2,925 research outputs found
Improved Convergence Rates for Distributed Resource Allocation
In this paper, we develop a class of decentralized algorithms for solving a
convex resource allocation problem in a network of agents, where the agent
objectives are decoupled while the resource constraints are coupled. The agents
communicate over a connected undirected graph, and they want to collaboratively
determine a solution to the overall network problem, while each agent only
communicates with its neighbors. We first study the connection between the
decentralized resource allocation problem and the decentralized consensus
optimization problem. Then, using a class of algorithms for solving consensus
optimization problems, we propose a novel class of decentralized schemes for
solving resource allocation problems in a distributed manner. Specifically, we
first propose an algorithm for solving the resource allocation problem with an
convergence rate guarantee when the agents' objective functions are
generally convex (could be nondifferentiable) and per agent local convex
constraints are allowed; We then propose a gradient-based algorithm for solving
the resource allocation problem when per agent local constraints are absent and
show that such scheme can achieve geometric rate when the objective functions
are strongly convex and have Lipschitz continuous gradients. We have also
provided scalability/network dependency analysis. Based on these two
algorithms, we have further proposed a gradient projection-based algorithm
which can handle smooth objective and simple constraints more efficiently.
Numerical experiments demonstrates the viability and performance of all the
proposed algorithms
Distributed Delay-Tolerant Strategies for Equality-Constraint Sum-Preserving Resource Allocation
This paper proposes two nonlinear dynamics to solve constrained distributed
optimization problem for resource allocation over a multi-agent network. In
this setup, coupling constraint refers to resource-demand balance which is
preserved at all-times. The proposed solutions can address various model
nonlinearities, for example, due to quantization and/or saturation. Further, it
allows to reach faster convergence or to robustify the solution against
impulsive noise or uncertainties. We prove convergence over weakly connected
networks using convex analysis and Lyapunov theory. Our findings show that
convergence can be reached for general sign-preserving odd nonlinearity. We
further propose delay-tolerant mechanisms to handle general bounded
heterogeneous time-varying delays over the communication network of agents
while preserving all-time feasibility. This work finds application in CPU
scheduling and coverage control among others. This paper advances the
state-of-the-art by addressing (i) possible nonlinearity on the agents/links,
meanwhile handling (ii) resource-demand feasibility at all times, (iii)
uniform-connectivity instead of all-time connectivity, and (iv) possible
heterogeneous and time-varying delays. To our best knowledge, no existing work
addresses contributions (i)-(iv) altogether. Simulations and comparative
analysis are provided to corroborate our contributions
Distributed Energy Resource Management: All-Time Resource-Demand Feasibility, Delay-Tolerance, Nonlinearity, and Beyond
In this work, we propose distributed and networked energy management
scenarios to optimize the production and reservation of energy among a set of
distributed energy nodes. In other words, the idea is to optimally allocate the
generated and reserved powers based on nodes' local cost gradient information
while meeting the demand energy. One main concern is the all-time (or anytime)
resource-demand feasibility, implying that at all iterations of the scheduling
algorithm, the balance between the produced power and demand plus reserved
power must hold. The other concern is to design algorithms to tolerate
communication time-delays and changes in the network. Further, one can
incorporate possible model nonlinearity in the algorithm to address both
inherent (e.g., saturation and quantization) and purposefully-added (e.g.,
signum-based) nonlinearities in the model. The proposed optimal allocation
algorithm addresses all the above concerns, while it benefits from possible
features of the distributed (or networked) solutions such as
no-single-node-of-failure and distributed information processing. We show both
the all-time feasibility of the proposed scheme and its convergence under
certain bound on the step-rate using Lyapunov-type proofs.Comment: IEEE LCSS 202
Asynchronous Distributed Power Control of Multimicrogrid Systems
Asynchrony widely exists in microgrids (MGs), such as nonidentical sampling rates and communication delays, which challenges the MG control. This article addresses the asynchronous distributed power control problem of hybrid microgrids, considering different kinds of asynchrony, such as nonidentical sampling rates, and random time delays. To this end, we first formulate the economic dispatch problem of MGs, and devise a synchronous algorithm. Then, we analyze the impact of asynchrony, and propose an asynchronous iteration algorithm based on the synchronous version. By introducing a random clock at each iteration, different types of asynchrony are fitted into a unified framework, where the asynchronous algorithm is converted into a fixed-point iteration problem with a nonexpansive operator, leading to a convergence proof. We further provide an upper bound estimation of the time delay. Moreover, the real-time implementation of the proposed algorithm in both ac and dc MGs is introduced. By measuring the frequency/voltage, the controller is simplified by reducing one order, and adapting to the fast varying load demand. Finally, simulations on a benchmark MG, and experiments are utilized to verify the effectiveness, and advantages of the proposed algorithm
Distributed Control Strategies for Microgrids: An Overview
There is an increasing interest and research effort focused on the analysis, design and implementation of distributed control systems for AC, DC and hybrid AC/DC microgrids. It is claimed that distributed controllers have several advantages over centralised control schemes, e.g., improved reliability, flexibility, controllability, black start operation, robustness to failure in the communication links, etc. In this work, an overview of the state-of-the-art of distributed cooperative control systems for isolated microgrids is presented. Protocols for cooperative control such as linear consensus, heterogeneous consensus and finite-time consensus are discussed and reviewed in this paper. Distributed cooperative algorithms for primary and secondary control systems, including (among others issues) virtual impedance, synthetic inertia, droop-free control, stability analysis, imbalance sharing, total harmonic distortion regulation, are also reviewed and discussed in this survey. Tertiary control systems, e.g., for economic dispatch of electric energy, based on cooperative control approaches, are also addressed in this work. This review also highlights existing issues, research challenges and future trends in distributed cooperative control of microgrids and their future applications
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