Priority-based Energy Allocation in Buildings for Distributed Model Predictive Control

แนวคิดหลัก

Developing a priority-based energy allocation strategy for buildings using distributed model predictive control.

บทคัดย่อ

The content discusses the challenges of energy shortage in buildings and proposes a priority-based energy allocation strategy using distributed model predictive control. It compares centralized, decentralized, and distributed strategies in small-scale and large-scale scenarios, showing that the distributed strategy performs well in ensuring comfort while optimizing energy distribution.

Introduction to energy shortage in buildings and proposed solution.
Comparison of centralized, decentralized, and distributed strategies.
Simulation results showing the effectiveness of the distributed strategy.

สถิติ

"Many countries are facing energy shortage today."
"The experiment shows that our scheme has good scalability."
"The building sector accounts for more than 40% of global energy consumption."
"Due to the advantages of low cost, simple operation and easy implementation..."
"In recent years, model predictive control (MPC) has received considerable attention from researchers..."

คำพูด

"Renewable energy systems are increasingly popular worldwide..."
"Well-designed control rules applied to the building HVAC module offer a promising approach..."

ข้อมูลเชิงลึกที่สำคัญจาก

Priority-based Energy Allocation in Buildings for Distributed Model Predictive Control

by Hongyi Li,Ju... ที่ arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13648.pdf

สอบถามเพิ่มเติม

How can renewable energy systems be integrated into this priority-based allocation strategy

Incorporating renewable energy systems into the priority-based allocation strategy involves considering the availability and variability of renewable energy sources. The strategy can be adapted to prioritize the use of renewable energy when it is abundant and cost-effective. This can be achieved by adjusting the optimization objectives to favor renewable sources over traditional energy sources based on factors such as weather forecasts, solar radiation levels, or wind speeds. By integrating real-time data on renewable energy generation into the decision-making process, the system can allocate energy in a way that maximizes the utilization of clean and sustainable resources.

What are the limitations of the centralized and decentralized strategies compared to the proposed distributed strategy

The limitations of centralized and decentralized strategies compared to distributed strategies lie in their scalability, flexibility, and efficiency. Centralized strategies may struggle with large-scale systems due to computational complexity and communication overheads. Decentralized approaches lack coordination between subsystems, leading to suboptimal solutions when faced with shared constraints or conflicting objectives. In contrast, distributed strategies like the one proposed offer better scalability by breaking down complex problems into smaller subproblems that can be solved independently but coordinated effectively through information exchange. This results in more efficient resource allocation while maintaining system-wide coherence.

How can this priority-based allocation strategy impact overall sustainability goals beyond just energy efficiency

This priority-based allocation strategy has broader implications for overall sustainability goals beyond just improving energy efficiency. By intelligently distributing limited resources based on priority needs, it ensures critical areas receive adequate support when resources are constrained. This approach aligns well with sustainability principles by optimizing resource usage, reducing waste, and enhancing resilience against fluctuations in supply or demand. Additionally, by incorporating renewable energy sources into the allocation scheme as discussed earlier, it promotes environmental stewardship and contributes to long-term sustainability objectives related to carbon footprint reduction and climate change mitigation efforts.