Distributed Finite-Time Economic Dispatch of a Network of Energy Resources

Chen, Gang; Ren, Jing; Feng, Eryan

doi:10.1109/tsg.2016.2516017

Cited by 111 publications

(73 citation statements)

References 30 publications

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“…In this paper, we focus on a class of distributed convex optimization problem with the following optimization objective:

x^{*} = \underset{x \in R^{n}}{arg min} \sum_{i = 1}^{N} f_{i} false(x false),

where f i : R n → R is a local cost function and is assumed to be strongly convex; x ∗ ∈ R n is the optimal value of

\sum_{i = 1}^{N} f_{i} false(x false)

. This optimal problem widely exists in real‐world applications such as sensor scheduling, source localization, distributed active power optimal control in power systems, and so on. For the distributed optimization problem , a large number of work have been carried out.…”

Section: Introductionmentioning

confidence: 99%

“…where f i ∶ R n → R is a local cost function and is assumed to be strongly convex; x * ∈ R n is the optimal value of ∑ N i=1 i (x). This optimal problem widely exists in real-world applications such as sensor scheduling, 1,2 source localization, 3 distributed active power optimal control in power systems, 4 and so on. For the distributed optimization problem (1), a large number of work have been carried out.…”

Section: Introductionmentioning

confidence: 99%

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Distributed zero‐gradient‐sum algorithm for convex optimization with time‐varying communication delays and switching networks

Guo

Chen

2018

Intl J Robust & Nonlinear

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Summary The distributed convex optimization problem subject to time‐varying communication delays and switching network topologies is addressed in this paper. Based on continuous‐time Zero‐Gradient‐Sum scheme, the novel distributed algorithms are proposed to minimize the global objective function which is composed of a sum of strictly convex local cost functions. In the fixed network topology case, by constructing a new Lyapunov‐Krasovskii function, two explicit sufficient conditions for the maximum admissible time delay are derived to guarantee that all agents' states converge to the optimal solution. In the switching network topology case, the stability condition is derived by the common Lyapunov function theory. In addition, two sufficient conditions about the maximum admissible time delays are also derived for the fixed and switching weight‐balanced network topologies, respectively. Several simulation tests are used to illustrate the effectiveness of our obtained theoretical results.

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“…In this paper, we focus on a class of distributed convex optimization problem with the following optimization objective:

x^{*} = \underset{x \in R^{n}}{arg min} \sum_{i = 1}^{N} f_{i} false(x false),

where f i : R n → R is a local cost function and is assumed to be strongly convex; x ∗ ∈ R n is the optimal value of

\sum_{i = 1}^{N} f_{i} false(x false)

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed zero‐gradient‐sum algorithm for convex optimization with time‐varying communication delays and switching networks

Guo

Chen

2018

Intl J Robust & Nonlinear

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“…The ED solves an optimisation problem where the goal is to achieve the minimum operating cost of the MG, subject to some operating constraints. It is worth to mention that the ED can be implemented using centralised, decentralised [262]- [267] and distributed control approaches [44], [112], [132], [134], [135], [138], [139], [148], [169], [268]- [281].…”

Section: Distributed Tertiary Controlmentioning

confidence: 99%

Distributed Control Strategies for Microgrids: An Overview

et al. 2020

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There is an increasing interest and research effort focused on the analysis, design and implementation of distributed control systems for AC, DC and hybrid AC/DC microgrids. It is claimed that distributed controllers have several advantages over centralised control schemes, e.g., improved reliability, flexibility, controllability, black start operation, robustness to failure in the communication links, etc. In this work, an overview of the state-of-the-art of distributed cooperative control systems for isolated microgrids is presented. Protocols for cooperative control such as linear consensus, heterogeneous consensus and finitetime consensus are discussed and reviewed in this paper. Distributed cooperative algorithms for primary and secondary control systems, including (among others issues) virtual impedance, synthetic inertia, droopfree control, stability analysis, imbalance sharing, total harmonic distortion regulation, are also reviewed and discussed in this survey. Tertiary control systems, e.g., for economic dispatch of electric energy, based on cooperative control approaches, are also addressed in this work. This review also highlights existing issues, research challenges and future trends in distributed cooperative control of microgrids and their future applications.

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“…Thus, after replacing (9) into (8), and some re-arrange, the dynamics of the consensus variable of each DG unit can be updated using (10), which can be seen as the weighted average of its current state and the current state of its neighbors units.…”

Section: A First-order Consensus Algorithmmentioning

confidence: 99%

“…In [9], a term is added to the consensus algorithm using only local information based on the nodal power balance equation, which plays the role of a gradient. In [10], [11] a modified consensus algorithm with finitetime convergence characteristics is presented, while in [12] a distributed gradient-based algorithm is developed, taken the derivative of the cost function of each DG unit as the consensus variable.…”

mentioning

confidence: 99%