Environmental and sustainability concerns have caused a recent surge in the penetration of distributed energy resources into the power grid. This may lead to voltage violations in the distribution systems making volta...
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Environmental and sustainability concerns have caused a recent surge in the penetration of distributed energy resources into the power grid. This may lead to voltage violations in the distribution systems making voltage regulation more relevant than ever. Owing to this and rapid advancements in sensing, communication, and computation technologies, the literature on voltage control techniques is growing at a rapid pace in distribution networks. In particular, there is a paradigm shift from traditional offline centralized approaches to distributed ones leveraging increased and varied types of actuators, real-time sensing, fast and efficient computations, and an overall distributed situational awareness. This paper reviews state-of-the-art voltage control algorithms, summarizes the underlying methods, and classifies their coordination mechanisms into local, centralized, distributed, and decentralized. The underlying solution methodologies are further classified into two categories, open-loop and feedback-based. Two specific example workflows are provided to illustrate these solutions for voltage regulation.
Optimal transport (OT) is a framework that can be used to guide the optimal allocation of a limited amount of resources. The classical OT paradigm does not consider malicious attacks in its formulation and thus the de...
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Optimal transport (OT) is a framework that can be used to guide the optimal allocation of a limited amount of resources. The classical OT paradigm does not consider malicious attacks in its formulation and thus the designed transport plan lacks resiliency to an adversary. To address this concern, we establish an OT framework that explicitly accounts for the adversarial and stealthy manipulation of participating nodes in the network during the transport strategy design. Specifically, we propose a game-theoretic approach to capture the strategic interactions between the transport planner and the deceptive attacker. We analyze the properties of the established two-person zero-sum game thoroughly. We further develop a fully distributed algorithm to compute the optimal resilient transport strategies, and show the convergence of the algorithm to a saddle-point equilibrium. Finally, we demonstrate the effectiveness of the designed algorithm using case studies.
This letter addresses the problem of economic dispatch for network systems where the local cost functions are coupled through their decision variables. Using Lyapunov based techniques, a continuous-time algorithm is d...
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This letter addresses the problem of economic dispatch for network systems where the local cost functions are coupled through their decision variables. Using Lyapunov based techniques, a continuous-time algorithm is developed which guarantees finite-time convergence to the optimal solution. This makes it possible to predetermine the system convergence time based on the initial conditions. Unlike many previous studies, the local cost functions in this letter can be nonconvex and coupled through their decision variables. It is only required that their sum is strongly convex. To verify the effectiveness of the proposed results, an illustrative example is studied.
The safe-consensus task was introduced by Afek, Gafni and Lieber (DISC' 09) as a weakening of the classic consensus. When there is concurrency, the consensus output can be arbitrary, not even the input of any proc...
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The safe-consensus task was introduced by Afek, Gafni and Lieber (DISC' 09) as a weakening of the classic consensus. When there is concurrency, the consensus output can be arbitrary, not even the input of any process. They showed that safe-consensus is equivalent to consensus, in a wait-free system. We study the solvability of consensus in three shared memory iterated models extended with the power of safe-consensus black boxes. In the first iterated model, for the i-th iteration, the processes write to memory, then they snapshot it and finally they invoke safe-consensus boxes. We prove that in this model, consensus cannot be implemented. In a second iterated model, processes first invoke safe-consensus, then they write to memory and finally they snapshot it. We show that this model is equivalent to the previous model and thus consensus cannot be implemented. In the last iterated model, processes write to the memory, invoke safe-consensus boxes and finally they snapshot the memory. We show that in this model, any wait-free implementation of consensus requires O(n(2)) safe-consensus black-boxes and this bound is tight.
In this article, we consider a resilient consensus problem for the multi-agent network where some of the agents are subject to Byzantine attacks and may transmit erroneous state values to their neighbors. In particula...
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In this article, we consider a resilient consensus problem for the multi-agent network where some of the agents are subject to Byzantine attacks and may transmit erroneous state values to their neighbors. In particular, we develop two event-triggered update schemes to tackle this problem as well as reduce the communication for each agent. Our approach is based on the mean subsequence reduced (MSR) algorithm with agents being capable to communicate with multi-hop neighbors through relaying process. Since communication delays are critical in such an environment, we provide necessary graph conditions for the proposed algorithms to perform well with delays in the communication. Moreover, a novel multi-hop relay scheme with event-triggered feature is proposed. It can reduce more transmissions than the conventional one-hop event-triggered algorithm. We also highlight that through multi-hop communication, the network connectivity can be reduced especially in comparison with the common one-hop communication case. Lastly, we show the effectiveness of the proposed algorithms by numerical examples.
This paper considers dynamic optimization of random access in deadline-constrained broadcasting with frame-synchronized traffic. Under the non-retransmission setting, we define a dynamic control scheme that allows eac...
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This paper considers dynamic optimization of random access in deadline-constrained broadcasting with frame-synchronized traffic. Under the non-retransmission setting, we define a dynamic control scheme that allows each active node to determine the transmission probability based on the local knowledge of current delivery urgency and contention intensity (i.e., the number of active nodes). For an idealized environment where the contention intensity is completely known, we develop a Markov Decision Process (MDP) framework, by which an optimal scheme for maximizing the timely delivery ratio (TDR) can be explicitly obtained. For a realistic environment where the contention intensity is incompletely known, we develop a Partially Observable MDP (POMDP) framework, by which an optimal scheme can only in theory be found. To overcome the infeasibility in obtaining an optimal or near-optimal scheme from the POMDP framework, we investigate the behaviors of the optimal scheme for extreme cases in the MDP framework, and leverage intuition gained from these behaviors together with an approximation on the contention intensity knowledge to propose a heuristic scheme for the realistic environment with TDR close to the maximum TDR in the idealized environment. We further generalize the heuristic scheme to support retransmissions. Numerical results are provided to validate our study.
Renewable energy sources (RES) are increasingly being developed and used to address the energy crisis and protect the environment. However, the large-scale integration of wind and solar energy into the power grid is s...
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Renewable energy sources (RES) are increasingly being developed and used to address the energy crisis and protect the environment. However, the large-scale integration of wind and solar energy into the power grid is still challenging and limits the adoption of these new energy sources. Microgrids (MGs) are small-scale power generation and distribution systems that can effectively integrate renewable energy, electric loads, and energy storage systems (ESS). By using MGs, it is possible to consume renewable energy locally and reduce energy losses from long-distance transmission. This paper proposes a deep reinforcement learning (DRL)-based energy management system (EMS) called DRL-MG to process and schedule energy purchase requests from customers in real-time. Specifically, the aim of this paper is to enhance the quality of service (QoS) for customers and reduce their electricity costs by proposing an approach that utilizes a Deep Q-learning Network (DQN) model. The experimental results indicate that the proposed method outperforms commonly used real-time scheduling methods significantly.
Clock synchronization is indispensable for numerous applications of wireless sensor networks (WSNs). When no common reference clock is available, the nodes must employ distributed synchronization techniques. This pape...
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Clock synchronization is indispensable for numerous applications of wireless sensor networks (WSNs). When no common reference clock is available, the nodes must employ distributed synchronization techniques. This paper proposes, a distributed pulse-based clock synchronisation approach, wherein the propagation delay is eliminated through signal ping-pongs between neighbouring nodes. Such an approach can jointly estimate the clock skew and offset without requiring any reference clock. The whole synchronization process is completed at the physical (PHY) layer, effectively avoiding the random delay caused by packet queuing and retransmission. Simulation results show that the proposed approach can achieve higher synchronization accuracy compared with other existing methods.
Leader election is one of the basic building blocks of distributed systems. Multiple different distributed applications employ a leader for decision making or as a coordinator. Traditional leader election algorithms a...
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ISBN:
(纸本)9798400717406
Leader election is one of the basic building blocks of distributed systems. Multiple different distributed applications employ a leader for decision making or as a coordinator. Traditional leader election algorithms are usually based on all-to-all communications and scales poorly. This work presents a hierarchical adaptive leader election algorithm for distributed systems under the crash-recovery fault model, which allows processes to maintain secondary non-volatile memory. The proposed solution is based on the vCube virtual topology, which presents multiple logarithmic properties, being scalable by definition. One of the contributions of the work is that it is the first to adapt the vCube to the crash-recovery model. The leader is the correct process with the smallest identifier, among those that are most stable, i.e. have failed and recovered the least number of times. The algorithm is adaptive in the sense that processes that change from stable to unstable receive a penalty in order to avoid slowing down the election. Simulation results comparing with the traditional approach show that the proposed solution significantly reduces the number of messages required for leader election, as well as the time to execute a single testing round.
We consider minimizing a sum of agent-specific nondifferentiable merely convex functions over the solution set of a variational inequality (VI) problem in that each agent is associated with a local monotone mapping. T...
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We consider minimizing a sum of agent-specific nondifferentiable merely convex functions over the solution set of a variational inequality (VI) problem in that each agent is associated with a local monotone mapping. This problem finds an application in computation of the best equilibrium in nonlinear complementarity problems arising in transportation networks. We develop an iteratively regularized incremental gradient method where at each iteration, agents communicate over a directed cycle graph to update their solution iterates using their local information about the objective and the mapping. The proposed method is single-timescale in the sense that it does not involve any excessive hard-to-project computation per iteration. We derive nonasymptotic agent-wise convergence rates for the suboptimality of the global objective function and infeasibility of the VI constraints measured by a suitably defined dual gap function. The proposed method appears to be the first fully iterative scheme equipped with iteration complexity that can address distributed optimization problems with VI constraints over cycle graphs.
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