ADMM-based fully decentralized Peer to Peer energy trading considering a shared CAES in a local community

Document Type : Original Article

Authors

1 Faculty of Electrical and Computer Engineering, Energy Systems Research Institute, Smart Energy Systems lab, University of Tabriz, Tabriz, Iran

2 Faculty of Electrical and Computer Engineering, Smart Energy systems lab, University of Tabriz, Tabriz, Iran

10.22109/jemt.2023.394182.1444

Abstract

Peer-to-peer (P2P) energy trading markets have emerged locally as a result of the higher usage of renewable energy sources in low-voltage networks. P2P energy trading systems have been increasingly popular in recent years, allowing consumers in residential and industrial types to trade electricity with each other. P2P energy trading has become feasible due to several developments in communication technology and the increased acceptance of renewable energy sources like solar and wind power. In this market, Consumers have been more interested in sharing their extra energy with others to get access to the new market and increase their profit. There are two approaches to P2P energy trading. The centralized approach involves a third-party entity, typically a network operator, that manages the trading platform. This approach offers a reliable option but may pose certain shortcomings such as limited privacy. In contrast, the decentralized approach empowers consumers to transact their surplus energy directly to one another, without requiring the intervention of a centralized authority. Such an approach endows participants with greater flexibility and preserves their privacy. This paper presents a fully decentralized approach for a local P2P energy trading market using the alternating direction method of multipliers (ADMM) algorithm. This paper also considers a compressed air energy storage (CAES) technology to increase flexibility and reduce peak demand. In the following, Numerical studies are carried out for a local community in a distribution network. Simulation results demonstrate how the P2P markets can facilitate the customers to manage their energy in the local community.

Keywords

Main Subjects


1. M. M. Hayati, A. Aminlou, K. Zare, and M. Abapour, “A two-stage stochastic optimization scheduling approach for integrating renewable energy sources and deferrable demand in the spinning reserve market,” in 2023 8th International Conference on Technology and Energy Management (ICTEM), pp. 1–7, IEEE, 2023.
2. S. Hosseinalipour, M. Rashidinejad, A. Abdollahi, and P. Afzali, “Optimal risk-constrained peer-to-peer energy trading strategy for a smart microgrid,” Journal of Energy Management and Technology, vol. 7, no. 4, pp. 227–236, 2023.
3. M. Domènech Monfort, C. De Jesús, N. Wanapinit, and N. Hartmann, “A review of peer-to-peer energy trading with standard terminology proposal and a techno-economic characterisation matrix,” Energies, vol. 15, no. 23, p. 9070, 2022.
4. Y. Zhou and P. D. Lund, “Peer-to-peer energy sharing and trading of renewable energy in smart communities trading pricing models, decision-making and agent-based collaboration,” Renewable Energy, vol. 207, pp. 177–193, 2023.
5. Á. Ordóñez, E. Sánchez, L. Rozas, R. García, and J. Parra-Domínguez, “Net-metering and net-billing in photovoltaic self-consumption: The cases of ecuador and spain,” Sustainable Energy Technologies and Assessments, vol. 53, p. 102434, 2022.
6. L. P. M. I. Sampath, A. Paudel, H. D. Nguyen, E. Y. Foo, and H. B. Gooi, “Peer-to-peer energy trading enabled optimal decentralized operation of smart distribution grids,” IEEE Transactions on Smart Grid, vol. 13, no. 1, pp. 654–666, 2021.
7. B. Zafar and S. Ben Slama, “Energy internet opportunities in distributed peer-to-peer energy trading reveal by blockchain for future smart grid 2.0,” Sensors, vol. 22, no. 21, p. 8397, 2022.
8. M. J. A. Baig, M. T. Iqbal, M. Jamil, and J. Khan, “A low-cost, open source peer-to-peer energy trading system for a remote community using the internet-of-things, blockchain, and hypertext transfer protocol,” Energies, vol. 15, no. 13, p. 4862, 2022.
9. M. J. A. Baig, M. T. Iqbal, M. Jamil, and J. Khan, “A low-cost, opensource peer-to-peer energy trading system for a remote community using the internet-of-things, blockchain, and hypertext transfer protocol,” Energies, vol. 15, no. 13, p. 4862, 2022.
10. M. Mehdinejad, H. Shayanfar, and B. Mohammadi-Ivatloo, “Decentralized blockchain-based peer-to-peer energy-backed token trading for active prosumers,” Energy, vol. 244, p. 122713, 2022.
11. K. Singh and A. Singh, “Building blocks of peer-to-peer energy trading in a smart grid,” Energy Proc, vol. 28, pp. 1–6, 2022.
12. A. Aminlou, M. M. Hayati, and K. Zare, “Local peer-to-peer energy trading evaluation in micro-grids with centralized approach,” in 2023 8th International Conference on Technology and Energy Management (ICTEM), pp. 1–6, IEEE, 2023.
13. A. Aminlou, B. Mohammadi-Ivatloo, K. Zare, R. Razzaghi, and A. AnvariMoghaddam, “Peer-to-peer decentralized energy trading in industrial town considering central shared energy storage using alternating direction method of multipliers algorithm,” IET Renewable Power Generation, vol. 16, no. 12, pp. 2579–2589, 2022.
14. M. Mehdinejad, H. A. Shayanfar, B. Mohammadi-Ivatloo, and H. Nafisi, “Designing a robust decentralized energy transactions framework for active prosumers in peer-to-peer local electricity markets,” IEEE Access, vol. 10, pp. 26743–26755, 2022.
15. Y. Liu, H. B. Gooi, and H. Xin, “Distributed energy management for the multi-microgrid system based on admm,” in 2017 IEEE Power & Energy Society General Meeting, pp. 1–5, IEEE, 2017.
16. R. Zhang and J. Kwok, “Asynchronous distributed admm for consensus optimization,” in International conference on machine learning, pp. 1701–1709, PMLR, 2014.
17. S. Boyd, N. Parikh, E. Chu, B. Peleato, J. Eckstein, et al., “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends® in Machine learning, vol. 3, no. 1, pp. 1–122, 2011.
18. F. Shen, Q. Wu, X. Jin, B. Zhou, C. Li, and Y. Xu, “Admm-based market clearing and optimal flexibility bidding of distribution-level flexibility market for day-ahead congestion management of distribution networks,” International Journal of Electrical Power & Energy Systems, vol. 123, p. 106266, 2020.
19. M. Latifi, A. Khalili, A. Rastegarnia, W. M. Bazzi, and S. Sanei, “A robust scalable demand-side management based on diffusion-admm strategy for smart grid,” IEEE Internet of Things Journal, vol. 7, no. 4, pp. 3363–3377, 2020.
20. J. F. Mota, J. M. Xavier, P. M. Aguiar, and M. Püschel, “D-admm: A communication-efficient distributed algorithm for separable optimization,” IEEE Transactions on Signal processing, vol. 61, no. 10, pp. 2718–2723, 2013.
21. D. H. Nguyen, “Optimal solution analysis and decentralized mechanisms for peer-to-peer energy markets,” IEEE Transactions on Power Systems, vol. 36, no. 2, pp. 1470–1481, 2020.
22. J. A. Aguado and Á. Paredes, “Coordinated and decentralized trading of flexibility products in inter-dso local electricity markets via admm,” Applied Energy, vol. 337, p. 120893, 2023.
23. S. Suthar, S. H. C. Cherukuri, and N. M. Pindoriya, “Peer-to-peer energy trading in smart grid: Frameworks, implementation methodologies, and demonstration projects,” Electric Power Systems Research, vol. 214, p. 108907, 2023.
24. Z. Liu, N. C. Luong, W. Wang, D. Niyato, P. Wang, Y.-C. Liang, and D. I. Kim, “A survey on blockchain: A game theoretical perspective,” IEEE Access, vol. 7, pp. 47615–47643, 2019.
25. S. Malik, M. Duffy, S. Thakur, B. Hayes, and J. Breslin, “A priority-based approach for peer-to-peer energy trading using cooperative game theory in local energy community,” International Journal of Electrical Power & Energy Systems, vol. 137, p. 107865, 2022.
26. I. Hadjipaschalis, A. Poullikkas, and V. Efthimiou, “Overview of current and future energy storage technologies for electric power applications,” Renewable and sustainable energy reviews, vol. 13, no. 6-7, pp. 1513–1522, 2009.
27. M. Hemmati, B. Mohammadi-Ivatloo, M. Abapour, and M. Shafiee, “Thermodynamic modeling of compressed air energy storage for energy and reserve markets,” Applied Thermal Engineering, vol. 193, p. 116948, 2021.
28. S. Shafiee, H. Zareipour, A. M. Knight, N. Amjady, and B. MohammadiIvatloo, “Risk-constrained bidding and offering strategy for a merchant compressed air energy storage plant,” IEEE Transactions on Power Systems, vol. 32, no. 2, pp. 946–957, 2016.
29. A. M. Rabi, J. Radulovic, and J. M. Buick, “Comprehensive review of compressed air energy storage (caes) technologies,” Thermo, vol. 3, no. 1, pp. 104–126, 2023.
30. A. Hosseini and A. Sadeghi Yazdankhah, “Hybrid robust-stochastic bidding strategy for integrated power to gas and compressed air energy storage systems coordinated with wind farm,” Journal of Energy Management and Technology, vol. 5, no. 4, pp. 45–56, 2021.
31. Y. B. Choh, W. Yang, and X. Wang, “Integration of blockchain-based peer-to-peer energy markets in industrial water-energy-network,” 2021.
32. H. Rashidizadeh-Kermani, M. Vahedipour-Dahraie, M. Shafie-khah, and P. Siano, “A peer-to-peer energy trading framework for wind power producers with load serving entities in retailing layer,” IEEE Systems Journal, vol. 16, no. 1, pp. 649–658, 2021.
33. M. Jadidbonab, E. Babaei, and B. Mohammadi-ivatloo, “Cvarconstrained scheduling strategy for smart multi carrier energy hub considering demand response and compressed air energy storage,” Energy, vol. 174, pp. 1238–1250, 2019