Comparison of Hour-to-Hour and Hour-Block Energy Trading for Networked Microgrids to Optimize Profits
DOI:
https://doi.org/10.13052/dgaej2156-3306.37413Keywords:
Networked microgrid, energy trading, particle swarm optimization, demand response, renewable energy sources, time-of-use-tariff.Abstract
Networked Microgrids are a concept that emerged as a result of growing microgrid deployments in the distribution network. Microgrids in close geographical or electrical proximity are coupled to build networked microgrids. Networked microgrids offer coordinated energy management, as well as interaction and energy exchange across microgrids. This increases the reliability of microgrids and lowers their running costs. Hour-to-hour, depending on generation and load profiles, Time-of-Use pricing, microgrids can manage energy inside the microgrid and participate in energy trading with linked microgrids to reduce costs. Demand response is also utilized in energy management to achieve the above objectives. In contrast to the preceding hour-to-hour strategy, a unique hour-block-based demand response program in Networked Microgrids is suggested in this paper. In this paper, in contrast, to the above hour-to-hour approach a novel hour block-based demand response program in Networked Microgrids is proposed. Each hour block is formed based on generation and load imbalance, the role of Microgrids and the Time-of-Use tariff system. Both techniques are evaluated in terms of time and complexity using the Particle Swarm Optimization method. The economic benefits of individual microgrids and networked microgrids are also compared.
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Liang Che, Xiaping Zhang, Mohammad Shahidehpour, Ahmed Alabdulwahab, and Abdullah Abusorrah. Optimal interconnection planning of community microgrids with renewable energy sources. IEEE Transactions on Smart Grid, 8(3):1054–1063, 2015.
Michael Lee, Omar Aslam, Ben Foster, David Kathan, Jordan Kwok, Lisa Medearis, Ray Palmer, Pamela Sporborg, and Michael Tita. Assessment of demand response and advanced metering. Federal Energy Regulatory Commission, Tech. Rep, 2013.
A. Hussain, V.-H. Bui, and H.-M. Kim, “Impact analysis of demand response intensity and energy storage size on operation of networked microgrids,” Energies, vol. 10, no. 7, p. 882, 2017.
R. Bahmani, H. Karimi, and S. Jadid, “Stochastic electricity market model in networked microgrids considering demand response programs and renewable energy sources,” International Journal of Electrical Power & Energy Systems, vol. 117, p. 105606, 2020.
N. Nikmehr, S. Najafi-Ravadanegh, and A. Khodaei, “Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty,” Applied energy, vol. 198, pp. 267–279, 2017.
N. Nikmehr, L. Wang, S. Najafi-Ravadanegh, and S. Moradi-Moghadam, “Demand response enabled optimal energy management of networked microgrids for resilience enhancement,” in Operation of Distributed Energy Resources in Smart Distribution Networks. Elsevier, 2018, pp. 49–74.
A. Ajoulabadi, S. N. Ravadanegh, and B. Mohammadi-Ivatloo, “Flexible scheduling of reconfigurable microgrid-based distribution networks considering demand response program,” Energy, vol. 196, p. 117024, 2020.
S. E. Ahmadi and N. Rezaei, “A new isolated renewable based multi microgrid optimal energy management system considering uncertainty and demand response,” International Journal of Electrical Power & Energy Systems, vol. 118, p. 105760, 2020.
R. Bahmani, H. Karimi, and S. Jadid, “Stochastic electricity market model in networked microgrids considering demand response programs and renewable energy sources,” International Journal of Electrical Power & Energy Systems, vol. 117, p. 105606, 2020.
M. Movahednia, H. Karimi, and S. Jadid, “Optimal hierarchical energy management scheme for networked microgrids considering uncertainties, demand response, and adjustable power,” IET Generation, Transmission & Distribution, vol. 14, no. 20, pp. 4352–4362, 2020.
H. Karimi and S. Jadid, “Optimal energy management for multi-microgrid considering demand response programs: A stochastic multi-objective framework,” Energy, vol. 195, p. 116992, 2020.
M. Azimian, V. Amir, R. Habibifar, and H. Golmohamadi, “Probabilistic optimization of networked multi-carrier microgrids to enhance resilience leveraging demand response programs,” Sustainability, vol. 13, no. 11, p. 5792, 2021.
J. Faraji, A. Ketabi, H. Hashemi-Dezaki, M. Shafie-Khah, and J. P. Catalão, “Optimal day-ahead self-scheduling and operation of prosumer microgrids using hybrid machine learning-based weather and load forecasting,” IEEE Access, vol. 8, pp. 157 284–157 305, 2020.
J. Faraji, A. Ketabi, and H. Hashemi-Dezaki, “Developing an energy management system for optimal operation of prosumers based on a modified data-driven weather forecasting method,” in 2020 10th Smart Grid Conference (SGC). IEEE, 2020, pp. 1–6.
M. S. Misaghian, M. Saffari, M. Kia, M. S. Nazar, A. Heidari, M. Shafie- khah, and J. P. Catalão, “Hierarchical framework for optimal operation of multiple microgrids considering demand response programs,” Electric power systems research, vol. 165, pp. 199–213, 2018.
V.-H. Bui, A. Hussain, and H.-M. Kim, “A multiagent-based hierarchical energy management strategy for multi-microgrids considering adjustable power and demand response,” IEEE Transactions on Smart Grid, vol. 9, no. 2, pp. 1323–1333, 2016.
Hongbin Wu, Xin Liu, Bin Ye, and Bin Xu. Optimal dispatch and bidding strategy of a virtual power plant based on a stackelberg game. IET Generation, Transmission & Distribution, 14(4):552–563, 2019.
James Kennedy and Russell Eberhart. Particle swarm optimization. In Proceedings of ICNN’95-international conference on neural networks, volume 4, pages 1942–1948. IEEE, 1995.
Qinghai Bai. Analysis of particle swarm optimization algorithm. Computer and information science, 3(1):180, 2010.
Daniel Bratton and James Kennedy. Defining a standard for particle swarm optimization. In 2007 IEEE swarm intelligence symposium, pages 120–127. IEEE, 2007.
Mahamad Nabab Alam, Saikat Chakrabarti, and Xiaodong Liang. A benchmark test system for networked microgrids. IEEE Transactions on Industrial Informatics, 16(10):6217–6230, 2020.
