Distributed Multi-objective Optimization in Cyber-Physical Energy Systems: MO-COHDA Algorithm

Concepts de base

The authors present MO-COHDA, a fully distributed agent-based algorithm for solving multi-objective optimization problems in Cyber-Physical Energy Systems, emphasizing flexibility and adaptability.

Résumé

The content discusses the challenges of managing complex Cyber-Physical Energy Systems (CPES) and introduces MO-COHDA, a distributed algorithm for multi-objective optimization. It compares the effectiveness of MO-COHDA with a central NSGA-2 algorithm using benchmark functions and evaluates its application to real-world optimization problems in CPES.
Managing energy systems on microgrid or household level, optimal placement of DER, system restoration are key challenges. Population-based metaheuristics like evolutionary or swarm-based methods are used for finding Pareto-optimal solutions. Distributed control architectures offer flexibility and robustness compared to centralized ones.

Stats

MO-COHDA approximates reference front well.
Results show MO-COHDA suitable for multi-objective optimization.
HV values: A - 0.421 ± 0.006, B - 0.935 ± 0.004.

decide calls: A - 275 ± 70, B - 788 ± 125.

Citations

"MO-COHDA allows an easy and flexible adaptation to different use cases."
"Distributed control architectures offer more flexibility and robustness compared with centralized ones."

Idées clés tirées de

Distributed Multi-objective Optimization in Cyber-Physical Energy Systems

by Sanja Stark,... à arxiv.org 03-08-2024

https://arxiv.org/pdf/2403.04627.pdf

Questions plus approfondies

Scalability of MO-COHDA

To improve the scalability of MO-COHDA for larger instances, several strategies can be implemented. One approach is to optimize the communication process between agents by reducing unnecessary message exchanges and ensuring efficient data sharing protocols. Additionally, optimizing the decision-making process within each agent can help reduce computational complexity. This can involve fine-tuning parameters such as the number of iterations, solution points, and mutation functions to strike a balance between accuracy and efficiency. Implementing parallel processing techniques or distributing agents across multiple devices can also enhance scalability by leveraging available resources effectively.

Implications of Different Mutate Functions on MO-COHDA Performance

The choice of mutate functions in MO-COHDA plays a crucial role in determining its performance. Different mutate functions impact how solutions evolve over time and explore the solution space. For instance, a more aggressive mutate function that allows for significant changes in solutions may lead to faster exploration but could risk missing optimal solutions due to excessive randomness. On the other hand, a conservative mutate function might converge slower but with more stability towards better solutions. Therefore, selecting an appropriate mutate function involves balancing exploration-exploitation trade-offs based on problem characteristics and optimization goals.

Application of Findings Beyond CPES Optimization

The findings from this study on distributed multi-objective optimization in Cyber-Physical Energy Systems (CPES) have broader implications for optimizing other complex systems as well. The agent-based algorithm MO-COHDA's adaptability and flexibility make it suitable for various domains beyond CPES, such as supply chain management, transportation networks, healthcare systems optimization, or financial portfolio management.
By customizing parameters like pick functions and mutation strategies according to specific system requirements,
MO-COHDA can efficiently handle multi-objective optimization tasks in diverse applications where decentralized decision-making is advantageous.
This approach enables organizations to address complex challenges involving multiple conflicting objectives while considering constraints unique to each domain.