Optimal expansion planning of active distribution system considering coordinated bidding of downward active microgrids and demand response providers

Optimal expansion planning of active distribution system considering coordinated bidding of downward active microgrids and demand response providers

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This paper addresses a framework for expansion planning of an active distribution network (ADS) that supplies its downward active microgrids (AMGs) and it participates in the upward wholesale market to sell its surplus electricity. The proposed novel model considers the impact of coordinated and uncoordinated bidding of AMGs and demand response providers (DRPs) on the optimal expansion planning. The problem has six sources of uncertainty: upward electricity market prices, AMGs location and time of installation, AMGs power generation/consumption, ADS intermittent power generations, DRP biddings, and the ADS system contingencies. The model uses the conditional value at risk (CVaR) criterion in order to handle the trading risks of ADS with the wholesale market. The proposed formulation integrates the deterministic and stochastic parameters of the risk-based expansion planning of ADS that is rare in the literature on this field. The introduced method uses a four-stage optimisation algorithm that uses genetic algorithm, CPLEX and DICOPT solvers. The proposed method is applied to the 18-bus and 33-bus test systems to assess the proposed algorithm. The proposed method reduces the aggregated expansion planning costs for the 18-bus and 33-bus system about 44.04% and 11.82% with respect to the uncoordinated bidding of AMGs/DRPs costs, respectively.


    1. 1)
      • 1. Chicco, G., Mancarella, P.: ‘Distributed multi-generation: a comprehensive view’, Renew. Sust. Energy Rev., 2009, 13, pp. 535551.
    2. 2)
      • 2. Chowdhury, S., Chowdhury, S.P., Crossley, P.: ‘Microgrids and active distribution networks’ (The Institution Of Engineering And Technology Press, UK, 2009).
    3. 3)
      • 3. Wu, M., Kou, L., Hou, X., et al: ‘A bi-level robust planning model for active distribution networks considering uncertainties of renewable energies’, Int. J. Electr. Power Energy Syst., 2019, 105, pp. 814822.
    4. 4)
      • 4. Bahrami, S., Amini, M., Shafie-Khah, M., et al: ‘A decentralized renewable generation management and demand response in power distribution networks’, IEEE Trans. Power Syst., 2018, 33, (4), pp. 42184227.
    5. 5)
      • 5. Moradijoz, M., Parsa Moghaddam, M., Haghifam, M.R.: ‘A flexible active distribution system expansion planning model: A risk-based approach’, Energy, 2018, 145, pp. 442457.
    6. 6)
      • 6. Samper, M., Vargas, A.: ‘Investment decisions in distribution networks under uncertainty with distributed generation -part I: model formulation’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 23412351.
    7. 7)
      • 7. Zare, A., Chung, C.Y., Zhan, J., et al: ‘A distributionally robust chance-constrained MILP model for multistage distribution system planning with uncertain renewables and loads’, IEEE Trans. Power Syst., 2018, 33, (5), pp. 52485262.
    8. 8)
      • 8. Bahrami, S., Amini, M., Shafie-Khah, M., et al: ‘A decentralized electricity market scheme enabling demand response deployment’, IEEE Trans. Sust. Energy, 2018, 9, (4), pp. 17831797.
    9. 9)
      • 9. Amjady, N., Attarha, A., Dehghan, S., et al: ‘Adaptive robust expansion planning for a distribution network with DERs’, IEEE Trans. Power Syst., 2018, 33, (2), pp. 16981715.
    10. 10)
      • 10. Wang, H., Zhang, H., Gu, C., et al: ‘Optimal design and operation of CHPs and energy hub with multi objectives for a local energy system’, Energy Proc., 2017, 142, pp. 16151621.
    11. 11)
      • 11. Moradi, S., Ghaffarpour, R., Ranjbar, A.M., et al: ‘Optimal integrated sizing and planning of hubs with midsize/large CHP units considering reliability of supply’, Energy Convers. Manage., 2017, 148, pp. 974992.
    12. 12)
      • 12. Rastgou, A., Moshtagh, J.: ‘Improved harmony search algorithm for electrical distribution network expansion planning in the presence of distributed generators’, Energy, 2018, 151, pp. 178202.
    13. 13)
      • 13. Weber, C., Shah, N.: ‘Optimization based design of a district energy system for an eco-town in the United Kingdom’, Energy, 2011, 36, pp. 12921308.
    14. 14)
      • 14. Soderman, J., Petterson, F.: ‘Structural and operational optimization of distributed energy systems’, Appl. Thermal Eng., 2006, 26, pp. 14001408.
    15. 15)
      • 15. Bahrami, S., Wong, V.W.S., Huang, J.: ‘Data center demand response in deregulated electricity markets’, IEEE Trans. smart Grid, 2018, Early access.
    16. 16)
      • 16. Bracco, S., Gabriele, D., Silvia, S.: ‘Economic and environmental optimization model for the design and the operation of a combined heat and power distributed generation system in an urban area’, Energy, 2013, 55, pp. 10141024.
    17. 17)
      • 17. Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: ‘Time series Analysis’ (John Wiley & Sons, USA, 2013).
    18. 18)
      • 18. Heitsch, H., Römisch, W.: ‘Scenario reduction algorithms in stochastic programming’, Comput. Optim. Appl., 2003, 24, (2), pp. 187206.
    19. 19)
      • 19. Conejo, A.J., Carrión, M., Morales, J.M.: ‘Decision making under uncertainty in electricity markets’ (Springer, USA, 2010).
    20. 20)
      • 20. Setayesh Nazar, M., Haghifam, M.R., Nazar, M.: ‘A scenario driven multiobjective primary–secondary distribution system expansion planning algorithm in the presence of wholesale–retail market’, Int. J. Electr. Power Energy Syst., 2012, 40, pp. 2945.
    21. 21)
      • 21. ‘Generalized Algebraic Modeling Systems (GAMS)’., accessed 9 October 2018.
    22. 22)
      • 22. Masoum, M.A., Jafarian, A., Ladjevardi, M., et al: ‘Fuzzy approach for optimal placement and sizing of capacitor banks in the presence of harmonics’, IEEE Trans. Power. Deliv., 2004, 19, (2), pp. 822829.
    23. 23)
      • 23. Derakhshandeh, S.Y., Masoum, A.S., Deilami, S., et al: ‘Coordination of generation scheduling with PEVs charging in industrial microgrids’, IEEE Trans. Power. Syst., 2013, 28, (3), pp. 34513461.
    24. 24)
      • 24. Baran, M.E., Wu, F.F.: ‘Network reconfiguration in distribution systems for loss reduction and load balancing’, IEEE Trans. Power. Deliv., 1989, 4, (2), pp. 14011407.

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