A Distributed Event-Triggered Control Strategy for DC Microgrids Based on Publish-Subscribe Model Over Industrial Wireless Sensor Networks

This paper presents a complete design, analysis, and performance evaluation of a novel distributed event-triggered control and estimation strategy for DC microgrids. The primary objective of this work is to efficiently stabilize the grid voltage, and to further balance the energy level of the energy storage (ES) systems. The locally-installed distributed controllers are utilised to reduce the number of transmitted packets and battery usage of the installed sensors, based on a proposed event-triggered communication scheme. Also, to reduce the network traffic, an optimal observer is employed which utilizes a modified Kalman consensus filter (KCF) to estimate the state of the DC microgrid via the distributed sensors. Furthermore, in order to effectively provide an intelligent data exchange mechanism for the proposed event-triggered controller, the publish-subscribe communication model is employed to setup a distributed control infrastructure in industrial wireless sensor networks (WSNs). The performance of the proposed control and estimation strategy is validated via the simulations of a DC microgrid composed of renewable energy sources (RESs). The results confirm the appropriateness of the implemented strategy for the optimal utilization of the advanced industrial network architectures in the smart grids

A review on communication aspects of demand response management for future 5G IoT- based smart grids

In recent power grids, the need for having a two-way flow of information and electricity is crucial. This provides the opportunity for suppliers and customers to better communicate with each other by shifting traditional power grids to smart grids (SGs). In this paper, demand response management (DRM) is investigated as it plays an important role in SGs to prevent blackouts and provide economic and environmental benefits for both end-users and energy providers. In modern power grids, the development of communication networks has enhanced DRM programmes and made the grid smarter. In particular, with progresses in the 5G Internet of Things (IoT), the infrastructure for DRM programmes is improved with fast data transfer, higher reliability, increased security, lower power consumption, and a massive number of connections. Therefore, this paper provides a comprehensive review of potential applications of 5G IoT technologies as well as the computational and analytical algorithms applied for DRM programmes in SGs. The review holistically brings together sensing, communication, and computing (optimization, prediction), areas usually studied in a scattered way. A broad discussion on various DRM programmes in different layers of enhanced 5G IoT based SGs is given, paying particular attention to advances in machine learning (ML) and deep learning (DL) algorithms alongside challenges in security, reliability, and other factors that have a role in SGs’ performance

Self-organising multi-agent control for distribution networks with distributed energy resources

Recent years have seen an increase in the connection of dispersed distributed energy resources (DERs) and advanced control and operational components to the distribution network. These DERs can come in various forms, including distributed generation (DG), electric vehicles (EV), energy storage, etc. The conditions of these DERs can be varying and unpredictably intermittent. The integration of these distributed components adds more complexity and uncertainty to the operation of future power networks, such as voltage, frequency, and active/reactive power control. The stochastic and distributed nature of DGs and the difficulty in predicting EV charging patterns presents problems to the control and management of the distribution network. This adds more challenges to the planning and operation of such systems. Traditional methods for dealing with network problems such as voltage and power control could therefore be inadequate. In addition, conventional optimisation techniques will be difficult to apply successfully and will be accompanied with a large computational load. There is therefore a need for new control techniques that break the problem into smaller subsets and one that uses a multi-agent system (MAS) to implement distributed solutions. These groups of agents would coordinate amongst themselves, to regulate local resources and voltage levels in a distributed and adaptive manner considering varying conditions of the network. This thesis investigates the use of self-organising systems, presenting suitable approaches and identifying the challenges of implementing such techniques. It presents the development of fully functioning self-organising multi-agent control algorithms that can perform as effectively as full optimization techniques. It also demonstrates these new control algorithms on models of large and complex networks with DERs. Simulation results validate the autonomy of the system to control the voltage independently using only local DERs and proves the robustness and adaptability of the system by maintaining stable voltage control in response to network conditions over time.Recent years have seen an increase in the connection of dispersed distributed energy resources (DERs) and advanced control and operational components to the distribution network. These DERs can come in various forms, including distributed generation (DG), electric vehicles (EV), energy storage, etc. The conditions of these DERs can be varying and unpredictably intermittent. The integration of these distributed components adds more complexity and uncertainty to the operation of future power networks, such as voltage, frequency, and active/reactive power control. The stochastic and distributed nature of DGs and the difficulty in predicting EV charging patterns presents problems to the control and management of the distribution network. This adds more challenges to the planning and operation of such systems. Traditional methods for dealing with network problems such as voltage and power control could therefore be inadequate. In addition, conventional optimisation techniques will be difficult to apply successfully and will be accompanied with a large computational load. There is therefore a need for new control techniques that break the problem into smaller subsets and one that uses a multi-agent system (MAS) to implement distributed solutions. These groups of agents would coordinate amongst themselves, to regulate local resources and voltage levels in a distributed and adaptive manner considering varying conditions of the network. This thesis investigates the use of self-organising systems, presenting suitable approaches and identifying the challenges of implementing such techniques. It presents the development of fully functioning self-organising multi-agent control algorithms that can perform as effectively as full optimization techniques. It also demonstrates these new control algorithms on models of large and complex networks with DERs. Simulation results validate the autonomy of the system to control the voltage independently using only local DERs and proves the robustness and adaptability of the system by maintaining stable voltage control in response to network conditions over time