Demand response technology offers the exciting potential to reduce peak energy demand, electricity infrastructure expenditure, and household electricity bills. In this paper, a pricing mechanism that relies on non-coo...
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Demand response technology offers the exciting potential to reduce peak energy demand, electricity infrastructure expenditure, and household electricity bills. In this paper, a pricing mechanism that relies on non-cooperative heterogeneous loads knowledgeable of future energy consumption-such as electric vehicles-transferring minimal amounts of information to achieve peak demand response in a distributed fashion, whilst maintaining the privacy of the players. The existence of a Nash equilibrium is proven, as well as convergence conditions proving uniqueness of a Nash equilibrium and the stability of an "Iterated Synchronous Best Response Algorithm." The price of anarchy (PoA) is proven to approach 1 as the number of homogeneous players approaches infinity, indicating there is no advantage to cooperation for a large number of similar players. Finally, simulation results are presented which suggest that the PoA for a system with heterogeneous players is likely to be proportional to the spread of energy consumption constraints.
This paper presents a non- cooperative, zero-sum, symmetric game approach to projecting the flexible demand-side resources to improve the reliability and robustness of long-term strategic energy planning models. A spe...
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ISBN:
(纸本)9781728169194
This paper presents a non- cooperative, zero-sum, symmetric game approach to projecting the flexible demand-side resources to improve the reliability and robustness of long-term strategic energy planning models. A specifically devised distributed algorithm is put forward to determine the unique, pure-strategy Nash equilibrium of a non-cooperative game formulated for the delivery of aggregator-mediated demand response resources, at which no player can yield a higher payoff by deviating from its best-response strategy. The simulation for the operation of renewable energy systems is run over the course of a year. Furthermore, using a metaheuristic-based solution algorithm proposed for the optimal capacity planning of microgrids, the model was satisfactorily applied to the energy infrastructure planning of a test-case system conceptualized for the town of Ohakune, in New Zealand. The simulation results suggest that not only does the proposed market-directed, incentive-based, strategic demand- side management planning framework help elicit further contributions from the customers, which, in turn, reduces the reserve capacity requirements to meet the peak demand, it also guarantees the highest possible payoff for all players of the game. According to the simulation results, the test-case system's life-cycle cost can be reduced by up to similar to 29% compared to the case, where no demand response is considered.
Active noise control (ANC) is an effective way for reducing the noise level in electroacoustic or electromechanical systems. Since its first introduction in 1936, this approach has been greatly developed. This paper f...
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Active noise control (ANC) is an effective way for reducing the noise level in electroacoustic or electromechanical systems. Since its first introduction in 1936, this approach has been greatly developed. This paper focuses on discussing the development of ANC techniques over the past decade. Linear ANC algorithms, including the celebrated filtered-x least-mean-square (FxLMS)-based algorithms and distributed ANC algorithms, are investigated and evaluated. Nonlinear ANC (NLANC) techniques, such as functional link artificial neural network (FLANN)-based algorithms, are pursued in Part II. Furthermore, some novel methods and applications of ANC emerging in the past decade are summarized. Finally, future research challenges regarding the ANC technique are discussed. (C) 2021 Elsevier B.V. All rights reserved.
In this letter, we introduce a distributed Nesterov gradient method, ABN, that does not require doubly stochastic weights. Instead, the implementation is based on a simultaneous application of both row- and column-sto...
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In this letter, we introduce a distributed Nesterov gradient method, ABN, that does not require doubly stochastic weights. Instead, the implementation is based on a simultaneous application of both row- and column-stochastic weights that makes ABN applicable to arbitrary (strongly-connected) graphs. Since constructing column-stochastic weights needs additional information (the number of outgoing neighbors), not available in certain communication protocols, we derive a variation, FROZEN, that only requires row-stochastic weights, but at the expense of additional iterations for eigenvector estimation. We numerically study these algorithms for various objective functions and network parameters and show that the proposed distributed Nesterov gradient methods achieve acceleration compared to the current state-of-the-art methods for distributed optimization.
In this paper we design and analyze distributed algorithms to compute a Nash equilibrium in potential games. Our algorithms are based on best-response dynamics, with suitable revision sequences (orders of play). We co...
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ISBN:
(纸本)9783952426982
In this paper we design and analyze distributed algorithms to compute a Nash equilibrium in potential games. Our algorithms are based on best-response dynamics, with suitable revision sequences (orders of play). We compute the average complexity over all potential games of best response dynamics under a random i. i. d. revision sequence, since it can be implemented in a distributed way using Poisson clocks. We obtain a distributed algorithm whose execution time is within a constant factor of the optimal centralized one. We then show how to take advantage of the structure of the interactions between players in a network game: non-interacting players can play simultaneously. This improves best response algorithm, both in the centralized and in the distributed case.
Two distributed algorithms to estimate the optimal control input sequence that solves a finite horizon quadratic optimization are proposed. The first algorithm utilizes information from 2-hop neighbors, whereas the se...
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ISBN:
(数字)9781728113982
ISBN:
(纸本)9781728113999
Two distributed algorithms to estimate the optimal control input sequence that solves a finite horizon quadratic optimization are proposed. The first algorithm utilizes information from 2-hop neighbors, whereas the second only considers 1-hop neighbors. The estimates obtained from both algorithms converge asymptotically, under appropriate assumptions, for any initialization of the algorithm. For the 2-hop algorithm, we show that the converged estimate is the optimal solution to the original optimization problem, while for the 1-hop algorithm the result is generally a suboptimal solution. We evaluate the methods with simulations for a leader-follower model predictive control problem with unstable linear agents dynamics.
The vertex colouring problem (VCP) and its generalisations have myriad applications in computer networks. To solve the VCP with $\Delta + 1$Delta+1 colours, numerous distributed algorithms based on LOCAL model have be...
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The vertex colouring problem (VCP) and its generalisations have myriad applications in computer networks. To solve the VCP with $\Delta + 1$Delta+1 colours, numerous distributed algorithms based on LOCAL model have been proposed to reduce time complexity (the number of rounds), where $\Delta $Delta is the maximum vertex degree in the graph. In this paper, the authors present a distributed algorithm based on modified LOCAL model (DIAMOND) that reduces the number of rounds to one. It greedily solves the VCP with at most $\Delta + 1$Delta+1 colours. Computational results on Geometry (GEOM) graphs show that the number of used colours to colour each instance using DIAMOND is about $\left({\Delta + 1} \right)/2$mml:mfenced close=")" open="("Delta+1/2. DIAMOND is easily extended to solve greedily generalised VCPs in only one round. Moreover, they present two efficient resource allocation algorithms using DIAMOND. They allocate more resource to the graph compared with $\lpar \Delta + 1\rpar $(Delta+1)-colouring and even to $\lpar \bar d + 1\rpar $(d over bar +1)-colouring algorithms, where $\bar d$d over bar is the average vertex degree of the graph. They run in two and $\Delta $Delta rounds.
The consensus is a central problem of fault-tolerant distributed computing. Unfortunately, solving such a problem is impossible in asynchronous distributed systems prone to process failures. To circumvent this impossi...
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The consensus is a central problem of fault-tolerant distributed computing. Unfortunately, solving such a problem is impossible in asynchronous distributed systems prone to process failures. To circumvent this impossibility (known as FLP impossibility result) in a deterministic way, on top of asynchronous distributed systems enriched with additional assumptions, several protocols have been proposed. Actually, to solve the Byzantine Consensus problem, with a deterministic manner, in systems where at most t processes may exhibit a Byzantine behavior, two approaches have been investigated. The first relies on the addition of synchrony, called TimerBased, while the second, called Time-Free, is based on the pattern of message exchange. This paper shows that both types of assumptions are not antagonist and can be combined to solve authenticated Byzantine consensus. The combined assumption considers a correct process p(i), called lozenge < t+1 >-BW, and a set X of t+1 correct processes (including pi itself) such that, eventually, for each query broadcasted by a correct process p(j) of X, p(j) receives a response from p(i) is an element of X among the (n - t) first responses to that query or both links connecting p(i) and p(j) are timely. Based on this combination, a simple hybrid authenticated Byzantine consensus protocol benefiting from the best of both worlds is proposed. As a matter of fact, although numerous hybrid protocols have been designed for the consensus problem in the crash model, this is, to our knowledge, the first hybrid deterministic solution to the Byzantine consensus problem.
Optimization algorithms play a significant role in the optimal solution of various problems in Smart Grid. distributed algorithms are of considerable research interest as these algorithms replace the centrally compute...
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Optimization algorithms play a significant role in the optimal solution of various problems in Smart Grid. distributed algorithms are of considerable research interest as these algorithms replace the centrally computed algorithms. This paper surveys the literature on the Alternating Direction Method of Multipliers (ADMM) algorithm, its versions and their applications in Smart grid operation and control, particularly in Optimal Power Flow (OPF), Economic Dispatch (ED), Demand Response (DR), pricing mechanism, Electric Vehicles (EVs), Cyber-Physical Systems (CPS), Multi Energy Systems (MES), Peer to Peer (P2P) trading, resilience, forecast techniques, State Estimation (SE), and some miscellaneous topics. This paper presented research gaps and future directions on all these applications in smart grids. Furthermore, it provided a joint research gap on all these applications, such as the impact of communication failures or communication delays, effect of uncertainties, incorporating the fraudulent behaviour of participants, usage of Blockchain technology, implementation of smart contract on the performance of these algorithms. Furthermore, the work has developed a decentralized and distributed automatic electricity trading mechanism for the independent microgrid without needing a central aggregator.
Programmable matter based on modular self-reconfigurable robots could stand as the ultimate form of display system, through which humans could not only see the virtual world in 3D, but manipulate it and interact with ...
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Programmable matter based on modular self-reconfigurable robots could stand as the ultimate form of display system, through which humans could not only see the virtual world in 3D, but manipulate it and interact with it through touch. These systems rely on self-reconfiguration processes to reshape themselves and update their representation, using methods that we argue, are currently too slow for such applications due to a lack of parallelism in the motion of the robotic modules. Therefore, we propose a novel approach to the problem, promising faster and more efficient self-reconfigurations in programmable matter display systems. We contend that this can be achieved by using a dedicated platform supporting self-reconfiguration named a sandbox, acting as a reserve of modules, and by engineering the representation of objects using an internal scaffolding covered by a coating. This paper introduces a complete view of our framework for realizing this approach on quasi-spherical modules arranged in a face-centered cubic lattice. After thoroughly discussing the model, motivations, and making a case for our method, we synthesize results from published research highlighting its benefits and engage in an honest and critical discussion of its current state of implementation and perspectives. (C) 2021 Elsevier B.V. All rights reserved.
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