A Distributed Cooperative Control Framework for Synchronized Reconnection of a Multi-Bus Microgrid

Shi, Di; Chen, Xi; Wang, Zhiwei; Zhang, Xiaohu; Yu, Zhe; Wang, Xinan; Bian, Desong

doi:10.1109/tsg.2017.2717806

Cited by 60 publications

(43 citation statements)

References 23 publications

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“…The droop coefficients kpi's and kqi's are typically set inversely proportional to capacities of DGs for power sharing, although there can be many different ways [11]. In steady state, equalization of both kpi Pi =kpjPj and ωi=ωj for i, jN can be reached since the transmission of active power between buses or DGs is dependent upon phase angle differences, i.e., the time integration of output frequency.…”

Section: B Distributed Control With Communication Delaymentioning

confidence: 99%

“…x Ky 0 A D (11) where D is the degree matrix as defined in subsection II. A, x0 is the initial value of x, and , for ,…”

Section: A Proposed Microgrid Modelmentioning

confidence: 99%

“…where vdei and vqei are the d-axis and q-axis components of a voltage at the system equilibrium point, respectively. According to these definitions, a state vector made up of DGs' terminal voltages in the dq reference frame can be written as: 11 , ,..., , With voltage and current of DGi known, its active and reactive power outputs, which are elements of y, can be calculated as:…”

Section: A Proposed Microgrid Modelmentioning

confidence: 99%

“…The conclusions may not hold if communication delay is considered. Authors in [11] show that communication delay has an impact on the resilience of microgrid under distributed control with the shortage of theories. Reference [12] studies microgrid stability through eigenvalue analysis, which unfortunately is not eligible for microgrids under distributed control with communication latency.…”

mentioning

confidence: 99%

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Small-Signal Stability Analysis and Performance Evaluation of Microgrids Under Distributed Control

Yan

Shi

Bian

et al. 2019

IEEE Trans. Smart Grid

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Distributed control, as a potential solution to decreasing communication demands in microgrids, has drawn much attention in recent years. Advantages of distributed control have been extensively discussed, while its impacts on microgrid performance and stability, especially in the case of communication latency, have not been explicitly studied or fully understood yet. This paper addresses this gap by proposing a generalized theoretical framework for small-signal stability analysis and performance evaluation for microgrids using distributed control. The proposed framework synthesizes generator and load frequency-domain characteristics, primary and secondary control loops, as well as the communication latency into a frequency-domain representation which is further evaluated by the generalized Nyquist theorem. In addition, various parameters and their impacts on microgrid dynamic performance are investigated and summarized into guidelines to help better design the system. Case studies demonstrate the effectiveness of the proposed approach.

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Section: B Distributed Control With Communication Delaymentioning

confidence: 99%

“…x Ky 0 A D (11) where D is the degree matrix as defined in subsection II. A, x0 is the initial value of x, and , for ,…”

Section: A Proposed Microgrid Modelmentioning

confidence: 99%

Section: A Proposed Microgrid Modelmentioning

confidence: 99%

mentioning

confidence: 99%

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Small-Signal Stability Analysis and Performance Evaluation of Microgrids Under Distributed Control

Yan

Shi

Bian

et al. 2019

IEEE Trans. Smart Grid

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“…Theorem 1 (Optimal Control Law u * ): For a non-linear uncertain system given in Eqn. (2) with the cost function defined in Eqn. (5) and HJB equation defined in Eqn.…”

Section: A Optimal Control Formulation For Uncertain Systemmentioning

confidence: 99%

A Neuron-Network-Based Optimal Control of Ultra-Capacitors with System Uncertainties

Duan

Shi

et al. 2019

2019 IEEE Power &Amp; Energy Society Innovative Smart Grid Technologies Conference (ISGT)

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In this paper, a neural-network (NN)-based online optimal control method (NN-OPT) is proposed for ultracapacitors (UCs) energy storage system (ESS) in hybrid AC/DC microgrids involving multiple distributed generations (e.g., Photovoltaic (PV) system, battery storage, diesel generator). Conventional control strategies usually produce large disturbances to buses during charging and discharging (C&D) processes of UCs, which significantly degrades the power quality and system performance, especially under fast C&D modes. Therefore, the optimal control theory is adopted to optimize the C&D profile as well as to suppress the disturbances caused by UCs implementation. Specifically, an NN-based intelligent algorithm is developed to learn the optimal control policy for bidirectionalconverter-interfaced UCs. The inaccuracies of system modeling are also considered in the control design. Since the designed NN-OPT method is decentralized that only requires the local measurements, plug & play of UCs can be easily realized with minimal communication efforts. In addition, the PV system is under the maximum power point tracking (MPPT) control to extract the maximum benefit. Both islanded and grid-tied modes are considered during the controller design. Extensive case studies have been conducted to evaluate the effectiveness of proposed method.

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Synchronization in Interconnected Microgrids

2023

Microgrids

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A Distributed Cooperative Control Framework for Synchronized Reconnection of a Multi-Bus Microgrid

Cited by 60 publications

References 23 publications

Small-Signal Stability Analysis and Performance Evaluation of Microgrids Under Distributed Control

Small-Signal Stability Analysis and Performance Evaluation of Microgrids Under Distributed Control

A Neuron-Network-Based Optimal Control of Ultra-Capacitors with System Uncertainties

Synchronization in Interconnected Microgrids

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