طراحی ریزشبکه توزیع در حضور منابع انرژی هیبریدی به منظور کاهش هزینه بهره برداری و افزایش درآمد تجمیع کننده منابع
محورهای موضوعی : مهندسی برق و کامپیوتر
میثاق بن سعید
1
,
ناصر عرفانی مجد
2
1 - گروه مهندسی برق، پردیس صنعتی شهدای هویزه، دانشگاه شهید چمران اهواز، اهواز، ایران
2 - گروه مهندسی برق، پردیس صنعتی شهدای هویزه، دانشگاه شهید چمران اهواز، اهواز، ایران
کلید واژه: طراحی ریزشبکه, منابع انرژی هیبریدی, هزینه بهره‌برداری, درآمد تجمیعکننده منابع, الگوریتم بهینهسازی مبتنی بر یادگیری و آموزش.,
چکیده مقاله :
با توسعه منابع انرژی هیبریدی و ذخیره‌سازهای انرژی الکتریکی، بهره‌برداری از شبکه‌های توزیع انعطاف‌پذیری بیشتری پیدا کرده است. به‌منظور کاهش هزینه‌های بهره‌برداری، مدیریت بهینه این منابع در شبکه توزیع ضروری است. از آنجا که مالکیت این منابع معمولاً خصوصی می باشند، لازم است در تابع هدف، علاوه بر هزینه بهره‌برداری، درآمد مالکان نیز لحاظ شود. این تحقیق یک مدل بهینهسازی برای مکان‌یابی و ظرفیت‌یابی منابع انرژی هیبریدی در کنار نیروگاه تلمبه ذخیره‌ای ارائه می‌دهد تا ریزشبکه‌ای با قابلیت بهره‌برداری به صورت جزیره‌ای طراحی شود. مدل پیشنهادی از دیدگاه بهره‌برداری شبکه توسط تجمیعکننده منابع انرژی ارائه شده است. تابع هدف شامل کاهش هزینه تلفات، هزینه تأمین انرژی، افزایش منافع تجمیعکننده و بهبود قابلیت اطمینان شبکه می باشد. عدم قطعیت‌های توان مصرفی و تولیدی منابع در قالب سناریوهای زمانی مدل‌سازی شده و از الگوریتم بهینهسازی مبتنی بر یادگیری و آموزش در نرم‌افزار متلب برای بهینه سازی استفاده شده است. نتایج شبیه‌سازی روی شبکه توزیع 69 باس استاندارد، نشان‌دهنده برتری مدل پیشنهادی در کاهش هزینه‌ها و افزایش درامد تجمیعکننده منابع می باشد.
With the development of hybrid energy resources and electrical energy storage devices, the operation of distribution networks has become more flexible. In order to reduce operating costs, optimal management of these resources in the distribution network is essential. Since the ownership of these resources is usually private, it is necessary to include the owners' income in the objective function, in addition to the operating cost. This research presents an optimization model for locating and capacity finding hybrid energy resources next to a pumped storage power plant to design a micro-grid with the ability to operate as an island. The proposed model is presented from the perspective of network operation by the energy resource aggregator. The objective function includes reducing the cost of losses, the cost of energy supply, increasing the benefits of the aggregator, and improving the reliability of the network. The uncertainties of the consumed and produced power of the resources are modeled in the form of time scenarios, and the teaching learning based optimization algorithm in MATLAB software is used for optimization. Simulation results on a standard 69-bus distribution network indicate the superiority of the proposed model in reducing costs and increasing resource aggregator revenue.
[1] M. Sedighizadeh, M. Esmaili, A. Jamshidi, and M. H. Ghaderi, "Stochastic multi-objective economic-environmental energy and reserve scheduling of microgrids considering battery energy storage system," International Journal of Electrical Power & Energy Systems, vol. 106, pp. 1-16, Mar. 2019.
[2] H. Li, Zh. Ren, A. Trivedi, P. P. Verma, D. Srinivasan, and W. Li, "A Noncooperative game-Based approach for microgrid planning considering existing interconnected and clustered microgrids on an island," IEEE Trans. on Sustainable Energy, vol. 13, no. 4, pp. 2064-2078, Oct. 2022.
[3] F. Zainab, et al., "An optimal joint planning of DGs and electric vehicle charging stations in grid-connected and islanded microgrids, "IET Renewable Power Generation, vol. 17, no. 7, pp. 1623-1634, May 2023.
[4] S. M. R. H. Shawon, X. Liang, and M. Janbakhsh, "Optimal placement of distributed generation units for microgrid planning in distribution networks," IEEE Trans. on Industry Applications, vol. 59, no. 3, pp. 2785-2795, May 2023.
[5] B. Panagiotis, and K. L. Katsifarakis, "Optimizing operation of a large-scale pumped storage hydropower system coordinated with wind farm by means of genetic algorithms," Global NEST Journal, vol. 21, no. 4, pp. 405-504, Dec. 2019.
[6] S. Haji Mohammadi, and M. Modaresi, "Optimal planning of distributed energy resources in distribution microgrid to improve resource owner profit and network resiliency," Signal Processing and Renewable Energy, vol. 7, no. 4, pp. 23-40, Autumn 2023.
[7] S. F. Contreras, C. A. Cortes, and J. M. A. Myrzik, "Optimal microgrid planning for enhancing ancillary service provision," Journal of Modern Power Systems and Clean Energy, vol. 7, no. 4, pp. 864-875, Jul. 2019.
[8] Our World in Data, Renewable Energy, Available at https://ourworldindata.org/renewable-energy
[9] M. Adham, S. Keene, and R. B. Bass, "Distributed energy resources: a systematic literature review," Energy Reports, vol. 13, pp. 1980-1999, Jun. 2025.
[10] S. T. Saranya and P. Bhowmik, "A comprehensive review of microgrid architectures, power management and resilient operation," Energy Reports, vol. 15, Article ID: 109021, Jun. 2026.
[11] M. F. Shaaban, A. Saber, M. E. Ammar, and H. H. Zeineldin, "A multi-objective planning approach for optimal DG allocation for droop based microgrids," Electric Power Systems Research, vol. 200, Article ID: 107474, Nov. 2021.
[12] J. Gao, et al., "A cost-effective two-stage optimization model for microgrid planning and scheduling with compressed air energy storage and preventive maintenance," International Journal of Electrical Power & Energy Systems, vol. 125, Article ID: 106547, Feb. 2021.
[13] A. H. Yazdavar, M. F. Shaaban, E. F. Elsaadany, M. M. A. Salama, and H. H. Zeineldin, "Optimal planning of distributed generators and shunt capacitors in isolated microgrids with nonlinear loads," IEEE Trans. on Sustainable Energy, vol. 11, no. 4, pp. 2732-2744, Feb. 2020.
[14] A. Garry, F. Cadoux, M. C. Alvarez-Herault, and N. Hadjsaid, "Risk aversion model of distribution network planning rules considering distributed generation curtailment," International Journal of Electrical Power & Energy Systems, vol. 99, pp. 385-393, Jul. 2018.
[15] R. Fachrizal, U. H. Ramadhani, J. Munkhammar, and J. Widén, "Combined PV–EV hosting capacity assessment for a residential LV distribution grid with smart EV charging and PV curtailment," Sustainable Energy, Grids and Networks, vol. 26, Article ID: 100445, Jun. 2021.
[16] D. A. Quijano, O. D. Melgar-Dominguez, C. Sabillon, B. Venkatesh, and A. Padilha-Feltrin, "Increasing distributed generation hosting capacity in distribution systems via optimal coordination of electric vehicle aggregators," IET Generation, Transmission & Distribution, vol. 15, no. 2, pp. 359-370, Jan. 2021.
[17] M. Hosseina, M. S. Moghaddam, and A. Hassannia, "Optimizing energy and load management in island microgrids for enhancing resilience against resource interruptions," Scientific Reports, vol. 15, Article ID: 16297, May 2025.
[18] D. R. T. Ngueko, O. Boukar, G. D. L. Libouga, and N. Ndjiya "Optimizing decentralized energy: A comprehensive review of distributed energy resource modeling," Journal of Electrical Systems and Information Technology, vol. 12, no. 1, Article ID: 72, Sept. 2025.
[19] R.V. Rao, V.J. Savsani, and D.P. Vakharia, "Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems," Computer-Aided Design, vol. 43, no. 3, pp. 303-315, Mar. 2011.
