Skip to main content
SpringerLink
Log in

Operation Strategy of Park Microgrid with Multi‐stakeholder Based on Artificial Immune System

  • Chapter
  • First Online:
Design, Control, and Operation of Microgrids in Smart Grids

Part of the book series: Power Systems ((POWSYS))

Abstract

The penetration of distributed energy resources (DER) is growing worldwide, and microgrid (MG) is an approprate way to realize intergration of these DERs. The new reform of power system promotes the market-oriented operation of microgrids. This chapter takes the park microgrid with multi-stakeholder as the object, and to promote the interaction between the main grid and DERs in MG, a two-level optimization model of microgrid bidding transaction based on multi-agent system is established. In the lower-level optimization, considering the deviation penalty of power generation and the previous round bidding results, the optimal bidding strategy model is established to maximize the benefit of bidding unit agent. In the upper-level model, bidding strategies of DERs as constraints, a multiple objective mixed-integer linear programming model was built to optimize the overall objectives of clearing price and imbalanced deviation, searching for the optimal clearing price and the generation plan of DERs. Due to the complexity of the two-layer optimization model, a novel artificial immune system (AIS) was established and integrated into the multi-agent system to help DERs participate in the optimal bidding operation of MG. The antigen is transformed by the environmental information, the price of the main grid, other DERs’ bidding strategies, and the predicted deviation coefficient while considering the uncertainties of DER facilities. The proposed optimized operation mode is compared with the traditional operation mode in the case study, verifying that the proposed method can realize the optimal operation of the MG and the coordinated interaction with the main grid, increasing the benefit of stakeholders. The AIS algorithm is also compared with traditional algorithms, proving the superiority in optimizing.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. M. Di Somma, G. Graditi, P. Siano, Optimal bidding strategy for a DER aggregator in the day-ahead market in the presence of demand flexibility. IEEE Trans. Ind. Electron. 66, 1509–1519 (2019)

    Article  Google Scholar 

  2. Z. Xu, P. Yang, C. Zheng, Y. Zhang, J. Peng, Z. Zeng, Analysis on the organization and development of multi-microgrids. Renew. Sust. Energ. Rev. 81, 2204–2216 (2018)

    Article  Google Scholar 

  3. Z. Xu, Y. Zhang, Z. Chen, X. Lin, B. Chen, Bi-level optimal capacity configuration for power to gas facilities considering operation strategy and investment subject benefit. Dianli Xitong Zidonghua/automation of Electric Power Systems 42, 76–84 (2018)

    Google Scholar 

  4. K. Neuhoff, Opening the electricity market to renewable energy: Making better use of the grid. Diw Econ. Bull. 1, 16–23 (2011)

    Google Scholar 

  5. W. Zhu, X. Yuan, Y. Li, Comprehensive Benefit Evaluation Based on Interaction of MicroGrid and Distribution Network, Electr. Energy Manag. Technol. (2014)

    Google Scholar 

  6. C. Li, J. Zhang, P. Li, Multi-objective optimization model of micro-grid operation considering cost, pollution discharge and risk. Proc. CSEE 35, 1051–1058 (2015)

    Google Scholar 

  7. M. Rahmani-Andebili, An adaptive approach for PEVs charging management and reconfiguration of electrical distribution system penetrated by renewables, IEEE Trans. Ind. Inf. 14(5) (2018)

    Google Scholar 

  8. L. Jie, L. Yikui, W. Lei, Optimal operation for community-based multi-party microgrid in grid-connected and islanded modes. IEEE Trans. Smart Grid 9, 756–765 (2018)

    Google Scholar 

  9. S. Zhang, L. Shuaikang, Evaluation method of park-level integrated energy system for microgrid. Proc. CSEE 13, 1983–1996 (2018)

    Google Scholar 

  10. D. Gejirifu, Z. Tan, S. Yang, M. Li, B. Yang, W. Kong, N. Zhang, Robust optimization of micro-grid operation considering bidding decisions in electricity market environment, Renew. Energy Res. (2018)

    Google Scholar 

  11. K. Shi, N. Liu, J. Zhang, J. Lei, Random matching trading mechanism in microgrid of multi-operators. Power Syst. Technol. (2016)

    Google Scholar 

  12. X. Kong, Y. Zeng, N. Lu, J. Yan, Q. Hu, Optimal bidding Management for Agent-based Microgrid Operation. Zhongguo Dianji Gongcheng Xuebao/proceedings of the Chinese Society of Electrical Engineering 37, 1626–1633 (2017)

    Google Scholar 

  13. Mehdi Rahmani-Andebili, Analyzing the Effects of Problem Parameters on the Operation Cost of the Networked Microgrids, 2020 IEEE Kansas Power and Energy Conference (KPEC) (2020)

    Google Scholar 

  14. Yongjing, He, Wei, Wang, Xuezhi, and Wu, Multi-Agent Based Fully Distributed Economic Dispatch in Microgrid Using Exact Diffusion Strategy, IEEE Access, 7020–7031 (2019)

    Google Scholar 

  15. Z. Zhao, J. Guo, X. Luo, J. Xue, L.L. Lai, Energy transaction for multi-microgrids and internal microgrid based on Blockchain. IEEE Access 76, 17–29 (2020)

    Google Scholar 

  16. M. Jin, W. Feng, P. Liu, C. Marnay, C. Spanos, MOD-DR: Microgrid optimal dispatch with demand response. Appl. Energy 187, 758–776 (2017)

    Article  Google Scholar 

  17. C. Dou, X. Jia, L.I. Heng, Multi-agent-system-based market bidding strategy for distributed generation in microgrid. Power Syst. Technol. (2016)

    Google Scholar 

  18. W. Li, Y. Si, L. Ming, J. Zhe, N. Wu, J. Jiang, Microgrid Operational Benefits Distribution Based on the Theory of Cooperative Game, in 2016 China International Conference on Electricity Distribution (CICED), (2016)

    Google Scholar 

  19. M. Rahmani-Andebili, Chapter 9: Cooperative distributed energy scheduling in Microgrids, in Electric Distribution Network Management and Control, (Springer, Singapore, 2018), pp. 235–254

    Chapter  Google Scholar 

  20. T. Wakui, M. Hashiguchi, K. Sawada, R. Yokoyama, Two-stage design optimization based on artificial immune system and mixed-integer linear programming for energy supply networks. Energy 170, 1228–1248 (2019)

    Article  Google Scholar 

  21. A.A. Hafez, A.Y. Hatata, M.M. Aldl, Optimal sizing of hybrid renewable energy system via artificial immune system under frequency stability constraints. J. Renew. Sustain. Energy 11, 015905 (2019)

    Article  Google Scholar 

  22. C. Gao, D. Wu, T. Pan, Z. Ji, Optimal economic dispatch of microgrid based on immune particle swarm optimization algorithm. J. Syst. Simul. 17, 58–76 (2018)

    Google Scholar 

  23. L.W. de Oliveira, E.J. de Oliveira, F.V. Gomes, I.C. Silva, A.L.M. Marcato, P.V.C. Resende, Artificial immune systems applied to the reconfiguration of electrical power distribution networks for energy loss minimization. Int. J. Electr. Power Energy Syst. 56, 64–74 (2014)

    Article  Google Scholar 

  24. B. Ramachandran, S.K. Srivastava, C.S. Edrington, D.A. Cartes, An intelligent auction scheme for Smart grid market using a hybrid immune algorithm. IEEE Trans. Ind. Electron. 58, 4603–4612 (2011)

    Article  Google Scholar 

  25. L. Tao, C. Schwaegerl, S. Narayanan, J.H. Zhang, From Laboratory Microgrid to Real Markets — Challenges and Opportunities, in IEEE International Conference on Power Electronics & Ecce Asia (2011)

    Google Scholar 

  26. S. Parhizi, A. Khodaei, M. Shahidehpour, Market-based versus Price-based microgrid optimal scheduling. IEEE Trans. Smart Grid 9, 615–623 (2018)

    Article  Google Scholar 

  27. J.K. Liu, E.R. Lian-Jie, Overview of application of multiagent technology, Control & Decision (2001)

    Google Scholar 

  28. M. Rahmani-Andebili, Chapter 4: Robust operation of a reconfigurable electrical distribution system by optimal charging management of plug-in electric vehicles considering the technical, social, and geographical aspects, in Planning and Operation of Plug-In Electric Vehicles: Technical, Geographical, and Social Aspects, (Springer, Cham, 2019)

    Chapter  Google Scholar 

  29. M. Rahmani-Andebili, Dynamic and adaptive reconfiguration of electrical distribution system including renewables applying stochastic model predictive control. IET Gener. Trans. Distrib. 11(16), 3912–3921 (2017)

    Article  Google Scholar 

  30. Tan and Ying, Artificial Immune System (Springer, Berlin, 2016)

    Google Scholar 

  31. X. Fang, J. Li, L. Li, Restudying the Artificial Immune Model for Network Intrusion Detection, in 2009 International Workshop on Intelligent Systems and Applications Wuhan,China (2009)

    Google Scholar 

  32. X. Kong, D. Liu, J. Xiao, C. Wang, A multi-agent optimal bidding strategy in microgrids based on artificial immune system. Energy 189, 116154 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, China

    Xiangyu Kong, Dehong Liu, Fangyuan Sun & Chengshan Wang

  2. Tianjin Electric Power Company Electric Power Science Research Institute, Tianjin, China

    Xianxu Huo & Shupeng Li

Authors
  1. Xiangyu Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dehong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fangyuan Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chengshan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xianxu Huo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shupeng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiangyu Kong .

Editor information

Editors and Affiliations

  1. Department of Engineering Technology, State University of New York, Buffalo State, Buffalo, NY, USA

    Mehdi Rahmani-Andebili

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kong, X., Liu, D., Sun, F., Wang, C., Huo, X., Li, S. (2021). Operation Strategy of Park Microgrid with Multi‐stakeholder Based on Artificial Immune System. In: Rahmani-Andebili, M. (eds) Design, Control, and Operation of Microgrids in Smart Grids. Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-64631-8_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-64631-8_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64630-1

  • Online ISBN: 978-3-030-64631-8

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation