Smart grid deployment is the modernization process of the power grid with new power resources, technologies, and devices for efficient management of energy. DNP involves 50%~80% of processing delay in power devices over TCP/IP and Modbus is able to support only simple applications (e.g., programmable logic controllers) within the same network. Existing protocols such as Distributed Network Protocol (DNP) and Modbus in the traditional power grid are not suitable for smart grids due to the lack of support for a large amount of data and quality communication. In the current practice, there has not been a widely accepted communication platform for smart grids. Some environments have ample computing resources (e.g., memory, CPU clock speed) which allow heavy-weight applications to run, while other environments have limited computing capability which can accommodate only small and light-weight applications. Unlike the traditional power grid, A smart grid is data-centric involving significant data communication among various types of applications running on different computing environments across the grid. Smart grids have emerged as the next generation of power grids for improved efficiency, reliability, and flexibility of power production and consumption. The results show that configured DDS is viable for efficient and quality data communication for applications that run on an environment with limited computing capability. We also experimented the approach for the efficiency of configured DDS in terms of resource utilization. We implemented the approach in OpenDDS and demonstrate its application to different application environments. We define a formal notion of feature composition to build DDS configurations. We analyze DDS in terms of features and design them using Unified Modeling Language (UML) and Object Constraint Language (OCL) based on inheritance and overriding. This allows DDS to serve as a uniform communication middleware across the smart grid, which is critical for interoperability. In this paper, we present a feature-based approach for tailoring DDS to configure lightweight DDS by selecting only the necessary features for the application in consideration of the resource constraints of its running environment.

Some environments have limited computing resources such as small memory and low performance, which makes it difficult to accommodate DDS. However, a smart grid involves various types of communication applications running on different computing environments.

DDS is designed to support quality communication for large scale real-time systems through a wide range of QoS policies. Data Distribution Service (DDS) has emerged as a potential solution for data communication challenges in smart grids.

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