The economic dispatch problem (EDP) is one of the fundamental and important problems in power systems. The objective of EDP is to determine the output generation of generators to minimize the total generation cost und...
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The economic dispatch problem (EDP) is one of the fundamental and important problems in power systems. The objective of EDP is to determine the output generation of generators to minimize the total generation cost under various constraints. In this article, a finite-time consensus-based distributed optimization algorithm is proposed to solve EDP. It is only required that each device in the communication network has access to its own local generation cost function, designed virtual local demand and its neighbors' local optimization variables. The proposed finite-time algorithm can solve EDP, if the gain parameters in the algorithm satisfy some conditions under undirected and connected time-varying graphs. Moreover, the bounded or linear increasing assumption on the gradient and subgradient of objecive functions is relaxed in this algorithm. Examples under several cases are provided to verify the effectiveness of the proposed distributed optimization algorithm.
This article addresses the optimization problem with its global objective function being composed of multiple convex functions under multiple nonidentical local constraints. The specific purpose is to resolve the stud...
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This article addresses the optimization problem with its global objective function being composed of multiple convex functions under multiple nonidentical local constraints. The specific purpose is to resolve the studied optimization problem in a distributed manner in the presence of the time-varying unbalanced graph sequence. For this purpose, an efficient distributed discrete-time algorithm is developed over the time-varying unbalanced graph sequence by improving the classical push-pull algorithms. Moreover, for the developed distributed discrete-time algorithm, a rigorous analysis is made of its convergence property to the optimal solution as well as its convergence rate under some standard assumptions. Finally, numerical simulations are carried out to demonstrate the good performance of the designed algorithm.
In this paper, two predefined -time distributed optimization algorithms are proposed to solve a class of resource allocation problem (RAP) with equation constraint. One is a distributed predefined continuous -time opt...
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In this paper, two predefined -time distributed optimization algorithms are proposed to solve a class of resource allocation problem (RAP) with equation constraint. One is a distributed predefined continuous -time optimizationalgorithm and the other is an accelerated distributed predefined discrete iterative optimizationalgorithm. Compared with existing finite -time and fixed -time distributed optimization algorithms, the proposed algorithms can solve the RAP in any given time. The core of the optimizationalgorithms is to design two predefined -time multi -agent weighted consensus algorithms. Especially in the design of discrete algorithm, the performance of optimizationalgorithm has been greatly improved through information composite interaction. Finally, the proposed algorithms are used to solve the problem of power resource allocation in power grids, and five sets of simulation instances are provided to verify the effectiveness and advantages of the proposed algorithms.
This paper focuses on a distributed nonsmooth composite optimization problem over a multiagent networked system, in which each agent is equipped with a local Lipschitz-differentiable function and two possibly nonsmoot...
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This paper focuses on a distributed nonsmooth composite optimization problem over a multiagent networked system, in which each agent is equipped with a local Lipschitz-differentiable function and two possibly nonsmooth functions, one of which incorporates a linear mapping. To address this problem, we introduce a synchronous distributedalgorithm featuring uncoordinated relaxed factors. It serves as a generalized relaxed version of the recent method TriPD-Dist. Notably, the considered problem in the presence of asynchrony and delays remains relatively unexplored. In response, a new asynchronous distributed primal-dual proximal algorithm is first proposed, rooted in a comprehensive asynchronous model. It is operated under the assumption that agents utilize possibly outdated information from their neighbors, while considering arbitrary, time-varying, yet bounded delays. With some special adjustments, new asynchronous distributed extensions of existing centralized methods are obtained via the proposed asynchronous algorithm. Theoretically, a new convergence analysis technique of the proposed algorithms is provided. Specifically, a sublinear convergence rate is explicitly derived by showcasing that the iteration behaves as a nonexpansive operator. In addition, the proposed asynchronous algorithm converges the optimal solution in expectation under the same step-size conditions as its synchronous counterpart. Finally, numerical studies substantiate the efficacy of the proposed algorithms and validate their performance in practical scenarios.
This paper presents a distributed trajectory optimizationalgorithm for the reconfiguration of large-scale spacecraft formation, which uses a low thrust propulsion system to safely guide the spacecraft formation to a ...
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This paper presents a distributed trajectory optimizationalgorithm for the reconfiguration of large-scale spacecraft formation, which uses a low thrust propulsion system to safely guide the spacecraft formation to a desired formation. Formation reconfiguration is formulated as a trajectory optimization problem with complex constraints, and the relative motion is accurately described by a nonlinear relative dynamics considering J2 perturbation. The collision avoidance constraint is convexified into a tangent plane to ensure the accuracy of the solution. The resulting nonlinear trajectory optimization problem is solved by applying the hp-adaptive pseudospectral method to convert it into a nonlinear programming problem. In order to overcome the disadvantage of huge computation of centralized algorithm considering collision avoidance, "predicted trajectory" is introduced to transmit information between spacecraft, and the parallel computing is implemented. Finally, a numerical simulation is given to verify the computational efficiency and collision avoidance effectiveness of the proposed distributedalgorithm.
The utilization of distributed renewable energy sources such as photovoltaic and wind power is characterized by uncertainty and volatility, posing significant challenges to the reliability and stability of integrated ...
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Task allocation is a key aspect of Unmanned Aerial Vehicle(UAV)swarm collaborative *** an continuous increase of UAVs’scale and the complexity and uncertainty of tasks,existing methods have poor performance in comput...
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Task allocation is a key aspect of Unmanned Aerial Vehicle(UAV)swarm collaborative *** an continuous increase of UAVs’scale and the complexity and uncertainty of tasks,existing methods have poor performance in computing efficiency,robustness,and realtime allocation,and there is a lack of theoretical analysis on the convergence and optimality of the *** paper presents a novel intelligent framework for distributed decision-making based on the evolutionary game theory to address task allocation for a UAV swarm system in uncertain scenarios.A task allocation model is designed with the local utility of an individual and the global utility of the ***,the paper analytically derives a potential function in the networked evolutionary potential game and proves that the optimal solution of the task allocation problem is a pure strategy Nash equilibrium of a finite strategy ***,a PayOff-based Time-Variant Log-linear Learning algorithm(POTVLLA)is proposed,which includes a novel learning strategy based on payoffs for an individual and a time-dependent Boltzmann *** former aims to reduce the system’s computational burden and enhance the individual’s effectiveness,while the latter can ensure that the POTVLLA converges to the optimal Nash equilibrium with a probability of *** simulation results show that the approach is optimal,robust,scalable,and fast adaptable to environmental changes,even in some realistic situations where some UAVs or tasks are likely to be lost and increased,further validating the effectiveness and superiority of the proposed framework and algorithm.
Variable-coupled distributed resource allocations (VCDRAs) involve optimizing resource distribution among interconnected entities, reflecting the complex correlation in practical systems. Based on the consensus techno...
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Variable-coupled distributed resource allocations (VCDRAs) involve optimizing resource distribution among interconnected entities, reflecting the complex correlation in practical systems. Based on the consensus technology, this paper proposes a novel distributed optimization algorithm to address the VCDRA in scenarios where agents are confronted with admissible interaction ranges and external disturbances. Admissible interaction refers to agents only transmitting information within a specified range due to limited communication capabilities or resource availability. The proposed distributed optimization algorithm improves existing interaction mechanisms, reducing computational demands and unifying the communication schemes of the multi-agent system, thereby avoiding additional information exchange. Theoretically, it is proven that the algorithm can get the optimal solution of VCDRA with an exponential rate. Compared to existing algorithms for suppressing external disturbances, the robustness of the proposed distributed optimization algorithm no longer relies on the upper bound of external disturbances, allowing it to remain effective even in the presence of unbounded disturbances. Finally, smart grids and wireless communications applications demonstrate the convergence and robustness of the developed distributed optimization algorithm, further proving its superiority in practical applications.
The integration of renewable energy provides a fresh boost for the development of power systems. Economic dispatch (ED) is necessary to achieve optimal power allocation while meeting practical physical constraints and...
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The integration of renewable energy provides a fresh boost for the development of power systems. Economic dispatch (ED) is necessary to achieve optimal power allocation while meeting practical physical constraints and ensuring economic benefits and production safety. Traditional centralized ED methods are not always able to meet the ED demands of microgrids that mostly use distributed power sources. Consequently, distributed ED (i.e. DED) schemes are receiving more research attention because of their high reliability, scalability and uniformity in communication and computation loads. The latest theoretical advancements in DED algorithms are reviewed in this paper to give a comprehensive review. The survey focuses on design considerations of DED optimizationalgorithms for both discrete-time and continuous-time implementation scenarios and cutting-edge engineering applications by incorporating practical operational constraints or privacy-preserving mechanisms. Furthermore, deep insights are provided to reveal the intrinsic mechanism and exceptional performance of various DED algorithms. A brief outline of prospective research directions is provided based on the reviewed literature, which includes DED issues with non-convex and multi-objective functions, fast DED algorithms and DED algorithms without initialization.
The progressing energy transition induces a growing need for redispatch congestion management, and, thereby, a fair distribution of its respective costs among the different system operators. In this light, a very rece...
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ISBN:
(纸本)9798400700323
The progressing energy transition induces a growing need for redispatch congestion management, and, thereby, a fair distribution of its respective costs among the different system operators. In this light, a very recent paper uses the Shapley value as such a fair allocation rule to assign redispatch congestion costs to system operators. However, this approach is based on DC optimal power flow (OPF) and requires the sharing of detailed grid models from all system operators. This is not preferred by them due to data privacy concerns. W.r.t. real-world implementation, the present paper extends the method by using AC OPF problem formulations for more realistic results, and solving them by using a distributed optimization algorithm, i.e., Augmented Lagrangian based Alternating Direction Inexact Newton method (aladin), for preserving data privacy. Simulation results of an illustrative example show great potential of the proposed distributed approach in the aspects of both solution accuracy and computing time. This makes the presented approach generically feasible for real applications in the energy transition.
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