Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Hierarchical two-stage robust optimisation dispatch based on co-evolutionary theory for multiple CCHP microgrids

Combined cooling, heating, and power (CCHP) microgrids are a special form of a microgrid that is attracting increasing attention. This study contributes to the goal of minimising the operation cost of CCHP microgrids by proposing a hierarchical two-stage robust optimisation dispatch model for multiple CCHP microgrid systems. The uncertainties associated with wind power output, electric power, heating, and cooling loads, and transmission line failures are considered in the proposed model. Moreover, the electricity purchasing and selling prices of each microgrid are independently determined. The proposed model applies the outputs of fuel cells, energy storage devices, and gas turbines, the distribution factor of waste heat, and the power transmission between the microgrids and an external grid as control variables. The optimised dispatch problem is solved using McCormick envelopes relaxation and a novel column and constraint generation algorithm that provides enhanced optimisation performance by implementing co-evolutionary theory. In this way, the microgrid system is divided into several sections, and each section is represented as an individual min–max–min problem. The rationality and validity of the proposed model and the superiority of the solution performance of the improved algorithm are verified through simulation case studies involving a system composed of four CCHP microgrids.

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
      • 19. Wang, L., Zhang, B., Li, Q., et al: ‘Robust distributed optimization for energy dispatch of multi-stakeholder multiple microgrids under uncertainty’, Appl. Energy, 2019, 255, p. 113845, ISSN 0306-2619. Available at https://doi.org/10.1016/j.apenergy.2019.113845.
    7. 7)
    8. 8)
      • 2. Farmani, F., Parvizimosaed, M., Monsef, H., et al: ‘A conceptual model of a smart energy management system for a residential building equipped with CCHP system’, Int. J. Electr. Power Energy Syst., 2018, 95, pp. 523536, ISSN 0142-0615. Available at https://doi.org/10.1016/j.ijepes.2017.09.016.
    9. 9)
      • 28. Long, R., Yang, J., Chen, H., et al: ‘Co-evolutionary simulation study of multiple stakeholders in the take-out waste recycling industry chain’, J. Environ. Manage., 2019, 231, pp. 701713, ISSN 0301-4797. Available at https://doi.org/10.1016/j.jenvman.2018.10.061.
    10. 10)
      • 1. Zhou, X., Ai, Q.: ‘Distributed economic and environmental dispatch in two kinds of CCHP microgrid clusters’, Int. J. Electr. Power Energy Syst., 2019, 112, pp. 109126, ISSN 0142-0615. Available at https://doi.org/10.1016/j.ijepes.2019.04.045.
    11. 11)
    12. 12)
    13. 13)
      • 33. Marino, C., Marufuzzaman, M., Hu, M., et al: ‘Developing a CCHP-microgrid operation decision model under uncertainty’, Comput. Ind. Eng., 2018, 115, pp. 354367, ISSN 0360-8352. Available at https://doi.org/10.1016/j.cie.2017.11.021.
    14. 14)
      • 18. Aboli, R., Ramezani, M., Falaghi, H.: ‘A hybrid robust distributed model for short-term operation of multi-microgrid distribution networks’, Electr. Power Syst. Res., 2019, 177, p. 106011, ISSN 0378-7796. Available at https://doi.org/10.1016/j.epsr.2019.106011.
    15. 15)
      • 24. Li, Z., Xu, Y.: ‘Temporally-coordinated optimal operation of a multi-energy microgrid under diverse uncertainties’, Appl. Energy, 2019, 240, pp. 719729, ISSN 0306-2619. Available at https://doi.org/10.1016/j.apenergy.2019.02.085.
    16. 16)
      • 27. Zaman, F., Elsayed, S.M., Ray, T., et al: ‘Co-evolutionary approach for strategic bidding in competitive electricity markets’, Appl. Soft Comput., 2017, 51, pp. 122.
    17. 17)
      • 31. CFAS Enterprises Inc. GTG_1610DSS_KawM1A13A_50 Hz. Gas Turbines 1 Mw–5 Mw. 6 September 2017. Available at http://cfaspower.com/Gas_Turbine_CTG_1Mw_5Mw.html, accessed 12 December 2019.
    18. 18)
      • 34. Gu, W., Lu, S., Wu, Z., et al: ‘Residential CCHP microgrid with load aggregator: operation mode, pricing strategy, and optimal dispatch’, Appl. Energy, 2017, 205, pp. 173186, ISSN 0306-2619. Available at https://doi.org/10.1016/j.apenergy.2017.07.045.
    19. 19)
    20. 20)
      • 36. Castillo-Calzadilla, T., Macarulla, A.M., Kamara-Esteban, O., et al: ‘A case study comparison between photovoltaic and fossil generation based on direct current hybrid microgrids to power a service building’, J. Clean Prod., 2020, 244, p. 118870, ISSN 0959-6526. Available at https://doi.org/10.1016/j.jclepro.2019.118870.
    21. 21)
    22. 22)
    23. 23)
      • 25. Park, O., Shin, H.-S., Thourdos, A.: ‘Evolutionary game theory based multi-objective optimization for control allocation of over-actuated system’, IFAC-PapersOnLine, 2019, 52, (12), pp. 310315, ISSN 2405-8963. Available at https://doi.org/10.1016/j.ifacol.2019.11.261.
    24. 24)
      • 26. Janzen, D.H.: ‘When is it coevolution?’, Evolution, 1980, 34, pp. 611612.
    25. 25)
      • 20. Khavari, F., Badri, A., Zangeneh, A.: ‘Energy management in multi-microgrids considering point of common coupling constraint’, Int. J. Electr. Power Energy Syst., 2020, 115, p. 105465, ISSN 0142-0615. Available at https://doi.org/10.1016/j.ijepes.2019.105465.
    26. 26)
      • 15. Wang, L., Li, Q., Ding, R., et al: ‘Integrated scheduling of energy supply and demand in microgrids under uncertainty: a robust multi-objective optimization approach’, Energy, 2017, 130, pp. 114, ISSN 0360-5442. Available at https://doi.org/10.1016/j.energy.2017.04.115.
    27. 27)
    28. 28)
      • 29. Ji, L., Zhang, B.-B., Huang, G.-H., et al: ‘Explicit cost-risk tradeoff for optimal energy management in CCHP microgrid system under fuzzy-risk preferences’, Energy Econ., 2018, 70, pp. 525535, ISSN 0140-9883. Available at https://doi.org/10.1016/j.eneco.2018.01.017.
    29. 29)
    30. 30)
      • 5. Zheng, C.Y., Wu, J.Y., Zhai, X.Q., et al: ‘A novel thermal storage strategy for CCHP system based on energy demands and state of storage tank’, Int. J. Electr. Power Energy Syst., 2017, 85, pp. 117129, ISSN 0142-0615. Available at https://doi.org/10.1016/j.ijepes.2016.08.008.
    31. 31)
    32. 32)
      • 22. Zhou, X., Ai, Q., Yousif, M.: ‘Two kinds of decentralized robust economic dispatch framework combined distribution network and multi-microgrids’, Appl. Energy, 2019, 253, p. 113588, ISSN 0306-2619. Available at https://doi.org/10.1016/j.apenergy.2019.113588.
    33. 33)
    34. 34)
      • 23. Zhang, Y., Meng, F., Wang, R., et al: ‘Uncertainty-resistant stochastic MPC approach for optimal operation of CHP microgrid’, Energy, 2019, 179, pp. 12651278, ISSN 0360-5442. Available at https://doi.org/10.1016/j.energy.2019.04.151.
    35. 35)
    36. 36)
      • 30. Li, W., Wang, R., Zhang, T., et al: ‘Multi-scenario microgrid optimization using an evolutionary multi-objective algorithm’, Swarm. Evol. Comput., 2019, 50, p. 100570, ISSN 2210-6502. Available at https://doi.org/10.1016/j.swevo.2019.100570.
    37. 37)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2020.0283
Loading

Related content

content/journals/10.1049/iet-rpg.2020.0283
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address