This paper studies distributed resource allocation problem for agents with input dead-zone, which is not considered in the existing work. At first, the primal problem is transformed to an auxiliary problem by using th...
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This paper studies distributed resource allocation problem for agents with input dead-zone, which is not considered in the existing work. At first, the primal problem is transformed to an auxiliary problem by using the exact penalty method to deal with local inequality constraints. It is assumed that the explicit expressions of cost functions and local inequality constraints are unknown to agents but the values of the cost and constraint functions can be obtained. Under such a setup, the extremum seeking control is used to estimate the gradient information. Thus, to obtain the optimal allocation, a novel distributed algorithm is designed by the virtue of the extremum seeking control and a dynamic compensating mechanism which is used to handle the effects of the input dead-zone. Due to a two time-scale structure of the designed distributed algorithm, the semi-globally practically asymptotical convergence of all agents' decisions to the optimal allocation is obtained by the singular perturbation technique. Finally, numerical examples of economic dispatch in smart grids are given to verify the effectiveness of our proposed method.
To minimize the overall generation cost under the restrictions of supply and demand balance and indi-vidual generator capacity, the economic dispatch problem (EDP) over directed communication network is considered in ...
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To minimize the overall generation cost under the restrictions of supply and demand balance and indi-vidual generator capacity, the economic dispatch problem (EDP) over directed communication network is considered in this research. This article develops a distributed consensus-based algorithm to solve the EDP that can uniformly pre-assign the settling time. Different from the work with finite-time/fixed-time control, the proposed one allows the settling time to be a user-defined parameter independent of the initial states and other parameters. Moreover, the algorithm does not require initialization and allows the online changes of participating nodes and load demand, it can handle dynamic changes with a pre-arranged convergence time according to the need of the application. In addition, only a dual variable is exchanged with neighboring nodes, and no private information of the node is disclosed. Finally, the sim-ulation results verify the effectiveness of our algorithm.(c) 2023 Elsevier B.V. All rights reserved.
In this brief, the problem of distributively solving a mixed equilibrium problem (EP) with multiple sets is investigated. A network of agents is employed to cooperatively find a point in the intersection of multiple c...
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In this brief, the problem of distributively solving a mixed equilibrium problem (EP) with multiple sets is investigated. A network of agents is employed to cooperatively find a point in the intersection of multiple convex sets ensuring that the sum of multiple bifunctions with a free variable is nonnegative. Each agent can only access information associated with its own bifunction and a local convex set. To solve this problem, a distributed algorithm involving a fixed step size is proposed by combining the mirror descent algorithm, the primal-dual algorithm, and the consensus algorithm. Under mild conditions on bifunctions and the graph, we prove that all agents' states asymptotically converge to a solution of the mixed EP. A numerical simulation example is provided for demonstrating the effectiveness of theoretical results.
A distributed algorithm is presented to solve the economic dispatch problem in power systems. By selecting the incremental cost of each generation unit and the incremental benefit of each elastic load as the consensus...
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ISBN:
(纸本)9789881563897
A distributed algorithm is presented to solve the economic dispatch problem in power systems. By selecting the incremental cost of each generation unit and the incremental benefit of each elastic load as the consensus variable, the proposed algorithm is able to solve the conventional centralized economic dispatch problem in a distributed manner. The proposed algorithm is a first-order consensus protocol modified by a correction term which uses an estimation of the system power mismatch to ensure the generation-demand equality. The results of several simulations demonstrate the effectiveness of the proposed methodology.
With the exponential growth of graph structured data in recent years, parallel distributed techniques play an increasingly important role in processing large-scale graphs. Since strong connections exist between vertic...
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ISBN:
(纸本)9783319271613;9783319271606
With the exponential growth of graph structured data in recent years, parallel distributed techniques play an increasingly important role in processing large-scale graphs. Since strong connections exist between vertices in graph data, the high communication cost for transforming boundary data is unavoidable in the distributed techniques. How to partition a large graph into several partitions with low coupling and balanced scale becomes a critical problem. Most of research in the literature studies vertex partitioning methods, which leads us to reconsider an alternative approach for edge partitioning. In this paper, we propose a distributed algorithm for graph partition based on edge partitioning, named as VSEP. A novel vertex permutation method is used to partition the large graphs iteratively. Experimental results indicate that VSEP reduces the number of times vertices are cut by about 10% similar to 20% comparing with a state-of-the-art algorithm while retains the scale balance.
We investigate the problem of power balancing in a general renewable-integrated power grid with storage and flexible loads. We consider a power grid that is supplied by one conventional generator (CG) and multiple ren...
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ISBN:
(纸本)9781479975914
We investigate the problem of power balancing in a general renewable-integrated power grid with storage and flexible loads. We consider a power grid that is supplied by one conventional generator (CG) and multiple renewable generators (RGs) each co-located with storage, and is additionally connected with external markets. An aggregator operates the power grid and aims at minimizing the long-term system cost. We propose a distributed real-time power balancing solution, taking into account the uncertainty of the renewable generation, loads, and energy prices. We demonstrate that our proposed algorithm is asymptotically optimal as the storage capacity increases and the CG ramping constraint loosens. The distributed implementation enjoys a fast convergence rate and enables each RG and the aggregator to make their own decisions.
This paper deals with iterative formation control problems for multi-agent systems with switching topologies. Our approach combines consensus protocol algorithms conducted with an iterative learning control update. Th...
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ISBN:
(纸本)9781479986842
This paper deals with iterative formation control problems for multi-agent systems with switching topologies. Our approach combines consensus protocol algorithms conducted with an iterative learning control update. This leads to networks dynamically changing with evolution along two directions: a finite time axis and an infinite iteration axis. A distributed algorithm is constructed based on nearest neighbor information, and its convergence is established via a Roesser system-based two-dimensional approach. It is shown that a desired relative formation between agents can be achieved along a finite time trajectory if and only if the union of the interaction graphs between the agents over iteration intervals of finite length has a spanning tree for every time step. Simulations are included to illustrate the effectiveness of the derived results.
This paper presents an algorithmic development in the framework of computationally efficient robust Nonlinear Model Predictive Control (NMPC) which deals with a parametric plant-model mismatch, where the description o...
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ISBN:
(纸本)9783952426937
This paper presents an algorithmic development in the framework of computationally efficient robust Nonlinear Model Predictive Control (NMPC) which deals with a parametric plant-model mismatch, where the description of the evolution of the uncertainty is done using a scenario tree, known as multi-stage approach. In order to reduce the computational time and memory requirements of the multistage NMPC, the calculations of the optimal control inputs can be done scenario-wise in parallel. These parallelized calculations must enforce the satisfaction of the non-anticipativity constraints, which is negotiated iteratively among the scenarios using Lagrangean or price-driven decomposition. The main challenge in using such scheme is the determination of the values of the aggregate variables that are used to coordinate the scenario-wise computations for convergence to the feasibility of the non-anticipativity constraints. The proposed approach uses parametric sensitivities of the optimal model states with respect to the control inputs which are used for the iterative determination of the values of aggregated variables. The proposed method achieves good performance and faster convergence compared to traditional decomposition schemes. The potential of the approach is demonstrated in simulation example of an hydrodesulphurisation unit.
In this article, the problem of distributed generalized Nash equilibrium (GNE) seeking in noncooperative games is investigated via multiagent networks, where each player aims to minimize his or her own cost function w...
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In this article, the problem of distributed generalized Nash equilibrium (GNE) seeking in noncooperative games is investigated via multiagent networks, where each player aims to minimize his or her own cost function with a nonsmooth term. Each player's cost function and feasible action set in the noncooperative game are both determined by actions of others who may not be neighbors, as well as his/her own action. Particularly, feasible action sets are constrained by private convex inequalities and shared linear equations. Each player can only have access to his or her own cost function, private constraint, and a local block of shared constraints, and can only communicate with his or her neighbours via a digraph. To address this problem, a novel continuous-time distributed primal-dual algorithm involving Clarke's generalized gradient is proposed based on consensus algorithms and the primal-dual algorithm. Under mild assumptions on cost functions and graph, we prove that players' actions asymptotically converge to a GNE. Finally, a simulation is presented to demonstrate the effectiveness of our theoretical results.
distributed algorithms are gaining increasing research interests in the area of power system optimization and dispatch. Existing distributed power dispatch algorithms (DPDAs) usually assume that suppliers/consumers bi...
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distributed algorithms are gaining increasing research interests in the area of power system optimization and dispatch. Existing distributed power dispatch algorithms (DPDAs) usually assume that suppliers/consumers bid truthfully. However, this article shows the need for DPDAs to consider strategic players and to take account of their behavior deviation from what the DPDAs expect. To address this, we propose a distributed strategy update algorithm (DSUA) on top of a DPDA. The DSUA considers strategic suppliers who optimize their bids in a DPDA, using only the information accessible from a DPDA, that is, price. The DSUA also considers the cases when suppliers update bids alternately or simultaneously. Under both cases, we show the closeness of supplier bids to the Nash equilibrium via game-theoretic analysis as well as simulation.
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