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

An overview of distributed microgrid state estimation and control for smart grids

© 2015 by the authors; licensee MDPI, Basel, Switzerland. Given the significant concerns regarding carbon emission from the fossil fuels, global warming and energy crisis, the renewable distributed energy resources (DERs) are going to be integrated in the smart grid. This grid can spread the intelligence of the energy distribution and control system from the central unit to the long-distance remote areas, thus enabling accurate state estimation (SE) and wide-area real-time monitoring of these intermittent energy sources. In contrast to the traditional methods of SE, this paper proposes a novel accuracy dependent Kalman filter (KF) based microgrid SE for the smart grid that uses typical communication systems. Then this article proposes a discrete-time linear quadratic regulation to control the state deviations of the microgrid incorporating multiple DERs. Therefore, integrating these two approaches with application to the smart grid forms a novel contributions in green energy and control research communities. Finally, the simulation results show that the proposed KF based microgrid SE and control algorithm provides an accurate SE and control compared with the existing method

Applications of Agent-Based Methods in Multi-Energy Systems—A Systematic Literature Review

The need for a greener and more sustainable energy system evokes a need for more extensive energy system transition research. The penetration of distributed energy resources and Internet of Things technologies facilitate energy system transition towards the next generation of energy system concepts. The next generation of energy system concepts include “integrated energy system”, “multi-energy system”, or “smart energy system”. These concepts reveal that future energy systems can integrate multiple energy carriers with autonomous intelligent decision making. There are noticeable trends in using the agent-based method in research of energy systems, including multi-energy system transition simulation with agent-based modeling (ABM) and multi-energy system management with multi-agent system (MAS) modeling. The need for a comprehensive review of the applications of the agent-based method motivates this review article. Thus, this article aims to systematically review the ABM and MAS applications in multi-energy systems with publications from 2007 to the end of 2021. The articles were sorted into MAS and ABM applications based on the details of agent implementations. MAS application papers in building energy systems, district energy systems, and regional energy systems are reviewed with regard to energy carriers, agent control architecture, optimization algorithms, and agent development environments. ABM application papers in behavior simulation and policy-making are reviewed with regard to the agent decision-making details and model objectives. In addition, the potential future research directions in reinforcement learning implementation and agent control synchronization are highlighted. The review shows that the agent-based method has great potential to contribute to energy transition studies with its plug-and-play ability and distributed decision-making process

Analysis of Privacy-aware Data Sharing in Cyber-physical Energy Systems

In this thesis, we determine the key factors and correlations among the privacy, security, and utility requirements of grid networks to ensure effective inter-and intra-actions within physical layer equipment (e.g., distributed energy resources (DERs), intelligent electronic devices (IEDs), etc.). We have conducted a comprehensive analysis of the existing consensus mechanisms in blockchain-enabled smart grids while pointing out the potential research gaps. We develop a practical and effective consensus mechanism for a private and permissioned blockchain-enabled Supervisory control and data acquisition (SCADA) system. Moreover, we bridge a common and popular industrial control system (ICS) protocol, distributed network protocol 3 (DNP3) with the blockchain network to ensure smooth operation. In addition, we develop differential privacy (DP)-enabled strategies to achieve data security, privacy, and utility requirements of the power system network under an adversarial setting. Specifically, we aim to analyze and develop a provable correlation between privacy loss and other DP parameters considering the variations of attacks and their impacts along with DP constraints. This will enable modern power grid designers to develop, design, and employ DP-based fault-tolerant models in data-driven power grid operation and control. Furthermore, we conduct feasibility and quality-of-service (QoS) analysis of the DP mechanism and the grid to achieve certified robustness. Feasibility analysis of the privacy measure provides an assessment of the practicability of differential privacy in grid operation and warns the operators about the possible failures and incoming attacks on physical layer operations. QoS is analyzed in the power grid in terms of data accuracy, computational overhead, and resource utilization

Smart grid state estimation and its applications to grid stabilization

University of Technology Sydney. Faculty of Engineering and Information Technology.The smart grid is expected to modernize the current electricity grid by commencing a new set of technologies and services that can make the electricity networks more secure, automated, cooperative and sustainable. The smart grid can integrate multiple distributed energy resources (DERs) into the main grid. The need for DERs is expected to become more important in the future smart grid due to the global warming and energy problems. Basically, the smart grid can spread the intelligence of the energy distribution and control system from the central unit to long-distance remote areas, thus enabling accurate state estimation and wide-area real-time monitoring of these intermittent energy sources. Reliable state estimation is a key technique to fulfil the control requirement and hence is an enabler for the automation of power grids. Driven by these motivations, this research explores the problem of state estimation and stabilization taking disturbances, cyber attacks and packet losses into consideration for the smart grid. The first contribution of this dissertation is to develop a least square based Kalman filter (KF) algorithm for state estimation, and an optimal feedback control framework for stabilizing the microgrid states. To begin with, the environment-friendly renewable microgrid incorporating multiple DERs is modelled to obtain discrete-time state-space linear equations where sensors are deployed to obtain system state information. The proposed smart grid communication system provides an opportunity to address the state regulation challenge by offering two-way communication links for microgrid information collection, estimation and stabilization. Interestingly, the developed least square based centralised KF algorithm is able to estimate the system states properly even at the beginning of the dynamic process, and the proposed H2 based optimal feedback controller is able to stabilize the microgrid states in a fairly short time. Unfortunately, the smart grid is susceptible to malicious cyber attacks, which can create serious technical, economic, social and control problems in power network operations. In contrast to the traditional cyber attack minimization techniques, this study proposes a recursive systematic convolutional (RSC) code and KF based method in the context of smart grids. The proposed RSC code is used to add redundancy in the microgrid states, and the log maximum a-posterior is used to recover the state information which is affected by random noises and cyber attacks. Once the estimated states are obtained, a semidefinite programming (SDP) based optimal feedback controller is proposed to regulate the system states. Test results show that the proposed approach can accurately mitigate the cyber attacks and properly estimate as well as regulate the system states. The other significant contribution of this dissertation is to develop an adaptive-then-combine distributed dynamic approach for monitoring the grid under lossy communication links between wind turbines and the energy management system. Based on the mean squared error principle, an adaptive approach is proposed to estimate the local state information. The global estimation is designed by combining local estimation results with weighting factors, which are calculated by minimizing the estimation error covariances based on SDP. Afterwards, the convergence analysis indicates that the estimation error is gradually decreased, so the estimated state converges to the actual state. The efficacy of the developed approach is verified using the wind turbine and IEEE 6-bus distribution system. Furthermore, the distribution power sub-systems are usually interconnected to each other, so this research investigates the interconnected optimal filtering problem for distributed dynamic state estimation considering packet losses. The optimal local and neighbouring gains are computed to reach a consensus estimation after exchanging their information with the neighbouring estimators. Then the convergence of the developed algorithm is theoretically proved. Afterwards, a distributed controller is designed based on the SDP approach. Simulation results demonstrate the accuracy of the developed approaches. The penultimate contribution of this dissertation is to develop a distributed state estimation algorithm for interconnected power systems that only needs a consensus step. After modelling the interconnected synchronous generators, the optimal gain is determined to obtain a distributed state estimation. The consensus of the developed approach is proved based on the Lyapunov theory. From the circuit and system point of view, the proposed framework is useful for designing a practical energy management system as it has less computational complexity and provides accurate estimation results. The distributed state estimation algorithm is further modified by considering different observation matrices with both local and consensus steps. The optimal local gain is computed after minimizing the mean squared error between the true and estimated states. The consensus gain is determined by a convex optimization process with a given local gain. Moreover, the convergence of the proposed scheme is analysed after stacking all the estimation error dynamics. The efficacy of the developed approach is demonstrated using the environment-friendly renewable microgrid and IEEE 30-bus power system. Overall, the findings, theoretical development and analysis of this research represent a comprehensive source of information for smart grid state estimation and stabilization schemes, and will shed light on green smart energy management systems and monitoring centre design in future smart grid implementations. It is worth pointing out that the aforementioned contributions are very important in the smart grid community as communication impairments have a significant impact on grid stability and the distributed strategies can reduce communication burden and offer a sparse communication network