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Utilising reliability-constrained optimisation approach to model microgrid operator and private investor participation in a planning horizon

Utilising reliability-constrained optimisation approach to model microgrid operator and private investor participation in a planning horizon

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A huge motivation has recently made on microgrid (MG) financial issues, aimed to investigate the contribution of MG operator (MGO) and private investor to reach an optimal operational strategy. Motivating the private investors to contribute in an energy production, is a considering benefit sharing factor by MGO to satisfy both of MGO and private investor. In this study, a reliability-constrained optimisation approach is presented to calculate the number and size of MG system components. To this aim, planning problem is solved in two cases; full available state and state with considering random outage of units. Furthermore, all uncertainties of generation units are considered in the problem formulation. Non-sequential Monte Carlo method is used to generate all scenarios. The proposed model simultaneously optimises two objectives, namely the benefits of MGO. The two-stage heuristic method is used to solve the objective function. In the first stage, by utilising genetic algorithm, the solution to form the Pareto optimal front is found. In the second stage, to select the trade-off solution among obtained Pareto solutions, the fuzzy satisfying method has been used. Simulations are carried out in two cases, with and without considering the share of a private investor of MGO's benefit, i.e. β.

References

    1. 1)
      • 1. Chen, J., Zhang, W., Li, J., et al: ‘Optimal sizing for grid-tied micro grids with consideration of joint optimization of planning and operation’, IEEE Trans. Sustain. Energy, 2018, 9, (1), pp. 237248.
    2. 2)
      • 2. Gazijahani, F.S., Salehi, J.: ‘Optimal bi-level model for stochastic risk-based planning of microgrids under uncertainty’, IEEE Trans. Ind. Inf., 2018, 14, (7), pp. 30543064.
    3. 3)
      • 3. Awasthi, A., Venkitusamy, K., Padmanaban, S., et al: ‘Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm’, Energy, 2017, 133, pp. 7078.
    4. 4)
      • 4. Mohammadi, S., Soleymani, S., Mozafari, B.: ‘Scenario-based stochastic operation management of microgrid including wind, photovoltaic, micro-turbine, fuel cell and energy storage devices’, Int. J. Electr. Power Energy Syst., 2014, 54, pp. 525535.
    5. 5)
      • 5. Zakariazadeh, A., Jadid, S., Siano, P.: ‘Stochastic multi-objective operational planning of smart distribution systems considering demand response programs’, Electr. Power Syst. Res., 2014, 111, pp. 156168.
    6. 6)
      • 6. Wang, B., Gayme, D.F., Liu, X., et al: ‘Optimal siting and sizing of demand response in a transmission constrained system with high wind penetration’, Int. J. Electr. Power Energy Syst., 2015, 68, pp. 7180.
    7. 7)
      • 7. Neyestani, N., Yazdani Damavandi, M., Shafie-Khah, M., et al: ‘Allocation of plug-in vehicles’ parking lots in distribution systems considering network-constrained objectives’, IEEE Trans. Power Syst., 2015, 30, (5), pp. 26432656.
    8. 8)
      • 8. Vahid-Pakdel, M.J., Nojavan, S., Mohammadi-ivatloo, B., et al: ‘Stochastic optimization of energy hub operation with consideration of thermal energy market and demand response’, Energy Convers. Manage., 2017, 145, pp. 117128.
    9. 9)
      • 9. Alipour, M., Zare, K., Seyedi, H.: ‘A multi follower Bi-level stochastic programming approach for energy management of combined heat and power micro-grids’, Energy, 2018, 149, pp. 135146.
    10. 10)
      • 10. Li, Y.Z., Zhao, T., Wang, P., et al: ‘Optimal operation of multi-microgrids via cooperative energy and reserve scheduling’, IEEE Trans. Ind. Inf., 2018, 14, (8), pp. 34593468.
    11. 11)
      • 11. Bahrami, S., Wong, V.W.S., Huang, J.: ‘An online learning algorithm for demand response in smart grid’, IEEE Trans. Smart Grid, 2018, 9, (5), pp. 47124725.
    12. 12)
      • 12. Bahrami, S., Hadi Amini, M., Shafie-khah, M., et al: ‘A decentralized electricity market scheme enabling demand response deployment’, IEEE Trans. Power Syst., 2018, 33, (4), pp. 42184227.
    13. 13)
      • 13. Nguyen, A.-D., Bui, V.H., Hussain, A., et al: ‘Impact of demand response programs on optimal operation of multi-microgrid system’, Energies, 2018, 11, (6), pp. 117.
    14. 14)
      • 14. Vahedipour-Dahraie, M., Anvari-Moghaddam, A., Guerrero, J.M.: ‘Evaluation of reliability in risk-constrained scheduling of autonomous microgrids with demand response and renewable resources’, IET Renew. Power Gener., 2018, 12, (6), pp. 657667.
    15. 15)
      • 15. Baghaee, H.R., Mirsalim, M., Gharehpetian, G.B., et al: ‘Three-phase AC/DC power-flow for balanced/unbalanced microgrids including wind/solar, droop-controlled and electronically-coupled distributed energy resources using radial basis function neural networks’, IET Power Electron., 2017, 10, (3), pp. 313328.
    16. 16)
      • 16. Baghaee, H.R., Mirsalim, M., Gharehpetian, G.B., et al: ‘Application of RBF neural networks and unscented transformation in probabilistic power-flow of microgrids including correlated wind/PV units and plug-in hybrid electric vehicles’, Simul. Modelling Pract. Theory, 2017, 72, pp. 5168.
    17. 17)
      • 17. Baghaee, H.R., Mirsalim, M., Gharehpetian, G.B., et al: ‘Fuzzy unscented transform for uncertainty quantification of correlated wind/PV microgrids: possibilistic–probabilistic power flow based on RBFNNs’, IET Renew. Power Gener., 2017, 11, (6), pp. 867877.
    18. 18)
      • 18. Aghaei, J., Agelidis, V.G., Charwand, M., et al: ‘Optimal robust unit commitment of CHP plants in electricity markets using information gap decision theory’, IEEE Trans. Smart Grid, 2017, 8, (5), pp. 22962304.
    19. 19)
      • 19. Nikmehr, N., Najafi-Ravadanegh, S., Khodaei, A.: ‘Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty’, Appl. Energy, 2017, 198, pp. 267279.
    20. 20)
      • 20. Zhang, Y., Lundblad, A., Campana, P.E., et al: ‘Battery sizing and rule-based operation of grid-connected photovoltaic-battery system: a case study in Sweden’, Energy Convers. Manage., 2017, 133, pp. 249263.
    21. 21)
      • 21. Moradi, H., Esfahanian, M., Abtahi, A., et al: ‘Optimization and energy management of a standalone hybrid microgrid in the presence of battery storage system’, Energy, 2018, 147, pp. 226238.
    22. 22)
      • 22. Xiao, H., Pei, W., Dong, Z., et al: ‘Application and comparison of metaheuristic and new metamodeling based global optimization methods to the optimal operation of active distribution networks’, Energies, 2018, 11, (1), p. 85.
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