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Cost optimization in microgrids: A scenario-based analysis by using polar the fox optimization algorithm

Yıl 2025, Sayı: 061, 34 - 59, 30.06.2025

Nisa Nacar Çıkan

Öz

Microgrids have come up as a promising solution for ensuring efficient, reliable, and sustainable energy management through the distributed energy resources integration. However, some challenges such as integration of distributed generators, economic efficacy and operational constraints cause the management and operation of microgrids remain as a complex problem. In this work, a comprehensive analysis is realized by using the Polar Fox Optimization algorithm to find solutions to these problems. Four different scenarios are analyzed to examine the effects of operational constraints on system performance and economic costs. In the first case, all distributed energy resources are operated within the specified limits and all power from renewable sources is injected into the microgrid. This scenario results in an operating cost of 269.76 €/day. In the second case, the output power of the renewable distributed energy sources is optimized. This case, a cost reduction of 42.5% is obtained when compared to the first scenario. In the third case, the energy exchange constraint between the grid and the microgrid is removed. Thus, a cost reduction of 74.7% is obtained when compared to the first case. In the fourth case, a detailed battery energy storage system model is added by considering technical parameters such as battery efficiency, state-of-charge limits, and charge/discharge rates. This case an operating cost of €107.08/day is obtained. Thus, a cost reduction of 60.3% is obtained when compared to the first case. The results show that changing the operational constraints significantly affects both system performance and economic efficiency. The proposed approach presents valuable perception for microgrid operators and planners. It points out the importance of the optimization algorithm in achieving economically efficient and reliable energy management.

Anahtar Kelimeler

Microgrid optimization Energy management Renewable integration Battery storage systems Grid exchange Operational constraints

Proje Numarası

Tubitak 124E002

Kaynakça

  • [1] S. Mohammadi, B. Mozafari, S. Solimani, and T. Niknam, ‘‘An adaptive modified firefly optimisation algorithm based on hong's point estimate method to optimal operation management in a microgrid with consideration of uncertainties,’’ Energy, vol.51, pp.339-348, March. 2013, doi: 10.1016/j.energy.2012.12.013.
  • [2] M. Cikan and N. N.Cikan, ‘‘Optimum allocation of multiple type and number of DG units based on IEEE 123-bus unbalanced multi-phase power distribution system’’ International Journal of Electrical Power & Energy Systems, vol.144, pp.1-17, Jan. 2023, doi: 10.1016/j.ijepes.2022.108564.
  • [3] M. Çıkan, “Çita optimizasyon algoritması kullanarak kısmi gölgelenme altındaki fotovoltaik sistemlerde maksimum güç noktası izleyicisinin tasarlanması,” Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol.40, no.1, pp. 555–572, Jan. 2025, doi: 10.17341/gazimmfd.1183267.
  • [4] M. Çıkan and N.N. Çıkan, ‘‘Elektrikli araç şarj istasyonlarının enerji dağıtım hatlarına optimum şekilde konumlandırılması’’ Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, vol.27, no.2, pp. 340-363, Jun. 2024, doi:10.17780/ksujes.1365209.
  • [5] M.Cikan, N.N. Cikan, B. Kekezoglu, “Determination of optimal island regions with simultaneous DG allocation and reconfiguration in power distribution networks,” IET Renewable Power Generation, vol.19, pp. e12942, 2025, doi:10.1049/rpg2.12942.
  • [6] C. Marnay, N. DeForest and J. Lai, “A green prison: The Santa Rita Jail campus microgrid,’’ IEEE Power and Energy Society General Meeting, 2012, pp.1-2, doi: 10.1109/PESGM.2012.6345235.
  • [7] T. Gabderakhmanova et al., “Demonstrations of DC Microgrid and Virtual Power Plant Technologies on the Danish Island of Bornholm,’’ 55th International Universities Power Engineering Conference (UPEC), 2020, pp. 1-6, doi: 10.1109/UPEC49904.2020.9209853.
  • [8] S. Ahmad, M. Shafiullah, C. B. Ahmed and M. Alowaifeer, “A Review of Microgrid Energy Management and Control Strategies,’’ IEEE Access, vol. 11, pp. 21729-21757, 2023, doi: 10.1109/ACCESS.2023.3248511
  • [9] N.N. Cikan and M. Cikan, ‘‘Reconfiguration of 123-bus unbalanced power distribution network analysis by considering minimization of current & voltage unbalanced indexes and power loss,’’ International Journal of Electrical Power & Energy Systems, vol.157, pp.1-15, Jun.2024, doi: 10.1016/j.ijepes.2024.109796.
  • [10] M. Cikan and B. Kekezoglu, ‘‘Comparison of metaheuristic optimization techniques including Equilibrium optimizer algorithm in power distribution network reconfiguration,’’ Alexandria Engineering Journal, vol.61, no.2, pp. 991-1031, Feb. 2022, doi: 10.1016/j.aej.2021.06.079.
  • [11] K. Doğanşahin and M. Çıkan, ‘‘A new line stability index for voltage stability analysis based on line loading,’’ Clean Energy Technologies Journal, vol.1, no.1, pp.23-30, 2023.
  • [12] A. Mortazavi, “Comparative assessment of five metaheuristic methods on distinct problems,” Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi (DUJE), vol. 10, pp. 879–898, 2019, doi: 10.24012/dumf.585790.
  • [13] A. Mortazavi, “A fuzzy reinforced Jaya algorithm for solving mathematical and structural optimization problems,’’ Soft Computing, vol. 28, pp. 2181–2206, 2024, doi:10.1007/s00500-023-09206-5.
  • [14] A. Akter et al., ‘‘A review on microgrid optimization with meta-heuristic techniques: Scopes, trends and recommendation,’’ Energy Strategy Reviews, vol. 51, pp. 1-27, Jan. 2024, doi: 10.1016/j.esr.2024.101298.
  • [15] S. Phommixay, M.L. Doumbia, and D. Lupien St-Pierre, ‘‘Review on the cost optimization of microgrids via particle swarm optimization,’’ International Journal of Energy and Environmental Engineering, vol. 11, pp. 73-89, 2020, doi: 0.1007/s40095-019-00332-1.
  • [16] M. A. Hossain, H. R. Pota, S. Squartini, F. Zaman, and K.M. Muttaqi, ‘‘Energy management of community microgrids considering degradation cost of battery,’’ Journal of Energy Storage, vol.22, pp.257-269, April 2019, doi: 10.1016/j.est.2018.12.021.
  • [17] S. Sharma, S. Bhattacharjee, and A. Bhattacharya, ‘‘Probabilistic operation cost minimization of Micro-Grid,’’ Energy, vol.148, pp. 1116-1139, 2018, doi: 10.1016/j.energy.2018.01.164.
  • [18] Z. Zheng, S.Yang, Y. Guo, X. Jin, and R. Wang, “Meta-heuristic Techniques in Microgrid Management: A Survey’’ Swarm and Evolutionary Computation, vol.78, pp.101256, 2023, doi: 10.1016/j.swevo.2023.101256.
  • [19] A. Mortazavi, “Marathon runner algorithm: theory and application in mathematical, mechanical and structural optimization problems,’’ Materials Testing, vol. 66, pp. 1267-1291,2024, doi: 10.1515/mt-2023-009.
  • [20] M. Moloodpoor, A. Mortazavi, and N. Ozbalta, “Thermo-economic optimization of double-pipe heat exchanger using a compound swarm intelligence,’’ Heat Transfer Research, vol.52, pp.1-20,2021, doi: 10.1615/HeatTransRes.2021037293.
  • [21] E. C. Kandemir, and A. Mortazavi, “Optimization of Seismic Base Isolation System Using a Fuzzy Reinforced Swarm Intelligence,’’ Advances in Engineering Software, vol.174, pp. 103323, 2022, doi: 10.1016/j.advengsoft.2022.103323.
  • [22] M. Cikan, and K. Dogansahin, “A Comprehensive Evaluation of Up-to-Date Optimization Algorithms on MPPT Application for Photovoltaic Systems,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol.45, pp.10381–10407, 2023, doi:10.1080/15567036.2023.2245771.
  • [23] N.N. Cikan, “Optimization of PV, Capacitor Bank, and EV Charging Station Allocation in a 33-Bus Power Distribution System Using the Slime Mould Algorithm” European Journal of Engineering and Natural Sciences (EJENS), vol.9, pp.55-60, 2024.
  • [24] N.N. Cikan, “Optimization of a 33-Bus Power Distribution System Using Artificial Hummingbird Algorithm for Power Loss and Voltage Stability Enhancement,” European Journal of Engineering and Natural Sciences (EJENS), vol.9, pp.41-46, 2024.
  • [25] M.Gendreau, and JY. Potvin, “Metaheuristics in Combinatorial Optimization,’’ Annals of Operation Research, vol. 140, pp.189–213, 2005, doi:10.1007/s10479-005-3971-7.
  • [26] I. Boussaïd, J. Lepagnot, and P. Siarry, “A survey on optimization metaheuristics,’’ Information Sciences, vol. 237, pp.82-117, 2013, doi: 10.1016/j.ins.2013.02.041.
  • [27] S. Yang, Y. Jiang, and T.T. Nguyen, “Metaheuristics for dynamic combinatorial optimization problems,’’ IMA Journal of Management Mathematics, vol.24, pp.451–480, 2013, doi: 10.1093/imaman/dps021.
  • [28] C. Gamarra, and J. M. Guerrero, “Computational optimization techniques applied to microgrids planning: A review,’’ Renewable and Sustainable Energy Reviews, vol. 48, pp. 413-424, 2015, doi: 10.1016/j.rser.2015.04.025.
  • [29] M. F. Zia, E. Elbouchikhi, and M. Benbouzid, “Microgrids energy management systems: A critical review on methods, solutions, and prospects,’’ Applied Energy, vol. 222, pp. 1033-1055, 2018, doi: 10.1016/j.apenergy.2018.04.103.
  • [30] T. Niknam, F. Golestaneh, and A. Malekpour, ‘‘Probabilistic energy and operation management of a microgrid containing wind/photovoltaic/fuel cell generation and energy storage devices based on point estimate method and self-adaptive gravitational search algorithm,’’ Energy, vol.43, no.1, pp. 427-437, July 2012, 10.1016/j.energy.2012.03.064.
  • [31] A. Ghiaskar, A. Amiri, S. Mirjalili, ‘‘Polar fox optimization algorithm: a novel meta-heuristic algorithm,’’ Neural Computing and Applications, vol.36, pp. 20983–21022, Aug. 2024, doi: 10.1007/s00521-024-10346-4.
  • [32] J. Radosavljevic, ‘‘Optimal energy and operation management of microgrids,’’ in Metaheuristic Optimization in Power Engineering, 1st ed. London, England: IET, ch.12, pp. 407-447.
  • [33] H. Saadat, Power system analysis. New York, NY, USA: McGraw-Hill,1999.
  • [34] J. Radosavljevic, ‘‘Optimal power flow in transmission networks,’’ in Metaheuristic Optimization in Power Engineering, 1st ed. London, England: IET, ch.6, pp. 177-233.
  • [35] S. Mohammadi, S. Soleymani, and B. Mozafari, ‘‘Scenario-based stochastic operation management of microgrid including wind, photovoltaic, micro-turbine, fuel cell and energy storage devices,’’ International Journal of Electrical Power & Energy Systems, vol. 54, pp.525-535, Jan.2014, doi: 10.1016/j.ijepes.2013.08.004.
  • [36] A. A. Moghaddam, A. Seifi, T. Niknam, and M. R. A. Pahlavani, ‘‘Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source,’’ Energy, vol.36, no.11, pp. 6490-6507, Nov.2011, doi: 10.1016/j.energy.2011.09.017.

Yıl 2025, Sayı: 061, 34 - 59, 30.06.2025

Nisa Nacar Çıkan

Öz

Proje Numarası

Tubitak 124E002

Kaynakça

  • [1] S. Mohammadi, B. Mozafari, S. Solimani, and T. Niknam, ‘‘An adaptive modified firefly optimisation algorithm based on hong's point estimate method to optimal operation management in a microgrid with consideration of uncertainties,’’ Energy, vol.51, pp.339-348, March. 2013, doi: 10.1016/j.energy.2012.12.013.
  • [2] M. Cikan and N. N.Cikan, ‘‘Optimum allocation of multiple type and number of DG units based on IEEE 123-bus unbalanced multi-phase power distribution system’’ International Journal of Electrical Power & Energy Systems, vol.144, pp.1-17, Jan. 2023, doi: 10.1016/j.ijepes.2022.108564.
  • [3] M. Çıkan, “Çita optimizasyon algoritması kullanarak kısmi gölgelenme altındaki fotovoltaik sistemlerde maksimum güç noktası izleyicisinin tasarlanması,” Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol.40, no.1, pp. 555–572, Jan. 2025, doi: 10.17341/gazimmfd.1183267.
  • [4] M. Çıkan and N.N. Çıkan, ‘‘Elektrikli araç şarj istasyonlarının enerji dağıtım hatlarına optimum şekilde konumlandırılması’’ Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, vol.27, no.2, pp. 340-363, Jun. 2024, doi:10.17780/ksujes.1365209.
  • [5] M.Cikan, N.N. Cikan, B. Kekezoglu, “Determination of optimal island regions with simultaneous DG allocation and reconfiguration in power distribution networks,” IET Renewable Power Generation, vol.19, pp. e12942, 2025, doi:10.1049/rpg2.12942.
  • [6] C. Marnay, N. DeForest and J. Lai, “A green prison: The Santa Rita Jail campus microgrid,’’ IEEE Power and Energy Society General Meeting, 2012, pp.1-2, doi: 10.1109/PESGM.2012.6345235.
  • [7] T. Gabderakhmanova et al., “Demonstrations of DC Microgrid and Virtual Power Plant Technologies on the Danish Island of Bornholm,’’ 55th International Universities Power Engineering Conference (UPEC), 2020, pp. 1-6, doi: 10.1109/UPEC49904.2020.9209853.
  • [8] S. Ahmad, M. Shafiullah, C. B. Ahmed and M. Alowaifeer, “A Review of Microgrid Energy Management and Control Strategies,’’ IEEE Access, vol. 11, pp. 21729-21757, 2023, doi: 10.1109/ACCESS.2023.3248511
  • [9] N.N. Cikan and M. Cikan, ‘‘Reconfiguration of 123-bus unbalanced power distribution network analysis by considering minimization of current & voltage unbalanced indexes and power loss,’’ International Journal of Electrical Power & Energy Systems, vol.157, pp.1-15, Jun.2024, doi: 10.1016/j.ijepes.2024.109796.
  • [10] M. Cikan and B. Kekezoglu, ‘‘Comparison of metaheuristic optimization techniques including Equilibrium optimizer algorithm in power distribution network reconfiguration,’’ Alexandria Engineering Journal, vol.61, no.2, pp. 991-1031, Feb. 2022, doi: 10.1016/j.aej.2021.06.079.
  • [11] K. Doğanşahin and M. Çıkan, ‘‘A new line stability index for voltage stability analysis based on line loading,’’ Clean Energy Technologies Journal, vol.1, no.1, pp.23-30, 2023.
  • [12] A. Mortazavi, “Comparative assessment of five metaheuristic methods on distinct problems,” Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi (DUJE), vol. 10, pp. 879–898, 2019, doi: 10.24012/dumf.585790.
  • [13] A. Mortazavi, “A fuzzy reinforced Jaya algorithm for solving mathematical and structural optimization problems,’’ Soft Computing, vol. 28, pp. 2181–2206, 2024, doi:10.1007/s00500-023-09206-5.
  • [14] A. Akter et al., ‘‘A review on microgrid optimization with meta-heuristic techniques: Scopes, trends and recommendation,’’ Energy Strategy Reviews, vol. 51, pp. 1-27, Jan. 2024, doi: 10.1016/j.esr.2024.101298.
  • [15] S. Phommixay, M.L. Doumbia, and D. Lupien St-Pierre, ‘‘Review on the cost optimization of microgrids via particle swarm optimization,’’ International Journal of Energy and Environmental Engineering, vol. 11, pp. 73-89, 2020, doi: 0.1007/s40095-019-00332-1.
  • [16] M. A. Hossain, H. R. Pota, S. Squartini, F. Zaman, and K.M. Muttaqi, ‘‘Energy management of community microgrids considering degradation cost of battery,’’ Journal of Energy Storage, vol.22, pp.257-269, April 2019, doi: 10.1016/j.est.2018.12.021.
  • [17] S. Sharma, S. Bhattacharjee, and A. Bhattacharya, ‘‘Probabilistic operation cost minimization of Micro-Grid,’’ Energy, vol.148, pp. 1116-1139, 2018, doi: 10.1016/j.energy.2018.01.164.
  • [18] Z. Zheng, S.Yang, Y. Guo, X. Jin, and R. Wang, “Meta-heuristic Techniques in Microgrid Management: A Survey’’ Swarm and Evolutionary Computation, vol.78, pp.101256, 2023, doi: 10.1016/j.swevo.2023.101256.
  • [19] A. Mortazavi, “Marathon runner algorithm: theory and application in mathematical, mechanical and structural optimization problems,’’ Materials Testing, vol. 66, pp. 1267-1291,2024, doi: 10.1515/mt-2023-009.
  • [20] M. Moloodpoor, A. Mortazavi, and N. Ozbalta, “Thermo-economic optimization of double-pipe heat exchanger using a compound swarm intelligence,’’ Heat Transfer Research, vol.52, pp.1-20,2021, doi: 10.1615/HeatTransRes.2021037293.
  • [21] E. C. Kandemir, and A. Mortazavi, “Optimization of Seismic Base Isolation System Using a Fuzzy Reinforced Swarm Intelligence,’’ Advances in Engineering Software, vol.174, pp. 103323, 2022, doi: 10.1016/j.advengsoft.2022.103323.
  • [22] M. Cikan, and K. Dogansahin, “A Comprehensive Evaluation of Up-to-Date Optimization Algorithms on MPPT Application for Photovoltaic Systems,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol.45, pp.10381–10407, 2023, doi:10.1080/15567036.2023.2245771.
  • [23] N.N. Cikan, “Optimization of PV, Capacitor Bank, and EV Charging Station Allocation in a 33-Bus Power Distribution System Using the Slime Mould Algorithm” European Journal of Engineering and Natural Sciences (EJENS), vol.9, pp.55-60, 2024.
  • [24] N.N. Cikan, “Optimization of a 33-Bus Power Distribution System Using Artificial Hummingbird Algorithm for Power Loss and Voltage Stability Enhancement,” European Journal of Engineering and Natural Sciences (EJENS), vol.9, pp.41-46, 2024.
  • [25] M.Gendreau, and JY. Potvin, “Metaheuristics in Combinatorial Optimization,’’ Annals of Operation Research, vol. 140, pp.189–213, 2005, doi:10.1007/s10479-005-3971-7.
  • [26] I. Boussaïd, J. Lepagnot, and P. Siarry, “A survey on optimization metaheuristics,’’ Information Sciences, vol. 237, pp.82-117, 2013, doi: 10.1016/j.ins.2013.02.041.
  • [27] S. Yang, Y. Jiang, and T.T. Nguyen, “Metaheuristics for dynamic combinatorial optimization problems,’’ IMA Journal of Management Mathematics, vol.24, pp.451–480, 2013, doi: 10.1093/imaman/dps021.
  • [28] C. Gamarra, and J. M. Guerrero, “Computational optimization techniques applied to microgrids planning: A review,’’ Renewable and Sustainable Energy Reviews, vol. 48, pp. 413-424, 2015, doi: 10.1016/j.rser.2015.04.025.
  • [29] M. F. Zia, E. Elbouchikhi, and M. Benbouzid, “Microgrids energy management systems: A critical review on methods, solutions, and prospects,’’ Applied Energy, vol. 222, pp. 1033-1055, 2018, doi: 10.1016/j.apenergy.2018.04.103.
  • [30] T. Niknam, F. Golestaneh, and A. Malekpour, ‘‘Probabilistic energy and operation management of a microgrid containing wind/photovoltaic/fuel cell generation and energy storage devices based on point estimate method and self-adaptive gravitational search algorithm,’’ Energy, vol.43, no.1, pp. 427-437, July 2012, 10.1016/j.energy.2012.03.064.
  • [31] A. Ghiaskar, A. Amiri, S. Mirjalili, ‘‘Polar fox optimization algorithm: a novel meta-heuristic algorithm,’’ Neural Computing and Applications, vol.36, pp. 20983–21022, Aug. 2024, doi: 10.1007/s00521-024-10346-4.
  • [32] J. Radosavljevic, ‘‘Optimal energy and operation management of microgrids,’’ in Metaheuristic Optimization in Power Engineering, 1st ed. London, England: IET, ch.12, pp. 407-447.
  • [33] H. Saadat, Power system analysis. New York, NY, USA: McGraw-Hill,1999.
  • [34] J. Radosavljevic, ‘‘Optimal power flow in transmission networks,’’ in Metaheuristic Optimization in Power Engineering, 1st ed. London, England: IET, ch.6, pp. 177-233.
  • [35] S. Mohammadi, S. Soleymani, and B. Mozafari, ‘‘Scenario-based stochastic operation management of microgrid including wind, photovoltaic, micro-turbine, fuel cell and energy storage devices,’’ International Journal of Electrical Power & Energy Systems, vol. 54, pp.525-535, Jan.2014, doi: 10.1016/j.ijepes.2013.08.004.
  • [36] A. A. Moghaddam, A. Seifi, T. Niknam, and M. R. A. Pahlavani, ‘‘Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source,’’ Energy, vol.36, no.11, pp. 6490-6507, Nov.2011, doi: 10.1016/j.energy.2011.09.017.
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Enerjisi Taşıma, Şebeke ve Sistemleri, Elektrik Mühendisliği (Diğer)
Bölüm Research Articles
Yazarlar

Nisa Nacar Çıkan 0000-0002-9641-4616

Proje Numarası Tubitak 124E002
Yayımlanma Tarihi 30 Haziran 2025
Gönderilme Tarihi 24 Aralık 2024
Kabul Tarihi 21 Mart 2025
Yayımlandığı Sayı Yıl 2025 Sayı: 061

Kaynak Göster

IEEE N. Nacar Çıkan, “Cost optimization in microgrids: A scenario-based analysis by using polar the fox optimization algorithm”, JSR-A, sy. 061, ss. 34–59, Haziran 2025.
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