برنامه‌ریزی اقتصادی- زیست‌محیطی چندهدفه ریزشبکه‌ها در حضور خودروهای الکتریکی هیبریدی و برنامه پاسخگویی بار در جهت هموارسازی قیمت‌های گرهی توزیع
محورهای موضوعی : مهندسی برق و کامپیوتر
علی میرزایی
1
(، دانشکده فنی و مهندسی، دانشگاه شهید مدنی آذربایجان)
نوید تقی زادگان کلانتری
2
(، دانشکده فنی و مهندسی، دانشگاه شهید مدنی آذربایجان)
سجاد نجفی روادانق
3
(، دانشکده فنی و مهندسی، دانشگاه شهید مدنی آذربایجان)
کلید واژه: ریزشبکه, خودروی الکتریکی, الگوریتم بهینه‌سازی مرغ دریایی چندهدفه تکاملی, انرژی تجدیدپذیر, قیمت‌های گرهی توزیع, برنامه پاسخگویی بار,
چکیده مقاله :
امروزه با رشد تقاضای خودروهای الکتریکی هیبریدی در ریزشبکه‌ها، تأمین برق، مسائل زیست‌محیطی و زمان‌بندی مجدد از جمله چالش‌های ریزشبکه‌هاست که باید حل و راه حل‌های مناسبی ارائه شود. برای غلبه بر این چالش‌ها، این مقاله یک مدل بهینه‌سازی چندهدفه جدید را معرفی می‌کند که در هدف اول، هزینه کل بهره‌برداری ریزشبکه را به حداقل می‌رساند و در هدف دوم با کاهش مقدار انرژی تأمین‌نشده، مقدار شاخص قابلیت اطمینان را بهبود می‌بخشد. به دلیل این دو هدف، الگوریتم بهینه‌سازی مرغ دریایی چندهدفه تکاملی برای یافتن بهترین راه حل‌های محلی مورد استفاده قرار می‌گیرد. در این راستا خودروهای الکتریکی هیبریدی و برنامه‌های پاسخ به تقاضا برای هموارسازی قیمت‌های گرهی توزیع و کاهش میزان انتشار دی‌اکسید کربن استفاده می‌شود. شبکه توزیع 69باسه برای ارزیابی کارایی روش پیشنهادی استفاده گردیده است.
Today, with the growing demand for hybrid electric vehicles in microgrids, electricity supply, environmental issues, and rescheduling are among the challenges of microgrids that must be solved and suitable solutions provided. To overcome these challenges, this paper introduces a new multi-objective optimization model, which in the first objective, minimizes the total operation cost of the microgrid, and in the second objective, improves the reliability index by reducing the amount of energy not supplied. Due to these two objectives, a multi-objective evolutionary seagull optimization algorithm is used to find the optimal global solutions. In this regard, hybrid electric vehicles and demand response programs are used to smooth out distribution nodal prices and reduce CO2 emissions. The 69-bus distribution network has been used to evaluate the efficiency of the proposed method.
[1] V. K. Prajapati and V. Mahajan, "Reliability assessment and congestion management of power system with energy storage system and uncertain renewable resources," Energy, pt B, vol. 215, Article ID: 119134, Jan. 2021.
[2] M. A. F. Ghazvini, et al., "Congestion management in active distribution networks through demand response implementation," Sustainable Energy, Grids and Networks, vol. 17, Article ID: 100185, Mar. 2019.
[3] K. Prakash, et al., "Bi-level planning and scheduling of electric vehicle charging stations for peak shaving and congestion management in low voltage distribution networks," Computers and Electrical Engineering, vol. 102, Article ID: pp. 108235, Sept. 2022.
[4] Z. Xiaoluan, H. Farajian, W. Xifeng, and K. Ohshima, "Scheduling of renewable energy and plug-in hybrid electric vehicles based microgrid using hybrid crow-pattern search method," J. of Energy Storage, vol. 47, Article ID: 103605, Mar. 2022.
[5] A. Ghazvini, M. Sedighizadeh, and J. Olamaei, "Semidefinite programing as a tool for economic-environmental operation of a microgrid including compressed air energy storage and electric vehicle," J. of Energy Storage, vol. 43, Article ID: 103215, Nov. 2021.
[6] Q. Guo, S. Nojavan, S. Lei, and X. Liang, "Economic-environmental analysis of renewable-based microgrid under a CVaR-based two-stage stochastic model with efficient integration of plug-in electric vehicle and demand response," Sustainable Cities and Society, vol. 75, Article ID:103276, Dec. 2021.
[7] N. Li, Z. Su, H. Jerbi, R. Abbassi, M. Latifi, and N. Furukawa, "Energy management and optimized operation of renewable sources and electric vehicles based on microgrid using hybrid gravitational search and pattern search algorithm," Sustainable Cities and Society, vol. 75, Article ID: 103279, Dec. 2021.
[8] Y. Mei, B. Li, H. Wang, X. Wang, and M. Negnevitsky, "Multi-objective optimal scheduling of microgrid with electric vehicles," Energy Reports, vol. 8, pp. 4512-4524, Nov. 2022.
[9] E. A. Mahdiraji and M. S. Amiri, "Optimization of electric vehicles along with power generation units to improve microgrid reliability," Quantum J. of Engineering, Science and Technology, vol. 2, no. 2, pp. 1-15, Apr. 2021.
[10] A. Aldosary, M. Rawa, Z. M. Ali, A. Razmjoo, and A. Rezvani, "Energy management strategy based on short-term resource scheduling of a renewable energy-based microgrid in the presence of electric vehicles using θ-modified krill herd algorithm," Neural Computing and Applications, vol. 33, pp. 10005-10020, Aug. 2021.
[11] M. Gonzalez Vaya and G. Andersson, "Optimal bidding strategy of a plug-in electric vehicle aggregator in day-ahead electricity markets under uncertainty," IEEE Trans. Power Syst., vol. 30, no. 5, pp. 2375-2385, Sep. 2015.
[12] M. A. Elfarra and M. Kaya, "Estimation of electricity cost of wind energy using Monte Carlo simulations based on nonparametric and parametric probability density functions," Alexandria Engineering J., vol. 60, no. 4, pp. 3631-3640, Aug. 2021.
[13] G. Li and X. P. Zhang, "Modeling of plug-in hybrid electric vehicle charging demand in probabilistic power flow calculations," IEEE Trans. on Smart Grid, vol. 3, no. 1, pp. 492-499, Mar. 2012.
[14] S. Rezaee, E. Farjah, and B. Khorramdel, "Probabilistic analysis of plug-in electric vehicles impact on electrical grid through homes and parking lots," IEEE Trans. on Sustainable Energy, vol. 4, no. 4, pp. 1024-1033, Oct. 2013.
[15] A. Ghaedi, S. D. Dehnavi, and H. Fotoohabadi, "Probabilistic scheduling of smart electric grids considering plug-in hybrid electric vehicles," J. of Intelligent & Fuzzy Systems, vol. 31, no. 3, pp. 1329-1340, 2016.
[16] M. A. Rostami, A. Kavousi-Fard, and T. Niknam, "Expected cost minimization of smart grids with plug-in hybrid electric vehicles using optimal distribution feeder reconfiguration," IEEE Trans. on Industrial Informatics, vol. 11, no. 2, pp. 388-397, Apr. 2015.
[17] A. Abdolahi, J. Salehi, F. S. Gazijahani, and A. Safari, "Assessing the potential of merchant energy storage to maximize social welfare of renewable-based distribution networks considering risk analysis," Electric Power Systems Research, vol. 188, Article ID: 106522, Nov. 2020.
[18] Z. Zhao, et al., "Distribution locational marginal pricing under uncertainty considering coordination of distribution and wholesale markets," IEEE Trans. on Smart Grid, vol. 14, no. 2, pp. 1590-1606, Aug. 2022.
[19] S. N. Chegini, A. Bagheri, and F. Najafi, "PSOSCALF: a new hybrid PSO based on sine cosine algorithm and levy flight for solving optimization problems," Applied Soft Computing, vol. 73, pp. 697-726, Dec. 2018.
[20] G. Dhiman and V. Kumar, "Seagull optimization algorithm: theory and its applications for large-scale industrial engineering problems," Knowledge-Based Systems, vol. 165, pp. 169-196, Feb. 2019.
[21] J. D. Knowles and D. W. Corne, "Approximating the nondominated front using the Pareto archived evolution strategy," Evolutionary Computation, vol. 8, no. 2, pp. 149-172, Jun. 2000.
[22] C. A. C. Coello, G. T. Pulido, and M. S. Lechuga, "Handling multiple objectives with particle swarm optimization," IEEE Trans. on Evolutionary Computation, vol. 8, no. 3, pp. 256-279, Jun. 2004.
[23] G. Dhiman, et al., "EMoSOA: a new evolutionary multi-objective seagull optimization algorithm for global optimization," International J. of Machine Learning and Cybernetics, vol. 12, pp. 571-596, Feb. 2021.
[24] N. Taghizadegan, S. Cheshmeh Khavar, A. Abdolahi, F. Arasteh, and R. Ghoreyshi, "Dominated GSO algorithm for optimal scheduling of renewable microgrids with penetration of electric vehicles and energy storages considering DRP," International J. of Ambient Energy, vol. 43, no. 1, pp. 1-12, 2022.
[25] M. Shamshirband, J. Salehi, and F. S. Gazijahani, "Decentralized trading of plug-in electric vehicle aggregation agents for optimal energy management of smart renewable penetrated microgrids with the aim of CO2 emission reduction," J. of Cleaner Production, vol. 200, pp. 622-640, Nov. 2018.
[26] A. Abdolahi, J. Salehi, F. Samadi Gazijahani, and A. Safari, "Probabilistic multi-objective arbitrage of dispersed energy storage systems for optimal congestion management of active distribution networks including solar/wind/CHP hybrid energy system," J. of Renewable and Sustainable Energy, vol. 10, no. 4, Article ID: 045502, Jul. 2018.
[27] A. J. Pimm, T. T. Cockerill, and P. G. Taylor, "The potential for peak shaving on low voltage distribution networks using electricity storage," J. of Energy Storage, vol. 16, pp. 231-242, Apr. 2018.
[28] X. Xu, D. Niu, L. Peng, S. Zheng, and J. Qiu, "Hierarchical multi-objective optimal planning model of active distribution network considering distributed generation and demand-side response," Sustainable Energy Technologies and Assessments, pt A, vol. 53, Article ID: 102438, Oct. 2022.
[29] T. Hai, A. K. Alazzawi, J. M. Zain, and H. Oikawa, "A stochastic optimal scheduling of distributed energy resources with electric vehicles based on microgrid considering electricity price," Sustainable Energy Technologies and Assessments, vol. 55, Article ID: 102879, Feb. 2023.
[30] A. Azizivahed, E. Naderi, H. Narimani, M. Fathi, and M. R. Narimani, "A new bi-objective approach to energy management in distribution networks with energy storage systems," IEEE Trans. on Sustainable Energy, vol. 9, no. 1, pp. 56-64, Jun. 2017.
[31] M. A. Hamidan and F. Borousan, "Optimal planning of distributed generation and battery energy storage systems simultaneously in distribution networks for loss reduction and reliability improvement," J. of Energy Storage, vol. 46, Article ID: 103844, Feb. 2022.
[32] H. Farzin and M. Monadi, "Reliability enhancement of active distribution grids via emergency V2G programs: an analytical cost/worth evaluation framework," Scientia Iranica, vol. 26, Special Issue on Machine Learning, Data Analytics, and Advanced Optimization Techniques, pp. 3635-3645, Nov./Dec. 2019.