Abstract
This paper describes a proposed particle swarm optimization (PSO)-based PID controller to solve the LFC issues of two-area interconnected power systems that operate under deregulated scenarios. This work proposes a particle swarm optimization-based PID controller to handle LFC concerns in a two-area linked power system that operates under deregulated scenarios. Deregulated power system comprises many power generating sources and many retailers or power sellers so it is not fruitful to use conventional methods for LFC. It is also very difficult to find the optimized parameters of the controller for the LFC of a multi-area deregulated power system. So, a PSO method is implemented in this work to tune the parameters of the PID controller. ITAE is used as an index of performance in function defining the objective. Optimization is done with the objective to minimize ITAE. A two-area deregulated electricity system has been mimicked in MATLAB/Simulink to evaluate the working of developed controllers and dynamic responses obtained under various contractual conditions because the effects of contracts are quite important in the dynamics of a restructured power system. The results approve that the controllers developed using PSO technique are capable of maintaining the frequency variation within the range and also keep tie-line exchange of power between the different areas as per the contractual conditions which are the main objectives of LFC. The proposed controller is highly insensitive to large and sudden load changes and parameter manipulations of the system. The controller is flexible with quite simple structure and very simple to apply. It may thus be highly beneficial for the real power systems. The suggested controller's dynamic behaviors are contrasted with GA-based controllers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jain D, Dr MK, Bhaskar MP (2022) Comparative analysis of load frequency control problem of multi area deregulated power system using soft computing techniques. Math Statistician Eng Appl 71(4):10713–10729
Donde V, Pai MA, Hiskens IA (2001) Simulation and optimization in an AGC system after deregulation. IEEE Trans Power Syst 16(3):481–489
Pal AK, Bera P, Chakraborty K (2014) AGC in two-area deregulated power system using reinforced learning neural network controller. Proc IEEE 1:1–6
Bhagya Shree S, Kamaraj N (2016) Hybrid neuro fuzzy approach for automatic generation control in restructured power system. Electr Power Energy Syst 74:274–285
Sekhar GC, Sahu RK, Baliarsingh A, Panda S (2016) Load frequency control of power system under deregulated environment using optimal firefly algorithm. Int J Electr Power Energy Syst 74:195–211
Bhatt P, Roy R, Ghoshal SP (2010) Optimized multi area AGC simulation in restructured power systems. Electr Power Energy Syst 32:311–322
Cohn N (1957) Some aspects of tie-line bias control on interconnected power systems. Am Inst Elect Eng Trans 75:1415–1436
Sood YR (2007) Evolutionary programming based optimal power flow and its validation for deregulated power system analysis. Int J Electr Power Energy Syst 29(1):65–75
Jain D et al (2023) Analysis of load frequency control problem for two area deregulated power system using genetic algorithm. Int J Creative Res Thoughts (IJCRT) 11(2):c57–c64. ISSN:2320–2882
Kothari ML, Sinha N, Rafi M (1998) Automatic generation control of an interconnected power system under deregulated environment. Proc IEEE 6:95–102
Ravi S, Kalyan C, Ravi B (2016) Impact of energy storage system on load frequency control for diverse sources of interconnected power system in deregulated power environment. Int J Electr Power Energy Syst 79(1):11–26
Deepak M, Abraham RJ (2015) Load following in a deregulated power system with thyristor controlled series compensator. Int J Electr Power Energy Syst 65:136–145
Abedinia O, Naderi MS, Ghasemi A (2011) Robust LFC in deregulated environment: fuzzy PID using HBMO. Proc IEEE 1:1–4
Abd-Elazim S, Ali E (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
Babahajiani P, Shafiee Q, Bevrani H (2018) Intelligent demand response contribution in frequency control of multiarea power systems. IEEE Trans Smart Grid 9:1282–1291
Sahoo PK (2018) Application of soft computing neural network tools to line congestion study of electrical power systems. Int J Inf Commun Technol 13(2)
Concordia C, Kirchmayer LK (1953) Tie line power and frequency control of electric power systems. Am Inst Elect Eng Trans Pt II 72:562–572
Rakhshani E, Sadeh J (2008) Simulation of two-area AGC system in a competitive environment using reduced-order observer method. Proc IEEE 1:1–6
Zribi M, Al-Rashed M, Alrifai M (2005) Adaptive decentralized load frequency control of multi-area power systems. Int J Electr Power Energy Syst 27(8):575–583
Pathak N, Nasiruddin I, Bhatti TS (2015) A more realistic model of centralized automatic generation control in real-time environment. Electr Power Compon Syst 43:2205–2213
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
IEEE PES Committee Report (1979) Current operating problems associated with automatic generation control. IEEE Trans Power App Syst PAS-98
Elgerd OI, Fosha C (1970) Optimum megawatt frequency control of multiarea electric energy systems. IEEE Trans Power App Syst PAS-89(4):556–563
Fosha CE, Elgerd OI (1970) The megawatt-frequency control problem: a new approach via optimal control theory. IEEE Trans Power App Syst PAS-89(4):563–567
Mishra AK, Das SR, Ray PK, Mallick RK, Mohanty A, Mishra DK (2020) PSO-GWO optimized fractional order PID based hybrid shunt active power filter for power quality improvements. IEEE Access 8:74497–74512
Suid MH, Ahmad MA (2022) Optimal tuning of sigmoid PID controller using nonlinear sine cosine algorithm for the automatic voltage regulator system. ISA Trans 128:265–286
Dhanasekaran B, Kaliannan J, Baskaran A, Dey N, Tavares JMR (2023) Load frequency control assessment of a PSO-PID controller for a standalone multi-source power system. Technologies 11(1):22
Bhatt P, Ghoshal SP, Roy R (2012) Coordinated control of TCPS and SMES for frequency regulation of interconnected restructured power systems with dynamic participation from DFIG based wind farm. Renew Energy 40:40–50
Tan W, Zhang H, Yu M (2012) Decentralized load frequency control in deregulated environments. Int J Electr Power Energy Syst 41(1):16–26
Arya Y, Kumar N (2016) Fuzzy gain scheduling controllers for automatic generation control of two-area interconnected electrical power systems. Electr Power Compon Syst 44:737–751
Jain D et al (2014) Comparative analysis of different methods of tuning the PID controller parameters for load frequency control problem. IJAREEIE 3(11). https://doi.org/10.15662/ijareeie.2014.0311030
Jain D et al (2014) Analysis of load frequency control problem for interconnected power system using PID controller. IJETAE 4(11):2250–2459. ISO 9001: 2008 Certified J. https://ijetae.com/files/Volume4Isue11/IJETAE_1114_42.pdf
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jain, D., Bhaskar, M.K., Parihar, M. (2023). PSO-Based Controller for LFC of Deregulated Power System. In: Yadav, A., Nanda, S.J., Lim, MH. (eds) Proceedings of International Conference on Paradigms of Communication, Computing and Data Analytics. PCCDA 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-4626-6_50
Download citation
DOI: https://doi.org/10.1007/978-981-99-4626-6_50
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-4625-9
Online ISBN: 978-981-99-4626-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)