Cost Optimal Hybrid Communication Model for Smart Distribution Grid

Devidas, Aryadevi Remanidevi; Ramesh, Maneesha Vinodini

doi:10.1109/tsg.2022.3185740

Cited by 12 publications

(3 citation statements)

References 37 publications

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“…However, these authors did not consider RES at different locations, implying that all sources are connected to common AC/DC buses with respective converters. A smart distribution grid architecture with a network of Microgrids, presenting a cost-optimal model, has been proposed [27], and a smart grid connecting RES at various locations with an implemented battery management system has been established [28]. A novel smart grid control alongside a battery energy management system (BEMS), aiming to provide reliable and high-quality power to multiple load buses.…”

Section: Related Workmentioning

confidence: 99%

Design and control of LSTM-ANN controllers for an efficient energy management system in a smart grid based on hybrid renewable energy sources

Wesley,

Babu,

Kumar

2024

Eng. Res. Express

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Many isolated locations, including hilly areas, remote sites, and military camps, lack feasible access to the main power grid. In these conditions, locally established Microgrids can provide the necessary power supply. However, to meet the demands of these isolated areas, numerous individual Microgrids are required. Although some of these Microgrids might be integratable, geographical constraints may preclude full integration. Under these circumstances, the establishment of a smart grid – integrating multiple Microgrids with a battery management system – can potentially solve many power supply issues. A smart grid control system paired with a centralized battery management system is proposed in this paper. The system considers the use of multiple renewable energy sources at various locations for stable and reliable power generation. The proposed method incorporates a long short-term memory (LSTM)-based artificial neural network (ANN) to ensure a stable, high-quality power supply at different load buses. Additionally, this work introduces an artificial intelligence-based operating system designed to maintain energy management under various conditions. To enhance voltage quality, a 7-level aligned multilevel inverter is incorporated into the system. As compared with PI and Fuzzy controllers, the proposed method with LSTM-ANN controller is improved the power quality under sudden changes in the system which are also presented in results. The effectiveness of the proposed energy management system (EMS) is verified by using a hardware-in the-loop approach with OPAL-RT modules, yielding realistic results.

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Section: Related Workmentioning

confidence: 99%

Design and control of LSTM-ANN controllers for an efficient energy management system in a smart grid based on hybrid renewable energy sources

Wesley,

Babu,

Kumar

2024

Eng. Res. Express

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“…This distributed validation process involves the active participation of microgrid entities, accessing smart meter data through an Application Programming Interface [29]. Leveraging an IoT network and APIs enables efficient data collection, facilitating prompt consensus validation [45]. The algorithm 1 shows the steps in the energy balance consensus.…”

Section: Energy Balance Consensus In the Microgridmentioning

confidence: 99%

Optimizing Microgrid Resilience: Integrating IoT, Blockchain, and Smart Contracts for Power Outage Management

Shibu,

Devidas,

Balamurugan

et al. 2024

IEEE Access

Self Cite

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Power outages can severely affect individuals, businesses, and communities, leading to disruptions, economic losses, and safety risks. The existing power recovery strategies often fail to adequately address the challenges associated with such outages. These challenges encompass a range of complexities, including resource allocation disparities, efficient prosumer integration, energy demand variability, and isolated generators. This paper presents a microgrid-centric power recovery strategy that leverages IoT, blockchain, smart contracts, and optimisation techniques for peer-to-peer energy sharing within the microgrid. The proposed strategy comprehensively addresses the challenges associated with the existing power recovery strategies. The paper outlines the system architecture for IoT and blockchain-enabled microgrids, discusses the mathematical modelling for energy sharing, and explores cost-optimal power restoration strategies. An incentive mechanism motivates prosumers to support restoration strategies during outages. Furthermore, the paper describes a blockchain smart contract facilitating peer-to-peer energy exchange in regions affected by power outages. This approach can mitigate the disruptive impact of power outages by providing reliable and community-centric power recovery solutions. Through validation with real-world data from our university's distribution grid test bed, Mean Time To Recover (MTTR) analysis and performance evaluations using the Hyperledger Caliper benchmark tool, this paper demonstrates its feasibility and effectiveness, paving the way for enhanced power recovery strategies and increased resilience in the face of energy disruptions.

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“…Compared with the traditional grid, Grid 2.0 adopts a combination of distributed and centralized generation, which can effectively integrate various generation systems such as wind energy, solar energy, and fuel cells. Moreover, through information integration and process optimization, it can intelligently coordinate various electrical systems, optimize energy scheduling, and improve the energy utilization rate and overall operating efficiency of electrical system devices [5].…”

Section: A Background Presentationmentioning

confidence: 99%

Multi-Target-Aware Energy Orchestration Modeling for Grid 2.0: A Network Virtualization Approach

et al. 2023

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Smart Grid -Grid 2.0, is a promising grid scenario due to its low cost, high reliability, and high security. However, the high-intensity complex task interaction and the differentiated quality of service (QoS) requirements pose challenges to the energy orchestration of the dynamic and time-varying smart grid. In addition, Network Virtualization (NV) technology is widely utilized to decouple physical network resources. In this work, based on Virtual Network Embedding (VNE), we propose an energy orchestration strategy to better support the operation and maintenance of Grid 2.0. Specifically, a series of constraint indicators for energy orchestration is first formulated. Second, based on the advantages of deep reinforcement learning (DRL), a five-layer architecture energy orchestration model is proposed. It frequently extracts the grid energy matrix as the state input, adopts two-stage actions to reasonably orchestrate the grid energy, and combines the reward mechanism of DRL to effectively interact with the environment. Finally, we conduct experiments in a simulation environment and demonstrate the advantages of the proposed strategy through analysis and comparison.

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Cost Optimal Hybrid Communication Model for Smart Distribution Grid

Cited by 12 publications

References 37 publications

Design and control of LSTM-ANN controllers for an efficient energy management system in a smart grid based on hybrid renewable energy sources

Design and control of LSTM-ANN controllers for an efficient energy management system in a smart grid based on hybrid renewable energy sources

Optimizing Microgrid Resilience: Integrating IoT, Blockchain, and Smart Contracts for Power Outage Management

Multi-Target-Aware Energy Orchestration Modeling for Grid 2.0: A Network Virtualization Approach

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