Tackling optimal power flow in modern power systems using a new optimization strategy

Abstract

This paper examines the intricate issue of Optimal Power Flow (OPF) optimization concerning the incorporation of renewable energy sources (RESs) into power networks. We present the Boosting Circulatory System Based Optimization (BCSBO) method, a novel modification of the original Circulatory System Based Optimization (CSBO) algorithm. The BCSBO algorithm has innovative movement techniques that markedly improve its exploration and exploitation skills, making it an effective instrument for addressing intricate optimization challenges. The suggested technique is thoroughly assessed utilizing five different objective functions alongside the IEEE 30-bus and IEEE 118-bus systems as test examples. The performance of the BCSBO algorithm is evaluated against many recognized optimization approaches, including CSBO, Moth-Flame Optimization (MFO), Particle Swarm Optimization (PSO), Thermal Exchange Optimization (TEO), and Elephant Herding Optimization (EHO). For the first case with minimizing the fuel cost associated with the thermal power generators, the total cost reported by the BCBSO is obtained as \mathrm{\$}781.8610, which is lower than other algorithms. For the second case, aimed at minimizing the total generating cost while also imposing a fixed carbon tax for thermal units, the derived total cost by the BCBSO is \mathrm{\$}810.7654. For the third case, aimed at minimizing the total cost considering prohibited operating zones of thermal units with RESs, the obtained total cost using the BCBSO is \mathrm{\$}781.9315. For case 4, with network losses included, the value of total costs obtained using the BCBSO is \mathrm{\$}880.4864. The value of total costs considering voltage deviation in case 5 is also obtained as \mathrm{\$}961.4354. For the IEEE 118-bus test system, the total cost is obtained \mathrm{\$}103,415.9315 using the BCBSO. These values reported by the BCBSO are all lower than those obtained by other methods addressed in this paper. The findings highlight the BCSBO algorithm’s potential as a crucial tool for enhancing power systems with renewable energies.

Graphical abstract

Keywords

optimal power flow (OPF) / optimization / boosting circulatory system based optimization / solar energy / wind energy

References

[1] Abdo M, Kamel S, Ebeed M, Yu J, Jurado F, (2018). Solving non-smooth optimal power flow problems using a developed grey wolf optimizer. Energies, 11( 7): 1692

[2] Abid M, Belazzoug M, Mouassa S, Chanane A, Jurado F, (2024a). Optimal power flow of thermal-wind-solar power system using enhanced Kepler optimization algorithm: Case study of a large-scale practical power system. Wind Engineering, 48( 5): 708–739

[3] Abid M, Belazzoug M, Mouassa S, Chanane A, Jurado F, (2024b). Multi-objective optimal power flow analysis incorporating renewable energy sources and FACTS devices using non-dominated sorting kepler optimization algorithm. Sustainability, 16( 21): 9599

[4] Ahmad M, Javaid N, Niaz I A, Almogren A, Radwan A, (2021). A bio-inspired heuristic algorithm for solving optimal power flow problem in hybrid power system. IEEE Access: Practical Innovations, Open Solutions, 9: 159809–159826

[5] Ahmadi A, Ahmadi M R, Nezhad A E, (2014). A lexicographic optimization and augmented ϵ-constraint technique for short-term environmental/economic combined heat and power scheduling. Electric Power Components and Systems, 42( 9): 945–958

[6] Alanazi A, Alanazi M, Memon Z A, Mosavi A, (2022). Determining optimal power flow solutions using new adaptive Gaussian TLBO method. Applied Sciences, 12( 16): 7959

[7] Alghamdi A S, (2023). Optimal power flow of hybrid wind/solar/thermal energy integrated power systems considering costs and emissions via a novel and efficient search optimization algorithm. Applied Sciences, 13( 8): 4760

[8] Alghamdi A S, Zohdy M A, (2024). Boosting cuckoo optimization algorithm via Gaussian mixture model for optimal power flow problem in a hybrid power system with solar and wind renewable energies. Heliyon, 10( 11): e31755

[9] Belagra E A, Mouassa S, Chettih S, Jurado F, (2024). Optimal power flow calculation in hybrid power system involving solar, wind, and hydropower plant using weighted mean of vectors algorithm. Wind Engineering, 48( 3): 468–493

[10] Biswas P P, Suganthan P N, Amaratunga G A J, (2017). Optimal power flow solutions incorporating stochastic wind and solar power. Energy Conversion and Management, 148: 1194–1207

[11] Biswas P P, Suganthan P N, Mallipeddi R, Amaratunga G A J, (2018). Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques. Engineering Applications of Artificial Intelligence, 68: 81–100

[12] Charwand M, Ahmadi A, Sharaf A M, Gitizadeh M, Esmaeel Nezhad A, (2016). Robust hydrothermal scheduling under load uncertainty using information gap decision theory. International Transactions on Electrical Energy Systems, 26( 3): 464–485

[13] Chen G, Qian J, Zhang Z, Sun Z, (2019a). Multi-objective optimal power flow based on hybrid firefly-bat algorithm and constraints- prior object-fuzzy sorting strategy. IEEE Access: Practical Innovations, Open Solutions, 7: 139726–139745

[14] Chen M R, Zeng G Q, Lu K D, (2019b). Constrained multi-objective population extremal optimization based economic-emission dispatch incorporating renewable energy resources. Renewable Energy, 143: 277–294

[15] Duman S, Rivera S, Li J, Wu L, (2020). Optimal power flow of power systems with controllable wind-photovoltaic energy systems via differential evolutionary particle swarm optimization. International Transactions on Electrical Energy Systems, 30( 4): e12270

[16] Esmaeel Nezhad A, Jowkar S, Tavakkoli Sabour T, Rahimi E, Ghanavati F, Esmaeilnezhad F, (2024a). A short-term wind-hydrothermal operational framework in the presence of pumped-hydro storage. e-Prime — Advances in Electrical Engineering, Electronics and Energy, 8: 100577

[17] Esmaeel Nezhad A, Mobtahej M, Javadi M S, Nardelli P H J, Sahoo S, (2024b). Optimal operation of lithium-ion batteries in microgrids using a semidefinite thermal model. International Journal of Electrical Power & Energy Systems, 155: 109630

[18] Esmaeel Nezhad A, Rahimnejad A, Nardelli P H J, Gadsden S A, Sahoo S, Ghanavati F, (2022). A shrinking horizon model predictive controller for daily scheduling of home energy management systems. IEEE Access: Practical Innovations, Open Solutions, 10: 29716–29730

[19] EsmaeelNezhad ATavakkoliSabour TJavadiM S H.JNardelliPJowkarSGhanavatiF (2025). Water–energy nexus. In: Tostado-Véliz M, Rezaee Jordehi A, Mansouri S A, Ramos A, Jurado F, eds. Towards Future Smart Power Systems with High Penetration of Renewables. Academic Press, 75–102

[20] Fei X, Wang J, Ying S, Hu Z, Shi J, (2020). Projective parameter transfer based sparse multiple empirical kernel learning machine for diagnosis of brain disease. Neurocomputing, 413: 271–283

[21] Ghasemi M, Akbari E, Faraji Davoudkhani I, Rahimnejad A, Asadpoor M B, Gadsden S A, (2022a). Application of Coulomb’s and Franklin’s laws algorithm to solve large-scale optimal reactive power dispatch problems. Soft Computing, 26( 24): 13899–13923

[22] Ghasemi M, Akbari M A, Jun C, Bateni S M, Zare M, Zahedi A, Pai H, Band S S, Moslehpour M, Chau K, (2022b). Circulatory system based optimization (CSBO): An expert multilevel biologically inspired meta-heuristic algorithm. Engineering Applications of Computational Fluid Mechanics, 16( 1): 1483–1525

[23] Ghasemi M, Davoudkhani I F, Akbari E, Rahimnejad A, Ghavidel S, Li L, (2020). A novel and effective optimization algorithm for global optimization and its engineering applications: Turbulent flow of water-based optimization (TFWO). Engineering Applications of Artificial Intelligence, 92: 103666

[24] Ghasemi M, Ghavidel S, Gitizadeh M, Akbari E, (2015). An improved teaching–learning-based optimization algorithm using Lévy mutation strategy for non-smooth optimal power flow. International Journal of Electrical Power & Energy Systems, 65: 375–384

[25] Ghasemi M, Trojovský P, Trojovská E, Zare M, (2023a). Gaussian bare-bones Levy circulatory system-based optimization for power flow in the presence of renewable units. Engineering Science and Technology, An International Journal, 47: 101551

[26] GhasemiMZareMKadkhoda MohammadiSMirjaliliS (2024a). Applications of whale migration algorithm in optimal power flow problems of power systems (2024a). In: Mirjalili S, ed. Handbook of Whale Optimization Algorithm: Variants, Hybrids, Improvements, and Applications. Academic Press, 347–64

[27] GhasemiMZareMZahediAAkbariMMirjaliliSAbualigahL (2023b). Geyser inspired algorithm: A new geological-inspired meta-heuristic for real-parameter and constrained engineering optimization. Journal of Bionic Engineering, 1–35

[28] Ghasemi M, Zare M, Zahedi A, Trojovský P, Abualigah L, Trojovská E, (2024b). Optimization based on performance of lungs in body: Lungs performance-based optimization (LPO). Computer Methods in Applied Mechanics and Engineering, 419: 116582

[29] Guvenc U, Duman S, Kahraman H T, Aras S, Katı M, (2021). Fitness–Distance Balance based adaptive guided differential evolution algorithm for security-constrained optimal power flow problem incorporating renewable energy sources. Applied Soft Computing, 108: 107421

[30] Hassan M H, Elsayed S K, Kamel S, Rahmann C, Taha I B M, (2022). Developing chaotic Bonobo optimizer for optimal power flow analysis considering stochastic renewable energy resources. International Journal of Energy Research, 46( 8): 11291–11325

[31] Hassan M H, Kamel S, Selim A, Khurshaid T, Domínguez-garcía J L, (2021). A modified rao-2 algorithm for optimal power flow incorporating renewable energy sources. Mathematics, 9( 13): 1532

[32] Houssein E H, Oliva D, Samee N A, Mahmoud N F, Emam M M, (2023). Liver cancer algorithm: A novel bio-inspired optimizer. Computers in Biology and Medicine, 165: 107389

[33] Javadi M S, Nezhad A E, Gough M, Lotfi M, Anvari-Moghaddam A, Nardelli P H J, Sahoo S, Catalão J P S, (2021). Conditional value-at-risk model for smart home energy management systems. e-Prime-Advances in Electrical Engineering, Electronics and Energy, 1: 100006

[34] Jin X, He T, Lin Y, (2019). Multi-objective model selection algorithm for online sequential ultimate learning machine. EURASIP Journal on Wireless Communications and Networking, 2019( 1): 1–7

[35] Kathiravan R, Kumudini Devi R P, (2017). Optimal power flow model incorporating wind, solar, and bundled solar-thermal power in the restructured Indian power system. International Journal of Green Energy, 14( 11): 934–950

[36] Kaveh A, Dadras A, (2017). A novel meta-heuristic optimization algorithm: Thermal exchange optimization. Advances in Engineering Software, 110: 69–84

[37] Kouadri R, Mouassa S, Jurado F, (2024). Optimal power dispatch in hybrid power system for medium-and large-scale practical power systems using self-adaptive bonobo optimizer algorithm. Wind Engineering, 48( 6): 1118–1140

[38] Li S, Chen H, Wang M, Heidari A A, Mirjalili S, (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111: 300–323

[39] Lian J, Hui G, Ma L, Zhu T, Wu X, Heidari A A, Chen Y, Chen H, (2024). Parrot optimizer: Algorithm and applications to medical problems. Computers in Biology and Medicine, 172: 108064

[40] Mirjalili S, (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89: 228–249

[41] Mohamed A A A, Mohamed Y S, El-Gaafary A A M, Hemeida A M, (2017). Optimal power flow using moth swarm algorithm. Electric Power Systems Research, 142: 190–206

[42] Mouassa S, Alateeq A, Alassaf A, Bayindir R, Alsaleh I, Jurado F, (2024). Optimal power flow analysis with renewable energy resource uncertainty using dwarf mongoose optimizer: Case of Adrar isolated electrical network. IEEE Access: Practical Innovations, Open Solutions, 12: 10202–10218

[43] Mouassa S, Althobaiti A, Jurado F, Ghoneim S S M, (2022). Novel design of slim mould optimizer for the solution of optimal power flow problems incorporating intermittent sources: A case study of Algerian electricity grid. IEEE Access: Practical Innovations, Open Solutions, 10: 22646–22661

[44] MouassaSBouktirT (2015). Artificial bee colony algorithm for solving OPF problem considering the valve point effect. International Journal of Computer Applications, 112(1)

[45] Mouassa S, Makhloufi S, Djabali C, Jurado F, (2023). Optimal power flow solution based on gorilla troops optimization technique considering uncertainty of renewable energy sources: A case study of Adrar’s isolated power network. Wind Engineering, 47( 5): 913–934

[46] Muttaqi K M, Esmaeel Nezhad A, Aghaei J, Ganapathy V, (2014). Control issues of distribution system automation in smart grids. Renewable & Sustainable Energy Reviews, 37: 386–396

[47] Naderi E, Narimani H, Pourakbari-Kasmaei M, Cerna F V, Marzband M, Lehtonen M, (2021). State-of-the-art of optimal active and reactive power flow: A comprehensive review from various standpoints. Processes, 9( 8): 1319

[48] Nezhad A E, Nardelli P H J, Sahoo S, Ghanavati F, (2022). Scheduling of energy hub resources using robust chance-constrained optimization. IEEE Access: Practical Innovations, Open Solutions, 10: 129738–129753

[49] Nguyen T T, (2019). A high performance social spider optimization algorithm for optimal power flow solution with single objective optimization. Energy, 171: 218–240

[50] Niknam T, Narimani M R, Aghaei J, Tabatabaei S, Nayeripour M, (2011). Modified honey bee mating optimisation to solve dynamic optimal power flow considering generator constraints. IET Generation, Transmission & Distribution, 5( 10): 989–1002

[51] Razavi S E, Esmaeel Nezhad A, Mavalizadeh H, Raeisi F, Ahmadi A, (2018). Robust hydrothermal unit commitment: A mixed-integer linear framework. Energy, 165: 593–602

[52] Saddique M S, Bhatti A R, Haroon S S, Sattar M K, Amin S, Sajjad I A, Ul Haq S S, Awan A B, Rasheed N, (2020). Solution to optimal reactive power dispatch in transmission system using meta-heuristic techniques—Status and technological review. Electric Power Systems Research, 178: 106031

[53] Salkuti S R, (2019). Optimal power flow using multi-objective glowworm swarm optimization algorithm in a wind energy integrated power system. International Journal of Green Energy, 16( 15): 1547–1561

[54] Sarda J, Pandya K, Lee K Y, (2023). Hybrid cross entropy—Cuckoo search algorithm for solving optimal power flow with renewable generators and controllable loads. Optimal Control Applications & Methods, 44( 2): 508–532

[55] Sarhan S, El-Sehiemy R, Abaza A, Gafar M, (2022). Turbulent flow of water-based optimization for solving multi-objective technical and economic aspects of optimal power flow problems. Mathematics, 10( 12): 2106

[56] Shi B, Chen J, Chen H, Lin W, Yang J, Chen Y, Wu C, Huang Z, (2022). Prediction of recurrent spontaneous abortion using evolutionary machine learning with joint self-adaptive sime mould algorithm. Computers in Biology and Medicine, 148: 105885

[57] Su H, Zhao D, Heidari A A, Liu L, Zhang X, Mafarja M, Chen H, (2023). RIME: A physics-based optimization. Neurocomputing, 532: 183–214

[58] Ullah Z, Wang S, Radosavljevic J, Lai J, (2019). A solution to the optimal power flow problem considering WT and PV generation. IEEE Access: Practical Innovations, Open Solutions, 7: 46763–46772

[59] WangG GDebSCoelhoL D S (2016). Elephant herding optimization. In: Proceedings 2015 3rd International Symposium on Computational and Business Intelligence, ISCBI 2015. Indonesia, 1–5

[60] Yang Y, Chen H, Heidari A A, Gandomi A H, (2021). Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Systems with Applications, 177: 114864

[61] Zahedibialvaei A, Trojovský P, Hesari-Shermeh M, Matoušová I, Trojovská E, Hubálovský Š, (2023). An enhanced turbulent flow of water-based optimization for optimal power flow of power system integrated wind turbine and solar photovoltaic generators. Scientific Reports, 13( 1): 14635

[62] Zimmerman R D, Murillo-Sánchez C E, Thomas R J, (2011). MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education. IEEE Transactions on Power Systems, 26( 1): 12–19

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The Author(s). This article is published with open access at link.springer.com and journal.hep.com.cn