WP 3

This WP focuses on the improved robustness, reliability, scalability and affordability of the underlying wireless and wired telecommunication networks for real-time smart grid operation. Presently used solutions are not sufficiently robust and scalable for future control of the smart grid, which includes massive amount of traffic sources such as smart meters, DEGs and other relevant infrastructure. This WP will investigate the potential for the use of existing telecom operator communication networks by the smart grid operators as their infrastructure most often coexists but remains isolated form one another. This opens a new design space with a plethora of possibilities for the implementation of collaborative approaches that, on one hand, support future operation of smart grids and, on the other hand, enable new services. The potential design space in SUNSEED is further scoped by the output of the T2.1 (key requirements) and T2.3 (business models and techno-economic analysis) in WP2. The investigated approaches include introduction of communication path diversity via reuse of the existing DSO and telecom infrastructure, use of self-optimization and self-healing concepts in heterogeneous networks, and reengineering protocol and network solutions where required and feasible. The allowed system and standardization impact of derived solutions will follow the constraints derived from the techno-economic analysis in WP2.
Methods and tools for planning, design, and optimization of (combined DSO and telecom operator) communication networks for smart grid support will be developed on the basis of existing infrastructure, traffic requirements and real-time response. Additionally, the aim is to derive enhancements for the networked components involved in the smart grid operation such as the advanced metering and control (AMC) platform (as part of the wide area measurement system WAMS node). The enhancements will enable precise 3-phase AC or multiple channel DC monitoring of power quality and energy consumption/production in real-time, control of individual loads via integrated switches or device APIs, and basic processing of measurement data to support autonomous local decisions and alarming.