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The elimination of traditional thermal generators and the increase of renewable energy source access reduced the inertia of the power system, and thermostatically controlled loads have the potential to provide ancillary services to the power system. This paper introduces a decentralized control strategy for thermostatically controlled loads (TCLs), which focuses on assisting large-scale TCLs in providing frequency support to power systems while considering energy efficiency ratio and measurement error correction. Initially, the nonlinear relationship between heat flow and TCLs' power is modeled using a polynomial relationship, and the coefficients are determined through a data-driven method to enhance accuracy in TCLs modeling and assess their regulation potential precisely. Subsequently, a decentralized control strategy tailored for power system frequency regulation is developed. To account for measurement errors in TCLs controllers, a measurement error back-correction method is proposed. Simulation examples demonstrate the effectiveness of the proposed control strategy in achieving accurate modeling and control of TCLs and reducing power system frequency fluctuations. [ABSTRACT FROM AUTHOR]

The existing research on orderly participation of air conditioning load in frequency regulation needs a relatively complete communication architecture, but it cannot be achieved on a large scale in practice. This paper proposes a decentralized control method for the air-conditioning load control to participate in the power system primary frequency regulation, so that the air-conditioning loads can respond to system frequency deviation in an orderly manner without communication. Firstly, the triggering frequency is set to prevent the air-conditioning load from being over-or under-adjusted based on the frequency response interval, which is determined according to the outdoor temperature. Secondly, the response priority is arranged according to the holding time to ensure an orderly response of the air-conditioning load without communication. Finally, a simulation example of a thousand air-conditioning loads under different control strategy proves that the proposed method can achieve better frequency regulation effect. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are increasingly applied to surveillance tasks, thanks to their excellent mobility and flexibility. Different from existing works using UAVs for video surveillance, this paper employs a UAV team to carry out collaborative radio surveillance on ground moving nodes and disguise the purpose of surveillance. We consider two aspects of disguise. The first is that the UAVs do not communicate with each other (or the ground nodes can notice), and each UAV plans its trajectory in a decentralized way. The other aspect of disguise is that the UAVs avoid being noticed by the nodes for which a metric quantifying the disguising performance is adopted. We present a new decentralized method for the online trajectory planning of the UAVs, which maximizes the disguising metric while maintaining uninterrupted surveillance and avoiding UAV collisions. Based on the model predictive control (MPC) technique, our method allows each UAV to separately estimate the locations of the UAVs and the ground nodes, and decide its trajectory accordingly. The impact of potential estimation errors is mitigated by incorporating the error bounds into the online trajectory planning, hence achieving a robust control of the trajectories. Computer-based simulation results demonstrate that the developed strategy ensures the surveillance requirement without losing disguising performance, and outperforms existing alternatives. Note to Practitioners—The paper is motivated by the covertness requirement in the radio surveillance (also called eavesdropping) by UAVs. In some situations, the UAV user (such as the police department) wishes to disguise the surveillance intention from the targets, and the trajectories of UAVs play a significant role in the disguising. However, the typical UAV trajectories such as standoff tracking and orbiting can easily be noticed by the targets. Considering this gap, we focus on how to plan the UAVs’ trajectories so that they are less noticeable while conducting effective eavesdropping. We formulate a path planning problem aiming at maximizing a disguising metric, which measures the magnitude of the relative position change between a UAV and a target. A decentralized method is proposed for the online trajectory planning of the UAVs based on MPC, and its robust version is also presented to account for the uncertainty in the estimation and prediction of the nodes’ states. [ABSTRACT FROM AUTHOR]

The future grid is evolving into a smart distribution network that integrates multiple distributed energy resources ensuring at the same time reliable operation and increased power quality. In recent years, many research papers have addressed the voltage violation problems that arise from the high penetration of distributed generation. In view of the transition to active network management and the increase in the quantity of collected data, distributed control schemes have been proposed that use pervasive communications to deal with the complexity of smart grid. This paper reviews the recent publications on distributed and decentralized voltage control of smart distribution networks, summarizes their control models, and classifies the solution methodologies. Moreover, it comments on issues that should be addressed in the future and the perspectives of industry applications. [ABSTRACT FROM PUBLISHER]

The objective of this paper is to provide a review of distributed control and management strategies for the next generation power system in the context of microgrids. This paper also identifies future research directions. The next generation power system, also referred to as the smart grid, is distinct from the existing power system due to its extensive use of integrated communication, advanced components such as power electronics, sensing, and measurement, and advanced control technologies. At the same time, the need for increased number of small distributed and renewable energy resources can exceed the capabilities of an available computational resource. Therefore, the recent literature has seen a significant research effort on dividing the control task among different units, which gives rise to the development of several distributed techniques. This paper discusses features and characteristics of these techniques, and identifies challenges and opportunities ahead. The paper also discusses the relationship between distributed control and hierarchical control. [ABSTRACT FROM PUBLISHER]

Broadcast control (BC) is a bio-inspired coordination technique for a swarm of agents in which a single coordinator broadcasts an identical scalar signal to all performing agents without discrimination, and the agents make appropriate moves towards the agents' collective optimal state without communicating with one another. The BC technique aims to accomplish a globally assigned task for which BC utilizes a stochastic optimization algorithm to coordinate a group of agents. However, the challenge intensifies as the system becomes larger: it requires a larger number of agents, which protracts the converging time for a single coordinator-based BC model. This paper proposes a revamped version of BC model, which assimilates distributed multiple coordinators to control a larger multi-agent system efficiently in a pragmatic manner. Precisely, in this hierarchical BC scheme, the distributed multiple sub-coordinators broadcast the identical feedback signal to the agents, which they receive from the global coordinator to accomplish the coverage control task of the ordinary agents. The dual role of sub-coordinators is manipulated by introducing weighted averaging of the gradient estimation under the stochastic optimization mechanism. The potency of the proposed model is analyzed with numerical simulation for a coverage control task, and various performance aspects are compared with the typical BC schemes to demonstrate its practicability and performance improvement. Particularly, the proposed scheme shows the same convergence with about 30% less traveling costs, and the near convergence is reached by only about one-third of iteration steps compared to the typical BC. [ABSTRACT FROM AUTHOR]

This paper introduces a new consensus-based control scheme for distributed power systems to solve the distributed economic dispatch (DED) problem. With the development of renewable energy, smart grid, and microgrid, more and more distributed generators will be connected to power systems. Thus the distributed control schemes become more and more important for power systems to control and manage the distributed equipment. Therefore, some consensus-based methods are published previously to solve the DED problem. But the challenge to apply the consensus-based method is to meet the power balance constraint without the centralized facility, since the value of power mismatch is hard to be obtained for a distributed control system. To solve the problem, this paper proposes the method to combine the frequency control methods with the consensus protocol from graph theory to develop the DED control method, so that the real power between the load and the generation can be balanced during the economic dispatch. With this method, a totally distributed optimal control scheme is obtained. The PI frequency controller and neural network frequency controller are employed to develop the DED control method. Then the communication failure problem is considered in this paper. The robust DED control method is proposed to improve the robustness of this distributed control method, allowing the system to stay at the optimal dispatching even if the communication is failed. In addition, the model of transmission line losses is incorporated in the DED problem to reduce the line losses. Finally, several case studies are given in this paper to verify the proposed method. [ABSTRACT FROM AUTHOR]