http://iet.metastore.ingenta.com
1887

Chance-constrained scheduling model of grid-connected microgrid based on probabilistic and robust optimisation

Chance-constrained scheduling model of grid-connected microgrid based on probabilistic and robust optimisation

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Generation, Transmission & Distribution — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This study presents a chance-constrained scheduling model based on probabilistic and robust optimisation to handle the uncertainty of renewable energy generation and loads in microgrids. In order to generate appropriate scenarios, a large number of scenarios are generated by a Latin hypercube sampling Monte Carlo method and reduced by a fast forward selection algorithm. With the aggregated scenarios, a probabilistic scheduling model is established to obtain the expectation of schedules in different probability scenario. Aiming at taking full use of the generated scenarios, a robust optimisation is applied to the probabilistic model to consider the worst situations. The scheduling model proposed in this study combines the probabilistic and robust optimisation, in which the probabilistic one utilises the aggregated scenarios to introduce the probability characteristic of uncertainty and the robust one utilises the eliminated scenarios to consider the worst case of uncertainty. Finally, the proposed scheduling model is applied to a designed grid-connected microgrid, and the simulation results demonstrate the effectiveness of the proposed scheduling model.

References

    1. 1)
      • 1. Hatziargyriou, N., Asano, H., Iravani, R., et al: ‘Microgrids’, IEEE Power Energy Mag., 2007, 5, (4), pp. 7894.
    2. 2)
      • 2. Katiraei, F., Iravani, R., Hatziargyriou, N., et al: ‘Microgrids management’, IEEE Power Energy Mag., 2008, 6, (3), pp. 5465.
    3. 3)
      • 3. Arabali, A., Ghofrani, M., Etezadi-Amoli, M., et al: ‘Genetic-algorithm-based optimization approach for energy management’, IEEE Trans. Power Deliv., 2013, 28, (1), pp. 162170.
    4. 4)
      • 4. Alavi, S.A., Ahmadian, A., Aliakbar-Golkar, M.: ‘Optimal probabilistic energy management in a typical micro-grid based-on robust optimization and point estimate method’, Energy Convers. Manage., 2015, 95, pp. 314325.
    5. 5)
      • 5. Niknam, T., Azizipanah-Abarghooee, R., Narimani, M.R.: ‘An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation’, Appl. Energy, 2012, 99, pp. 455470.
    6. 6)
      • 6. Su, W., Wang, J., Roh, J.: ‘Stochastic energy scheduling in microgrids with intermittent renewable energy resources’, IEEE Trans. Smart Grid, 2014, 5, (4), pp. 18761883.
    7. 7)
      • 7. Li, P., Xu, D., Zhou, Z., et al: ‘Stochastic optimal operation of microgrid based on chaotic binary particle swarm optimization’, IEEE Trans. Smart Grid, 2016, 7, (1), pp. 6673.
    8. 8)
      • 8. Kuznetsova, E., Ruiz, C., Li, Y.-F., et al: ‘Analysis of robust optimization for decentralized microgrid energy management under uncertainty’, Int. J. Electr. Power Energy Syst., 2015, 64, pp. 815832.
    9. 9)
      • 9. Khodaei, A., Bahramirad, S., Shahidehpour, M.: ‘Microgrid planning under uncertainty’, IEEE Trans. Power Syst., 2015, 30, (5), pp. 24172425.
    10. 10)
      • 10. Bertsimas, D., Litvinov, E., Sun, X.A., et al: ‘Adaptive robust optimization for the security constrained unit commitment problem’, IEEE Trans. Power Syst., 2013, 28, (1), pp. 5263.
    11. 11)
      • 11. Liu, J., Chen, H., Zhang, W., et al: ‘Energy management problems under uncertainties for grid-connected microgrids: a chance constrained programming approach’, IEEE Trans. Smart Grid, 2016, PP, (99), pp. 11.
    12. 12)
      • 12. Guo, L., Liu, W., Jiao, B., et al: ‘Multi-objective stochastic optimal planning method for stand-alone microgrid system’, IET Gener. Transm. Distrib., 2014, 8, (7), pp. 12631273.
    13. 13)
      • 13. Khan, S., Gawlik, W., Palensky, P.: ‘Reserve capability assessment considering correlated uncertainty in microgrid’, IEEE Trans. Sustain. Energy, 2016, 7, (2), pp. 637646.
    14. 14)
      • 14. Yang, Z., Wu, R., Yang, J., et al: ‘Economical operation of microgrid with various devices via distributed optimization’, IEEE Trans. Smart Grid, 2016, 7, (2), pp. 857867.
    15. 15)
      • 15. Zachar, M., Daoutidis, P.: ‘Microgrid/macrogrid energy exchange: a novel market structure and stochastic scheduling’, IEEE Trans. Smart Grid, 2016, PP, (99), pp. 11.
    16. 16)
      • 16. Wu, Z., Zeng, P., Zhang, X.P., et al: ‘A solution to the chance-constrained two-stage stochastic program for unit commitment with wind energy integration’, IEEE Trans. Power Syst., 2016, 31, (6), pp. 41854196.
    17. 17)
      • 17. Hojjat, M., Javidi, M.H.: ‘Chance-constrained programming approach to stochastic congestion management considering system uncertainties’, IET Gener. Transm. Distrib., 2015, 9, (12), pp. 14211429.
    18. 18)
      • 18. Dall'Anese, E., Giannakis, G.B.: ‘Risk-constrained microgrid reconfiguration using group sparsity’, IEEE Trans. Sustain. Energy, 2014, 5, (4), pp. 14151425.
    19. 19)
      • 19. Ravichandran, A., Sirouspour, S., Malysz, P., et al: ‘A chance-constraints-based control strategy for microgrids with energy storage and integrated electric vehicles’, IEEE Trans. Smart Grid, 2016, PP, (99), pp. 11.
    20. 20)
      • 20. Mohan, V., Singh, J., Ongsakul, W.: ‘Sortino ratio based portfolio optimization considering Evs and renewable energy in microgrid power market’, IEEE Trans. Sustain. Energy, 2016, PP, (99), pp. 11.
    21. 21)
      • 21. Khodabakhsh, R., Sirouspour, S.: ‘Optimal control of energy storage in a microgrid by minimizing conditional value-at-risk’, IEEE Trans. Sustain. Energy, 2016, 7, (3), pp. 12641273.
    22. 22)
      • 22. Mashayekh, S., Butler-Purry, K.L.: ‘An integrated security-constrained model-based dynamic power management approach for isolated microgrids in all-electric ships’, IEEE Trans. Power Syst., 2015, 30, (6), pp. 29342945.
    23. 23)
      • 23. Zhao, C., Guan, Y.: ‘Unified stochastic and robust unit commitment’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 33533361.
    24. 24)
      • 24. Jiang, R., Wang, J., Zhang, M., et al: ‘Two-stage minimax regret robust unit commitment’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 22712282.
    25. 25)
      • 25. Jiang, Q., Xue, M., Geng, G.: ‘Energy management of microgrid in grid-connected and stand-alone modes’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 33803389.
    26. 26)
      • 26. Shamshad, A., Bawadi, M.A., Hussin, W.M.A.W., et al: ‘First and second order Markov chain models for synthetic generation of wind speed time series’, Energy, 2005, 30, (5), pp. 693708.
    27. 27)
      • 27. Nfaoui, H., Essiarab, H., Sayigh, A.A.M.: ‘A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco’, Renew. Energy, 2004, 29, (8), pp. 14071418.
    28. 28)
      • 28. Liu, C., Wang, X., Wu, X., et al: ‘Robust optimal dispatch model of islanded microgrids with uncertain renewable energy sources’. 2016 China Int. Conf. on Electricity Distribution (CICED), Xi'an, China, August 2016, pp. 15.
    29. 29)
      • 29. Liu, C., Wang, X., Wu, X., et al: ‘Economic scheduling model of microgrid considering the lifetime of batteries’, IET Gener. Transm. Distrib., 2017, 11, (3), pp. 759767.
    30. 30)
      • 30. Ben-Tal, A., Nemirovski, A.: ‘Robust solutions of linear programming problems contaminated with uncertain data’, Math. Program., 2000, 88, (3), pp. 411424.
    31. 31)
      • 31. Bertsimas, D., Sim, M.: ‘The price of robustness’, Oper. Res., 2004, 52, (1), pp. 3553.
    32. 32)
      • 32. Mckay, M.D., Beckman, R.J., Conover, W.J.: ‘A comparison of three methods for selecting values of input variables in the analysis of output from a computer code’, Technometrics, 1979, 21, (1), pp. 239245.
    33. 33)
      • 33. Papaefthymiou, G., Klockl, B.: ‘MCMC for wind power simulation’, IEEE Trans. Energy Convers., 2008, 23, (1), pp. 234240.
    34. 34)
      • 34. Heitsch, H., Misch, W.: ‘Scenario reduction algorithms in stochastic programming’, Comput. Optim. Appl., 2003, 24, (2-3), pp. 187206.
    35. 35)
      • 35. Bertsimas, D., Brown, D.B., Caramanis, C.: ‘Theory and applications of robust optimization’, SIAM Rev., 2011, 53, (3), pp. 464501.
    36. 36)
      • 36. Zeng, B., Zhao, L.: ‘Solving two-stage robust optimization problems using a column-and-constraint generation method’, Oper. Res. Lett., 2013, 41, (5), pp. 457461.
    37. 37)
      • 37. Houwing, M., Negenborn, R.R., Schutter, B.D.: ‘Demand response with micro-chip systems’, Proc. IEEE, 2011, 99, (1), pp. 200213.
    38. 38)
      • 38. Zhang, J., Cheng, H., Wang, C.: ‘Technical and economic impacts of active management on distribution network’, Int. J. Electr. Power Energy Syst., 2009, 31, (2-3), pp. 130138.
    39. 39)
      • 39. Thorstensen, B.: ‘A parametric study of fuel cell system efficiency under full and part load operation’, J. Power Sources, 2001, 92, (1-2), pp. 916.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2017.1039
Loading

Related content

content/journals/10.1049/iet-gtd.2017.1039
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address