access icon free Optimal operation of hybrid power systems including renewable sources in the sugar cane industry

This study presents a control structure, based on model predictive control, applied to energy management optimisation in a sugar cane processing plant including renewable sources. The proposed energy plant is set upon a sugar cane processing industry and has to produce and maintain an amount of electric power throughout the year, defined by contract. This plant is, also, bound to produce flows of steam in different pressures, to comply to the demands of the production process of ethanol and sugar, from the sugar cane. The renewable sources in the system include photovoltaic, wind power generation and the use of biomass, from the remains of the sugar cane. The proposed control algorithm has the task of performing the management of which energy system to use (combined heat and power generation systems, boilers or others), maximise the use of renewable energy sources, maximise the gains of the boilers (that vary according to the biomass mixture used), manage the use of energy storages and supply the defined amount of energy. Simulation results show the satisfactory operation of the proposed control structure.

Inspec keywords: energy management systems; bioenergy conversion; wind power plants; photovoltaic power systems; boilers; cogeneration; energy storage; predictive control; power generation control; hybrid power systems; optimisation; sugar industry

Other keywords: control structure; model predictive control; hybrid power systems; control algorithm; sugar cane processing plant; renewable energy sources; steam flows; photovoltaic power generation; boilers; energy system management; sugar cane industry; wind power generation; energy management optimisation; biomass; combined heat and power generation systems; optimal operation; energy storages; ethanol production process

Subjects: Power system management, operation and economics; Control applications in food processing industries; Food industry; Wind power plants; Optimisation techniques; Optimal control; Control of electric power systems; Other direct energy conversion; Power applications in food processing industries; Optimisation techniques; Solar power stations and photovoltaic power systems; Optimisation

References

    1. 1)
      • 6. González, J.R.P.: ‘Libro Blanco de la Automatización y Control en la Industria de la Caña de Azúcar’. Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo (CYTED), 2011.
    2. 2)
      • 22. Galus, M.D., La Fauci, R., Andersson, G., et al: ‘Investigating PHEV wind balancing capabilities using heuristics and model predictive control’. Proc. Int. of Power and Energy Society General Meeting, IEEE, 2010, pp. 18.
    3. 3)
      • 3. Jiayi, H., Chuanwen, J., Rong, X., et al: ‘A review on distributed energy resources and Microgrid’, Renew. Sustain. Energy Rev., 2008, 12(9), pp. 24722483.
    4. 4)
      • 33. Geidl, M.: ‘Integrated modeling and optimization of multi-carrier energy systems’ (TU Graz, 2007).
    5. 5)
      • 23. Morais, H., Kadar, P., Faria, P., et al: ‘Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming’, Renew. Energy, 2010, 35(1) pp. 151156.
    6. 6)
      • 15. Bakken, B.H., Haugstad, A., Hornnes, K.S., et al: ‘Simulation and optimization of systems with multiple energy carriers’. Proc. Int. of Scandinavian Conf. on Simulation and Modeling, Sweden, Citeseer, 1999.
    7. 7)
      • 36. ILOG, IBM: ‘CPLEX’, 2007.
    8. 8)
      • 21. Da Costa Mendes, P., Normey-Rico, J.E., Bordons, C., et al: ‘Economic energy management of a microgrid including electric vehicles’. ‘Innovative Smart Grid Technologies Latin America (ISGT LATAM) IEEE PES’, 2015, pp. 869874.
    9. 9)
      • 29. Schulze, M., Del Granado, P.C.: ‘Multi-period optimization of cogeneration systems: considering biomass energy for district heating’. Proc. Int. on 2nd Power Systems Modeling Conf., 2009.
    10. 10)
      • 19. Petrollese, M.: ‘Optimal generation scheduling for renewable microgrids using hydrogen storage systems’, 2015.
    11. 11)
      • 4. Ministério de Minas e Energia, Governo Federal: ‘Resenha Energética Brasileira: Exercício de 2014’, 2015, pp. 619.
    12. 12)
      • 25. Parisio, A., Rikos, E., Glielmo, L., et al: ‘A model predictive control approach to microgrid operation optimization’. Proc. Int. of IEEE Transactions on Control Systems Technology, 2014, 22(5) pp. 18131827.
    13. 13)
      • 17. Valverde, L., Rosa, F., Del Real, A., et al: ‘Modeling, simulation and experimental set-up of a renewable hydrogen-based domestic microgrid’, Int. J. Hydrog. Energy, 2012, 38(27), pp. 1167211684.
    14. 14)
      • 34. Mathworks: ‘Matlab’, 2009.
    15. 15)
      • 24. Arnold, M., Negenborn, R.R., Andersson, G., et al: ‘Model-based predictive control applied to multi-carrier energy systems’. Power Energy Society General Meeting, PES ’09, IEEE, 2009, pp. 18.
    16. 16)
      • 12. Ferrari-Trecate, G., Gallestey, E., Letizia, P., et al: ‘Modeling and control of co-generation power plants: a hybrid system approach’, IEEE Trans. Control Syst. Technol., 2004, 12(5), pp. 694705.
    17. 17)
      • 13. Del Real, A., Galus, M.D., Bordons, C., et al: ‘Optimal power dispatch of energy networks including external power exchange’. Proc. Int. European Control Conf. (ECC), 2009, pp. 36163621.
    18. 18)
      • 32. Júnior, R., Agudo, R.: ‘Análise da viabilidade do aproveitamento da palha da cana de açúcar para cogeração de energia numa usina sucroalcooleira’ (Universidade Estadual Paulista, 2009).
    19. 19)
      • 5. Tiba, C.: ‘Atlas Solarimétrico do Brasil’, 2000.
    20. 20)
      • 16. Lasseter, R.H.: ‘Microgrid’. Proc. Int. of Power Engineering Society Winter Meeting, IEEE, 2002, pp. 305308.
    21. 21)
      • 26. Bozchalui, M.C., Hashmi, S.A., Hassen, H., et al: ‘Optimal operation of residential energy hubs in smart grids’, IEEE Trans. Smart Grid, 2012, 3(4), pp. 17551766.
    22. 22)
      • 11. Greenweel, W., Vahidi, A.: ‘Predictive control of voltage and current in a fuel cell-ultracapacitor hybrid’, IEEE Trans. Ind. Electron., 2010, 57(6), pp. 19541963.
    23. 23)
      • 30. Viana, L.F.: ‘Potencial energético do bagaço e palhiço de cana-de-açúcar, cv. SP80-1842, em área de alambique artesanal’ (Universidade Federal de Lavras, 2014).
    24. 24)
      • 14. Manwell, J.F.: ‘Hybrid energy systems’, Encycl. Energy, 2004, 3, pp. 215229.
    25. 25)
      • 1. Johansson, T.B.: ‘Renewable energy: sources for fuels and electricity’ (Island press, 1993).
    26. 26)
      • 31. Tolentino, G., Florentino, H.D.O., Sartori, M.M.P., et al: ‘Modelagem matemática para o aproveitamento da biomassa residual de colheita da cana-de-açúcar com menor custo’ (Bragantia, 2007), pp. 729735.
    27. 27)
      • 18. Garcia-Torres, F., Bordons, C.: ‘Optimal Economical Schedule of Hydrogen-Based Microgrids With Hybrid Storage Using Model Predictive Control’, IEEE Trans. Ind. Electron., 2015, 62(8), pp. 51955207.
    28. 28)
      • 27. Parisio, A., Glielmo, L.: ‘A mixed integer linear formulation for microgrid economic scheduling’. Proc. Int. of IEEE Int. Conf. on Smart Grid Communications (SmartGridComm), 2011, pp. 505510.
    29. 29)
      • 35. Lofberg, J.: ‘YALMIP: a toolbox for modeling and optimization in MATLAB’. IEEE Int. Symp. on Computer Aided Control Systems Design, 2004, pp. 284289.
    30. 30)
      • 8. Rego, E.E., Hernandez, F., Del, M.: ‘Eletricidade por digestão anaeróbia da vinhaça de cana-de-açúcar: contornos técnicos, econômicos e ambientais de uma opção’. Proc. Int. 6 Encontro de Energia no Meio Rural, Brazil, SciELO Brasil, 2006.
    31. 31)
      • 28. Sánchez, M., Bernardo, J. : ‘Optimización de metodologías para la caracterización de biocombustibles sólidos procedentes de la industria del olivar’ (Servicio de Publicaciones Universidad de Córdoba, 2015).
    32. 32)
      • 10. Costa Filho, M.V.A.da: ‘Modelagem, controle e otimização de processos da indústria do etanol’, 2013.
    33. 33)
      • 2. Shafiee, S., Topal, E.: ‘When will fossil fuel reserves be diminished?’ (Elsevier, 2009).
    34. 34)
      • 20. Galus, M.D., Andersson, G. : ‘Power system considerations of plug-in hybrid electric vehicles based on a multi energy carrier model’. ‘Power Energy Society General Meeting. PES ’09 IEEE’, 2009, pp. 18.
    35. 35)
      • 7. Alves, J.M.: ‘Paradigma técnico e co-geração de energia com bagaço de cana de açúcar em Goiás’. Proc. Int. 6 Encontro de Energia no Meio Rural, Brazil, 2006.
    36. 36)
      • 9. Pinto, C.P.: ‘Tecnologia da digestão anaeróbia da vinhaça e desenvolvimento sustentável’,1999.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2016.0443
Loading

Related content

content/journals/10.1049/iet-rpg.2016.0443
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading