Improving the Reliability of Smart Distribution Grids Using Software-based Networking - Brown, Gerhard Errol (Department of Electrical Engineering, 2021)
Smart grid is a combination of technologies that emerged in response to the rapid changes in the way humans generate, transfer, distribute and use energy. The smart grid paradigm shifts the focus from bulk generation with centralised grid control to distributed electricity generation, energy storage and greater consumer participation in grid operations. Many organisations and institutions are contributing to this initiative by developing new frameworks, architectures and standards that aim to support its adoption and improvement. Most of these new developments are based on modern Information Communication Technology (ICT) such as the Internet of Things (IoT). Although many governments have made commitments to pursue smart grid as part of their national policies and strategies, some nations, especially those with developing economies, have struggled to make significant strides in smart grid deployment. An important characteristic of smart grid is its resilience and its ability to self-heal, enabled by better use of grid knowledge and the distribution of grid intelligence. This creates a challenge for many utilities that need to improve their existing grid ICT infrastructure to meet more stringent communication requirements. These requirements will likely include very high communication reliability with high throughput and low latency. Although most networks can be developed or improved to meet these requirements using modern network hardware, the cost and complexity involved in implementing such designs in large-scale distribution grid networks may be too high. To overcome this challenge alternative ways of designing smart grid communication networks using software approaches are needed. This study proposes a new software-based networking platform, based on Industrial Internet of Things (IIoT) technology, that aims to support smart grid reliability by enabling reliability-centred smart grid systems and by reacting immediately to communication problems using real-time monitoring techniques. Using the principles of software defined networking (SDN), network functions virtualisation (NFV) and machine-tomachine communication (M2M), the design proposed in this work aims to provide a more flexible and affordable approach to developing and maintaining large-scale grid communication networks while offering several features that improve grid reliability and performance. Experimental simulations were conducted with this architecture implemented in an emulated network environment, using a topology based on a model of a real city distribution grid. Results from the experimental evaluation show that a software-based communication network is easy to set up, maintain and scale using virtual machines capable of running on existing grid IT infrastructure. Furthermore, the results show that by using the features of SDN, NFV and M2M a smart grid communication network can be designed that can automatically detect and recover from at least six different simulated communication failures without impacting the operation of a functional smart grid application supported by the network. The results also support this platform's capability to reduce network congestion using a scheduled network data buffering service, resulting in end-to-end network latency improvements from 0.6 seconds to 0.05 milliseconds. From these results, we conclude that software-based networking can offer promising design alternatives for smart distribution grids, capable of improving the grid's overall reliability. This conclusion is drawn from the fact that software-based networks not only offer many features that can improve communication reliability and performance, but also have the potential to reduce the cost and complexity of network implementation and maintenance. This study can potentially improve the uptake of smart grid as it offers utilities design options that are more flexible and affordable to implement and maintain.