An Adaptive Approach for Effective Power Management in DC Microgrid Based on Virtual Generation in Distributed Energy Sources

Deshmukh, Rohit R.; Ballal, Makarand Sudhakar; Suryawanshi, Hiralal Murlidhar; Mishra, Mahesh K.

doi:10.1109/tii.2019.2919647

Cited by 42 publications

(21 citation statements)

References 23 publications

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“…In proposed method, the output power from individual sources is categorized into three different powers. The addition of these three different powers from individual sources defines the power reference for the converter [29]. It is expressed as…”

Section: Proposed Methodologymentioning

confidence: 99%

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Improved Dynamic Response of DC Microgrid Under Transient Condition Using Inertia by Virtual Generation

Deshmukh

Ballal

2021

IEEE Access

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This article proposes new control architecture to improve the dynamic performance of DC microgrid by enhancing system inertia. The DC microgrids are gaining more importance due to the controlled and combined operation of multiple distributed energy resources (DERs). The DC microgrid is dominated by controlled power electronics converters. These converters are not having inbuilt inertia like rotating generators. Therefore, the DC microgrid has a fast rate of change of bus voltage under fluctuations of load, change in available power capacity of sources due to their randomness, etc. This affects the voltage profile and stability of the system under dynamic variation. The proposed closed loop control scheme estimates the amount of inertia required in the DC microgrid to minimize the voltage variation. The new power references are given to sources based on virtual generation to improve the inertia in the system. The proposed control scheme maintains the stability of DC microgrid. It also achieves effective power management based on available power in DERs with minimum burden on the energy storage system (ESS). The bus voltages are regulated at reference value. The stability analysis of the system is carried out for DC microgrid under different operating conditions. The proposed control method is tested experimentally under diverse operating conditions.INDEX TERMS DC microgrid, inertia, power management, transient stability, virtual generation.

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Section: Proposed Methodologymentioning

confidence: 99%

“…The required inertia can be supplied by proper power management of virtual generation in the DC microgrid. In [29], the virtual generation based power management control method is proposed. The sudden variation of the power output of all sources reflects in voltage oscillations.…”

Section: A State Of the Artmentioning

confidence: 99%

Improved Dynamic Response of DC Microgrid Under Transient Condition Using Inertia by Virtual Generation

Deshmukh

Ballal

2021

IEEE Access

Self Cite

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“…Reference [11] proposed multi-level queues to tackle data rates and reduce the communication delay for IoT sensor devices. Reference [12] focused on the energy efficiency of photovoltaic storage systems by using machine learning methods and proposed an approximation offline policy to handle energy management, whereas [13] introduced the abstraction of virtual energy generation and designed an adaptive algorithm to improve the energy efficiency of dedicated microgrid networks. In addition, [14] improved the performance of hybrid energy storage systems by optimizing the energy cost and data size together, and [15] proposed a novel energy management approach using an intelligent agent and improved the efficiency of customized microgrid networks.…”

Section: Introductionmentioning

confidence: 99%

Learning-based Green Workload Placement for Energy Internet in Smart Cities

Zhou

Sun

et al. 2022

Journal of Modern Power Systems and Clean Energy

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The Energy Internet is a fundamental infrastructure for deploying green city applications, where energy saving and job acceleration are two critical issues to address. In contrast to existing approaches that focus on static metrics with the assumption of complete prior knowledge of resource information, both application-level properties and energy-level requirements are realized in this paper by jointly considering energy saving and job acceleration during job runtime. Considering the online environment of smart city applications, the main objective is transferred as an optimization problem with a model partition and function assignment. To minimize the energy cost and job completion time together, a green workload placement approach is proposed by using the multi-action deep reinforcement learning method. Evaluations with real-world applications demonstrate the superiority of this method over state-ofthe-art methods.

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“…Integration of various renewable energy sources to the power distribution network has many advantages like it reduces the power share of the conventional sources, improves additional load sharing, increases active power sharing of the distribution network, which improves the power quality of the photovoltaic array (PVA) tied grid under unbalanced non‐linear load, and continuous harnessing of power from locally available sources to meet the load demand. Due to easy availability, an integration of photovoltaic energy conversion system (PVECS) to the distribution network has been very common [1–4]. Generally, PVA is connected to the distribution network through an interfacing voltage source converter (VSC) to feed the power to the grid.…”

Section: Introductionmentioning

confidence: 99%