Machina Economicus: AI Impact on Energy Internet of Smart Cities

Основні поняття

AI integration in Energy Internet reshapes prosumer behaviors and introduces Machina Economicus paradigm.

Анотація

• Introduction to Energy Internet (EI) as a share economy platform for energy supplies in smart cities.
• Challenges faced in modeling, analyzing, and designing efficient platforms for energy sharing.
• Introduction of Machina Economicus paradigm to study economic rationality in AI/IoT-based EI prosumer behaviors.
• Focus on how AI reshapes prosumer behaviors and reveals new research directions.
• Detailed exploration of key applications like energy management, demand response, V2X technologies, hydrogen-electrical systems, and blockchain-based trading.
• Discussion on the challenges faced by machina economicus in D3 environments.
• Overview of system modeling methodologies like mechanism design, machine learning, and data-driven optimization.
• Proposal of an integrated solution framework for managing large-scale machina economicus systems in EI.

Статистика

"EI promotes the formation of more flexible, personalized, and efficient energy production and consumption."
"AIs will gradually replace human decision-making and interact with each other as homo economicus."
"The equilibrium-based solutions are not always optimal from the system-level perspective."

Цитати

"AIs should not just approach homo economicus-like ability but evolve into an entirely new species of economic agent: Machina Economicus."
"The key contribution is to present a cutting-edge economic-AI perspective to the management and operation issues of the EI."

Ключові висновки, отримані з

Machina Economicus

by Luyang Hou,J... о arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.14660.pdf

Глибші Запити

How can Machina Economicus address the trade-off between system-wide efficiency and individual well-being?

Machina Economicus, as a new paradigm that combines AI and economics, can help address the trade-off between system-wide efficiency and individual well-being in several ways. Firstly, by incorporating machine learning algorithms into decision-making processes within Energy Internet (EI), Machina Economicus can optimize resource allocation and management to maximize overall system efficiency while considering the preferences and benefits of individual prosumers. This approach allows for a more balanced distribution of resources that benefits both the collective system performance and individual participants.
Secondly, Machina Economicus can introduce mechanisms designed to incentivize cooperation among agents in EI. By utilizing game theory principles, it can encourage prosumers to collaborate for mutual benefit rather than solely pursuing their self-interests. This cooperative behavior contributes to improved overall efficiency without compromising individual well-being.
Furthermore, through data-driven optimization techniques, Machina Economicus can analyze large datasets from various sources within EI to make informed decisions that balance system-wide goals with individual needs. By leveraging advanced machine learning models, it can adapt its strategies based on changing conditions in real-time environments, ensuring continuous optimization while addressing the dynamic nature of energy markets.
In summary, Machina Economicus offers a comprehensive framework that integrates economic rationality with AI capabilities to navigate the complex interplay between system-wide efficiency and individual well-being in Energy Internet settings.

What are the implications of integrating electric mobility with EI on pricing/dispatching decisions?

Integrating electric mobility with Energy Internet (EI) has significant implications for pricing/dispatching decisions within smart cities. One key implication is the need for dynamic pricing strategies that consider factors such as electricity demand patterns influenced by charging behaviors of electric vehicles (EVs). Pricing mechanisms must be flexible enough to incentivize off-peak charging or vehicle-to-grid interactions while maintaining grid stability.
Moreover, dispatching decisions become more complex when incorporating electric mobility into EI systems. Operators must manage not only traditional energy generation but also coordinate EV charging/discharging schedules effectively. This requires intelligent algorithms capable of optimizing dispatching strategies based on real-time data from EVs' battery levels, grid constraints, and renewable energy availability.
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