Öz
The aim of this study is to provide economic optimization in the energy exchange to be realized in the day-ahead and intra-day electricity markets by incorporating an energy storage system into an active electric distribution system. The two-stage stochastic programming problem developed for this purpose was formulated with Mixed Integer Linear Programming (MILP) through the General Algebraic Modeling System (GAMS) and solved with the CPLEX solver. Uncertain parameters in modeling were handled with Monte Carlo Simulation and scenario selection was made in this direction. In order to confirm the accuracy and effectiveness of the proposed model, simulation studies were carried out on a selected pilot feeder of the considered distribution system and with real data. The operating costs, which are considered in the simulation studies, are calculated separately and compared in cases where energy storage is used in the grid. According to the results obtained, when energy storage is integrated into the grid, the average daily operation is reduced by more than 600 dollars compared to the case where no storage is available. Thus, it is confirmed that the proposed model can effectively support the economic operation of the electric distribution system.
Anahtar Kelimeler
Energy storage Electricity market Distribution system Economic optimization Operating cost
Kaynakça
- 1. Koohi-Fayegh, S., Rosen, M.A., 2020. A Review of Energy Storage Types, Applications and Recent Developments. Energy Storage, 27, 101047.
- 2. Chamandoust, H., Derakhshan, G., Hakimi, S.M., Bahramara S., 2020. Tri-objective Scheduling of Residential Smart Electrical Distribution Grids with Optimal Joint of Responsive Loads with Renewable Energy Sources. Energy Storage, 27, 101112.
- 3. Jing, W., Lai, C.H., Ling, D.K.X., Wong, W.S.H., Wong, M.L.D., 2019. Battery Lifetime Enhancement Via Smart Hybrid Energy Storage Plug-in Module in Standalone Photovoltaic Power System. Energy Storage, 21, 586-98.
- 4. Shao, Z., Wakil, K., Usak, M., Amin Heidari, M., Wang B., Simoes, R., 2018. Kriging Empirical Mode Decomposition Via Support Vector Machine Learning Technique for Autonomous Operation Diagnosing of CHP in Microgrid. Appl Therm Eng, 145, 58-70.
- 5. Moutis, P., Hadi Amini, M., Khan, I.A., He, G., Mohammadi, J., Kar, S., 2019. A Survey of Recent Developments and Requirements for Modern Power System Control. Pathways to a Smarter Power System. Elsevier Ltd., 289-316
- 6. Malekpour, A.R., Niknam, T., 2011. A Probabilistic Multi-Objective Daily Volt/Var Control at Distribution Networks Including Renewable Energy Sources. Energy, 36(5), 3477-3488.
- 7. Tan, K.M., Babu, T.S., Ramachandaramurthy, V.K., Kasinathan, P., Solanki, S.G., Raveendran, S.K., 2021. Empowering Smart Grid: A Comprehensive Review of Energy Storage Technology and Application with Renewable Energy Integration. Energy Storage 39, 102591.
- 8. Li, Y., Yang, Z., Li, G., Zhao, D., Tian, W., 2019. Optimal Scheduling of an Isolated Microgrid with Battery Storage Considering Load and Renewable Generation Uncertainties. IEEE Trans Ind Electron. 66(2), 1565-1675.
- 9. Farzin, H., Fotuhi-Firuzabad, M., Moeini-Aghtaie, M., 2017. A Stochastic Multi-Objective Framework for Optimal Scheduling of Energy Storage Systems in Microgrids. IEEE Trans Smart Grid, 8(1), 117-27.
- 10. Raghavan A., Maan P., Shenoy A. 2016. Optimal Scheduling of Energy Storage for Renewable Energy Distributed Energy Generation System. Renewable and Sustainable Energy Reviews, 58, 1100-1107.
- 11. Wang, Y., Zhao, J., Zheng, T., Fan, K., Zhang, K., 2022. Optimal Planning of Integrated Energy System Considering Convertibility Index. Energy Res. 10, 1–17.
- 12. Ho, W.S., Macchietto, S., Lim, J.S., Hashim, H., Muis, Z.A., Liu, W.H., 2016. Optimal scheduling of energy storage for renewable energy distributed energy generation system. Renew Sustain Energy Rev., 58, 1100-7.
- 13. Zhang, X., Son, Y., Choi, S., 2022. Optimal Scheduling of Battery Energy Storage Systems and Demand Response for Distribution Systems with High Penetration of Renewable Energy Sources. Energies. 15(6).
- 14. Teimourzadeh, S., Tor, O.B., Cebeci, M.E., Bara, A., Oprea S.V., Kisakurek, S.M., 2020. Enlightening Customers on Merits of Demand-Side Load Control: A Simple-But-Efficient-Platform. IEEE Access, 8, 193238-47.
- 15. Gholami, A., Shekari, T., Aminifar, F., Shahidehpour, M., 2016. Microgrid Scheduling with Uncertainty: The Quest for Resilience. IEEE Trans Smart Grid, 7(6), 2849-2858.
- 16. Teimourzadeh, S., Tor, O.B., Cebeci, M.E., Bara, A., Oprea, S.V., 2019. A Three-Stage Approach for Resilience-Constrained Scheduling of Networked Microgrids. Mod Power Syst Clean Energy, 7(4), 705-715.
- 17. Binder, K., 2005. Monte-Carlo Methods. Guide to Monte Carlo Simulations in Statistical Physics. Cambridge University, 51-208.
- 18. Kroese, D.P., Rubinstein, R.Y., 2012. Monte Carlo methods. Wiley Interdiscip Rev Comput Stat., 4(1), 48-58.
- 19. Conejo, A.J., Carrión, M., Morales, J.M. 2010. Decision Making Under Uncertainty in Electricity Markets. New York: Springer. 1, 27-61.
Öz
Bu çalışmanın amacı, aktif bir elektrik dağıtım sistemine bir enerji depolama sisteminin dâhil edilmesiyle gün öncesi ve gün içi elektrik piyasalarında gerçekleştirilecek enerji alışverişinde ekonomik optimizasyonu sağlamaktır. Bu amaçla geliştirilen iki aşamalı stokastik programlama problem, Genel Cebirsel Modelleme Sistemi (GAMS) aracılığıyla Karma Tamsayılı Doğrusal Programlama (MILP) ile formüle edilmiş ve CPLEX çözücüsü ile çözülmüştür. Modellemedeki belirsiz parametreler, Monte Carlo Simülasyonu ile ele alınarak bu yönde senaryo seçimi gerçekleştirilmiştir. Önerilen modelin doğruluğunu ve etkinliğini teyit etmek için, ele alınan dağıtım sisteminin seçili bir pilot fideri üzerinde ve gerçek verilerle simülasyon çalışmaları gerçekleştirilmiştir. Simülasyon çalışmalarında ele alınan işletme maliyetleri, şebekede enerji depolama kullanılıp kullanılmadığı durumlarda ayrı olarak hesaplanarak karşılaştırılmıştır. Edinilen sonuçlara göre, şebekeye enerji depolama sistemi entegre edildiği durumlarda, depolama sisteminin hiç bulunmadığı durumlara göre günlük ortalama işletme 600 doları aşkın bir düşüş gözlenmiştir. Böylelikle, önerilen modelin elektrik dağıtım sisteminin ekonomik işletimini desteklemeyi etkin bir şekilde gerçekleştirilebileceği de doğrulanmıştır.
Anahtar Kelimeler
Enerji depolama Elektrik piyasası Dağıtım sistemi Ekonomik optimizasyon İşletme maliyeti
Kaynakça
- 1. Koohi-Fayegh, S., Rosen, M.A., 2020. A Review of Energy Storage Types, Applications and Recent Developments. Energy Storage, 27, 101047.
- 2. Chamandoust, H., Derakhshan, G., Hakimi, S.M., Bahramara S., 2020. Tri-objective Scheduling of Residential Smart Electrical Distribution Grids with Optimal Joint of Responsive Loads with Renewable Energy Sources. Energy Storage, 27, 101112.
- 3. Jing, W., Lai, C.H., Ling, D.K.X., Wong, W.S.H., Wong, M.L.D., 2019. Battery Lifetime Enhancement Via Smart Hybrid Energy Storage Plug-in Module in Standalone Photovoltaic Power System. Energy Storage, 21, 586-98.
- 4. Shao, Z., Wakil, K., Usak, M., Amin Heidari, M., Wang B., Simoes, R., 2018. Kriging Empirical Mode Decomposition Via Support Vector Machine Learning Technique for Autonomous Operation Diagnosing of CHP in Microgrid. Appl Therm Eng, 145, 58-70.
- 5. Moutis, P., Hadi Amini, M., Khan, I.A., He, G., Mohammadi, J., Kar, S., 2019. A Survey of Recent Developments and Requirements for Modern Power System Control. Pathways to a Smarter Power System. Elsevier Ltd., 289-316
- 6. Malekpour, A.R., Niknam, T., 2011. A Probabilistic Multi-Objective Daily Volt/Var Control at Distribution Networks Including Renewable Energy Sources. Energy, 36(5), 3477-3488.
- 7. Tan, K.M., Babu, T.S., Ramachandaramurthy, V.K., Kasinathan, P., Solanki, S.G., Raveendran, S.K., 2021. Empowering Smart Grid: A Comprehensive Review of Energy Storage Technology and Application with Renewable Energy Integration. Energy Storage 39, 102591.
- 8. Li, Y., Yang, Z., Li, G., Zhao, D., Tian, W., 2019. Optimal Scheduling of an Isolated Microgrid with Battery Storage Considering Load and Renewable Generation Uncertainties. IEEE Trans Ind Electron. 66(2), 1565-1675.
- 9. Farzin, H., Fotuhi-Firuzabad, M., Moeini-Aghtaie, M., 2017. A Stochastic Multi-Objective Framework for Optimal Scheduling of Energy Storage Systems in Microgrids. IEEE Trans Smart Grid, 8(1), 117-27.
- 10. Raghavan A., Maan P., Shenoy A. 2016. Optimal Scheduling of Energy Storage for Renewable Energy Distributed Energy Generation System. Renewable and Sustainable Energy Reviews, 58, 1100-1107.
- 11. Wang, Y., Zhao, J., Zheng, T., Fan, K., Zhang, K., 2022. Optimal Planning of Integrated Energy System Considering Convertibility Index. Energy Res. 10, 1–17.
- 12. Ho, W.S., Macchietto, S., Lim, J.S., Hashim, H., Muis, Z.A., Liu, W.H., 2016. Optimal scheduling of energy storage for renewable energy distributed energy generation system. Renew Sustain Energy Rev., 58, 1100-7.
- 13. Zhang, X., Son, Y., Choi, S., 2022. Optimal Scheduling of Battery Energy Storage Systems and Demand Response for Distribution Systems with High Penetration of Renewable Energy Sources. Energies. 15(6).
- 14. Teimourzadeh, S., Tor, O.B., Cebeci, M.E., Bara, A., Oprea S.V., Kisakurek, S.M., 2020. Enlightening Customers on Merits of Demand-Side Load Control: A Simple-But-Efficient-Platform. IEEE Access, 8, 193238-47.
- 15. Gholami, A., Shekari, T., Aminifar, F., Shahidehpour, M., 2016. Microgrid Scheduling with Uncertainty: The Quest for Resilience. IEEE Trans Smart Grid, 7(6), 2849-2858.
- 16. Teimourzadeh, S., Tor, O.B., Cebeci, M.E., Bara, A., Oprea, S.V., 2019. A Three-Stage Approach for Resilience-Constrained Scheduling of Networked Microgrids. Mod Power Syst Clean Energy, 7(4), 705-715.
- 17. Binder, K., 2005. Monte-Carlo Methods. Guide to Monte Carlo Simulations in Statistical Physics. Cambridge University, 51-208.
- 18. Kroese, D.P., Rubinstein, R.Y., 2012. Monte Carlo methods. Wiley Interdiscip Rev Comput Stat., 4(1), 48-58.
- 19. Conejo, A.J., Carrión, M., Morales, J.M. 2010. Decision Making Under Uncertainty in Electricity Markets. New York: Springer. 1, 27-61.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Elektrik Enerjisi Taşıma, Şebeke ve Sistemleri |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 28 Mart 2024 |
| Yayımlandığı Sayı | Yıl 2024 Cilt: 39 Sayı: 1 |
