Advanced Demand Response Mechanisms Using Blockchain and IoT: Enhancing Grid Efficiency and Transparency

The increasing demand for electricity, particularly during peak periods, has created significant challenges in maintaining grid stability and efficiency. Traditional Demand Response (DR) systems are often centralized, resulting in inefficiencies and delays in managing real-time energy consumption. This research presents an innovative solution by integrating blockchain and Internet of Things (IoT) technologies to enhance the effectiveness of DR systems. IoT devices are used for real-time data collection on energy consumption, while blockchain provides a secure, decentralized framework for automating DR actions through smart contracts. The proposed system enables autonomous load management, ensuring that energy consumption is reduced during peak periods without the need for manual intervention. Simulations conducted in MATLAB demonstrate that this approach leads to significant reductions in power consumption during high-demand periods, preventing grid overloads and improving overall energy efficiency. The use of blockchain enhances transparency and security by providing a tamperproof ledger of all DR transactions, which fosters trust among consumers, utilities, and grid operators. Additionally, advanced optimization algorithms, including linear programming and genetic algorithms, ensure that DR actions are both efficient and minimally disruptive to consumers. While this system shows great promise, challenges related to blockchain scalability and IoT data processing remain, indicating areas for future research.

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Published
2024-11-20
DOI
https://doi.org/10.1109/icic63915.2024.11116604
Primary Topic
Blockchain Technology Applications and Security
Type
article
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article

Advanced Demand Response Mechanisms Using Blockchain and IoT: Enhancing Grid Efficiency and Transparency

Songsong Chen, Bin Li, Muhammad Athar, Habib Ullah Khan et al.
Blockchain Technology Applications and Security
article

Advanced Demand Response Mechanisms Using Blockchain and IoT: Enhancing Grid Efficiency and Transparency

Songsong Chen, Bin Li, Muhammad Athar, Habib Ullah Khan, Shiming Tian, Zaid Bin Tariq Baig
article en

Abstract

The increasing demand for electricity, particularly during peak periods, has created significant challenges in maintaining grid stability and efficiency. Traditional Demand Response (DR) systems are often centralized, resulting in inefficiencies and delays in managing real-time energy consumption. This research presents an innovative solution by integrating blockchain and Internet of Things (IoT) technologies to enhance the effectiveness of DR systems. IoT devices are used for real-time data collection on energy consumption, while blockchain provides a secure, decentralized framework for automating DR actions through smart contracts. The proposed system enables autonomous load management, ensuring that energy consumption is reduced during peak periods without the need for manual intervention. Simulations conducted in MATLAB demonstrate that this approach leads to significant reductions in power consumption during high-demand periods, preventing grid overloads and improving overall energy efficiency. The use of blockchain enhances transparency and security by providing a tamperproof ledger of all DR transactions, which fosters trust among consumers, utilities, and grid operators. Additionally, advanced optimization algorithms, including linear programming and genetic algorithms, ensure that DR actions are both efficient and minimally disruptive to consumers. While this system shows great promise, challenges related to blockchain scalability and IoT data processing remain, indicating areas for future research.

North China Electric Power University (CN), University of Central Punjab (PK)
Affordable and clean energy
Openalex Percentile: Top 49%
Blockchain Technology Applications and Security
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Advanced Demand Response Mechanisms Using Blockchain and IoT: Enhancing Grid Efficiency and Transparency — Songsong Chen, Bin Li, et al. · (2024) | TGRS Research Map | TGRS