Soft Computing

, Volume 23, Issue 24, pp 13459–13475 | Cite as

Optimal control strategies for a hybrid renewable energy system: an ALANN/RNN technique

  • K. MahendranEmail author
  • S. U. Prabha
Methodologies and Application


In this paper, optimal control strategy for the power flow management of the grid-connected hybrid renewable energy system (HRES) is proposed. The proposed control strategy is the parallel execution of both the ant lion optimization with artificial neural network (ANN) and recurrent neural network (RNN), and hence it is named as ALANN/RNN. Here, the HRES is made out of photovoltaic, wind turbine, fuel cell, and battery which is associated with the DC link and can adjust the real and reactive power. The proposed ALANN/RNN technique predicts the required control gain parameters of the HRES to maintain the power flow based on the active and reactive power variation in the load side. To predict the control gain parameters, the proposed technique considers power balance constraints like renewable energy accessibility and load side power demand. By using the proposed technique, power flow variations between the source side and the load side and the operational cost of HRES in light of weekly and daily prediction grid electricity prices have been minimized. In the HRES unit, the power flow management of grid is accomplished while controlling the PI controller for producing the optimal control pulses of the DC/DC converter. The proposed method is actualized in MATLAB/Simulink working stage, and the effectiveness is analyzed via the comparison analysis using the existing techniques such as SPC, GA, PSO and BAT technique. The comparison results demonstrate that the occurrence of proposed approach confirms its ability for controlling the power flow in the HRES system.

Graphical Abstract

An optimal control strategy for the power flow management of the grid-connected hybrid renewable energy system (HRES) is proposed. The proposed control strategy is the joined execution of both the ant lion optimization (ALO) with artificial neural network (ANN) and recurrent neural network (RNN), and hence it is named as ALANN/RNN. ALO is utilized to minimizing the power variations of the HRES units. ANN is used for real power prediction, and RNN is utilized for reactive power prediction.


HRES PV WT FC Battery ANN RNN ALO Power flow Active and reactive power control strategy 


Compliance with ethical standards

Conflict of interest

Authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.


  1. Aboubakeur H, Khaled A, Mohamed B (2018) A WCA-based optimization of a fuzzy sliding-mode controller for stand-alone hybrid renewable power system. Soft Comput. CrossRefGoogle Scholar
  2. Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773–4795. CrossRefGoogle Scholar
  3. Abualigah LM, Khader AT, Hanandeh ES (2018a) A combination of objective functions and hybrid krill herd algorithm for text document clustering analysis. Eng Appl Artif Intell 73:111–125. CrossRefGoogle Scholar
  4. Abualigah LM, Khader AT, Hanandeh ES (2018b) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell. CrossRefGoogle Scholar
  5. Aktas A, Erhan K, Ozdemir S, Ozdemir E (2017) Experimental investigation of a new smart energy management algorithm for a hybrid energy storage system in smart grid applications. Electr Power Syst Res 144:185–196. CrossRefGoogle Scholar
  6. Al-Sai ZA, Abualigah LM (2017) Big data and E-government: a review. In: Proceedings of the 8th international conference on information technology (ICIT), Amman, pp 580–587.
  7. Baghaee H, Mirsalim M, Gharehpetian G, Talebi H (2017) A decentralized power management and sliding mode control strategy for hybrid AC/DC microgrids including renewable energy resources. IEEE Trans Ind Inform. CrossRefGoogle Scholar
  8. Bahmani-Firouzi B, Azizipanah-Abarghooee R (2014) Optimal sizing of battery energy storage for micro-grid operation management using a new improved bat algorithm. Int J Electr Power Energy Syst 56:42–54. CrossRefGoogle Scholar
  9. Barklund E, Pogaku N, Prodanovic M et al. (2007) Energy management system with stability constraints for stand-alone autonomous microgrid. In: 2007 IEEE international conference on system of systems engineering, Oshawa.
  10. Basaran K, Cetin N, Borekci S (2017) Energy management for on-grid and off-grid wind/PV and battery hybrid systems. IET Renew Power Gener 11:642–649. CrossRefGoogle Scholar
  11. Blaabjerg F, Chen Z, Kjaer S (2004) Power electronics as efficient interface in dispersed power generation systems. IEEE Trans Power Electron 19:1184–1194. CrossRefGoogle Scholar
  12. Blaabjerg F, Teodorescu R, Liserre M, Timbus A (2006) Overview of control and grid synchronization for distributed power generation systems. IEEE Trans Ind Electron 53:1398–1409. CrossRefGoogle Scholar
  13. Bohte S, Kok J, La Poutré H (2002) Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing 48:17–37. CrossRefzbMATHGoogle Scholar
  14. Bojoi R, Limongi L, Roiu D, Tenconi A (2011) Enhanced power quality control strategy for single-phase inverters in distributed generation systems. IEEE Trans Power Electron 26:798–806. CrossRefGoogle Scholar
  15. Caisheng W, Nehrir M (2008) Power management of a stand-alone wind/photovoltaic/fuel cell energy system. IEEE Trans Energy Convers 23:957–967. CrossRefGoogle Scholar
  16. Camacho A, Castilla M, Miret J et al (2015) Active and reactive power strategies with peak current limitation for distributed generation inverters during unbalanced grid faults. IEEE Trans Ind Electron 62:1515–1525. CrossRefGoogle Scholar
  17. Camacho-Gómez C, Jiménez-Fernández S, Mallol-Poyato R, Del Ser J, Salcedo-Sanz S (2018) Optimal design of Microgrid’s network topology and location of the distributed renewable energy resources using the Harmony Search algorithm. Soft Comput. CrossRefGoogle Scholar
  18. Dubey H, Pandit M, Panigrahi B (2016) Hydro-thermal-wind scheduling employing novel ant lion optimization technique with composite ranking index. Renew Energy 99:18–34. CrossRefGoogle Scholar
  19. Emadi A, Williamson S, Khaligh A (2006) Power electronics intensive solutions for advanced electric, hybrid electric, and fuel cell vehicular power systems. IEEE Trans Power Electron 21:567–577. CrossRefGoogle Scholar
  20. Harrison G, Piccolo A, Siano P, Wallace A (2008) Hybrid GA and OPF evaluation of network capacity for distributed generation connections. Electr Power Syst Res 78:392–398. CrossRefGoogle Scholar
  21. Higuita Cano M, Agbossou K, Kelouwani S, Dubé Y (2017) Experimental evaluation of a power management system for a hybrid renewable energy system with hydrogen production. Renew Energy 113:1086–1098. CrossRefGoogle Scholar
  22. Kefayat M, Lashkar Ara A, Nabavi Niaki S (2015) A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources. Energy Convers Manag 92:149–161. CrossRefGoogle Scholar
  23. Khooban M, Niknam T (2015) A new intelligent online fuzzy tuning approach for multi-area load frequency control: self Adaptive Modified Bat Algorithm. Int J Electr Power Energy Syst 71:254–261. CrossRefGoogle Scholar
  24. Lu B, Shahidehpour M (2005) Short-term scheduling of battery in a grid-connected PV/battery system. IEEE Trans Power Syst 20:1053–1061. CrossRefGoogle Scholar
  25. Mandal D, Pal S, Saha P (2007) Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II. J Mater Process Technol 186:154–162. CrossRefGoogle Scholar
  26. Menezes Morato M, da Costa Mendes P, Normey-Rico J, Bordons C (2017) Optimal operation of hybrid power systems including renewable sources in the sugar cane industry. IET Renew Power Gener 11:1237–1245. CrossRefGoogle Scholar
  27. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98. CrossRefGoogle Scholar
  28. Muthukumar R, Balamurugan P (2018) A model predictive controller for improvement in power quality from a hybrid renewable energy system. Soft Comput. CrossRefGoogle Scholar
  29. Nick M, Cherkaoui R, Paolone M (2014) Optimal allocation of dispersed energy storage systems in active distribution networks for energy balance and grid support. IEEE Trans Power Syst 29:2300–2310. CrossRefGoogle Scholar
  30. Perera A, Nik V, Mauree D, Scartezzini J (2017) Electrical hubs: an effective way to integrate non-dispatchable renewable energy sources with minimum impact to the grid. Appl Energy 190:232–248. CrossRefGoogle Scholar
  31. Praveen Kumar T, Subrahmanyam N, Sydulu M (2018) CMBSNN for power flow management of the hybrid renewable energy—storage system-based distribution generation. IETE Tech Rev. CrossRefGoogle Scholar
  32. Riffonneau Y, Bacha S, Barruel F, Ploix S (2011) Optimal power flow management for grid connected PV systems with batteries. IEEE Trans Sustain Energy 2:309–320. CrossRefGoogle Scholar
  33. Selva Santhose Kumar R, Girirajkumar S (2015) Z-source inverter fed induction motor drive control using particle swarm optimization recurrent neural network. J Intell Fuzzy Syst 28:2749–2760. CrossRefGoogle Scholar
  34. Sun K, Zhang L, Xing Y, Guerrero J (2011) A distributed control strategy based on DC bus signaling for modular photovoltaic generation systems with battery energy storage. IEEE Trans Power Electron 26:3032–3045. CrossRefGoogle Scholar
  35. Timbus A, Liserre M, Teodorescu R et al (2009) Evaluation of current controllers for distributed power generation systems. IEEE Trans Power Electron 24:654–664. CrossRefGoogle Scholar
  36. Torres-Jimenez J, Rodriguez-Cristerna A (2017) Metaheuristic post-optimization of the NIST repository of covering arrays. CAAI Trans Intell Technol 2(1):31–38. CrossRefGoogle Scholar
  37. Zhang J (2006) Optimal power flow control for congestion management by interline power flow controller (IPFC). In: International conference on power system technology, 2006. PowerCon 2006, Chongqing, 22 Oct 2006Google Scholar
  38. Zhang L, Gari N, Hmurcik L (2014) Energy management in a microgrid with distributed energy resources. Energy Convers Manag 78:297–305. CrossRefGoogle Scholar
  39. Zhao C, Round S, Kolar J (2008) An isolated three-port bidirectional DC–DC converter with decoupled power flow management. IEEE Trans Power Electron 23:2443–2453. CrossRefGoogle Scholar
  40. Zheng W, Wu W, Zhang B et al (2015) A fully distributed reactive power optimization and control method for active distribution networks. IEEE Trans Smart Grid. CrossRefGoogle Scholar
  41. Zhu X, Han S, Liu Y, Chen G (2017) Effects of laccase incubated from white rot fungi on the mechanical properties of fiberboard. J For Res 28(6):1293–1300. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical and Electronics EngineeringJansons Institute of TechnologyCoimbatoreIndia
  2. 2.Department of Electrical and Electronics EngineeringSri Ramakrishna Engineering CollegeCoimbatoreIndia

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