A Survey on Information Communication Technologies in Modern Demand-Side Management for Smart Grids: Challenges, Solutions, and Opportunities

Said, Dhaou

doi:10.1109/emr.2022.3186154

Cited by 37 publications

(11 citation statements)

References 165 publications

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“…The benchmark used to evaluate the models is the simplest forecast baseline model, Naive Persistence (Persistence), it is based on the most trivial assumption that can be made when forecasting, it takes the value at the current time-step (t) and assigns it to the predicted value for the next step (t +1), in our case the value of the time-step (t) is assigned to the time-step that has to be predicted 24 hours ahead (t + 96) as described in (6), where y p is the predicted value and y the actual one.…”

Section: Model Tuning and Evaluation On Jpl Datasetmentioning

confidence: 99%

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Electric Vehicle Supply Equipment Day-Ahead Power Forecast Based on Deep Learning and the Attention Mechanism

Matrone,

Ogliari,

Nespoli

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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Transports is one of the sectors that produce the highest emissions of CO 2 ; in the last ten years, there has been a process of decarbonization which has led to a considerable increase in Electric Vehicles (EVs). However, the sudden introduction of a large number of Electric vehicle supply equipment (EVSE) supplying electrical energy to EVs could cause problems in the management of the electric grid which must cope with the consequent increase in the electrical load demand. In this context, the 24 hour ahead forecast of the power curve associated with the recharge of EVs becomes of vital importance to ensure the reliability of the electric grid. In this paper, different Machine Learning models based on Recurrent Neural Networks (LSTM, GRU) and with different architectures, are compared based on their capability to accurately predict the power curve of an EV charging station one day in advance. A Sequence to Sequence model has been implemented and a thorough analysis of an Attention layer has been detailed. The models are tested on a real world open dataset.

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Section: Model Tuning and Evaluation On Jpl Datasetmentioning

confidence: 99%

“…EV load forecast is a form of Demand Side Management (DSM) [6], [7], [8] that can be crucial to balance load demand and power production. It is particularly relevant when the vehicles are integrated into Distributed Generation (DG) such as a micro-grid [9].…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle Supply Equipment Day-Ahead Power Forecast Based on Deep Learning and the Attention Mechanism

Matrone,

Ogliari,

Nespoli

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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“…Future renewable energy control systems will likely focus on improving grid flexibility and resilience. Smart grids, enabled by advanced control technologies, will facilitate the seamless integration of renewable energy sources, energy storage systems, and demand-side management (Panda et al, 2022;Said, 2022). This holistic approach will contribute to grid stability, enable efficient energy trading, and enhance the resilience of the overall energy infrastructure.…”

Section: Future Prospects In Renewable Energy Control Systemsmentioning

confidence: 99%

Control systems in renewable energy: A review of applications in Canada, USA, and Africa

Onyinyechukwu Chidolue,

Cosmas Dominic Daudu,

Valentine Ikenna Illojianya

et al. 2024

World J. Adv. Eng. Technol. Sci.

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This research explores the applications of control systems in renewable energy across Canada, the United States, and Africa. It underscores their pivotal role in optimizing efficiency and reliability by examining supervisory, predictive, and adaptive control strategies. The literature review delves into global and regional renewable energy landscapes, emphasizing unique challenges and opportunities. Technological innovations, including advanced monitoring, artificial intelligence, and blockchain, are investigated, highlighting their transformative impact. The paper anticipates prospects such as quantum computing, decentralized systems, and heightened cybersecurity measures. The findings contribute to understanding the nuanced interplay between control systems and renewable energy, offering insights for policymakers, researchers, and industry stakeholders as they navigate the evolving landscape of sustainable energy solutions.

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“…Demand-side management (DSM), a combination of software and hardware models and data analytics capabilities, is an essential facilitator of the energy transaction and the transfer from fossil to renewable energy sources. In addition to demand response capabilities, the DSM manages the grid infrastructure, reducing customer pain and optimizing grid stability in light of environmental obligations [28]. Any network system loses efficiency, dependability, and resilience when cybersecurity is neglected.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence-Based Secured Power Grid Protocol for Smart City

Sulaiman,

Nagu,

Kaur

et al. 2023

Sensors

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Due to the modern power system’s rapid development, more scattered smart grid components are securely linked into the power system by encircling a wide electrical power network with the underpinning communication system. By enabling a wide range of applications, such as distributed energy management, system state forecasting, and cyberattack security, these components generate vast amounts of data that automate and improve the efficiency of the smart grid. Due to traditional computer technologies’ inability to handle the massive amount of data that smart grid systems generate, AI-based alternatives have received a lot of interest. Long Short-Term Memory (LSTM) and recurrent Neural Networks (RNN) will be specifically developed in this study to address this issue by incorporating the adaptively time-developing energy system’s attributes to enhance the model of the dynamic properties of contemporary Smart Grid (SG) that are impacted by Revised Encoding Scheme (RES) or system reconfiguration to differentiate LSTM changes & real-time threats. More specifically, we provide a federated instructional strategy for consumer sharing of power data to Power Grid (PG) that is supported by edge clouds, protects consumer privacy, and is communication-efficient. They then design two optimization problems for Energy Data Owners (EDO) and energy service operations, as well as a local information assessment method in Federated Learning (FL) by taking non-independent and identically distributed (IID) effects into consideration. The test results revealed that LSTM had a longer training duration, four hidden levels, and higher training loss than other models. The provided method works incredibly well in several situations to identify FDIA. The suggested approach may successfully induce EDOs to employ high-quality local models, increase the payout of the ESP, and decrease task latencies, according to extensive simulations, which are the last points. According to the verification results, every assault sample could be effectively recognized utilizing the current detection methods and the LSTM RNN-based structure created by Smart.

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A Survey on Information Communication Technologies in Modern Demand-Side Management for Smart Grids: Challenges, Solutions, and Opportunities

Cited by 37 publications

References 165 publications

Electric Vehicle Supply Equipment Day-Ahead Power Forecast Based on Deep Learning and the Attention Mechanism

Electric Vehicle Supply Equipment Day-Ahead Power Forecast Based on Deep Learning and the Attention Mechanism

Control systems in renewable energy: A review of applications in Canada, USA, and Africa

Artificial Intelligence-Based Secured Power Grid Protocol for Smart City

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