Understanding Microgrids and Their Future Trends

Shrivastwa, Ritu Raj; Hably, Ahmad; Melizi, Kaouthar; Bacha, Seddik

doi:10.1109/icit.2019.8754952

Cited by 19 publications

(13 citation statements)

References 54 publications

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“…A rapidly rising technique utilized in the optimization of a microgrid is machine learning. Effective energy management inside the microgrid is achieved by machine learning through approaches including linear and dynamic programming which enable schedule economic dispatch and unit commitments of the microgrid's components (Shrivastwa et al, 2019). An example of the energy scheduling strategies has been reported in a DC community solar microgrid in the city of Wisconsin, Milwaukee.…”

Section: Optimization Of Microgrids Through Energy Management System ...mentioning

confidence: 99%

“…Linear programming Optimization of problems subject to certain limitations (Mashayekh, Stadler, Goncalo Cardoso et al, 2017;Shrivastwa et al, 2019;Wang et al, 2020;Santos et al, 2021;Shufian and Mohammad, 2022) Dynamic programming Process of resolving problems and finding the best decision one after another Borra and Debnath, 2018;Zeng et al, 2019;Wang et al, 2020;Davari et al, 2021 Optimal system simulation on HOMERPRO Simulation analysis to identify the least levelized Cost of Energy (COE) Baral, Behera and Kisku, (2022) Frontiers in Energy Research frontiersin.org protection of the components in the microgrid settlement (Hasan and Arif, 2018).…”

Section: Optimization Technique Description List Of Referencesmentioning

confidence: 99%

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A review of modeling and simulation tools for microgrids based on solar photovoltaics

2022

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Solar Photo Voltaic (PV) powered community microgrids are a promising sustainable solution for neighborhoods, residential quarters, and cities in sub-Saharan Africa (SSA) to meet their energy demands locally and to increase energy independence and resilience. This review provides a comprehensive study on the nature of solar PV community microgrids. Through their capacity to operate in both grid-connected and island modes, community microgrids improve utility system resiliency while also boosting energy security in local states and towns. The integration of solar PV microgrids with the electricity utility grid requires control strategies to facilitate the load sharing between distributed generation units, voltage and frequency control, as well as emergency islanding. Control strategies such as hierarchical control and droop are discussed in the review article. To identify the effectiveness of control strategies through system simulation, a review of various modeling designs of individual components in a solar PV microgrid system is discussed. The article goes on to talk about energy optimization approaches and their economic impact on microgrid systems. Finally, the review concludes with an overview of the technical challenges encountered in the integration of solar PV systems in microgrids.

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Section: Optimization Of Microgrids Through Energy Management System ...mentioning

confidence: 99%

Section: Optimization Technique Description List Of Referencesmentioning

confidence: 99%

A review of modeling and simulation tools for microgrids based on solar photovoltaics

2022

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“…Due to the AC nature of most loads and the main grid, this MG can be easily integrated with the main grid. 25,26 For an AC MG, the generation of all RE sources and grid couple need to be synchronized and should have the capability to diminish the harmonics and inrush current for better operation.…”

Section: Based On the Typesmentioning

confidence: 99%

Microgrid to smart grid's evolution: Technical challenges, current solutions, and future scopes

Badal

Sarker

Nayem

et al. 2022

Energy Science & Engineering

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Renewable energy (RE) sources play an important role that not only reduces the pressure on fossil fuels but also produces safe and clean energy by developing the microgrid (MG) technology that fulfills the excess demand for power throughout the world. The technological development and the blessing of information and communication technology converts the MG technology to a smarter one, termed as smart grid (SG) and virtual power plant, by establishing a two‐way communication between the consumers and service provider with the aid of smart metering infrastructure, dynamic pricing scheme, energy management system, smart transmission and distribution system. The transformation of the traditional power system to SG enhances the efficiency, flexibility, resiliency, and robustness of the power system due to the increasing level of cyber security and automation. A directional pathway from conventional to smart power system has been carried out in this paper by addressing the present status of the power system, challenges during the operations, and possible solutions. Initially, the control operation of MG with RE and energy storage system has been investigated which is essential to understand the operation complexity and challenges due to the variable nature of the RE, load dynamics, and uncertainties. But the facilities of end‐user feedback, dynamic pricing scheme, cyber security, and smart metering system are not present in MG technology that hampers the proper regulation of controlling operation, generating and distributing energy as well as energy losses. The development of SG with smart technologies overcomes these problems by proper controlling of power usage. Finally, this paper provides a path to move the MG toward SG by analyzing the technical and critical challenges of smart devices, equipment integration, and their control issues and provides possible solutions to overcome the integration and control challenges for a reliable and sustainable operation.

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“…Recently, microgrid (MG) systems have been widely established for distributed power grids [1,2]. Compared with conventional grid systems, energy transmission losses and carbon emissions can be minimized by the MG [3].…”

Section: Introductionmentioning

confidence: 99%

Novel Energy Trading System Based on Deep-Reinforcement Learning in Microgrids

et al. 2021

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Inefficiencies in energy trading systems of microgrids are mainly caused by uncertainty in non-stationary operating environments. The problem of uncertainty can be mitigated by analyzing patterns of primary operation parameters and their corresponding actions. In this paper, a novel energy trading system based on a double deep Q-networks (DDQN) algorithm and a double Kelly strategy is proposed for improving profits while reducing dependence on the main grid in the microgrid systems. The DDQN algorithm is proposed in order to select optimized action for improving energy transactions. Additionally, the double Kelly strategy is employed to control the microgrid’s energy trading quantity for producing long-term profits. From the simulation results, it is confirmed that the proposed strategies can achieve a significant improvement in the total profits and independence from the main grid via optimized energy transactions.

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Understanding Microgrids and Their Future Trends

Cited by 19 publications

References 54 publications

A review of modeling and simulation tools for microgrids based on solar photovoltaics

A review of modeling and simulation tools for microgrids based on solar photovoltaics

Microgrid to smart grid's evolution: Technical challenges, current solutions, and future scopes

Novel Energy Trading System Based on Deep-Reinforcement Learning in Microgrids

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