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Influence of energy storage device on load frequency control of an interconnected dual-area thermal and solar photovoltaic power system

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Abstract

The mismatch between power generation and load demand causes unwanted fluctuations in frequency and tie-line power, and load frequency control (LFC) is an inevitable mechanism to compensate the mismatch. For this issue, this paper explores the influence of energy storage device (ESD) on ameliorating the LFC performance for an interconnected dual-area thermal and solar photovoltaic (PV) power system. Initially, to alleviate the frequency and tie-line power deviations, a proportional-integral (PI) controller is chosen and utilized in the system due to its effectiveness and simplicity in practice. For achieving the highest performance from this controller, salp swarm algorithm (SSA) is employed to search for optimal controller parameters by using integral of time-multiplied absolute error (ITAE) criterion. To affirm the contribution of SSA optimized PI controller, it is contrasted with a recent approach utilizing PI controller optimized by genetic algorithm (GA) and firefly algorithm (FA). It is observed that the results acquired for SSA are better than for GA and FA. To improve the system performance further, ESD such as redox flow battery (RFB) famous for its excellent disturbance rejection capability is integrated with the thermal power unit for the first time in the literature. It is divulged from the results that the system performance with RFB has boosted considerably with regard to shorter settling time, less undershoot/overshoot and smaller ITAE value of the frequency and tie-line power fluctuations. According to the sensitivity analysis, our proposal is found robust against system parameters variations and different loading conditions.

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References

  1. Çelik E (2020) Improved stochastic fractal search algorithm and modified cost function for automatic generation control of interconnected electric power systems. Eng Appl Artif Intell 88:103407

    Article  Google Scholar 

  2. Abd-Elazim SM, Ali ES (2018) Load frequency controller design of a two-area system composing of PV grid and thermal generator via firefly algorithm. Neural Comput Appl 30:607–616

    Article  Google Scholar 

  3. Selvaraju RK, Somaskandan G (2016) Impact of energy storage units on load frequency control of deregulated power systems. Energy 97:214–228

    Article  Google Scholar 

  4. Abd-Elazim SM, Ali ES (2016) Load frequency controller design via BAT algorithm for nonlinear interconnected power system. Int J Electr Power Energy Syst 77:166–177

    Article  Google Scholar 

  5. Guha D, Roy PK, Banerjee S (2016) Load frequency control of interconnected power system using grey wolf optimization. Swarm Evol Comput 27:97–115

    Article  Google Scholar 

  6. Sahu RK, Panda S, Padhan S (2015) A hybrid firefly algorithm and pattern search technique for automatic generation control of multi area power systems. Int J Electr Power Energy Syst 64:9–23

    Article  Google Scholar 

  7. Guha D, Roy PK, Banerjee S (2016) Quasi-oppositional differential search algorithm applied to load frequency control. Eng Sci Technol Int J 19(4):1635–1654

    Google Scholar 

  8. Oozeer MY, Ramjug-Ballgobin R (2019) A jaya-based invasive weed optimization technique for load frequency control. In: Fleming P, Lacquet B, Sanei S, Deb K, Jakobsson A (eds) Smart and sustainable engineering for next generation applications ELECOM 2018, lecture notes elect eng, vol 561. Springer, Singapore, pp 11–21

    Google Scholar 

  9. Ozdemir MT, Ozturk D (2017) Comparative performance analysis of optimal PID parameters tuning based on the optics inspired optimization methods for automatic generation control. Energies 10(12):2134

    Article  Google Scholar 

  10. Yılmaz ZY, Bal G, Çelik E, Öztürk N, Güvenç U, Arya Y (2021) A new objective function design for optimization of secondary controllers in load frequency control. J Fac Eng Archit Gazi Univ 36(4):2053–2067

    Google Scholar 

  11. Hasanien HM, El-Fergany A (2017) Symbiotic organisms search algorithm for automatic generation control of interconnected power systems including wind farms. IET Gener Transm Distrib 11(7):1692–1700

    Article  Google Scholar 

  12. Guha D, Roy PK, Banerjee S (2018) Symbiotic organism search algorithm applied to load frequency control of multi-area power system. Energy Syst 9(2):439–468

    Article  Google Scholar 

  13. Manoharan N, Dash SS, Rajesh KS, Panda S (2017) Automatic generation control by hybrid invasive weed optimization and pattern search tuned 2-DOF PID controller. Int J Comput Commun Control 12(4):533–549

    Article  Google Scholar 

  14. Rahman A, Saikia LC, Sinha N (2016) AGC of dish-Stirling solar thermal integrated thermal system with biogeography based optimised three degree of freedom PID controller. IET Renew Power Gener 10(8):1161–1170

    Article  Google Scholar 

  15. Satapathy P, Debnath MK, Mohanty PK (2018) Design of PD-PID controller with double derivative filter for frequency regulation. In 2nd IEEE international conference on power electronics, Intelligent Control and Energy Systems, Delhi, India, 2018, pp. 1142–1147

  16. Çelik E, Öztürk N, Arya Y, Ocak C (2021) (1+PD)-PID cascade controller design for performance betterment of load frequency control in diverse electric power systems. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06168-3

    Article  Google Scholar 

  17. Dahiya P, Sharma V, Naresh R (2019) Optimal sliding mode control for frequency regulation in deregulated power systems with DFIG-based wind turbine and TCSC-SMES. Neural Comput Appl 31:3039–3056

    Article  Google Scholar 

  18. Rosaline AD, Somarajan UK (2019) Structured H-infinity controller for an uncertain deregulated power system. IEEE Trans Ind Appl 55(1):892–906

    Article  Google Scholar 

  19. Çelik E (2021) Design of new fractional order PI-fractional order PD cascade controller through dragonfly search algorithm for advanced load frequency control of power systems. Soft Comput 25:1193–1217

    Article  Google Scholar 

  20. Arya Y, Dahiya P, Çelik E, Sharma G, Gözde H, Nasiruddin I (2021) AGC performance amelioration in multi-area interconnected thermal and thermal-hydro-gas power systems using a novel controller. Eng Sci Technol Int J 24(2):384–396

    Google Scholar 

  21. Arya Y, Kumar N, Dahiya P, Sharma G, Çelik E, Dhundhara S, Sharma M (2021) Cascade- IλDμN controller design for AGC of thermal and hydrothermal power systems integrated with renewable energy sources. IET Renew Power Gener 15(3):504–520

    Article  Google Scholar 

  22. Saikia LC, Mishra S, Sinha N, Nanda J (2011) Automatic generation control of a multi area hydrothermal system using reinforced learning neural network controller. Int J Electr Power Energy Syst 33(4):1101–1108

    Article  Google Scholar 

  23. Rajesh KS, Dash SS, Rajagopal R (2019) Hybrid improved firefly-pattern search optimized fuzzy aided PID controller for automatic generation control of power systems with multi-type generations. Swarm Evol Comput 44:200–211

    Article  Google Scholar 

  24. Sharma D, Mishra S (2019) Non-linear disturbance observer-based improved frequency and tie-line power control of modern interconnected power systems. IET Gener Transm Distrib 13(16):3564–3573

    Article  Google Scholar 

  25. Guha D, Roy PK, Banerjee S (2021) Disturbance observer aided optimised fractional-order three-degree-of-freedom tilt-integral-derivative controller for load frequency control of power systems. IET Gener Transm Distrib 15(4):716–736

    Article  Google Scholar 

  26. Gozde H, Taplamacioglu MC, Kocaarslan I (2012) Comparative performance analysis of Artificial Bee Colony algorithm in automatic generation control for interconnected reheat thermal power system. Int J Electr Power Energy Syst 42:167–178

    Article  Google Scholar 

  27. Ali ES, Abd-Elazim SM (2011) Bacteria foraging optimization algorithm based load frequency controller for interconnected power system. Int J Electr Power Energy Syst 33:633–638

    Article  Google Scholar 

  28. Panda S, Mohanty B, Hota PK (2013) Hybrid BFOA-PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems. Appl Soft Comput 13:4718–4730

    Article  Google Scholar 

  29. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Article  Google Scholar 

  30. Ismael S, Aleem S, Abdelaziz A, Zobaa A (2018) Practical considerations for optimal conductor reinforcement and hosting capacity enhancement in radial distribution systems. EEE Access 6:27268–27277

    Google Scholar 

  31. Ekinci S, Hekimoglu B (2018) Parameter optimization of power system stabilizer via salp swarm algorithm, 5th International Conference on Electrical and Electronic Engineering, Istanbul, Turkey, 2018, pp. 143–147

  32. Asaithambi S, Rajappa M (2018) Swarm intelligence-based approach for optimal design of cmos differential amplifier and comparator circuit using a hybrid salp swarm algorithm. Rev Sci Instrum 89(5):054702

    Article  Google Scholar 

  33. El-Fergany AA (2018) Extracting optimal parameters of PEM fuel cells using salp swarm optimizer. Renew Energy 119:641–648

    Article  Google Scholar 

  34. Reddy YVK, Reddy MD (2018) Solving economic load dispatch problem with multiple fuels using teaching learning based optimization and salp swarm algorithm. J Intell Syst Theory Appl 1(1):5–15

    Google Scholar 

  35. Guha D, Roy PK, Banerjee S (2019) Maiden application of SSA-optimised CC-TID controller for load frequency control of power systems. IET Gener Transm Distrib 13(7):1110–1120

    Article  Google Scholar 

  36. Çelik E (2022) Performance analysis of SSA optimized fuzzy 1PD-PI controller on AGC of renewable energy assisted thermal and hydro-thermal power systems. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-022-03751-x

    Article  Google Scholar 

  37. Çelik E, Öztürk N (2022) Novel fuzzy 1PD-TI controller for AGC of interconnected electric power systems with renewable power generation and energy storage devices. Eng Sci Technol Int J. https://doi.org/10.1016/j.jestch.2022.101166

    Article  Google Scholar 

  38. Chidambaram IA, Paramasiyam B (2012) Control performance standards based on load-frequency controller considering redox flow batteries coordinate with interline power flow controller. J Power Sources 219:292–304

    Article  Google Scholar 

  39. Gorripotu TS, Sahu RK, Panda S (2015) AGC of a multi-area power system under deregulated environment using redox flow batteries and interline power flow controller. Eng Sci Technol Int J 18(4):555–578

    Google Scholar 

  40. Arya Y (2017) AGC performance enrichment of multi-source hydrothermal gas power systems using new optimized FOFPID controller and redox flow batteries. Energy 127:704–715

    Article  Google Scholar 

  41. Tasnin W, Saikia LC (2018) Performance comparison of several energy storage devices in deregulated AGC of a multi-area system incorporating geothermal power plant. IET Renew Power Gener 12(7):761–772

    Article  Google Scholar 

  42. Al Shaqsi AZ, Sopian K, Al-Hinai A (2020) Review of energy storage services, applications, limitations, and benefits. Energy Rep 6:288–306

    Article  Google Scholar 

  43. Luo X, Wang J, Dooner M, Clarke J (2015) Overview of current development in electrical energy storage technologies and the application potential in power system operation. Appl Energy 137:511–536

    Article  Google Scholar 

  44. Çelik E, Öztürk N, Arya Y (2021) Advancement of the search process of salp swarm algorithm for global optimization problems. Expert Syst Appl 182:115292

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Faculty of Engineering, Düzce University, Düzce, Turkey

    Emre Çelik

  2. Department of Electrical and Electronics Engineering, Faculty of Technology, Gazi University, Ankara, Turkey

    Nihat Öztürk

  3. Department of Computer Science, Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam H. Houssein

Authors
  1. Emre Çelik
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  2. Nihat Öztürk
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  3. Essam H. Houssein
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Correspondence to Emre Çelik.

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Appendices

Appendix 1

The system data and the algorithms’ control parameters used in the compared work [2] are shown below:

  1. 1.

    \({T}_{ps}=20\) s, \({K}_{ps}=120\) Hz/puMW, \({T}_{g}=0.08\) s, \({T}_{t}=0.3\) s, \({K}_{r}=0.33\), \({T}_{r}=10\) s, \({T}_{12}=0.545\) puMW/Hz, \(B=0.8\) puMW/Hz, \(R=0.4\) Hz/puMW, \({F}^{0}=60\) Hz

  2. 2.

    The parameters of FA: the contrast of the attractiveness = 1.0, the attractiveness = 0.1 at \(r\) = 0, randomization parameter (\(\alpha \)) = 0.1, maximum number of generations = 100, number of fireflies = 50.

  3. 3.

    The parameters of GA are as follows: maximum generation number = 100, population size = 50, crossover probability = 0.75, mutation probability = 0.1.

There are only two control parameters in SSA such as number of search agents and maximum iteration. They are set equally for both cases with and without RFB as: maximum iteration number (\({M}_{iter}\)) = 50, number of salps (\(n\)) = 50.

The RFB parameters are set in the current work as follows:

\({T}_{d}=0.025\) s, \({T}_{r1}=0.1\) s, \({K}_{r1}=0.5\), \({K}_{RFB}=1.8\)

Appendix 2: Validation of the superior performance of SSA

For the justification of employing SSA as optimizer, a comparative study is established by applying well-known and recent algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), artificial bee colony (ABC) algorithm, firefly algorithm (FA), flower pollination algorithm (FPA), gray wolf optimizer (GWO) and moth-flame optimization (MFO) to the optimization of PI controller parameters in the studied system without RFB. These are good comparative algorithms in the literature able to challenge the SSA and accordingly to see how competitive SSA is in comparison with the state-of-the-art variants. For the fairness of comparison, number of fitness function evaluations (NFFE) is set to 5000 in all the algorithms.

For eliminating the situation in which best solution is found by chance, each algorithm is run 20 times and the results are averaged over 20 independent runs. The average and standard deviation (std) of the best solutions found in the last iteration are employed to qualify the overall performance of SSA against other set of algorithms. Although these two metrics give us a credible conclusion, the collected results have been also examined from nonparametric statistical point of view to check how competitive the results are. To this end, Wilcoxon rank-sum test is applied at the 5% significance level. According to the test decision, p values less than 0.05 suggest a rejection of the null hypothesis, whereas those greater than 0.05 signify a failure to reject the null hypothesis at the given level of significance.

The numerical results for the minimization of ITAE value are presented in Table

Table 5 Results offered by various algorithms for the ITAE minimization
Full size table

5. For convenience, better results are highlighted by bold numbers and p values greater than 0.05 are made Italic. N/A (not applicable) shown in the table states that the best algorithm cannot be compared with itself in the rank-sum test. Inspecting the results of average and std, SSA provides better results than all other algorithms. As per the relevant p values, the performance of SSA is significant compared with that of the remaining algorithms excepting GWO. SSA cannot outperform GWO remarkably, and they show identical performance for the current problem. However, considering the complexity of coding, computational expense and number of algorithmic parameters, SSA is more advantageous than GWO and thus, SSA is the best choice as optimizer in the present work.

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Çelik, E., Öztürk, N. & Houssein, E.H. Influence of energy storage device on load frequency control of an interconnected dual-area thermal and solar photovoltaic power system. Neural Comput & Applic 34, 20083–20099 (2022). https://doi.org/10.1007/s00521-022-07558-x

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