Güç Sisteminde Sezgisel Algoritmalarla Sürdürülebilir Ekonomi Amaçlı Maliyet Optimizasyonu

Year 2024, Volume: 3 Issue: 1, 26 - 37, 28.05.2024

Serkan Iscan , Gürcan Lokman

Abstract

Güç üretim birimlerinden elde edilen enerjinin iletim ve dağıtım hatları üzerinden ekonomik bir şekilde iletilmesi, çevre dostu ve sürdürülebilir enerji yönetimi açısından kritiktir. Elektrik enerjisinin üretim birimlerinden dağıtım ve tüketim birimlerine ulaştırılmasında önemli bir rol oynayan bileşen, iletim/dağıtım şebekesidir. Bu aşamada, üretilen aktif ve reaktif gücün ekonomik bir şekilde sürdürülebilirliği, işletme maliyetleri ve kayıp giderlerinin kontrol altında tutulması ile mümkündür. Güç üretim birimlerinin yetersiz olması veya kayıpların artması, güç sistemlerinde işletme maliyetini artırır. Kapasite aşımı ve maliyet artışı ise sistem güvenilirliğini azaltarak kararlılığı etkiler. Bu olumsuzluklar, güç sistemlerinde sorunlara yol açabilir ve güç iletim şebekesini kullanılamaz hale getirerek tüketicileri olumsuz etkiler. Gelişen teknoloji ve artan enerji talepleri, güç sistemlerinde kalite sorunlarını beraberinde getirmektedir. Artan talep gücünü en uygun maliyet ve güç üretimiyle sağlayacak mevcut güç üretim ünitelerinin işletme maliyetlerinin optimizasyon teknikleri ile revize edilmesi gerekmektedir. Böylelikle güç sistemlerinin verimliliği artırılabilir. Güç sistemlerinin yetersiz kalması durumunda yeni ve yenilenebilir güç üretim üniteleri güç sistemine dahil edilmelidir. Bu çalışmada, IEEE 30-bara test sistemleri üzerinde sürü zekasını kullanan Parçacık Sürü Optimizasyonu (PSO) ve Gri Kurt Optimizasyonu (GKO) algoritmaları ile güç sistemi işletme ve maliyet optimizasyonları gerçekleştirilmiştir. PSO ve GKO algoritmaları için popülasyon ve tekrar çalıştırma sayısı sırasıyla 20, 30 ve 50 değerlerinde seçildiğinde sonuçlarda önemli farklılıklar gözlemlenmiştir. Üç farklı durum için oluşan sonuçların algoritmalar bazında karşılaştırılması yapıldığında; PSO algoritmasının 50 popülasyon ve yeniden çalıştırma değerlerinin kullanıldığı üçüncü durum için yapılan simülasyon testlerinde en uygun işletme maliyet değeri olan 800,47 $/Saat’e ulaşılmıştır. Çalışma sonucunda güç sisteminde kullanılan PSO ve GKO algoritmalarının popülasyon ve tekrar çalıştırma sayısının artışıyla toplam işletme maliyetini asgari değerlere yaklaştırdığı ve güç üretimini daha sürdürülebilir hale getirdiği görülmüştür.

Keywords

Güç Sistemleri, Sürdürülebilir Enerji, Maliyet Optimizasyonu, PSO, GKO

Cost Optimization for Sustainable Economy with Heuristic Algorithms in Power System

Abstract

Economically transmitting the energy obtained from power generation units through transmission and distribution lines is critical for environmentally friendly and sustainable energy management. The component that plays an important role in delivering electrical energy from production units to distribution and consumption units is the transmission/distribution network. At this stage, economic sustainability of the generated active and reactive power is possible by keeping operating costs and loss expenses under control. Insufficient power generation units or increased losses increase the operating costs in power systems. Capacity excess and cost increase affect stability by reducing system reliability. These negativities can cause problems in power systems and negatively affect consumers by making the power transmission network unusable. Developing technology and increasing energy demands bring quality problems in power systems. The operating costs of existing power generation units, which will provide the increasing demand power with the most appropriate cost and power generation, need to be revised with optimization techniques. Thus, the efficiency of power systems can be increased. If power systems are inadequate, new and renewable power generation units should be included in the power system. In this study, power system operation and cost optimizations were carried out with Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms that use swarm intelligence on IEEE 30-bus test systems. Significant differences in the results were observed when the number of population and re-runs were selected as 20, 30 and 50 for the PSO and GWO algorithms, respectively. When the results for three different situations are compared on the basis of algorithms; In the simulation tests conducted for the third case, where 50 population and re-run values of the PSO algorithm were used, the optimal operating cost value of 800.47 $/h was reached. As a result of the study, it was seen that the PSO and GWO algorithms used in the power system brought the total operating cost closer to minimum values and made power production more sustainable by increasing the number of population and re-runs.

Keywords

Power Systems, Sustainable Energy, Cost Optimization, PSO, GWO.

References

• S. Iscan, O. Kaplan, G. Lokman, “Power loss and voltage stability optimization with meta-heuristic algorithms in power system,” PAJES, vol. 27, no. 2, pp. 199-209, 2021.
• E. Mahboubi-Moghaddam, M. R. Narimani, M. H. Khooban, A. Azizivahed, M. Javid sharifi, “Multi-Objective distribution feeder reconfiguration to improve transient stability, and minimize power loss and operation cost using an enhanced evolutionary algorithm at the presence of distributed generations,” IJEPES, vol. 76, pp. 35–43, Mar. 2016.
• T. Sen, H. D. Mathur, “A new approach to solve Economic Dispatch problem using a Hybrid ACO–ABC–HS optimization algorithm,” IJEPES, vol. 78, pp. 735–744, Jun. 2016.
• H. A. Garmejani, S. Hossainpour, “Single and multi-objective optimization of a TEG system for optimum power, cost and second law efficiency using genetic algorithm,” Energy Convers. Manag., vol. 228, p. 113658, Jan. 2021.
• X. He, Y. Rao, J. Huang, “A novel algorithm for economic load dispatch of power systems,” Neurocomputing, vol. 171, pp. 1454–1461, Jan. 2016.
• F. Tariq, S. Alelyani, G. Abbas, A. Qahmash, M. R. Hussain, “Solving renewables-integrated economic load dispatch problem by variant of metaheuristic bat-inspired algorithm,” Energies, vol. 13, no. 23, p. 6225, Nov. 2020.
• A. Srivastava, D. K. Das, “An adaptive chaotic class topper optimization technique to solve economic load dispatch and emission economic dispatch problem in power system,” Soft Comput., vol. 26, pp. 2913–2934, Jan. 2022.
• A. Srivastava, S. Singh, “Implementation of ant colony optimization and particle swarm optimization in economic load dispatch problem using renewable source,” IEEE 17th India Council International Conference (INDICON), New Delhi, India, Dec. 2020, pp. 1–6.
• C. Chen, L. Qu, M.-L. Tseng, L. Li, C.-C. Chen, M. K. Lim, “Reducing fuel cost and enhancing the resource utilization rate in energy economic load dispatch problem,” J. Clean. Prod., vol. 364, p. 132709, Sep. 2022.
• J. N. Kuk, R. A. Gonçalves, L. M. Pavelski, S. M. G. S. Venske, C. P. de Almeida, A. T. R. Pozo, “An empirical analysis of constraint handling on evolutionary multi-objective algorithms for the environmental/economic load dispatch problem,” Expert Syst. Appl., vol. 165, p. 113774, Mar. 2021.
• J. Yu, C.-H. Kim, S.-B. Rhee, “Clustering cuckoo search optimization for economic load dispatch problem,” Neural Comput. Appl., vol. 32, pp. 16951–16969, Jun. 2020.
• R. P. Parouha, P. Verma, “An innovative hybrid algorithm to solve nonconvex economic load dispatch problem with or without valve point effects,” Int. Trans. Electr. Energy Syst., vol. 31, no. 1, p. e12682, 2021.
• R. Al-Nahhal, A. F. Naiem, Y. G. Hegazy, “Economic load dispatch problem using particle swarm optimization technique considering wind power penetration,” 2019 International Conference on Smart Energy Systems and Technologies (SEST), Porto, Portugal, Sep. 2019, pp. 1–6.
• S. Lalwani, H. Sharma, S. C. Satapathy, K. Deep, J. C. Bansal, “A survey on parallel particle swarm optimization algorithms,” Arab. J. Sci. Eng., vol. 44, pp. 2899–2923, 2019.
• O. Ceylan, A. Ozdemir, H. Dag, “Heuristic methods for postoutage voltage magnitude calculations,” Turk J Elec Eng & Comp Sci, vol. 24, pp. 105-120, 2016.
• M. H. Nadimi-Shahraki, S. Taghian, S. Mirjalili, “An improved grey wolf optimizer for solving engineering problems,” Expert Syst. Appl., vol. 166, p. 113917, Mar. 2021.
• M. B. Atsever, M. H. Hocaoglu, “Comprehensive performance analysis of greywolf optimizer for overcurrent relay coordination”, Turkish Journal of Engineering Research and Education., vol.1, no.2, 52-61, Nov. 2022.
• Q. Li, X. Li, Z. Guo, H. Du, “An improved whale optimization algorithm and its application on ELD,” 10th Frontier Academic Forum of Electrical Engineering (FAFEE2022), Singapore, 2022, pp. 1287–1295.
• L. dos Santos Coelho, V. C. Mariani, “Particle swarm approach based on quantum mechanics and harmonic oscillator potential well for economic load dispatch with valve-point effects,” Energy Convers. Manag., vol. 49, no. 11, pp. 3080–3085, Nov. 2008.
• S. K. Elsayed, S. Kamel, A. Selim, M. Ahmed, “An improved heap-based optimizer for optimal reactive power dispatch”, IEEE Access, vol. 9, pp. 58319-58336, Apr. 2021.
• Power system test case archive, labs.ece.uw.edu/pstca/pf14/pg_tca14bus.htm, Last access 04.12.2023.
• D. C. Walters, G. B. Sheble, "Genetic algorithm solution of economic dispatch with valve point loading," IEEE T Power Syst, vol. 8, no. 3, pp. 1325-1332, Aug. 1993.
• D. P. Kothari, J. S. Dhillon, “Power System Optimization," 2nd ed., PHI, Delhi, 2013.
• L. C. A. Ferreira, A. C. Z. de Souza, S. Granville, J. W. M. Lima, “Interior point method applied to voltage collapse problems and losses reduction,” IEE Gener Transm Dis, vol. 149, no. 2, pp. 165-170, Mar. 2002.
• D. Aydin, S. Ozyon, C. Yasar, T. Liao, “Artificial bee colony algorithm with dynamic population size to combined economic and emission dispatch problem,” IJEPES, vol. 54, pp. 144–153, Jan. 2014.
• M. Basu, “Economic environmental dispatch using multi-objective differential evolution,” Appl. Soft Comput., vol. 11, no. 2, pp. 2845–2853, Mar. 2011.
• N. I. Nwulu, X. Xia, “Multi-objective dynamic economic emission dispatch of electric power generation integrated with game theory based demand response programs,” Energy Convers. Manag., vol. 89, pp. 963-974, Jan. 2015.
• P. Kundur, “Power System Stability and Control”, McGrawHill, New York, 1994.
• J. d. A. B. Júnior, M. V. A. Nunes, M. H. R. Nascimento, J. L. M. Rodriques, J. C. Leite “Solution to economic emission load dispatch by simulated annealing: case study,” Electr Eng, vol. 100, pp. 749–761, 2018.
• C. W. Taylor, “Power System Voltage Stability”, McGraw-Hill, New York, 1994.
• P. Kundur, “A survey of utility experience with power plant response during partial load rejections and system disturbances”, IEEE Trans. Power App. and Syst., vol. PAS-100, no. 5, pp. 2471-2475, May 1981.
• S. Ganguly, “Multi-Objective planning for reactive power compensation of radial distribution networks with unified power quality conditioner allocation using particle swarm optimization”, IEEE T Power Syst., vol. 29, no. 4, pp. 1801-1810, Jan. 2014.
• S. Mirjalili, S. M. Mirjalili, A. Lewis, “Grey wolf optimizer”, Adv. Eng. Softw., vol. 69, pp. 46-61, Mar. 2014.
• R. Eberhart, J. Kennedy, “A new optimizer using particle swarm theory”, Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, Oct. 1995, pp. 39-43.
• J. Kennedy, R. C. Eberhart, “Particle swarm optimization,” ICNN'95, Perth, WA, Australia, Nov. 1995, pp. 1942-1948.
• Y. Labbi, D. Attous, “A hybrid GA-PS method to solve the economic load dispatch problem,” J. Theor. Appl. Inf. Technol., vol. 15, no.1, 61-68, 2010.
• W. Ongsakul, T. Tantimaporn, “Optimal powers flow by improved evolutionary programming,” Electr Pow Compo Syst, vol. 34, no. 1, pp. 79-95, Jan. 2006.
• G. P. Dixit, H. M. Dubey, M. Pandit, B. K. Panigrahi, “Economic load dispatch using artificial bee colony optimization," International Journal of Advances in electronics Engineering, vol.1, no. 1, pp. 119-124, 2011.