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Operation Planning of a Smart Microgrid Including Controllable Loads and Intermittent Energy Resources by Considering Uncertainties

  • Research Article - Electrical Engineering
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Abstract

This paper presents a new model for optimum operation of a microgrid, consisting dispatchable supplier (microturbine), non-dispatchable supplier (wind turbine), energy storage system, and loads. It has the capability of energy exchanging with upstream distribution network and contains both controllable and uncontrollable loads. For the controllable loads by presenting a new controlling algorithms, the consumption of these loads is changed or postponed to another time, with regard to the uncertainties of wind generation and the energy price of upstream distribution network, and of course by considering the welfare level of consumers. On the other hand, Monte Carlo simulation method has been used, in order to model the uncertainties of wind generation, energy price of the upstream distribution network, power consumption of uncontrollable loads, and also the failure probability of units and disconnection probability from the network. In this method, various scenarios have been generated and involved in the operation optimization program with other required inputs for the next 24h. Finally, the proposed models have been simulated on a typical microgrid with two 200kW microturbines, one 400kW wind turbine, 300kWh battery bank, and some loads with about 420kW peak demand. Simulation results by considering uncertainties for the various proposed load management programs have been analyzed and show that by implementing these programs, total operation profit of microgrid is increased from about 47$ to 54$ per day.

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References

  1. Albadi, M.H.; El-Saadany, E.F.: Demand response in electricity markets: an overview. In: Power Engineering Society General Meeting, 24–28 June, 1–5 (2007)

  2. Burns, R.M.; Gibson, C.A.: Optimization of priority list for a unit commitment program. In: IEEE PES Summer Meeting (1975)

  3. Borghetti A., Frangioni A., Lacalandra F., Nucci C.A.: Lagrangian heuristics based on disaggregated Bundle methods for hydrothermal unit commitment. Proc. IEEE Trans. Power Syst. 18, 313–323 (2003)

    Article  Google Scholar 

  4. Quyang Z., Shahidepour S.M.: An intelligent dynamic programming for unit commitment application. Proc. IEEE Trans. Power Syst. 6, 1203–1209 (1991)

    Article  Google Scholar 

  5. Lowery P.G.: Generating Unit Commitment by Dynamic Programming. Proc. IEEE Trans. Power Appar. Syst. PAS-85, 422–426 (1966)

    Article  Google Scholar 

  6. Ongsakul W., Petcharaks N.: Unit commitment by enhanced adaptive Lagrangian relaxation. Proc. IEEE Trans. Power Syst. 19, 620–628 (2004)

    Article  Google Scholar 

  7. Chuan-Ping C., Chih-Wen L., Chun-Chang L.: Unit commitment by Lagrangian relaxation and genetic algorithms. Proc. IEEE Trans. Power Syst. 15, 707–714 (2000)

    Article  Google Scholar 

  8. Zhen, L.; Na, L.; Chaohai, Z.: Unit commitment scheduling using a hybrid ANN and Lagrangian relaxation method. In: Proceeding of International Conference on Multimedia and Ubiquitous Engineering, pp. 481–484 (2008)

  9. Chen C.L., Wang S.C.: Branch-and-bound scheduling for thermal generating units. Proc. IEEE Trans. Energy Convers. 8, 184–189 (1993)

    Article  Google Scholar 

  10. Arif S., Mohammedi R.D., Hellal A., Choucha A.: A memory simulated annealing method to the unit commitment problem with ramp constraints. Proc. Arab. J. Sci. Eng. (AJSE) 37, 1021–1031 (2012)

    Article  Google Scholar 

  11. Uyar A.S., Turkay B.: Evolutionary algorithms for the unit commitment problem. Turk J. Elec. Eng. 16, 239–255 (2008)

    Google Scholar 

  12. Khorasani J.: A new heuristic approach for unit commitment problem using particle swarm optimization. Proc. Arab. J. Sci. Eng. 37, 1033–1042 (2012)

    Article  Google Scholar 

  13. Park J.B., Lee K.S., Shin J.R.: A particle swarm optimization for economic dispatch with nonsmooth cost functions. In: Proceedings of the IEEE Trans. Power Syst. 20, 34–42 (2005)

    Google Scholar 

  14. Bagherian, A.; Moghaddas Tafreshi, S.M.: A developed energy management system for a microgrid in the competitive electricity market. In: Power Tech Conference, Bucharest, 1–6 (2009)

  15. Logenthiran, T.; Srinivasan, D.: Short term generation scheduling of a Microgrid. In: TENCON IEEE Region 10 Conference, Singap, pp. 1–6 (2009)

  16. Mohamed, F.A.; Koivo, H.N.: Online management of microgrid with battery storage using multi objective optimization. In: Proceeding of International Conference on Power Engineering, Energy and Electrical Drives. POWERENG, pp. 231–236 (2007)

  17. Ding, M.; Zhang, Y.Y.; Mao, M.Q.; Yang, W.; Liu, X.P.: Operation optimization for microgrids under centralized control. In: Proceeding of 2nd IEEE International Symposium on PEDG Systems, Hefei, China, pp. 984–987 (2010)

  18. Mohamed, F.A.; Koivo, H.N.: MicroGrid online management and balancing using multiobjective optimization. In: Power Tech, Lausanne, Switz, pp. 1–6 (2007)

  19. Mohamed, F.A.; Koivo, H.N.: System modeling and online optimal management of microgrid using multiobjective optimization. In: Proceeding of Clean Electrical Power, Capri, pp. 148–153, 16 July (2007)

  20. Chen C., Duan S., Cai T., Liu B., Hu G.: Smart energy management system for optimal microgrid economic operation. IET Renew Power Gener. 5, 258–267 (2011)

    Article  Google Scholar 

  21. Xiaoying D., Wei-Jen L., Wang J., Lin L.: Studies on stochastic unit commitment formulation with flexible generating units. ELSEVIER Electr. Power Syst. Res. 80, 130–141 (2010). doi:10.1016/j.epsr.2009.08.015

    Article  Google Scholar 

  22. Pindoriya, N.M.; Singh, S.N.: MOPSO based day-ahead optimal self-scheduling of generators under electricity price forecast uncertainty. In: Power & Energy Society General Meeting, October (2009)

  23. Siahkali H., Vakilian M.: Stochastic unit commitment of wind farms integrated in power system. Electr. Power Syst. Res. 80, 1006–1017 (2010)

    Article  Google Scholar 

  24. Palensky, P.; Kupzog, F.; Zaidi, A.A.; Zhou, K.: Modeling domestic housing loads for demand response. In: Proceeding of 34th Annual Conference of IEEE Industrial Electronics (IECON) (2008)

  25. Omagari, Y.; Sugihara, H.; Tsuji, K.; Funaki, T.: An economic effect of demand response with Thermal Storage Air-conditioning systems in electricity markets. In: IEEE PES/IAS Conference on SAE, Valencia, Spain, 1–6 (2009)

  26. Cobelo N., Oyarzabal I., Ruiz J.: A direct load control model for virtual power plant management. Proc. IEEE Trans. Power Syst. 24, 959–966 (2009)

    Article  Google Scholar 

  27. Fuller, K.P.; Chassin, J.C.; Schneider, D.: Analysis of distribution level residential demand response. In: PSCE, Phoenix, AZ, pp. 1–6 (2011)

  28. Asano, H.; Takahashi, H.; Yamaguchi, N.; Bando, S.: Demand participation in the power market by load curtailment of building energy use and distributed generation of commercial customers in Japan. In: Proceeding of International Conference on Clean Electrical Power, Capri, pp. 520–525 (2009)

  29. Bagen: Reliability and Cost/Worth Evaluation of Generating Systems Utilizing Wind and Solar Energy. PhD Thesis, Department of Electrical Engineering, University of Saskatchewan, Saskatoon (2005)

  30. Roofegari Nejad, R.; Moghaddas Tafreshi, S. M.: A novel method for demand response by air-conditioning systems in a microgrid with considering wind power generation variation. In: Innovative Smart Grid Technologies (ISGT Asia) (2012)

  31. Moon P.: The Scientific Basis of Illumination Engineering. McGraw-Hill, New York (1936)

    Google Scholar 

  32. Billinton R., Li W.: Reliability Assessment of Electrical Power Systems Using Monte Carlo Methods. Springer, New York (1994)

    Book  Google Scholar 

  33. Hyung R.J., Shahidehpour M., Lei W.: Market-based generation and transmission planning with uncertainties. Proc. IEEE Trans. Power Syst. 24, 1587–1598 (2009)

    Article  Google Scholar 

  34. Masters G.M.: Renewable and Efficient Electric Power Systems. Wiley, Hoboken (2004)

    Book  Google Scholar 

Download references

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Correspondence to Reza Roofegari Nejad.

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Roofegari Nejad, R., Moghaddas Tafreshi, S.M. Operation Planning of a Smart Microgrid Including Controllable Loads and Intermittent Energy Resources by Considering Uncertainties. Arab J Sci Eng 39, 6297–6315 (2014). https://doi.org/10.1007/s13369-014-1267-4

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  • DOI: https://doi.org/10.1007/s13369-014-1267-4

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