A Framework for Distributed Orchestration of Cyber-Physical Systems: An Energy Trading Case Study

The increasing number of active energy consumers, also known as energy prosumers, is dramatically changing the electricity system. New products and services that adopt the concept of dynamic pricing are available to the market, where demand and price forecasting are applied to determine schedule loads and prices. Throughout this manuscript, a novel framework for energy trading among prosumers is introduced. Rather than solving the problem in a centralized manner, the proposed orchestrator relies on a distributed game theory to determine optimal bids. Experimental results validate the efficiency of proposed solution, since it achieves average energy cost reduction of 2×, as compared to the associated cost from the main grid. Additionally, the hardware implementation of the introduced framework onto a low-cost embedded device achieves near real-time operation with comparable performance to state-of-the-art computational intensive solvers.

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Publication Details

Journal
Technologies
Published
2024-11-13
DOI
https://doi.org/10.3390/technologies12110229
Primary Topic
Smart Grid Energy Management
Type
article
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article

A Framework for Distributed Orchestration of Cyber-Physical Systems: An Energy Trading Case Study

Kostas Siozios
Technologies
Smart Grid Energy Management
article

A Framework for Distributed Orchestration of Cyber-Physical Systems: An Energy Trading Case Study

Kostas Siozios
article en

Abstract

The increasing number of active energy consumers, also known as energy prosumers, is dramatically changing the electricity system. New products and services that adopt the concept of dynamic pricing are available to the market, where demand and price forecasting are applied to determine schedule loads and prices. Throughout this manuscript, a novel framework for energy trading among prosumers is introduced. Rather than solving the problem in a centralized manner, the proposed orchestrator relies on a distributed game theory to determine optimal bids. Experimental results validate the efficiency of proposed solution, since it achieves average energy cost reduction of 2×, as compared to the associated cost from the main grid. Additionally, the hardware implementation of the introduced framework onto a low-cost embedded device achieves near real-time operation with comparable performance to state-of-the-art computational intensive solvers.

TechnologiesVol. 12(11)
Aristotle University of Thessaloniki (GR)
Affordable and clean energy
Openalex Percentile: Top 82%
Smart Grid Energy Management
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