Araştırma Makalesi
BibTex RIS Kaynak Göster

Güç Sistemi Kayıplarında Belirsizlik Etkisinin Analizi: Yenilenebilir Enerji ve Yük Belirsizlikleri

Yıl 2022, , 62 - 71, 07.05.2022
https://doi.org/10.31590/ejosat.1051410

Öz

Yenilenebilir enerji kaynaklarının güç sistemlerine yaygın şekilde entegre edilmesi sonucunda enerji kaybı minimizasyonu problemi giderek daha fazla önem kazanmaktadır. Bu nedenle, fotovoltaik (PV) ve rüzgar türbini (WT) sistemlerindeki kesikli karakteristikler ve yük taleplerindeki belirsizlikler nedeniyle teknik sorunları ele almak için güç sisteminin optimal planlanması gerekmektedir. Bu makalede, PV ve WT sistemlerinin kurulu olduğu güç şebekesindeki toplam enerji kayıplarının azaltılmasında çeşitli belirsizlik senaryolarının etkileri dikkate alınmıştır. Güç sistemi teknik kısıtları dikkate alınarak kontrol değişkenlerinin optimal değerlerinin belirlenmesi için Parçacık Sürü Optimizasyonu (PSO) algoritması uygulanmıştır. Planlamanın uygulanmasında toplam enerji kayıpları azaltılırken farklı belirsizlik senaryolarının etkileri dikkate alınmıştır.

Kaynakça

  • Kiwan, S., Al-Gharibeh, E., & Abu-Lihia, E. (2021). Wind Energy Potential in Jordan: analysis of the first large-scale wind farm and techno-economic assessment of potential farms. Journal of Solar Energy Engineering, 143(1), 011007.
  • Akdemir, H., Durusu, A., Erduman, A., & Nakir, I. (2018). Effect of energy management of a grid connected photovoltaic/battery/load system on the optimal photovoltaic placement on a national scale: The case of Turkey. Journal of Solar Energy Engineering, 140(2), 021009.
  • Liu, J., Tang, Z., Zeng, P. P., Li, Y., & Wu, Q. (2022). Distributed adaptive expansion approach for transmission and distribution networks incorporating source-contingency-load uncertainties. International Journal of Electrical Power & Energy Systems, 136, 107711.
  • Sanjari, M. J., & Karami, H. (2020). Optimal control strategy of battery-integrated energy system considering load demand uncertainty. Energy, 210, 118525.
  • Abedi, M. H., Hosseini, H., & Jalilvand, A. (2019). Sub-transmission substation expansion planning considering load center uncertainties of size and location. International Journal of Electrical Power & Energy Systems, 109, 413-422.
  • Esmaeili, M., Sedighizadeh, M., & Esmaili, M. (2016). Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty. Energy, 103, 86-99.
  • Mehrjerdi, H., & Rakhshani, E. (2019). Correlation of multiple time-scale and uncertainty modelling for renewable energy-load profiles in wind powered system. Journal of Cleaner Production, 236, 117644.
  • Shargh, S., Mohammadi-Ivatloo, B., Seyedi, H., & Abapour, M. (2016). Probabilistic multi-objective optimal power flow considering correlated wind power and load uncertainties. Renewable Energy, 94, 10-21.
  • Mohseni-Bonab, S. M., Rabiee, A., & Mohammadi-Ivatloo, B. (2016). Voltage stability constrained multi-objective optimal reactive power dispatch under load and wind power uncertainties: A stochastic approach. Renewable Energy, 85, 598-609.
  • Huang, S., & Abedinia, O. (2021). Investigation in economic analysis of microgrids based on renewable energy uncertainty and demand response in the electricity market. Energy, 225, 120247.
  • Zhang, S., Cheng, H., Li, K., Tai, N., Wang, D., & Li, F. (2018). Multi-objective distributed generation planning in distribution network considering correlations among uncertainties. Applied energy, 226, 743-755.
  • Ebrahimi, J., Abedini, M., Rezaei, M. M., & Nasri, M. (2020). Optimum design of a multi-form energy in the presence of electric vehicle charging station and renewable resources considering uncertainty. Sustainable Energy, Grids and Networks, 23, 100375.
  • Jithendranath, J., Das, D., & Guerrero, J. M. (2021). Probabilistic optimal power flow in islanded microgrids with load, wind and solar uncertainties including intermittent generation spatial correlation. Energy, 222, 119847.
  • Nikmehr, N., & Ravadanegh, S. N. (2016). Reliability evaluation of multi-microgrids considering optimal operation of small scale energy zones under load-generation uncertainties. International Journal of Electrical Power & Energy Systems, 78, 80-87.
  • Yang, J., & Su, C. (2021). Robust optimization of microgrid based on renewable distributed power generation and load demand uncertainty. Energy, 223, 120043.
  • Alabi, T. M., Lu, L., & Yang, Z. (2021). Stochastic optimal planning scheme of a zero-carbon multi-energy system (ZC-MES) considering the uncertainties of individual energy demand and renewable resources: an integrated chance-constrained and decomposition algorithm (CC-DA) approach. Energy, 121000.
  • Salau, A. O., Gebru, Y. W., & Bitew, D. (2020). Optimal network reconfiguration for power loss minimization and voltage profile enhancement in distribution systems. Heliyon, 6(6), e04233.
  • Ali, M. H., Mehanna, M., & Othman, E. (2020). Optimal Network Reconfiguration Incorporating with Renewable Energy Sources in Radial Distribution Networks. International Journal of Advanced Science and Technology, 29(12s), 3114-3133.
  • Moussa, S. A. M., & Abdelwahed, A. (2017). DG Allocation Based on Reliability, Losses and Voltage Sag Considerations: an expert system approach. Renewable Energy and Sustainable Development, 3(1), 33-38.
  • Wang, Y., Luo, H., & Xiao, X. (2021). Joint Optimal Planning of Distributed Generations and Sensitive Users Considering Voltage Sag. IEEE Transactions on Power Delivery.
  • HassanzadehFard, H., & Jalilian, A. (2021). Optimization of DG units in distribution systems for voltage sag minimization considering various load types. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 45(2), 685-699.
  • Nikoukar, J., Haghifam, M. R., & Panahi, A. (2011). Transmission expansion cost allocation based on economic benefit and use of system. Journal of American Science, 7(4).
  • Hamzaoğlu, A., Erduman, A., & Alçı, M. (2021). Reduction of distribution system losses using solar energy cooperativity by home user. Ain Shams Engineering Journal, 12(4), 3737-3745.
  • Khaled, U., Eltamaly, A. M., & Beroual, A. (2017). Optimal power flow using particle swarm optimization of renewable hybrid distributed generation. Energies, 10(7), 1013.
  • Sadeghi, D., Naghshbandy, A. H., & Bahramara, S. (2020). Optimal sizing of hybrid renewable energy systems in presence of electric vehicles using multi-objective particle swarm optimization. Energy, 209, 118471.
  • Nasri, A., Hamedani Golshan, M. E., & Mortaza Saghaian Nejad, S. (2014). Optimal planning of dispatchable and non‐dispatchable distributed generation units for minimizing distribution system's energy loss using particle swarm optimization. International Transactions on Electrical Energy Systems, 24(4), 504-519.
  • Pandi, V. R., Zeineldin, H. H., Xiao, W., & Zobaa, A. F. (2013). Optimal penetration levels for inverter-based distributed generation considering harmonic limits. Electric Power Systems Research, 97, 68-75.

Analysis of the Uncertainty Effect in Power System Losses: Uncertainties of Renewable Energy and Load

Yıl 2022, , 62 - 71, 07.05.2022
https://doi.org/10.31590/ejosat.1051410

Öz

The energy loss minimization problem is increasingly gaining prominence as a result of widespread integration of renewable energy sources into the power systems. Thus, the optimal planning of power system is required to handle the technical issues due to the uncertainties in load demands and the intermittent characteristics in photovoltaic (PV) and wind turbine (WT) systems. In this paper, the impacts of various uncertainty scenarios have been considered while mitigating the total energy losses in the power network, where PV and WT systems are installed. Particle Swarm Optimization (PSO) algorithm has been implemented to determine the optimal values of control variables while taking into account the power system technical constraints. The influences of different uncertainty scenarios have been considered while alleviating total energy losses in the implementation of planning.

Kaynakça

  • Kiwan, S., Al-Gharibeh, E., & Abu-Lihia, E. (2021). Wind Energy Potential in Jordan: analysis of the first large-scale wind farm and techno-economic assessment of potential farms. Journal of Solar Energy Engineering, 143(1), 011007.
  • Akdemir, H., Durusu, A., Erduman, A., & Nakir, I. (2018). Effect of energy management of a grid connected photovoltaic/battery/load system on the optimal photovoltaic placement on a national scale: The case of Turkey. Journal of Solar Energy Engineering, 140(2), 021009.
  • Liu, J., Tang, Z., Zeng, P. P., Li, Y., & Wu, Q. (2022). Distributed adaptive expansion approach for transmission and distribution networks incorporating source-contingency-load uncertainties. International Journal of Electrical Power & Energy Systems, 136, 107711.
  • Sanjari, M. J., & Karami, H. (2020). Optimal control strategy of battery-integrated energy system considering load demand uncertainty. Energy, 210, 118525.
  • Abedi, M. H., Hosseini, H., & Jalilvand, A. (2019). Sub-transmission substation expansion planning considering load center uncertainties of size and location. International Journal of Electrical Power & Energy Systems, 109, 413-422.
  • Esmaeili, M., Sedighizadeh, M., & Esmaili, M. (2016). Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty. Energy, 103, 86-99.
  • Mehrjerdi, H., & Rakhshani, E. (2019). Correlation of multiple time-scale and uncertainty modelling for renewable energy-load profiles in wind powered system. Journal of Cleaner Production, 236, 117644.
  • Shargh, S., Mohammadi-Ivatloo, B., Seyedi, H., & Abapour, M. (2016). Probabilistic multi-objective optimal power flow considering correlated wind power and load uncertainties. Renewable Energy, 94, 10-21.
  • Mohseni-Bonab, S. M., Rabiee, A., & Mohammadi-Ivatloo, B. (2016). Voltage stability constrained multi-objective optimal reactive power dispatch under load and wind power uncertainties: A stochastic approach. Renewable Energy, 85, 598-609.
  • Huang, S., & Abedinia, O. (2021). Investigation in economic analysis of microgrids based on renewable energy uncertainty and demand response in the electricity market. Energy, 225, 120247.
  • Zhang, S., Cheng, H., Li, K., Tai, N., Wang, D., & Li, F. (2018). Multi-objective distributed generation planning in distribution network considering correlations among uncertainties. Applied energy, 226, 743-755.
  • Ebrahimi, J., Abedini, M., Rezaei, M. M., & Nasri, M. (2020). Optimum design of a multi-form energy in the presence of electric vehicle charging station and renewable resources considering uncertainty. Sustainable Energy, Grids and Networks, 23, 100375.
  • Jithendranath, J., Das, D., & Guerrero, J. M. (2021). Probabilistic optimal power flow in islanded microgrids with load, wind and solar uncertainties including intermittent generation spatial correlation. Energy, 222, 119847.
  • Nikmehr, N., & Ravadanegh, S. N. (2016). Reliability evaluation of multi-microgrids considering optimal operation of small scale energy zones under load-generation uncertainties. International Journal of Electrical Power & Energy Systems, 78, 80-87.
  • Yang, J., & Su, C. (2021). Robust optimization of microgrid based on renewable distributed power generation and load demand uncertainty. Energy, 223, 120043.
  • Alabi, T. M., Lu, L., & Yang, Z. (2021). Stochastic optimal planning scheme of a zero-carbon multi-energy system (ZC-MES) considering the uncertainties of individual energy demand and renewable resources: an integrated chance-constrained and decomposition algorithm (CC-DA) approach. Energy, 121000.
  • Salau, A. O., Gebru, Y. W., & Bitew, D. (2020). Optimal network reconfiguration for power loss minimization and voltage profile enhancement in distribution systems. Heliyon, 6(6), e04233.
  • Ali, M. H., Mehanna, M., & Othman, E. (2020). Optimal Network Reconfiguration Incorporating with Renewable Energy Sources in Radial Distribution Networks. International Journal of Advanced Science and Technology, 29(12s), 3114-3133.
  • Moussa, S. A. M., & Abdelwahed, A. (2017). DG Allocation Based on Reliability, Losses and Voltage Sag Considerations: an expert system approach. Renewable Energy and Sustainable Development, 3(1), 33-38.
  • Wang, Y., Luo, H., & Xiao, X. (2021). Joint Optimal Planning of Distributed Generations and Sensitive Users Considering Voltage Sag. IEEE Transactions on Power Delivery.
  • HassanzadehFard, H., & Jalilian, A. (2021). Optimization of DG units in distribution systems for voltage sag minimization considering various load types. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 45(2), 685-699.
  • Nikoukar, J., Haghifam, M. R., & Panahi, A. (2011). Transmission expansion cost allocation based on economic benefit and use of system. Journal of American Science, 7(4).
  • Hamzaoğlu, A., Erduman, A., & Alçı, M. (2021). Reduction of distribution system losses using solar energy cooperativity by home user. Ain Shams Engineering Journal, 12(4), 3737-3745.
  • Khaled, U., Eltamaly, A. M., & Beroual, A. (2017). Optimal power flow using particle swarm optimization of renewable hybrid distributed generation. Energies, 10(7), 1013.
  • Sadeghi, D., Naghshbandy, A. H., & Bahramara, S. (2020). Optimal sizing of hybrid renewable energy systems in presence of electric vehicles using multi-objective particle swarm optimization. Energy, 209, 118471.
  • Nasri, A., Hamedani Golshan, M. E., & Mortaza Saghaian Nejad, S. (2014). Optimal planning of dispatchable and non‐dispatchable distributed generation units for minimizing distribution system's energy loss using particle swarm optimization. International Transactions on Electrical Energy Systems, 24(4), 504-519.
  • Pandi, V. R., Zeineldin, H. H., Xiao, W., & Zobaa, A. F. (2013). Optimal penetration levels for inverter-based distributed generation considering harmonic limits. Electric Power Systems Research, 97, 68-75.
Toplam 27 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

İbrahim Çağrı Barutçu 0000-0001-6164-2048

Ali Erduman 0000-0003-4116-3159

Yayımlanma Tarihi 7 Mayıs 2022
Yayımlandığı Sayı Yıl 2022

Kaynak Göster

APA Barutçu, İ. Ç., & Erduman, A. (2022). Analysis of the Uncertainty Effect in Power System Losses: Uncertainties of Renewable Energy and Load. Avrupa Bilim Ve Teknoloji Dergisi(35), 62-71. https://doi.org/10.31590/ejosat.1051410