Abstract
The chapter presents a concept of swarm algorithms, which can be applied in energy systems. This includes the group of renewable energy sources controlled by a superior control system that forces proper generator operation according to existing conditions. Research has been presented on the impact of selected renewable energy sources on the power system. First, the chapter introduces the reader to the theory of Particle Swarm Optimisation (PSO) where the algorithm is explained. Then, the structure of modelled networks is presented on which tests were performed to verify the operation of the superior control system. Furthermore, research and analyses are carried out by using a swarm algorithm to determine the optimal reduction in active power output of each of the analysed power plants, i.e. photovoltaic, biogas, wind and hydro plants, in situations that require a limitation and costs are taking into account of electricity generation by these sources. The objective of second case is dedicated to the influence of phase shifter on the operation of a power system, which applies the particle swarm algorithm, that was used to optimize the location and selection of phase shifter parameters. Optimal location of the phase shifter in the test network for the multi-criteria objective function was selected. The end of the chapter presents conclusions of the investigated cases as well as an implementation example that is leaned on C-language syntax.
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
Abbreviations
- c1, c2:
-
Weighting coefficients
- \(i\) :
-
Individual number
- \(iteration_{i}\) :
-
Current number (i-th) of iteration
- \(iteration_{ \hbox{max} }\) :
-
Maximum number of iterations
- \(j\) :
-
N-dimensional space number
- \(k\) :
-
Iteration number
- K a, K b, K c, K d :
-
Costs of electricity produced, taking into account their changing nature depending on capacity
- k N, k M :
-
Gain (weight) coefficients of the power element and the cost element
- \(k_{vol}\) :
-
Penalty coefficient (the value of penalty kvol = 2 was adopted)
- \({\text{nn}}\) :
-
Number of nodes in the test network
- P a, P b, P c, P d :
-
Auxiliary variables describing the active powers of renewable sources
- P BG :
-
Active power of a biogas plant
- P desired :
-
Desired value of active power
- P FW :
-
Active power of the wind farm
- P HE :
-
Active power of the hydropower plant
- \(p_{k}^{gi}\) :
-
The best solution found by the swarm
- \(p_{k}^{li}\) :
-
The best solution found so far by the individual
- P PV :
-
Active power of photovoltaic farm
- \(r_{1} ,r_{2}\) :
-
Random values from the interval \(\left\langle {0,1} \right\rangle\)
- U 1 :
-
Voltage at the beginning of the power line
- U 2 :
-
Voltage at the end of the power line
- U 3 :
-
Voltage at the beginning of the power line with phase shifter
- \(u_{j}\) :
-
Voltage at the jth node [pu]
- \(u_{pen}\) :
-
Penalty for non-compliance with allowable voltage values in network nodes for the i-th iteration
- \(w\) :
-
Inertia coefficient
- \(W_{ \hbox{max} }\) :
-
Maximum value of the inertia coefficient
- \(W_{ \hbox{min} }\) :
-
Minimum value of the inertia coefficient
- \(v_{k}^{\text{ij}}\) :
-
Speed vector of each particle
- X :
-
Reactance of the power line
- \(x_{k}^{\text{ij}}\) :
-
Location vector of each particle
- δ 1 :
-
Power load of the power line
- δ 3 :
-
Power load of the power line with installed phase shifter
- \(\Delta P_{Li}\) :
-
Total active power losses for the i-th iteration
- \(\Delta P_{L0}\) :
-
Total active power losses for test network without phase shifter device
- \(\Delta Q_{Li}\) :
-
Total reactive power losses for the i-th iteration
- \(\Delta Q_{L0}\) :
-
Total reactive power losses for test network without phase shifter device
- ABC:
-
Artificial Bee Colony
- HBA:
-
Honey Bee Algorithm
- IIoT:
-
Industrial Internet of Things
- IRiESP:
-
Technical requirements set out in the Transmission Network Code
- OF:
-
Objective function
- PSO:
-
Particle Swarm Optimizations
- SI:
-
Swarm Intelligence
- VBA:
-
Virtual Bee Algorithm
References
S. Smriti et al., Special issue on intelligent tools and techniques for signals, machines and automation. J. Intell. Fuzzy Syst. 35(5), 4895–4899 (2018). https://doi.org/10.3233/JIFS-169773
H. Malik et al., PSO-NN-based hybrid model for long-term wind speed prediction: a study on 67 cities of India. Adv. Intell. Syst. Comput. 697, 319–327, 2018. https://doi.org/10.1007/978-981-13-1822-1_29. Book chapter in Applications of Artificial Intelligence Techniques in Engineering
T. Mahto et al., Load frequency control of a solar-diesel based isolated hybrid power system by fractional order control using particle swarm optimization. J. Intell. Fuzzy Syst. 35(5), 5055–5061 (2018). https://doi.org/10.3233/JIFS-169789
T. Mahto et al., Fractional order control and simulation of wind-biomass isolated hybrid power system using particle swarm optimization. Adv. Intell. Syst. Comput. 698, 277–287 (2018). https://doi.org/10.1007/978-981-13-1819-1_28. Book chapter in Applications of Artificial Intelligence Techniques in Engineering
A. Migacz, R. Tadeusiewicz, The computer model of the bee colony. Syst. Sci. 3, 83–95 (1983)
M Tomera, The Use of Swarm Algorithms to Optimize Parameters in Models of Control Systems (Zesz Nauk Wydz Elektrotechniki i Autom Politech Gdańskiej, 2015), pp. 97–102 (in Polish)
M. Gryniewicz-Jaworska, Overview of Selected Evolutionary Methods In Multi-Criterial Optimization (Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 2014), pp. 32–34 (in Polish)
J. Kennedy’, R. Eberhart, Particle swarm optimization, in Proceedings of ICNN’95—International Conference on Neural Networks (IEEE, Perth, WA, Australia, 1995), p. 7
R. Kumar, U. Agarwal, A.K. Sahu, R. Anand, Utility of PSO for power loss minimization in a power system network, in 1st International Conference on Automation, Control, Energy and Systems, ACES 2014 (IEEE, Hooghy, India, 2014), pp. 1–6
M. Sarnicki, R. Zajczyk, B. Tarakan, K. Tarakan, Comparison of transmission capacities of two regulation systems : lateral and in-phase control transformers. ActaEnergetica 27, 186–190 (2016). https://doi.org/10.12736/issn.2300-3022.2016217
J. Machowski, J. Białek, J. Bumby, Power System Dynamics: Stability and Control (Wiley, 1997)
A.R. Araghi, S.H. Hosseinian, G.B. Gharehpetian, B. Vahidi, Employing TCPS for suppressing oscillations in two-area system constitute of wind farm and thermal system. IEEJ Trans Electr Electron Eng 7, 130–135 (2012). https://doi.org/10.1002/tee.21707
P. Bhatt, S.P. Ghoshal, R. Roy, Optimized automatic generation control by SSSC and TCPS in coordination with SMES for two-area hydro-hydro power system, in ACT 2009—International Conference on Advances in Computing, Control and Telecommunication Technologies (IEEE, Trivandrum, Kerala, India, 2009), pp. 474–480
V. Jeyalakshmi, P. Subburaj, PSO-scaled fuzzy logic to load frequency control in hydrothermal power system. Soft. Comput. 20, 2577–2594 (2016). https://doi.org/10.1007/s00500-015-1659-8
J. Szczepanik, T. Sieńko, New multiphase matrix converter based device for power flow control. actaenergetica.org, pp. 158–165
P. Bhasaputra, W. Ongsakul, Optimal power flow with multi-type of FACTS devices by hybrid TS/SA approach. In: 2002 IEEE International Conference on Industrial Technology, 2002. IEEE ICIT’02 (IEEE, Bankok, Thailand, Thailand, 2002), pp. 285–290
P. Paterni, S. Vitet, M. Bena, A. Yokoyama, Optimal location of phase shifters in the French network by genetic algorithm. IEEE Trans. Power Syst. 14, 37–42 (1999). https://doi.org/10.1109/59.744481
K. Kalaiselvi, V. Kumar, K. Chandrasekar, Enhanced genetic algorithm for optimal electric power flow using TCSC and TCPS, in World Congress on Engineering, WCE, vol. II (INT ASSOC ENGINEERS-IAENG, Londyn, 2010), pp. 962–966
A. Mukherjee, V. Mukherjee, Solution of optimal power flow with FACTS devices using a novel oppositional krill herd algorithm. Int. J. Electr. Power Energy Syst. 78, 700–714 (2016). https://doi.org/10.1016/j.ijepes.2015.12.001
S. Sayah, K. Zehar, Modified differential evolution algorithm for optimal power flow with non-smooth cost functions. Energy Convers. Manag. 49, 3036–3042 (2008). https://doi.org/10.1016/j.enconman.2008.06.014
M Singh, P.K. Roy, Optimal power flow with FACTS devices using a Novel Grey Wolf Algorithm, in 2017 Third International Conference on Science Technology Engineering & Management (ICONSTEM) pp. 501–506. https://doi.org/10.1109/ICONSTEM.2017.8261361
R. Janiczek, A. Matczewski, Outline of a Power Plant (PŚ Publishing Department, Gliwice, 1979) (in Polish)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
The following appendix presents an implementation example of the PSO-algorithm that was used to find the optimal solution. The code was written in DPL (DIgSILENT Programming Language) and its syntax is close to the C-language. This programming/script language is included in the software PowerFactory (https://www.digsilent.de/de/powerfactory.html), which provides excellent energy system simulation capabilities.
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Tarakan, B., Sarnicki, M., Strankowski, P.D. (2021). Optimization Solutions Using Particle Swarm Optimization in Power Systems. In: Malik, H., Iqbal, A., Joshi, P., Agrawal, S., Bakhsh, F.I. (eds) Metaheuristic and Evolutionary Computation: Algorithms and Applications. Studies in Computational Intelligence, vol 916. Springer, Singapore. https://doi.org/10.1007/978-981-15-7571-6_17
Download citation
DOI: https://doi.org/10.1007/978-981-15-7571-6_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-7570-9
Online ISBN: 978-981-15-7571-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)