Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
119 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Network-Aware Asynchronous Distributed ADMM Algorithm for Peer-to-Peer Energy Trading (2312.06976v1)

Published 12 Dec 2023 in math.OC, cs.NI, cs.SY, and eess.SY

Abstract: The increasing uptake of distributed energy resources (DERs) in smart home prosumers calls for distributed energy management strategies, and the advances in information and communications technology enable peer-to-peer (P2P) energy trading and transactive energy management. Many works attempted to solve the transactive energy management problem using distributed optimization to preserve the privacy of DERs' operations. But such distributed optimization requires information exchange among prosumers, often via synchronous communications, which can be unrealistic in practice. This paper addresses a transactive energy trading problem for multiple smart home prosumers with rooftop solar, battery storage, and controllable load, such as heating, ventilation, and air-conditioning (HVAC) units, considering practical communication conditions. We formulate a network-aware energy trading optimization problem, in which a local network operator manages the network constraints supporting bidirectional energy flows. We develop an asynchronous distributed alternating direction method of multipliers (ADMM) algorithm to solve the problem under asynchronous communications, allowing communication delay and indicating a higher potential for real-world applications. We validate our design by simulations using real-world data. The results demonstrate the convergence of our developed asynchronous distributed ADMM algorithm and show that energy trading reduces the energy cost for smart home prosumers.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (29)
  1. A.-Y. Yoon, Y.-J. Kim, and S.-I. Moon, “Optimal retail pricing for demand response of hvac systems in commercial buildings considering distribution network voltages,” IEEE Transactions on Smart Grid, vol. 10, no. 5, pp. 5492–5505, 2018.
  2. H. Mansy and S. Kwon, “Optimal hvac control for demand response via chance-constrained two-stage stochastic program,” IEEE Transactions on Smart Grid, vol. 12, no. 3, pp. 2188–2200, 2020.
  3. W. Wu and H. M. Skye, “Net-zero nation: Hvac and pv systems for residential net-zero energy buildings across the united states,” Energy conversion and management, vol. 177, pp. 605–628, 2018.
  4. S. Li, J. Yang, W. Song, and A. Chen, “A real-time electricity scheduling for residential home energy management,” IEEE Internet of Things Journal, vol. 6, no. 2, pp. 2602–2611, 2018.
  5. S. Arun and M. Selvan, “Intelligent residential energy management system for dynamic demand response in smart buildings,” IEEE Systems Journal, vol. 12, no. 2, pp. 1329–1340, 2017.
  6. S. Paul and N. P. Padhy, “Real time energy management for smart homes,” IEEE Systems Journal, vol. 15, no. 3, pp. 4177–4188, 2021.
  7. A. C. Luna, N. L. Diaz, M. Graells, J. C. Vasquez, and J. M. Guerrero, “Cooperative energy management for a cluster of households prosumers,” IEEE transactions on consumer electronics, vol. 62, no. 3, pp. 235–242, 2016.
  8. M. R. Alam, M. St-Hilaire, and T. Kunz, “Peer-to-peer energy trading among smart homes,” Applied energy, vol. 238, pp. 1434–1443, 2019.
  9. A. Mondal, S. Misra, L. S. Patel, S. K. Pal, and M. S. Obaidat, “Demands: Distributed energy management using noncooperative scheduling in smart grid,” IEEE Systems Journal, vol. 12, no. 3, pp. 2645–2653, 2018.
  10. Q. Yang and H. Wang, “Distributed energy trading management for renewable prosumers with hvac and energy storage,” Energy Reports, vol. 7, pp. 2512–2525, 2021.
  11. ——, “Privacy-preserving transactive energy management for iot-aided smart homes via blockchain,” IEEE Internet of Things Journal, pp. 1–13, 2021.
  12. X. Kou, F. Li, J. Dong, M. Starke, J. Munk, Y. Xue, M. Olama, and H. Zandi, “A scalable and distributed algorithm for managing residential demand response programs using alternating direction method of multipliers (admm),” IEEE Transactions on Smart Grid, vol. 11, no. 6, pp. 4871–4882, 2020.
  13. L. Yu, D. Xie, T. Jiang, Y. Zou, and K. Wang, “Distributed real-time hvac control for cost-efficient commercial buildings under smart grid environment,” IEEE Internet of Things Journal, vol. 5, no. 1, pp. 44–55, 2017.
  14. K. Song, Y. Jang, M. Park, H.-S. Lee, and J. Ahn, “Energy efficiency of end-user groups for personalized hvac control in multi-zone buildings,” Energy, vol. 206, p. 118116, 2020.
  15. Z. Zhang, T. Ding, Q. Zhou, Y. Sun, M. Qu, Z. Zeng, Y. Ju, L. Li, K. Wang, and F. Chi, “A review of technologies and applications on versatile energy storage systems,” Renewable and Sustainable Energy Reviews, vol. 148, p. 111263, 2021.
  16. M. R. Alam, M. St-Hilaire, and T. Kunz, “Peer-to-peer energy trading among smart homes,” Applied Energy, vol. 238, pp. 1434 – 1443, 2019.
  17. H. Wang, G. Henri, C.-W. Tan, and R. Rajagopal, “Activity detection and modeling using smart meter data: concept and case studies,” in 2020 IEEE Power & Energy Society General Meeting (PESGM).   IEEE, 2020, pp. 1–5.
  18. T. Caldognetto, P. Tenti, A. Costabeber, and P. Mattavelli, “Improving microgrid performance by cooperative control of distributed energy sources,” IEEE Transactions on Industry Applications, vol. 50, no. 6, pp. 3921–3930, 2014.
  19. N. Liu, X. Yu, C. Wang, and J. Wang, “Energy sharing management for microgrids with pv prosumers: A stackelberg game approach,” IEEE Transactions on Industrial Informatics, vol. 13, no. 3, pp. 1088–1098, 2017.
  20. J. Mei, C. Chen, J. Wang, and J. L. Kirtley, “Coalitional game theory based local power exchange algorithm for networked microgrids,” Applied energy, vol. 239, pp. 133–141, 2019.
  21. J. Yang, Z. Dong, G. Chen, F. Wen, and C. Li, “A fully decentralized distribution market mechanism using admm,” in 2019 IEEE Power & Energy Society General Meeting (PESGM).   IEEE, 2019, pp. 1–5.
  22. Z. Wang, X. Zhang, and H. Wang, “Consensus-based decentralized energy trading for distributed energy resources,” Energy Conversion and Economics, vol. 2, no. 4, pp. 221–234, 2017.
  23. S. G. M. Rokni, M. Radmehr, and A. Zakariazadeh, “Optimum energy resource scheduling in a microgrid using a distributed algorithm framework,” Sustainable cities and society, vol. 37, pp. 222–231, 2018.
  24. H. Wang and J. Huang, “Incentivizing energy trading for interconnected microgrids,” IEEE Transactions on Smart Grid, vol. 9, no. 4, pp. 2647–2657, 2016.
  25. M. Qu, T. Ding, W. Jia, S. Zhu, Y. Yang, and F. Blaabjerg, “Distributed optimal control of energy hubs for micro-integrated energy systems,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 4, pp. 2145–2158, 2020.
  26. C. Mu, T. Ding, M. Qu, Q. Zhou, F. Li, and M. Shahidehpour, “Decentralized optimization operation for the multiple integrated energy systems with energy cascade utilization,” Applied Energy, vol. 280, p. 115989, 2020.
  27. W. Zhong, K. Xie, Y. Liu, C. Yang, and S. Xie, “Topology-aware vehicle-to-grid energy trading for active distribution systems,” IEEE Transactions on Smart Grid, vol. 10, no. 2, pp. 2137–2147, 2018.
  28. S. Kumar, R. Jain, and K. Rajawat, “Asynchronous optimization over heterogeneous networks via consensus admm,” IEEE Transactions on Signal and Information Processing over Networks, vol. 3, no. 1, pp. 114–129, 2016.
  29. H. Wang and J. Huang, “Cooperative planning of renewable generations for interconnected microgrids,” IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2486–2496, 2016.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (2)
  1. Zeyu Yang (27 papers)
  2. Hao Wang (1120 papers)

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets