Data and Communication Infrastructure in Smart Grids

The communication infrastructure constitutes the key element in smart grids. There have been great advances to enhance the way data is communicated among the different smart grid applications. The aim of this chapter is to present the data communication part of the smart grid with some pioneering developments in this topic. A succinct review of the state of art projects to improve the communication link is presented. An illustrative simulation using LABVIEW is included with a proposed idea of introducing some newly technologies involved in the current and future generations of wireless communication systems.

Stability Analysis of Power Hardware-in-the-Loop Simulations for Grid Applications

Power Hardware-in-the-Loop (PHiL) simulation is an emerging testing methodology of real hardware equipment within an emulated virtual environment. The closed loop interfacing between the Hardware under Test (HuT) and the Real Time Simulation (RTS) enables a realistic simulation but can also result in an unstable system. In addition to fundamentals in PHiL simulation and interfacing, this paper therefore provides a consistent and comprehensive study of PHiL stability. An analytic analysis is compared with a simulative approach and is supplemented by practical validations of the stability limits in PHiL simulation. Special focus is given on the differences between a switching and a linear amplifier as power interface (PI). Stability limits and the respective factors of influence (e.g., Feedback Current Filtering) are elaborated with a minimal example circuit with voltage-type Ideal Transformer Model (ITM) PHiL interface algorithm (IA). Finally, the findings are transferred to a real low-voltage grid PHiL application with residential load and photovoltaic system.

Towards online optimization for power grids

In this note, we discuss potential advantages in extending distributed optimization frameworks to enhance support for power grid operators managing an influx of online sequential decisions. First, we review the state-of-the-art distributed optimization frameworks for electric power systems, and explain how distributed algorithms deliver scalable solutions. Next, we introduce key concepts and paradigms for online optimization, and present a distributed online optimization framework highlighting important performance characteristics. Finally, we discuss the connection and difference between offline and online distributed optimization, showcasing the suitability of such optimization techniques for power grid applications.