framework which considered the signal interference of electromagnetic wave and delay-constraint of the smart grid system. Since the smart grid indicates the convergence of the network system and power distribution system, which were distinctive before, studies must be elaborately designed and clearly evaluated. The majority of the smart grid studies usually depend on sophisticated simulation tools [5], since it is very difficult to find out a well-defined theoretical model which can represent the complexity and dynamics of the smart grid. If we try to represent the smart grid system with a mathematical model, we need to use unrealistic assumptions and/or leave out subtle details of the system. Especially, the degree of analytic complexity becomes more intractable in the distribution and customer domain [6], since many applications, such as demand-response (DR), energy management, and smart metering, operate on the domains. The applications generally collect diverse data, perform information processing, and control power system elements through the network system. Therefore, the complexity of the analytic approach rapidly increases as the number of applications and information exchanges increase. Another approach of the smart grid research is testbed based verification where numerous applications can be implemented and studies can be performed elaborately. However, the cost to construct the testbed is too expensive, since new kinds of equipments and systems should be employed. Moreover, since the smart grid is a very large system and many elements are tightly coupled, it is very difficult to construct a well specified and designed smart grid testbed. Therefore, a simulation study is the first choice for investigating both structures and operations of the smart grid. However, since the network system and the power system are tightly coupled with each other in the smart grid, _organic_ combination of simulation tools for both systems is required to carry out an elaborate simulation study. In this paper, the word _organic_ is used to describe a cosimulation in which two simulators are necessary, fit together harmoniously as fundamental parts of a whole, and interact with each other systematically, so that any complex applications can be precisely modeled. However, there are several challenges to achieve the goal. One of the challenges is that the smart grid is an application-centric system [7]. Various applications would be introduced in the smart grid and each application would have different operations. Therefore, we are strongly convinced to build a cosimulation technique through which diverse applications can be easily modeled and simulated.