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Assessment of Economic, Technical, Social, and Environmental Impact of Flexibility-based Smart Microgrid Operation | ||
| International Journal of Industrial Electronics Control and Optimization | ||
| مقاله 5، دوره 8، شماره 1، خرداد 2025، صفحه 57-66 اصل مقاله (512.2 K) | ||
| نوع مقاله: Research Articles | ||
| شناسه دیجیتال (DOI): 10.22111/ieco.2024.48762.1568 | ||
| نویسندگان | ||
| Faezeh Gholipour Zarandi؛ Masoud Rashidinejad* ؛ Amir Abdollahi؛ Ali Yazhari Kermani | ||
| Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran | ||
| چکیده | ||
| The proliferation of renewable energy sources, with their inherent uncertainty in smart microgrids, necessitates the use of flexible resources to maintain grid stability. However, implementing these flexibility-based approaches can have a multidimensional impact, including economic, technical, social, and environmental considerations. This study investigates these effects, with a particular focus on how flexibility provision influences battery aging, which is a critical aspect since batteries are the primary source of flexibility in microgrids. Here, a Lexicographic approach is used to optimize the multi-objective operation problem by minimizing costs while maximizing flexibility. Batteries act as the main source of flexibility and compensate for the uncertainty associated with solar energy production; therefore, it is important to investigate the battery's aging upon flexibility provision. The analysis shows a trade-off between flexibility and economic efficiency. Hence, from an economic point of view, increasing reliance on batteries and micro turbine production to improve flexibility leads to higher operating costs. From a social perspective, the proposed approach increases microgrid reliability by minimizing the cost of energy not supplied. Considering the technical aspect, the results indicate that increasing the use of batteries in order to increase microgrids' flexibility accelerates their aging, hence decreasing their corresponding state of health. Further, the simulation results show that flexibility comes with an environmental cost. Therefore, increasing reliance on micro turbine production and the possibility of purchasing energy from sources with more emissions to provide the required flexibility can lead to an increase in the cost of pollution. | ||
| کلیدواژه‌ها | ||
| Flexibility؛ Battery Aging؛ Smart Micro grid؛ Uncertainty؛ Lexicographic Method | ||
| مراجع | ||
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[1] G. G. Kim et al., "Prediction Model for PV Performance With Correlation Analysis of Environmental Variables," in IEEE Journal of Photovoltaics, vol. 9, no. 3, pp. 832- 841, May 2019, doi: 10.1109/JPHOTOV.2019.2898521.
[2] S. B. Q. Naqvi and B. Singh, "An Improved Power Quality PV-Battery System Coupled to Weak Grid with Feed-in Tariff Flexibility and PV Intermittency Flattening," 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India, 2020, pp. 583-588, doi: 10.1109/ICCCA49541.2020.9250834. [3] D. Liu, X. Kong, D. Kong, S. Quan, C. YONG and D. Dong, "Bi-level optimized operation strategy for interconnected microgrids," 2019 22nd International Conference on Electrical Machines and Systems (ICEMS), Harbin, China, 2019, pp. 1-6, doi: 10.1109/ICEMS.2019.8921519. [4] T. Xing, Q. Caijuan, Z. Liang, G. Pengjiang, G. Jianfeng and J. Panlong, "A comprehensive flexibility optimization strategy on power system with high- percentage renewable energy," 2017 2nd International Conference on Power and Renewable Energy (ICPRE), Chengdu, China, 2017, pp. 553-558, doi: 10.1109/ICPRE.2017.8390596. [5] T. T. Pedersen, M. Victoria, M. G. Rasmussen and G. B. Andresen, "Exploring flexibility of near-optimal solutions to highly renewable energy systems," 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC), Fort Lauderdale, FL, USA, 2021, pp. 0387-0391, doi: 10.1109/PVSC43889.2021.9518745. [6] S. Paesschesoone, N. Kayedpour, C. Manna and G. Crevecoeur, "Improving Energy Flexibility in Photovoltaic-Battery Systems through Switching Reinforcement Learning Control," 2024 IEEE Conference on Technologies for Sustainability (SusTech), Portland, OR, USA, 2024, pp. 57-62, doi: 10.1109/SusTech60925.2024.10553555. [7] M. MansourLakouraj, M. J. Sanjari, M. S. Javadi, M. Shahabi and J. P. S. Catalão, "Exploitation of Microgrid Flexibility in Distribution System Hosting Prosumers," in IEEE Transactions on Industry Applications, vol. 57, no. 4, pp. 4222-4231, July-Aug. 2021, doi: 10.1109/TIA.2021.3073882. [8] S Poorvaezi Roukerd, A Abdollahi and M Rashidinejad, "Probabilistic-possibilistic flexibility-based unit commitment with uncertain negawatt demand response resources considering Z-number method", International Journal of Electrical Power & Energy Systems,Volume 113, 2019, Pages 71-89, ISSN 0142-0615. [9] M. Yu, J. Jiang, X. Ye, X. Zhang, C. Lee and S. H. Hong, "Demand Response Flexibility Potential Trading in Smart Grids: A Multileader Multifollower Stackelberg Game Approach," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 5, pp. 2664- 2675, May 2023, doi: 10.1109/TSMC.2022.3218039. [10] S. Luo, J. Zhou, N. Feng, Y. Su, D. Yang and B. Wang, "Power System Flexibility Assessment Method for Matching Supply and Demand with Flexibility," 2023 8th International Conference on Power and Renewable Energy (ICPRE), Shanghai, China, 2023, pp. 107-112, doi: 10.1109/ICPRE59655.2023.10353889. [11] H. Berahmandpour, S. M. Kouhsari and H. Rastegar, "A New Flexibility Index in Real Time Operation Incorporating Wind Farms," 2019 27th Iranian Conference on Electrical Engineering (ICEE), Yazd, Iran, 2019, pp. 549-553, doi: 10.1109/IranianCEE.2019.8786492. [12] X. Tang, Y. Hu and Z. Chen, "Multi-objective optimal configuration model of energy storage system considering flexibility and economy in distribution network," 2020 International Conference on Smart Grids and Energy Systems (SGES), Perth, Australia, 2020, pp. 374-378, doi: 10.1109/SGES51519.2020.00072. [13] A. Wang, J. Liu, and W. Wang, “Flexibility-based improved model of multi-energy hubs using linear weighted sum algorithm,” J. Renew. Sustain. Energy, vol. 10, no. 1, p. 15901, Jan. 2018, doi: 10.1063/1.5005571. [14] S. Ramírez-López, G. Gutiérrez-Alcaraz, M. Gough, M. S. Javadi, G. J. Osório and J. P. S. Catalão, "Bi-Level Approach for Flexibility Provision by Prosumers in Distribution Networks," in IEEE Transactions on Industry Applications, vol. 60, no. 2, pp. 2491-2500, March-April 2024, doi: 10.1109/TIA.2023.3330683. [15] C. Fournely, M. Pečjak and T. Medved, "New method for evaluating the value of flexibility and new services for DSOs on the LV network," 2022 18th International Conference on the European Energy Market (EEM), Ljubljana, Slovenia, 2022, pp. 1-5, doi: 10.1109/EEM54602.2022.9921072. [16] Y. Peng, Q. Zhou, X. Qin, B. Ding and Y. Zhang, "Power System Flexibility Indicators Considering Reliability in Electric Power System with High-Penetration New Energy," 2022 5th International Conference on Power and Energy Applications (ICPEA), Guangzhou, China, 2022, pp. 469-474, doi: 10.1109/ICPEA56363.2022.10052215. [17] K. Oikonomou, M. Parvania and R. Khatami, "Deliverable Energy Flexibility Scheduling for Active Distribution Networks," in IEEE Transactions on Smart Grid, vol. 11, no. 1, pp. 655-664, Jan. 2020, doi: 10.1109/TSG.2019.2927604. [18] B. Mohammadi-ivatloo and F. Jabari, Operation, Planning, and Analysis of Energy Storage Systems in Smart Energy Hubs. 2018. doi: 10.1007/978-3-319-75097-2. [19] C. Guenther, B. Schott, W. Hennings, P. Waldowski, and M. A. Danzer, “Model-based investigation of electric vehicle battery aging by means of vehicle-to-grid scenario simulations,” J. Power Sources, vol. 239, pp. 604–610, 2013, doi: https://doi.org/10.1016/j.jpowsour.2013.02.041. [20] L. Lam and P. Bauer, “Practical Capacity Fading Model for Li-Ion Battery Cells in Electric Vehicles,” IEEE Trans. Power Electron., vol. 28, no. 12, pp. 5910–5918, doi: 579 10.1109/TPEL.2012.2235083. [21] L. Vankudoth, A. Q. H. Badar, R. Kumar Chauhan and M. Hossain, "Economic Analysis of Energy Scheduling and Trading in Multiple-Microgrids Environment," 2021 XVIII International Scientific Technical Conference Alternating Current Electric Drives (ACED), Ekaterinburg, Russia, 2021, pp. 1-6, doi: 10.1109/ACED50605.2021.9462285. [22] Ma, T., Wu, J., & Hao, L. (2017). Energy flow modeling and optimal operation analysis of the micro energy grid based on energy hub. Energy conversion and management, 133, 292-306. [23] A. Sangwongwanich, Y. Yang and F. Blaabjerg, "High-Performance Constant Power Generation in Grid-Connected PV Systems," in IEEE Transactions on Power Electronics, vol. 31, no. 3, pp. 1822-1825, March 2016, doi: 10.1109/TPEL.2015.2465151. [24] L. Lai, L. Fiaschi, M. Cococcioni and K. Deb, "Solving Mixed Pareto-Lexicographic Multiobjective Optimization Problems: The Case of Priority Levels," in IEEE Transactions on Evolutionary Computation, vol. 25, no. 5, pp. 971-985, Oct. 2021, doi: 10.1109/TEVC.2021.3068816. [25] P. Aaslid, “Rye microgrid load and generation data, and meteorological forecasts.,” Zenodo (CERN European Organization for Nuclear Research). Jun. 21, 2021. doi: 10.5281/zenodo.4448894. [26] A. Yazhari Kermani, A. Abdollahi, and M. Rashidinejad, “Cyber-secure energy and flexibility scheduling of interconnected local energy networks with introducing an XGBoost-assisted false data detection and correction method,” International Journal of Electrical Power & Energy Systems, vol. 155, p. 109683, Jan. 2024, doi: 10.1016/j.ijepes.2023.109683. | ||
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