Next generation of smart grids face a number of challenges including co-generation from intermittent renewable power sources, a shift away from monolithic control due to increased market deregulation, and robust opera...
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ISBN:
(纸本)9781450337700
Next generation of smart grids face a number of challenges including co-generation from intermittent renewable power sources, a shift away from monolithic control due to increased market deregulation, and robust operation in the face of disasters. Such heterogeneous nature and high operational readiness requirement of smart grids necessitates decentralized control for critical tasks such as power supply restoration (PSR) after line failures. We present a novel multiagent system based approach for PSR using Lagrangian dual decomposition. Our approach works on general graphs, provides provable quality-bounds and requires only local message-passing among different connected sub-regions of a smart grid, enabling decentralized control. Using these quality bounds, we show that our approach can provide near-optimal solutions on a number of large real-world and synthetic benchmarks. Our approach compares favorably both in solution quality and scalability with previous best multiagent PSR approach.
We address the problem of solving math programs defined over a graph where nodes represent agents and edges represent interaction among agents. The objective and constraint functions of this program model the task age...
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ISBN:
(纸本)9781450363099
We address the problem of solving math programs defined over a graph where nodes represent agents and edges represent interaction among agents. The objective and constraint functions of this program model the task agent team must perform and the domain constraints. In this multiagent setting, no single agent observes the complete objective and all the constraints of the program. Thus, we develop a distributed message-passing approach to solve this optimization problem. We focus on the class of graph structured linear and quadratic programs (LPs/QPs) which can model important multiagent coordination frameworks such as distributed constraint optimization (DCOP). For DCOPs, our framework models functional constraints among agents (e.g. resource, network flow constraints) in a much more tractable fashion than previous approaches. Our iterative approach has several desirable properties---it is guaranteed to find the optimal solution for LPs, converges for general cyclic graphs, and is memory efficient making it suitable for resource limited agents, and has anytime property. Empirically, our approach provides solid empirical results on several standard benchmark problems when compared against previous approaches.
Complete algorithms have been proposed to solve problems modelled as distributed constraint optimization (DCOP). However, there are only few attempts to address real world scenarios using this formalism, mainly becaus...
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ISBN:
(纸本)9780981738116
Complete algorithms have been proposed to solve problems modelled as distributed constraint optimization (DCOP). However, there are only few attempts to address real world scenarios using this formalism, mainly because of the complexity associated with those algorithms. In the present work we compare three complete algorithms for DCOP, aiming at studying how they perform in complex and dynamic scenarios of increasing sizes. In order to assess their performance we measure not only standard quantities such as number of cycles to arrive to a solution, size and quantity of exchanged messages, but also computing time and quality of the solution which is related to the particular domain we use. This study can shed light in the issues of how the algorithms perform when applied to problems other than those reported in the literature (graph coloring, meeting scheduling, and distributed sensor network).
distributedconstraints optimization (DCOP) is a powerful framework for representing and solving distributed combinatorial problems, where the variables of the problem are owned by different agents. DCOP algorithms se...
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ISBN:
(纸本)9780982657119
distributedconstraints optimization (DCOP) is a powerful framework for representing and solving distributed combinatorial problems, where the variables of the problem are owned by different agents. DCOP algorithms search for the optimal solution, optimizing the total gain (or cost) that is composed of all gains of all agents. Local search (LS) DCOP algorithms search locally for an approximate such *** multi-agent problems include constraints that produce different gains (or costs) for the participating agents. Asymmetric gains of constrained agents cannot be naturally represented by the standard DCOP *** present paper proposes a general framework for Asymmetric DCOPs (ADCOPs). The new framework is described and its differences from former attempts are discussed. New local search algorithms for ADCOPs are introduced and their advantages over existing algorithms and over former representations are discussed in *** new proposed algorithms for the ADCOP framework are evaluated experimentally and their performance compared to existing algorithms. Two measures of performance are used: quality of solutions and loss of privacy. The results show that the new algorithms significantly outperform existing DCOP algorithms with respect to both measures.
Resource allocation problems on resource supply networks are formalized with distributed constraint optimization Problems. In previous studies, solution methods based on pseudo trees have been proposed. However, when ...
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ISBN:
(纸本)9781479939329
Resource allocation problems on resource supply networks are formalized with distributed constraint optimization Problems. In previous studies, solution methods based on pseudo trees have been proposed. However, when the pseudo trees contain nodes of high degree and a large number of cycles, those methods are not applicable due to the large size of local problems in agents. Here, we employ cluster trees that hierarchically divide the problem into sub-problems. With optimistic approximation, solution methods on the cluster tree are applied to several large problems.
Eco-driving is a driving style that can significantly reduce fuel consumption and CO_2 emission. Current methods for eco-driving practice are inefficient or not easily accessible. Therefore, we introduce iCO_2, an onl...
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ISBN:
(纸本)9781450319935
Eco-driving is a driving style that can significantly reduce fuel consumption and CO_2 emission. Current methods for eco-driving practice are inefficient or not easily accessible. Therefore, we introduce iCO_2, an online multi-user three-dimensional (3D) eco-driving training space, which was developed in Unity 3D and made available as a Facebook application since September 2012. In iCO_2, agents are trained to act as "opponents" that create eco-challenges for users, i.e. situations that make eco-driving difficult. The (eco-)challenge is optimized for all users using distributed constraint optimization. iCO_2 is the first application to address the problem of multiuser real-time challenge balancing. Visitors of our demo will be able to join the simulation via Facebook (web client) or iPad (iOS client), and compete for the best eco-score in a shared 3D virtual environment depicting a part of Tokyo.
In this paper, we formulate the microgrid islanding problem as distributed constraint optimization problem (DCOP) and investigate the feasibility of solving it using off-the-shelf DCOP algorithms. This paper puts forw...
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ISBN:
(纸本)9781479912537
In this paper, we formulate the microgrid islanding problem as distributed constraint optimization problem (DCOP) and investigate the feasibility of solving it using off-the-shelf DCOP algorithms. This paper puts forward the potential of distributedconstraint reasoning paradigm as a candidate for solving common microgrids problems.
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