The purpose of this paper is to utilize enhanced distributed neural dynamics to address the optimization scheduling problem of integrated energy systems (IES). The combination of neural dynamics algorithms and distrib...
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ISBN:
(数字)9798350351668
ISBN:
(纸本)9798350351675
The purpose of this paper is to utilize enhanced distributed neural dynamics to address the optimization scheduling problem of integrated energy systems (IES). The combination of neural dynamics algorithms and distributed algorithms can solve large-scale optimization problems more quickly. Incorporating a fractional order PI control mechanism into distributed neural dynamics enhances the selection range and robustness of the entire control system. When an IES unit transmits information to nearby units, there may be communication noise that could affect the IES. To mitigate the effects of communication noise on IES, an enhanced distributed neural dynamics algorithm incorporates a uniform gain function to suppress noise in network communication. Simulation examples show that communication noise can significantly impact the overall optimization scheduling of the IES. The proposed algorithm successfully mitigates communication noise.
A new and efficient distributed algorithm based on multi-parameter quadratic programming (MPQP) is proposed in this paper to realize distributed voltage optimization of coupled HVDNs and MVDNs. With the boundary varia...
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ISBN:
(数字)9798350386127
ISBN:
(纸本)9798350386134
A new and efficient distributed algorithm based on multi-parameter quadratic programming (MPQP) is proposed in this paper to realize distributed voltage optimization of coupled HVDNs and MVDNs. With the boundary variables between HVDNs and MVDNs as programming parameters, the proposed method transforms the solution of distributed voltage optimization model into the calculation and searches for the solution set for the parameters' corresponding quadratic functions, thus realizing efficient and distributed global iterative computation. Finally, the results show that our method can obtain a global optimal solution deviating from that provided by global centralized voltage regulation method by only 0.16%, and outperform the mainstream distributed algorithms in terms of the number of iterations and computation duration.
In this paper, we investigate the nonconvex L-2 - L-infinity containment control. The objective is to use local information to make all followers move to the convex hull formed by multiple stationary leaders while sat...
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ISBN:
(数字)9781665495721
ISBN:
(纸本)9781665495721
In this paper, we investigate the nonconvex L-2 - L-infinity containment control. The objective is to use local information to make all followers move to the convex hull formed by multiple stationary leaders while satisfying a desired L-2 - L-infinity performance index and the control input of each follower remain in their corresponding constraint set. Based on constraint operator and Lyapunov function, some sufficient condition are given to solve nonconvex L-2 - L-infinity containment control problem.
In this paper, we consider the prescribed-time distributed Nash equilibrium (PTDNE) seeking problem in noncooperative monotone games under directed graphs. For the purpose of compensating for the absence of strong mon...
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ISBN:
(数字)9781665464543
ISBN:
(纸本)9781665464550
In this paper, we consider the prescribed-time distributed Nash equilibrium (PTDNE) seeking problem in noncooperative monotone games under directed graphs. For the purpose of compensating for the absence of strong monotoncity condition, a time-varying decay regularization term is implemented in the merely monotone game. By using the regularization scheme, a new prescribed-time distributed algorithm is established for seeking the least-norm Nash equilibrium in monotone games, where the leader-following consensus protocol and gradient play are incorporated. Based on the proposed strategies, some sufficient conditions on time-varying parameters are provided through Lyapunov function analysis, under which the players’ actions are capable of converging to the least-norm one of the Nash equilibria in a prescribed time. Finally, numerical simulations on a network of players demonstrate the benefits of the proposed algorithm in achieving convergence at any arbitrary time.
With the increasing penetration of distributed generators, distributed algorithms have become essential for efficient power system operations. However, these algorithms are often vulnerable to noise and communication ...
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ISBN:
(数字)9798350373929
ISBN:
(纸本)9798350373936
With the increasing penetration of distributed generators, distributed algorithms have become essential for efficient power system operations. However, these algorithms are often vulnerable to noise and communication delays due to their dependence on communication infrastructures. A consensus-based decentralized method is presented in this paper to solve economic dispatch in microgrids, accounting for practical communication challenges such as dynamically switching topologies, communication delays, and noise. A proportional-integral (PI) consensus protocol is proposed to address these issues, which includes an integral term to mitigate the harmful effects of noise and delays. The multi-agent system (MAS) model is used to describe the microgrid. A distributed PI consensus approach with two primary consensus variables—the global power imbalance (∆P ) and the incremental cost (λ)—is employed to solve the economic dispatch problem. A key benefit of this method is that it allows generating units to maintain the confidentiality of their cost characteristics. The effectiveness of the presented approach is demonstrated by simulation results on a microgrid with five buses.
Recent studies have explored collaborative Transformer-based inference in edge computing (EC), but they often overlook the mobility of users and edge devices, leading to potential reliability issues. This paper aims t...
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ISBN:
(数字)9798331520861
ISBN:
(纸本)9798331520878
Recent studies have explored collaborative Transformer-based inference in edge computing (EC), but they often overlook the mobility of users and edge devices, leading to potential reliability issues. This paper aims to minimize inference latency in mobile edge computing (MEC) by considering heterogeneity in mobility, computation, and communication. We propose a task partitioning model utilizing the GPipe scheme for Transformer-based inference. The task partitioning and offloading problem is then formulated with constraints on computation resources and mobility, decomposed into a bin-packing problem and an integer optimization problem. To solve these subproblems, we introduce the distributed Aggregated Competition Algorithm (DACA). Extensive simulations and testbed experiments demonstrate the high performance of our proposed algorithm in minimizing inference latency across heterogeneous mobile edge devices and networks.
In this paper, we consider an energy trading problem in a network of interconnected MicroGrids. We consider a model in which each unit can produce, consume, or store energy and is classified as a seller or buyer, depe...
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ISBN:
(数字)9798350358513
ISBN:
(纸本)9798350358520
In this paper, we consider an energy trading problem in a network of interconnected MicroGrids. We consider a model in which each unit can produce, consume, or store energy and is classified as a seller or buyer, depending on its energy status. Indeed, the sellers have an excess of energy to be sold or stored, while the buyers, instead, need to buy energy from the other units or the main grid to satisfy their energy demand. In this setting, we formulate a cooperative optimization problem with the aim of finding the best tradeoff between the competitive objectives of (i) maximizing the sellers’ revenue, (ii) ensuring storage, (iii) minimizing the buyers’ energy cost, and (iv) satisfying the energy demand. Then, we recast the obtained problem in the so-called aggregative optimization scenario, a recently emerged framework in which a network of agents aims at cooperatively minimizing the sum of local functions each depending on both global (the so-called aggregative variable) and local quantities. Hence, we propose a distributed scheme tailored for aggregative optimization. The numerical simulations confirm the effectiveness of our approach showing the convergence of the chosen distributed algorithm to a stationary point of the problem. Finally, we test the flexibility of the model by considering scenarios where agents have different preferences.
This paper presents a fully distributed algorithm for scheduling electric vehicle (EV) charging and discharging to flatten the total load of the grid, while considering constraints on grid transmission capacity. As a ...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
This paper presents a fully distributed algorithm for scheduling electric vehicle (EV) charging and discharging to flatten the total load of the grid, while considering constraints on grid transmission capacity. As a fully distributed solution, the proposed algorithm operates without the need for a central unit. Instead, each agent only communicates a single dual variable with its neighboring agents based on a communication graph, and thus no private information is shared. In particular, the algorithm does not rely on initial conditions, ensuring robustness in online changes of operational conditions. Simulation results verify the effectiveness of the proposed algorithm.
In this paper, we study distributed prime-dual flows for multi-agent optimization with spatio-temporal compressions. The central aim of multi-agent optimization is for a network of agents to collaboratively solve a sy...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
In this paper, we study distributed prime-dual flows for multi-agent optimization with spatio-temporal compressions. The central aim of multi-agent optimization is for a network of agents to collaboratively solve a system-level optimization problem with local objective functions and node-to-node communication by distributed algorithms. The scalability of such algorithms crucially depends on the complexity of the communication messages, and a number of communication compressors for distributed optimization have recently been proposed in the literature. First of all, we introduce a general spatio-temporal compressor characterized by the stability of the resulting dynamical system along the vector field of the compressor. We show that several important distributed optimization compressors such as the greedy sparsifier, the uniform quantizer, and the scalarizer all fall into the category of this spatio-temporal compressor. Next, we propose two distributed prime-dual flows with the spatio-temporal compressors being applied to local node states and local error states, respectively, and prove (exponential) convergence of the node trajectories to the global optimizer for (strongly) convex cost functions. Finally, a few numerical examples are present to illustrate our theoretical results.
This paper presents a distributed algorithm based on mutual feedback mechanism for economic dispatch problem of power system. In such a mutual feedback mechanism, the average of the load demand and power generated are...
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ISBN:
(数字)9798350366204
ISBN:
(纸本)9798350366211
This paper presents a distributed algorithm based on mutual feedback mechanism for economic dispatch problem of power system. In such a mutual feedback mechanism, the average of the load demand and power generated are estimated, respectively. Simulation results on standard test cases show the convergence and effectiveness of the proposed algorithm.
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