Optimizing Unit Scheduling with Fuzzy Logic: A Strategic Approach for Efficient Power Network Operations

Authors

  • Sahbi Marrouchi LaTICE Laboratory, National Higher Engineering School of Tunis, University of Tunis, Tunisia | Higher Institute of Technological Studies of Kef, Tunisia
  • Moez ben Hessine Higher Institute of Applied Sciences and Technologies of Gafsa, University of Gafsa, Tunisia
  • Souad Chebbi LaTICE Laboratory, National Higher Engineering School of Tunis, University of Tunis, Tunisia
Volume: 14 | Issue: 2 | Pages: 13305-13312 | April 2024 | https://doi.org/10.48084/etasr.6894

Received: 12 January 2024 | Revised: 27 January 2024 | Accepted: 5 February 2024 | Online: 18 February 2024


Corresponding author: Sahbi Marrouchi

Abstract

This study delves into addressing the challenge of resolving the Unit Commitment (UC) problem, which focuses on enhancing the efficiency of production units and devising their operational schedules to accommodate fluctuations in consumption spanning from a day to a month. Given the intricate, combinatorial, and nonlinear constraints associated with each production unit, this study advocates an optimization approach rooted in fuzzy logic. A Langrangian function was established to simplify the UCP and to transform the different inequality into a linear unconstrained problem. The choice of fuzzy inputs was established using the partial derivatives of a Lagrangian function as a function of the powers injected into each node of the electrical network. This combination of the Lagrangian function and the input of the fuzzy regulator made it possible to control the different constraints in the total production cost function and to improve the operating efficiency of the different production units. This method was effectively applied to a 14-bus IEEE power network encompassing 5 generating units, to address the UC problem by optimizing generator load capacity (LCG) and minimizing Incremental Losses (IL). The numerical processing of the fuzzy linguistic variables was implemented using Mamdani-type fuzzy rules. This strategy stands out for its robust exploratory capability, facilitating the identification of optimal solutions to reduce production costs while ensuring optimal planning of production units.

Keywords:

unit commitment, optimization, fuzzy logic, energy management, production unit

Downloads

Download data is not yet available.

References

D. N. Simopoulos, S. D. Kavatza, and C. D. Vournas, "Unit commitment by an enhanced simulated annealing algorithm," IEEE Transactions on Power Systems, vol. 21, no. 1, pp. 68–76, Jan. 2006.

M. Premkumar et al., "An efficient and reliable scheduling algorithm for unit commitment scheme in microgrid systems using enhanced mixed integer particle swarm optimizer considering uncertainties," Energy Reports, vol. 9, pp. 1029–1053, Dec. 2023.

A. Abu-Jasser and M. M. Hijjo, "Fuzzy-Logic-Based Approach to Solve the Unit-Commitment Problem," International Journal of Engineering Research and Development, vol. 2, no. 1, pp. 22–29, Jul. 2012.

T. A. A. Victoire and A. E. Jeyakumar, "Unit commitment by a tabu-search-based hybrid-optimisation technique," IEE Proceedings - Generation, Transmission and Distribution, vol. 152, no. 4, pp. 563–574, Jul. 2005.

C. C. A. Rajan and M. R. Mohan, "An evolutionary programming-based tabu search method for solving the unit commitment problem," IEEE Transactions on Power Systems, vol. 19, no. 1, pp. 577–585, Feb. 2004.

S. Kharbash and W. Wang, "All-Terminal Network Reliability Optimization in Fading Environment via Cross Entropy Method," in 2010 IEEE International Conference on Communications, May 2010, pp. 1–5.

Ι. Marouani, A. Boudjemline, T. Guesmi, and H. H. Abdallah, "A Modified Artificial Bee Colony for the Non-Smooth Dynamic Economic/Environmental Dispatch," Engineering, Technology & Applied Science Research, vol. 8, no. 5, pp. 3321–3328, Oct. 2018.

S. Marrouchi and S. Chebbi, "New strategy based on fuzzy logic approach to solve the unit-commitment problem," presented at the International Conference on Control, Engineering Information Technology (CEIT’14), Sousse, Tunisia, Mar. 2014.

C. Abbey, F. Katiraei, C. Brothers, L. Dignard-Bailey, and G. Joos, "Integration of distributed generation and wind energy in Canada," in 2006 IEEE Power Engineering Society General Meeting, Jun. 2006.

X. Guan, P. B. Luh, H. Yan, and J. A. Amalfi, "An optimization-based method for unit commitment," International Journal of Electrical Power & Energy Systems, vol. 14, no. 1, pp. 9–17, Feb. 1992.

Z. Ouyang and S. M. Shahidehpour, "An intelligent dynamic programming for unit commitment application," IEEE Transactions on Power Systems, vol. 6, no. 3, pp. 1203–1209, Aug. 1991.

A. Ahmad, "Unit Commitment Using Hybrid Approaches," Ph.D. dissertation, University of Engineering and Technology Taxila, Pakistan, 2010.

S. Marrouchi, N. Amor, M. B. Hessine, and S. Chebbi, "Theoretical Investigation of Combined Use of PSO, Tabu Search and Lagrangian Relaxation methods to solve the Unit Commitment Problem," Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 1, pp. 357–365, Feb. 2018.

J. M. Arroyo and A. J. Conejo, "Optimal response of a thermal unit to an electricity spot market," IEEE Transactions on Power Systems, vol. 15, no. 3, pp. 1098–1104, Aug. 2000.

B. Li et al., "Fast Solution Method for the Large-Scale Unit Commitment Problem with Long-Term Storage," Chinese Journal of Electrical Engineering, vol. 9, no. 3, pp. 39–49, Sep. 2023.

A. Merlin and P. Sandrin, "A New Method for Unit Commitment at Electricite De France," IEEE Transactions on Power Apparatus and Systems, vol. PAS-102, no. 5, pp. 1218–1225, May 1983.

C. L. Chen and S. C. Wang, "Branch-and-bound scheduling for thermal generating units," IEEE Transactions on Energy Conversion, vol. 8, no. 2, pp. 184–189, Jun. 1993.

T. Senjyu, T. Miyagi, A. Y. Saber, N. Urasaki, and T. Funabashi, "Emerging solution of large-scale unit commitment problem by Stochastic Priority List," Electric Power Systems Research, vol. 76, no. 5, pp. 283–292, Mar. 2006.

S. S. Kumar and V. Palanisamy, "A hybrid fuzzy dynamic programming approach to unit commitment," Journal-Institution of Engineers India, vol. 88, Mar. 2008.

A. Abu-Jasser, "Solving the Unit Commitment Problem Using Fuzzy Logic," International Journal of Computer and Electrical Engineering, vol. 3, no. 6, pp. 824–829, Dec. 2011.

K. Venkatesan and C. C. Asir Rajan, "A Simulated Annealing Method for Solving Multi-Area Unit Commitment Problem," in 2011 International Conference on Process Automation, Control and Computing, Jul. 2011, pp. 1–7.

S. Yin Wa Wong, "An enhanced simulated annealing approach to unit commitment," International Journal of Electrical Power & Energy Systems, vol. 20, no. 5, pp. 359–368, Jun. 1998.

A. Torchani, A. Boudjemline, H. Gasmi, Y. Bouazzi, and T. Guesmi, "Dynamic Economic/Environmental Dispatch Problem Considering Prohibited Operating Zones," Engineering, Technology & Applied Science Research, vol. 9, no. 5, pp. 4586–4590, Oct. 2019.

C. C. A. Rajan, M. R. Mohan, and K. Manivannan, "Refined simulated annealing method for solving unit commitment problem," in Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN’02 (Cat. No.02CH37290), Honolulu, HI, USA, May 2002, vol. 1, pp. 333–338 vol.1.

M. Sudhakaran and P. Raj, "Integrating genetic algorithms and tabu search for unit commitment problem," International Journal of Engineering, Science and Technology, vol. 2, no. 1, pp. 57–69, 2010.

G. VenkataSubba Reddy, V. Ganesh, and C. SrinivasaRao, "Cost Reduction in Clustering Based Unit Commitment Employing Hybrid Genetic-Simulated Annealing Technique," Journal of Electrical Engineering & Technology, vol. 14, no. 1, pp. 27–35, Jan. 2019.

C. C. Asir Rajan, "Hydro-thermal unit commitment problem using simulated annealing embedded evolutionary programming approach," International Journal of Electrical Power & Energy Systems, vol. 33, no. 4, pp. 939–946, May 2011.

L. Montero, A. Bello, and J. Reneses, "A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods," Energies, vol. 15, no. 4, Jan. 2022, Art. no. 1296.

C. P. Cheng, C. W. Liu, and C. C. Liu, "Unit commitment by annealing-genetic algorithm," International Journal of Electrical Power & Energy Systems, vol. 24, no. 2, pp. 149–158, Feb. 2002.

L. Abdelhakem-Koridak and M. Rahli, "Optimisation d’un dispatching environnement/économique de la production d’énergie électrique par un algorithme génétique," presented at the Quatrième Conférence Internationale sur le Génie Electrique CIGE’10, Nov. 2010.

T. T. Maifeld and G. B. Sheble, "Genetic-based unit commitment algorithm," IEEE Transactions on Power Systems, vol. 11, no. 3, pp. 1359–1370, Aug. 1996.

A. Rudolf and R. Bayrleithner, "A genetic algorithm for solving the unit commitment problem of a hydro-thermal power system," IEEE Transactions on Power Systems, vol. 14, no. 4, pp. 1460–1468, Nov. 1999.

F. T. Lin, C. Y. Kao, and C. C. Hsu, "Applying the genetic approach to simulated annealing in solving some NP-hard problems," IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 6, pp. 1752–1767, Nov. 1993.

N. P. Padhy, "Unit commitment using hybrid models: a comparative study for dynamic programming, expert system, fuzzy system and genetic algorithms," International Journal of Electrical Power & Energy Systems, vol. 23, no. 8, pp. 827–836, Nov. 2001.

S. Marrouchi, M. Ben Hessine, and S. Chebbi, "New strategy based on combined use of Particle Swarm Optimization and Gradient methods to solve the unit commitment problem," in 2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC), Rome, Italy, Jun. 2015, pp. 1362–1367.

K. Alqunun, "Optimal Unit Commitment Problem Considering Stochastic Wind Energy Penetration," Engineering, Technology & Applied Science Research, vol. 10, no. 5, pp. 6316–6322, Oct. 2020.

S. Marrouchi and S. Ben Saber, "A Comparative Study of Fuzzy Logic, Genetic Algorithm, and Gradient-Genetic Algorithm Optimization Methods for Solving the Unit Commitment Problem," Mathematical Problems in Engineering, vol. 2014, Jul. 2014, Art. no. e708275.

S. Marrouchi and S. Chebbi, "Unit Commitment Optimization Using Gradient-Genetic Algorithm and Fuzzy Logic Approaches," in Complex System Modelling and Control Through Intelligent Soft Computations, Q. Zhu and A. T. Azar, Eds. Cham, Switzerland: Springer International Publishing, 2015, pp. 687–710.

A. H. Mantawy and Y. L. Abdel-Magid, "A New Fuzzy Unit Commitment Model and Solution," presented at the 14th PSCC, Seville, Spain, 2002.

A. C. G. C. Rajan, "Neural Based Tabu Search method for solving unit commitment problem with cooling-banking constraints," Serbian Journal of Electrical Engineering, vol. 6, no. 1, pp. 57–74, 2009.

H. Q. Truong and C. Jeenanunta, "Fuzzy mixed integer linear programming model for national level monthly unit commitment under price-based uncertainty: A case study in Thailand," Electric Power Systems Research, vol. 209, Aug. 2022, Art. no. 107963.

P. S. Kundur, "Power System Stability," in Power System Stability and Control, 3rd ed., CRC Press, 2012.

P. Wei, N. H. Li, H. Wu, Y. Zhang, X. Wang, and B. Zhu, "Daily generation scheduling based on genetic algorithm," Automation of Electric Power Systems, vol. 23, no. 10, pp. 23–27, 1999.

C. Cai and Y. Cai, "Optimization of unit commitment by genetic algorithm," Power System Technology, pp. 44–47, 1997.

K. Chandrasekaran, S. Hemamalini, S. P. Simon, and N. P. Padhy, "Thermal unit commitment using binary/real coded artificial bee colony algorithm," Electric Power Systems Research, vol. 84, no. 1, pp. 109–119, Mar. 2012.

S. Dekrajangpetch, G. B. Sheble, and A. Conejo, "Auction implementation problems using Lagrangian relaxation," IEEE Transactions on Power Systems, vol. 14, no. 1, pp. 82–88, Feb. 1999.

Downloads

How to Cite

[1]
S. Marrouchi, M. ben Hessine, and S. Chebbi, “Optimizing Unit Scheduling with Fuzzy Logic: A Strategic Approach for Efficient Power Network Operations”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 2, pp. 13305–13312, Apr. 2024.

Metrics

Abstract Views: 141
PDF Downloads: 270

Metrics Information

License

Copyright (c) 2024 Sahbi Marrouchi, Moez ben Hessine, Souad Chebbi

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.

Crossref
Scopus
Google Scholar
Europe PMC