Enerji Depolama ile Elektrik Dağıtım Şebekesinin İşletme Maliyeti Optimizasyonu

Yıl 2024, Cilt: 36 Sayı: 1, 105 - 120, 28.03.2024

Fatma Avli Fırış , İsrafil Karadöl , Ö. Fatih Keçecioğlu

https://doi.org/10.35234/fumbd.1294350

Öz

Bu çalışmanın amacı, yenilenebilir ve dağıtık enerji kaynaklarının bulunduğu elektrik dağıtım şebekesinin fider ölçeğinde enerji depolama sistemi kullanılarak minimum işletme maliyeti sağlamaktır. Şebekenin işletim optimizasyonu, çalışmada geliştirilen iki aşamalı stokastik programlama problemi ile ele alınmıştır. Problem, General Algebraic Modelling System (GAMS) aracılığıyla doğrusal bir model olan Mixed Integer Linear Programming (MILP) ile formüle edilmiş ve CPLEX çözücüsü ile çözülmüştür. Modellemedeki belirsizliklerin ele alınabilmesi için Monte Carlo Simülasyonu aracılığıyla senaryo üretimi ve azaltımı gerçekleştirilmiştir. Önerilen modelin etkinliğini doğrulamak için gerçekleştirilen simülasyon çalışmaları, IEEE-33 test baraları üzerinde uygulanmıştır. İşletme maliyetleri olası şebeke koşulları altında hesaplanmış ve kendi aralarında enerji depolamanın kullanımlarına göre karşılaştırılmıştır. Edinilen sonuçlara göre, şebekeye enerji depolama sistemi entegre edildiği durumlarda, depolama sisteminin hiç bulunmadığı durumlara göre işletme maliyetinde yalnıca bir günlük ortalama zaman periyodunda 200 doları aşkın bir düşüş gözlenmiştir. Böylece önerilen sistemle birlikte enerji depolamanın optimum şekilde programlanmasının; işletme maliyetlerini düşürmede ve dolayısıyla güç sistemlerinin en kritik konularından biri olan ekonomik optimizasyonun sağlanmasında etkin bir yöntem olduğu doğrulanmıştır.

Anahtar Kelimeler

Dağıtım sistemleri, monte carlo simülasyonu, optimal planlama, maliyet

Operating Cost Optimization of Electricity Distribution Network with Energy Storage

Öz

The aim of this study is to provide minimum operating cost by using the energy storage system at the feeder scale of the electricity distribution network with renewable and distributed energy sources. The operating optimization of the network is handled with the two-stage stochastic programming problem developed in the study. The problem was formulated with Mixed Integer Linear Programming (MILP), a linear model, through the General Algebraic Modeling System (GAMS) and solved with the CPLEX solver. In order to deal with the uncertainties in the modeling, scenario generation and reduction were carried out through Monte Carlo Simulation. Simulation studies carried out to verify the effectiveness of the proposed model were applied on IEEE-33 test busbars. Operating costs were calculated under possible grid conditions and compared among themselves according to the use of energy storage. According to the results, in cases where the energy storage system is integrated into the grid, a decrease of more than 200 dollars was observed in the operating cost in only one day's average time period compared to the cases where the storage system is not available at all. Thus, the optimum programming of energy storage with the proposed system; It has been proven to be an effective method in reducing operating costs and thus providing economic optimization, which is one of the most critical issues of power systems.

Anahtar Kelimeler

Distribution systems, monte carlo simulation, optimum planning, cost

Kaynakça

• S. Koohi-Fayegh and M. A. Rosen, “A review of energy storage types, applications and recent developments,” J. Energy Storage, vol. 27, no. July 2019, p. 101047, 2020, doi: 10.1016/j.est.2019.101047.
• E. D. M. SHURA, Yenilenebilir Dağıtık Enerji Üretiminin Şebeke ve Piyasa Entegrasyonu. 2021.
• S. Seyyedeh Barhagh, M. Abapour, and B. Mohammadi-Ivatloo, “Optimal scheduling of electric vehicles and photovoltaic systems in residential complexes under real-time pricing mechanism,” J. Clean. Prod., vol. 246, 2020, doi: 10.1016/j.jclepro.2019.119041.
• K. P. Kumar and B. Saravanan, “Day ahead scheduling of generation and storage in a microgrid considering demand Side management,” J. Energy Storage, vol. 21, no. June 2018, pp. 78–86, 2019, doi: 10.1016/j.est.2018.11.010.
• Y. Li, Z. Yang, G. Li, D. Zhao, and W. Tian, “Optimal Scheduling of an Isolated Microgrid with Battery Storage Considering Load and Renewable Generation Uncertainties,” IEEE Trans. Ind. Electron., vol. 66, no. 2, pp. 1565–1575, 2019, doi: 10.1109/TIE.2018.2840498.
• L. Luo et al., “Optimal scheduling of a renewable based microgrid considering photovoltaic system and battery energy storage under uncertainty,” J. Energy Storage, vol. 28, no. August 2019, p. 101306, 2020, doi: 10.1016/j.est.2020.101306.
• X. Zhang, Y. Son, and S. Choi, “Optimal Scheduling of Battery Energy Storage Systems and Demand Response for Distribution Systems with High Penetration of Renewable Energy Sources,” Energies, vol. 15, no. 6, 2022, doi: 10.3390/en15062212.
• Y. Wang, J. Zhao, T. Zheng, K. Fan, and K. Zhang, “Optimal Planning of Integrated Energy System Considering Convertibility Index,” Front. Energy Res., vol. 10, no. April, pp. 1–17, 2022, doi: 10.3389/fenrg.2022.855312.
• W. S. Ho, S. Macchietto, J. S. Lim, H. Hashim, Z. A. Muis, and W. H. Liu, “Optimal scheduling of energy storage for renewable energy distributed energy generation system,” Renew. Sustain. Energy Rev., vol. 58, pp. 1100–1107, 2016, doi: 10.1016/j.rser.2015.12.097.
• F. Avli Firiş, I. Karadöl, M. Şekkeli, and Ö. F. Keçecioğlu, “Optimal scheduling of active electricity distribution network at feeder scale under possible conditions and considering operating cost,” Electr. Eng., 2023, doi: 10.1007/s00202-023-01887-3.
• A. Hadjian, “Kastamonu,” Secret Nation, no. 2, pp. 545–556, 2019, doi: 10.5040/9781350987951.ch-016.
• Z. Wang and J. Wang, “Self-Healing Resilient Distribution Systems Based on Sectionalization into Microgrids,” IEEE Trans. Power Syst., vol. 30, no. 6, pp. 3139–3149, 2015, doi: 10.1109/TPWRS.2015.2389753.
• M. Di Somma, G. Graditi, E. Heydarian-Forushani, M. Shafie-khah, and P. Siano, “Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects,” Renew. Energy, vol. 116, pp. 272–287, 2018, doi: 10.1016/j.renene.2017.09.074.
• U. Shahzad and S. Asgarpoor, “Probabilistic Risk Assessment of an Active Distribution Network Using Monte Carlo Simulation Approach,” 51st North Am. Power Symp. NAPS 2019, 2019, doi: 10.1109/NAPS46351.2019.9000225.
• E. Zio, M. Delfanti, L. Giorgi, V. Olivieri, and G. Sansavini, “Monte Carlo simulation-based probabilistic assessment of DG penetration in medium voltage distribution networks,” Int. J. Electr. Power Energy Syst., vol. 64, pp. 852–860, 2015, doi: 10.1016/j.ijepes.2014.08.004.
• S. Conti and S. Raiti, “Probabilistic load flow using Monte Carlo techniques for distribution networks with photovoltaic generators,” Sol. Energy, vol. 81, no. 12, pp. 1473–1481, 2007, doi: 10.1016/j.solener.2007.02.007.
• A. Izadi and A. mohammad Kimiagari, “Distribution network design under demand uncertainty using genetic algorithm and Monte Carlo simulation approach: a case study in pharmaceutical industry,” J. Ind. Eng. Int., vol. 10, no. 1, pp. 1–9, 2014, doi: 10.1186/2251-712X-10-1.
• K. Nara, A. Shiose, M. Kitagawa, and T. Ishihara, “Implementation of Genetic Algorithm for Distribution Systems Loss Minimum Re-Configuration,” IEEE Trans. Power Syst., vol. 7, no. 3, pp. 1044–1051, 1992, doi: 10.1109/59.207317.
• S. Stentz, “Improving weighted caseload studies in limited jurisdiction courts,” Justice Syst. J., vol. 13, no. 3, pp. 79–385, 1988, doi: 10.1080/23277556.1989.10871109.
• A. Khodaei, S. Bahramirad, and M. Shahidehpour, “Microgrid Planning Under Uncertainty,” IEEE Trans. Power Syst., vol. 30, no. 5, pp. 2417–2425, 2015, doi: 10.1109/TPWRS.2014.2361094.
• Z. Liu, F. Wen, and G. Ledwich, “Optimal planning of electric-vehicle charging stations in distribution systems,” IEEE Trans. Power Deliv., vol. 28, no. 1, pp. 102–110, 2013, doi: 10.1109/TPWRD.2012.2223489.
• S. Wen, H. Lan, Q. Fu, D. C. Yu, and L. Zhang, “Economic allocation for energy storage system considering wind power distribution,” IEEE Trans. Power Syst., vol. 30, no. 2, pp. 644–652, 2015, doi: 10.1109/TPWRS.2014.2337936.
• P. Siano and G. Mokryani, “Assessing wind turbines placement in a distribution market environment by using particle swarm optimization,” IEEE Trans. Power Syst., vol. 28, no. 4, pp. 3852–3864, 2013, doi: 10.1109/TPWRS.2013.2273567.
• A. Gholami, T. Shekari, F. Aminifar, and M. Shahidehpour, “Microgrid Scheduling with Uncertainty: The Quest for Resilience,” IEEE Trans. Smart Grid, vol. 7, no. 6, pp. 2849–2858, 2016, doi: 10.1109/TSG.2016.2598802.
• K. Binder, Monte-Carlo Methods. 2005.
• D. P. Kroese and R. Y. Rubinstein, “Monte Carlo methods,” Wiley Interdiscip. Rev. Comput. Stat., vol. 4, no. 1, pp. 48–58, 2012, doi: 10.1002/wics.194.
• S. Teimourzadeh, O. B. Tor, M. E. Cebeci, A. Bara, and S. V. Oprea, “A three-stage approach for resilience-constrained scheduling of networked microgrids,” J. Mod. Power Syst. Clean Energy, vol. 7, no. 4, pp. 705–715, 2019, doi: 10.1007/s40565-019-0555-0.
• F. S. Hillier, Series Editor. 2019.