Optimal Short-Term Scheduling of Photovoltaic Powered Multi-chiller Plants in the Presence of Demand Response Programs

Chapter

Abstract

As we know, total cooling demand directly depends on solar irradiations in a way that when solar irradiance increases, the value of building cooling demand in different residential, commercial, and industrial sectors will be increased. Hence, use of solar energy for supplying total electricity requirement of chillers will be a cost-effective way in comparison with other energy resources. If solar photovoltaic panels are employed to produce electricity for driving chiller equipment, higher coefficient of performance for chillers will be attained and lower electricity cost will be paid while increasing the amount of cooling demand. In this chapter, short-term optimal scheduling of solar powered multi-chiller plants is presented. Moreover, real time demand response programs are introduced as a cooling-demand side management strategy in reducing total electricity consumptions of compression chillers by shifting a part of cooling load from on-peak hours to off-peak periods.

Keywords

Photovoltaic cells Multi-chiller plants Demand response programs 

References

  1. 1.
    Lo C-C, Tsai S-H, Lin B-S (2016) Economic dispatch of chiller plant by improved ripple bee swarm optimization algorithm for saving energy. Appl Therm Eng 100:1140–1148CrossRefGoogle Scholar
  2. 2.
    Niknam T, Golestaneh F (2013) Enhanced bee swarm optimization algorithm for dynamic economic dispatch. IEEE Syst J 7(4):754–762CrossRefGoogle Scholar
  3. 3.
    Lee W-S, Lin L-C (2009) Optimal chiller loading by particle swarm algorithm for reducing energy consumption. Appl Therm Eng 29(8):1730–1734CrossRefGoogle Scholar
  4. 4.
    Beghi A, Cecchinato L, Cosi G, Rampazzo M (2012) A PSO-based algorithm for optimal multiple chiller systems operation. Appl Therm Eng 32:31–40CrossRefGoogle Scholar
  5. 5.
    Askarzadeh A, dos Santos Coelho L (2015) Using two improved particle swarm optimization variants for optimization of daily electrical power consumption in multi-chiller systems. Appl Therm Eng 89:640–646CrossRefGoogle Scholar
  6. 6.
    Ardakani AJ, Ardakani FF, Hosseinian S (2008) A novel approach for optimal chiller loading using particle swarm optimization. Energy Build 40(12):2177–2187CrossRefGoogle Scholar
  7. 7.
    Powell KM, Cole WJ, Ekarika UF, Edgar TF (2013) Optimal chiller loading in a district cooling system with thermal energy storage. Energy 50:445–453CrossRefGoogle Scholar
  8. 8.
    dos Santos Coelho L, Klein CE, Sabat SL, Mariani VC (2014) Optimal chiller loading for energy conservation using a new differential cuckoo search approach. Energy 75:237–243CrossRefGoogle Scholar
  9. 9.
    Valian E, Tavakoli S, Mohanna S, Haghi A (2013) Improved cuckoo search for reliability optimization problems. Comput Ind Eng 64(1):459–468CrossRefGoogle Scholar
  10. 10.
    Chang Y-C, Lee C-Y, Chen C-R, Chou C-J, Chen W-H, Chen W-H (2009) Evolution strategy based optimal chiller loading for saving energy. Energy Convers Manag 50(1):132–139CrossRefGoogle Scholar
  11. 11.
    Chang Y-C (2005) Genetic algorithm based optimal chiller loading for energy conservation. Appl Therm Eng 25(17):2800–2815CrossRefGoogle Scholar
  12. 12.
    Chow T, Zhang G, Lin Z, Song C (2002) Global optimization of absorption chiller system by genetic algorithm and neural network. Energy Build 34(1):103–109CrossRefGoogle Scholar
  13. 13.
    Chang Y-C, Lin J-K, Chuang M-H (2005) Optimal chiller loading by genetic algorithm for reducing energy consumption. Energy Build 37(2):147–155CrossRefGoogle Scholar
  14. 14.
    Beghi A, Cecchinato L, Rampazzo M (2011) A multi-phase genetic algorithm for the efficient management of multi-chiller systems. Energy Convers Manag 52(3):1650–1661CrossRefGoogle Scholar
  15. 15.
    Chang Y-C (2006) An innovative approach for demand side management – optimal chiller loading by simulated annealing. Energy 31(12):1883–1896CrossRefGoogle Scholar
  16. 16.
    Chang Y-C, Chen W-H, Lee C-Y, Huang C-N (2006) Simulated annealing based optimal chiller loading for saving energy. Energy Convers Manag 47(15):2044–2058CrossRefGoogle Scholar
  17. 17.
    Lee W-S, Chen Y-T, Kao Y (2011) Optimal chiller loading by differential evolution algorithm for reducing energy consumption. Energy Build 43(2):599–604CrossRefGoogle Scholar
  18. 18.
    Chang Y-C, Chan T-S, Lee W-S (2010) Economic dispatch of chiller plant by gradient method for saving energy. Appl Energy 87(4):1096–1101CrossRefGoogle Scholar
  19. 19.
    Chang Y-C (2006) An outstanding method for saving energy-optimal chiller operation. IEEE Trans Energy Convers 21(2):527–532CrossRefGoogle Scholar
  20. 20.
    Wang H (2017) Empirical model for evaluating power consumption of centrifugal chillers. Energy Build 140:359–370CrossRefGoogle Scholar
  21. 21.
    Frey P, Fischer S, Drück H (2014) Artificial Neural Network modelling of sorption chillers. Sol Energy 108:525–537CrossRefGoogle Scholar
  22. 22.
    Wei X, Xu G, Kusiak A (2014) Modeling and optimization of a chiller plant. Energy 73:898–907CrossRefGoogle Scholar
  23. 23.
    Labus J, Hernández J, Bruno J, Coronas A (2012) Inverse neural network based control strategy for absorption chillers. Renew Energy 39(1):471–482CrossRefGoogle Scholar
  24. 24.
    Čongradac V, Kulić F (2012) Recognition of the importance of using artificial neural networks and genetic algorithms to optimize chiller operation. Energy Build 47:651–658CrossRefGoogle Scholar
  25. 25.
    Chang Y-C, Chen W-H (2009) Optimal chilled water temperature calculation of multiple chiller systems using Hopfield neural network for saving energy. Energy 34(4):448–456MathSciNetCrossRefGoogle Scholar
  26. 26.
    dos Santos Coelho L, Mariani VC (2013) Improved firefly algorithm approach applied to chiller loading for energy conservation. Energy Build 59:273–278CrossRefGoogle Scholar
  27. 27.
    Nguyen DT, Le LB (2014) Optimal bidding strategy for microgrids considering renewable energy and building thermal dynamics. IEEE Trans Smart Grid 5(4):1608–1620CrossRefGoogle Scholar
  28. 28.
    Kamyab F, Amini M, Sheykhha S, Hasanpour M, Jalali MM (2016) Demand response program in smart grid using supply function bidding mechanism. IEEE Trans Smart Grid 7(3):1277–1284CrossRefGoogle Scholar
  29. 29.
    Qdr Q (2006) Benefits of demand response in electricity markets and recommendations for achieving them. US Department of EnergyGoogle Scholar
  30. 30.
    Vlachos AG, Biskas PN (2013) Demand response in a real-time balancing market clearing with pay-as-bid pricing. IEEE Trans Smart Grid 4(4):1966–1975CrossRefGoogle Scholar
  31. 31.
    Strbac G (2008) Demand side management: benefits and challenges. Energy Policy 36(12):4419–4426CrossRefGoogle Scholar
  32. 32.
    Brook A, Kendrick D, Meeraus A (1988) GAMS, a user’s guide. ACM Signum Newsl 23(3–4):10–11CrossRefGoogle Scholar
  33. 33.
    Jabari F, Nojavan S, Ivatloo BM (2016) Designing and optimizing a novel advanced adiabatic compressed air energy storage and air source heat pump based μ-combined cooling, heating and power system. Energy 116:64–77CrossRefGoogle Scholar
  34. 34.
    Jabari F, Nojavan S, Ivatloo BM, Sharifian MB (2016) Optimal short-term scheduling of a novel tri-generation system in the presence of demand response programs and battery storage system. Energy Convers Manag 122:95–108CrossRefGoogle Scholar
  35. 35.
    Jabari F, Masoumi A, Mohammadi-ivatloo B (2017) Long-term solar irradiance forecasting using feed-forward back-propagation neural network. In: 3rd international conference of IEA technology and energy management. Shahid Beheshti University, TehranGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of TabrizTabrizIran

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