Hour Block Based Demand Response for Optimal Energy Trading Profits in Networked Microgrids

Authors

  • Lokesh Vankudoth Department of Electrical Engineering, National Institute of Technology Warangal, Warangal, Telangana, 506004, India
  • Altaf Q. H. Badar Department of Electrical Engineering, National Institute of Technology Warangal, Warangal, Telangana, 506004, India

DOI:

https://doi.org/10.13052/dgaej2156-3306.37511

Keywords:

Networked microgrid, energy trading, particle swarm optimization, demand response, renewable energy sources, time-of-use-tariff

Abstract

Microgrids are a small-scale power system that integrates Distributed Generation, Energy Storage Systems and controllable loads. The intermittent and variable nature of renewable generation leads to a complex control mechanism required for Microgrids. Microgrids that are geographically close to each other are interconnected to form Networked Microgrids. Networked Microgrids provide enhanced benefits of resource sharing to Microgrids, thus, improving the reliability and operation costs while reducing the environmental impact. The Microgrids, based on their generation and load profiles, can perform energy trading within the Networked Microgrid system for achieving optimized operational costs. In this paper, the impact of a novel hour block-based demand response program in Networked Microgrids is explored. In the proposed model, hour blocks are formed in a Networked Microgrids environment, dependent on generation and load imbalance, the role of Microgrids and the Time-of-Use tariff system. The Particle Swarm Optimization method is used to optimize the individual and overall economic benefits of Microgrids in the Networked Microgrid system. The simulations of the proposed method are performed on a Networked Microgrid system having 4 Microgrids. The results show a credible reduction in costs of operation for all Microgrids and the system as a whole.

Author Biographies

Lokesh Vankudoth, Department of Electrical Engineering, National Institute of Technology Warangal, Warangal, Telangana, 506004, India

Lokesh Vankudoth received the bachelor’s degree in Electrical and Electronics Engineering from Kakatiya University in 2013, the master’s degree in Integrated Power Systems from Visvesvaraya National Institute of Technology Nagpur in 2017, respectively. He is currently working as a Ph.D. Scholar at the Department of Electrical Engineering, National Institute of Technology Warangal.

Altaf Q. H. Badar, Department of Electrical Engineering, National Institute of Technology Warangal, Warangal, Telangana, 506004, India

Altaf Q. H. Badar received his bachelor’s degree in Electrical Engineering from RTM Nagpur University in 2009, the master’s degree in Power Systems from RTM Nagpur University in 2009, and the philosophy of doctorate from Visvesvaraya National Institute of Technology Nagpur 2015, respectively. He is currently working as an Assistant Professor at the Department of Electrical Engineering, National Institute of Technology Warangal. His research areas include Evolutionary Optimization Algorithms, Energy Management, Energy Trading, Smart Grids, Forecasting.

References

Yeliz Yoldas, Ahmet Önen, SM Muyeen, Athanasios V Vasilakos, and İrfan Alan. Enhancing smart grid with microgrids: Challenges and opportunities. Renewable and Sustainable Energy Reviews, 72:205–214, 2017.

Dan T Ton and Merrill A Smith. The us department of energy’s microgrid initiative. The Electricity Journal, 25(8):84–94, 2012.

Sina Parhizi, Hossein Lotfi, Amin Khodaei, and Shay Bahramirad. State of the art in research on microgrids: A review. Ieee Access, 3:890–925, 2015.

Nikos Hatziargyriou, Hiroshi Asano, Reza Iravani, and Chris Marnay. Microgrids. IEEE power and energy magazine, 5(4):78–94, 2007.

IRE Series. Microgrids and active distribution networks. The institution of Engineering and Technology, 2009.

HSVS Kumar Nunna and Suryanarayana Doolla. Energy management in microgrids using demand response and distributed storage—a multiagent approach. IEEE Transactions on Power Delivery, 28(2):939–947, 2013.

Ashok M Jadhav, Nita R Patne, and Josep M Guerrero. A novel approach to neighborhood fair energy trading in a distribution network of multiple microgrid clusters. IEEE Transactions on Industrial Electronics, 66(2):1520–1531, 2018.

Mehdi Jalali, Kazem Zare, and Heresh Seyedi. Strategic decision-making of distribution network operator with multi-microgrids considering demand response program. Energy, 141:1059–1071, 2017.

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.

Guodong Liu, Michael R Starke, Ben Ollis, and Yaosuo Xue. Networked microgrids scoping study. ORNL, TN.[Online]. Available: https://info.ornl.gov/sites/publications/files/Pub68339.pdf, 2016.

Mahamad Nabab Alam, Saikat Chakrabarti, and Arindam Ghosh. Networked microgrids: State-of-the-art and future perspectives. IEEE Transactions on Industrial Informatics, 15(3):1238–1250, 2018.

Eduard Bullich-Massagué, Francisco Díaz-González, Mònica Aragüés-Peñalba, Francesc Girbau-Llistuella, Pol Olivella-Rosell, and Andreas Sumper. Microgrid clustering architectures. Applied energy, 212: 340–361, 2018.

Hualei Zou, Shiwen Mao, Yu Wang, Fanghua Zhang, Xin Chen, and Long Cheng. A survey of energy management in interconnected multi-microgrids. IEEE Access 7:72158–72169, 2019.

John S Vardakas, Nizar Zorba, and Christos V Verikoukis. A survey on demand response programs in smart grids: Pricing methods and optimization algorithms. IEEE Communications Surveys & Tutorials, 17(1):152–178, 2014.

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.

Hossein Haddadian and Reza Noroozian. Multi-microgrid-based operation of active distribution networks considering demand response programs. IEEE Transactions on Sustainable Energy, 10(4):1804–1812, 2018.

Hamid Karimi and Shahram Jadid. Optimal energy management for multi-microgrid considering demand response programs: A stochastic multi-objective framework. Energy, 195:116992, 2020.

Nima Nikmehr, Sajad Najafi-Ravadanegh, and Amin Khodaei. Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty. Applied energy, 198:267–279, 2017.

Mohammad Saeed Misaghian, Mohammadali Saffari, Mohsen Kia, Mehrdad Setayesh Nazar, Alireza Heidari, Miadreza Shafie-khah, and João PS Catalão. Hierarchical framework for optimal operation of multiple microgrids considering demand response programs. Electric power systems research, 165:199–213, 2018.

Seyed Ehsan Ahmadi and Navid Rezaei. A new isolated renewable based multi microgrid optimal energy management system considering uncertainty and demand response. International Journal of Electrical Power & Energy Systems, 118:105760, 2020.

Ramin Bahmani, Hamid Karimi, and Shahram Jadid. Stochastic electricity market model in networked microgrids considering demand response programs and renewable energy sources. International Journal of Electrical Power & Energy Systems, 117:105606, 2020.

Van-Hai Bui, Akhtar Hussain, and Hak-Man Kim. A multiagent-based hierarchical energy management strategy for multi-microgrids considering adjustable power and demand response. IEEE Transactions on Smart Grid, 9(2):1323–1333, 2016.

HSVS Kumar Nunna and Suryanarayana Doolla. Demand response in smart distribution system with multiple microgrids. IEEE transactions on smart grid, 3(4):1641–1649, 2012.

HSVS Kumar Nunna, Amit Mohan Saklani, Anudeep Sesetti, Swathi Battula, Suryanarayana Doolla, and Dipti Srinivasan. Multi-agent based demand response management system for combined operation of smart microgrids. Sustainable Energy, Grids and Networks, 6:25–34, 2016.

Behzad Javanmard, Mohammad Tabrizian, Meghdad Ansarian, and Amir Ahmarinejad. Energy management of multi-microgrids based on game theory approach in the presence of demand response programs, energy storage systems and renewable energy resources. Journal of Energy Storage, 42:102971, 2021.

Ali Jani, Hamid Karimi, and Shahram Jadid. Hybrid energy management for islanded networked microgrids considering battery energy storage and wasted energy. Journal of Energy Storage, 40:102700, 2021.

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.

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.

Yuhui Shi and Russell C Eberhart. Empirical study of particle swarm optimization. In Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), volume 3, pages 1945–1950. IEEE, 1999.

Daniel Bratton and James Kennedy. Defining a standard for particle swarm optimization. In 2007 IEEE swarm intelligence symposium, pages 120–127. IEEE, 2007.

H Abdi, M Ranjbaran, P Nazari, and H Akbari. A review on pso models in power system operation. International Journal of Emerging Technology and Advanced Engineering 3(7), 2013.

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Published

2022-07-01

How to Cite

Vankudoth, L. ., & Badar, A. Q. H. . (2022). Hour Block Based Demand Response for Optimal Energy Trading Profits in Networked Microgrids. Distributed Generation &Amp; Alternative Energy Journal, 37(05), 1549–1576. https://doi.org/10.13052/dgaej2156-3306.37511

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