ارائه طرحی جدید برای پاسخگویی بار یک جامعه مسکونی هوشمند با انواع مدل‌های پاسخگویی بار

نوع مقاله : مقاله پژوهشی (کاربردی)

نویسندگان

1 دانشجوی دکتری، گروه مهندسی برق، واحد کرج، دانشگاه آزاد اسلامی، کرج، ایران.

2 استادیار، گروه مهندسی برق، واحد کرج، دانشگاه آزاد اسلامی، کرج، ایران.

10.48301/kssa.2021.287832.1547

چکیده

افزایش با استفاده از توانایی کنتورهای هوشمند و فناوری‌های شبکه هوشمند، یک شرکت‌کننده پاسخگویی بار (DR[1]) جدید با انعطاف‌پذیری بار قابل‌توجهی از سمت مسکونی ایجاد می‌شود که جامعه مسکونی هوشمند نام دارد. در این مقاله روشی جدید مبتنی‌بر الگوریتم بهینه‌سازی ازدحام ذرات باینری (BPSO[2]) برای حل مسئله DR ارائه شده است. در ابتدا با به‌دست‌آوردن متغیرهای باینری برای برداشت بار، نوع برداشت بار برای بارهای مختلف تعیین می‌شود. سپس با استفاده از حل متغیرهای پیوسته، میزان برداشت بار در بارهای مختلف به‌دست می‌آید. در سیستم پیشنهادی از منابع تولید پراکنده (DG[3])، ذخیره‌ساز باتری و خودروهای الکتریکی استفاده شده است. بارهای مسکونی هوشمند به سه دسته بارهای قابل وقفه (IL[4])، قابل جابه‌جایی و قابل کنترل تقسیم می‌شوند. در مدل پیشنهادی، روش بهینه‌سازی مقاوم ([5]RO) برای مقابله با عدم‌قطعیت‌ها ارائه شده است. همچنین، انواع طرح­های DR با قیمت‌گذاری زمان واقعی([6]RTP)، زمان استفاده (TOU[7]) و قیمت‌گذاری پیک بحرانی (CPP[8]) در قالب یک طرح مقاوم به‌کار رفته‌اند. انواع طرح‌های DR با یکدیگر مقایسه و بهترین طرح برای DR انتخاب می­شود. در طرح پیشنهادی برای اعتبارسنجی روش پیشنهادی، شبیه‌سازی روی یک سیستم آزمون انجام شد و نتایج نشان از کارایی روش پیشنهادی در برنامه‌ریزی و کاهش هزینه‌های مصرفی مشترکین دارند.
 
[1] Demand response
[2] Binary particle swarm optimization
[3] Distributed generation
[4] Interruptible load
[5] Robust optimization
[6] Real time pricing
[7] Time of use
[8] Critical peak pricing

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Introducing a New Scheme for Demand Response of a Smart Residential Community with a Variety of Demand Response Models

نویسندگان [English]

  • Navid Ahmadi Asl 1
  • Reza Effatnejad 2
  • Mahdi Hedayati 2
  • Peyman Hajihosseini 2
1 PhD Student, Department of Electrical Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran.
2 Assistant Professor, Department of Electrical Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran.
چکیده [English]

Using the capabilities of smart meters and smart grid technologies, a new demand response (DR) participant with considerable load flexibility was created from the residential side called the intelligent residential community. In this paper, smart residential loads were divided into three categories of shiftable, interruptible, and controllable loads and a new method based on binary particle swarm algorithm (BPSO) to solve the DR problem is presented. The type of load shedding was determined for different loads. Then, by solving the continuous variables of the problem, the amount of load shedding at different loads was determined. The proposed system uses distributed generation sources, battery storage, and electric vehicles. Furthermore, in the proposed model, a robust optimization method for dealing with uncertainties is presented using a variety of real-time pricing (RTP), time-of-use (TOU), and critical peak pricing (CPP) schemes in a robust design. A comparison of the different types of demand response schemes was made and the best design for optimal demand response was selected. To validate the proposed method, a simulation was performed on a test system and the results indicated the efficiency of the proposed method in planning and reducing users' consumption costs. 

کلیدواژه‌ها [English]

  • Smart residential community
  • Robust optimization
  • Demand response
  • Battery storage
  • Electric vehicles
  • Distributed generation resources

مراجع

[1] Lujano-Rojas, J. M., Monteiro, C., Dufo-López, R., & Bernal-Agustín, J. L. (2012). Optimum residential load management strategy for real time pricing (RTP) demand response programs. Energy Policy, 45, 671-679. https://doi.org/10.1016/j.enpol.2012.03.019
[2] Christakou, K., Tomozei, D. C., Boudec, J. Y. L., & Paolone, M. (2014). GECN: Primary Voltage Control for Active Distribution Networks via Real-Time Demand-Response. Institute of Electrical and Electronics Engineers Transactions on Smart Grid, 5(2), 622-631. https://doi.org/10.1109/TSG.2013.2275004
[3] Chen, Z., Wu, L., & Fu, Y. (2012). Real-Time Price-Based Demand Response Management for Residential Appliances via Stochastic Optimization and Robust Optimization. Institute of Electrical and Electronics Engineers Transactions on Smart Grid, 3(4), 1822-1831. https://doi.org/10.1109/TSG.2012.2212729
[4] Kim, S. J., & Giannakis, G. B. (2017). An Online Convex Optimization Approach to Real-Time Energy Pricing for Demand Response. Institute of Electrical and Electronics Engineers Transactions on Smart Grid, 8(6), 2784-2793. https://doi.org/10.1109/TS G.2016.2539948
[5] Nan, S., Zhou, M., & Li, G. (2018). Optimal residential community demand response scheduling in smart grid. Applied Energy, 210, 1280-1289. https://doi.org/10.1016/j.apenergy.2 017.06.066
[6] Zhao, L., Yang, Z., & Lee, W. J. (2017). The Impact of Time-of-Use (TOU) Rate Structure on Consumption Patterns of the Residential Customers. Institute of Electrical and Electronics Engineers Transactions on Industry Applications, 53(6), 5130-5138. htt ps://doi.org/10.1109/TIA.2017.2734039
[7] Derakhshan, G., Shayanfar, H. A., & Kazemi, A. (2016). The optimization of demand response programs in smart grids. Energy Policy, 94, 295-306. https://doi.org/10.1016/j.enpol .2016.04.009
[8] Tindemans, S. H., Trovato, V., & Strbac, G. (2015). Decentralized Control of Thermostatic Loads for Flexible Demand Response. Institute of Electrical and Electronics Engineers Transactions on Control Systems Technology, 23(5), 1685-1700. https://doi.org/10. 1109/TCST.2014.2381163
[9] Huang, Q., Roozbehani, M., & Dahleh, M. A. (2015). Efficiency-Risk Tradeoffs in Electricity Markets with Dynamic Demand Response. Institute of Electrical and Electronics Engineers Transactions on Smart Grid, 6(1), 279-290. https://doi.org/10.1109/TSG. 2014.2326614
[10] Alipour, M. (2017). Optimal allocation of SVC and TCSC in power system by means of fuzzy estimator with the approach of increasing the static stability of the voltage. Karafan Quarterly Scientific Journal, 14(2), 95-121. https://karafan.tvu.ac.ir/article_100507 .html?lang=en
[11] AlinaghizadehArdestani, M., & Vakili, A. (2020). Output feedback Controller design for HVAC system with delayed based Robust control approach. Karafan Quarterly Scientific Journal, 17(1), 85-95. https://doi.org/10.48301/kssa.2020.112758
[12] Mamun, A. A., Narayanan, I., Wang, D., Sivasubramaniam, A., & Fathy, H. K. (2018). A Stochastic Optimal Control Approach for Exploring Tradeoffs between Cost Savings and Battery Aging in Datacenter Demand Response. Institute of Electrical and Electronics Engineers Transactions on Control Systems Technology, 26(1), 360-367. https://doi .org/10.1109/TCST.2016.2643569
[13] Golmohamadi, H., Keypour, R., Bak-Jensen, B., Pillai, J. R., & Khooban, M. H. (2020). Robust Self-Scheduling of Operational Processes for Industrial Demand Response Aggregators. Institute of Electrical and Electronics Engineers Transactions on Industrial Electronics, 67(2), 1387-1395. https://doi.org/10.1109/TIE.2019.2899562
[14] Ju, L., Wu, J., Lin, H., Tan, Q., Li, G., Tan, Z., & Li, J. (2020). Robust purchase and sale transactions optimization strategy for electricity retailers with energy storage system considering two-stage demand response. Applied Energy, 271, 115155. https://doi.o rg/10.1016/j.apenergy.2020.115155
[15] Melhem, F. Y., Grunder, O., Hammoudan, Z., & Moubayed, N. (2018). Energy Management in Electrical Smart Grid Environment Using Robust Optimization Algorithm. Institute of Electrical and Electronics Engineers Transactions on Industry Applications, 54(3), 2714-2726. https://doi.org/10.1109/TIA.2018.2803728
[16] Nojavan, S., Nourollahi, R., Pashaei-Didani, H., & Zare, K. (2019). Uncertainty-based electricity procurement by retailer using robust optimization approach in the presence of demand response exchange. International Journal of Electrical Power & Energy Systems, 105(93), 237-248. https://doi.org/10.1016/j.ijepes.2018.08.041
[17] Yi, W., Zhang, Y., Zhao, Z., & Huang, Y. (2018). Multiobjective Robust Scheduling for Smart Distribution Grids: Considering Renewable Energy and Demand Response Uncertainty. Institute of Electrical and Electronics Engineers Access, 6, 45715-45724. https://doi.org/10.1109/ACCESS.2018.2865598
فصلنامه علمی کارافن
دوره 20، شماره 3
فنی و مهندسی
آذر 1402
صفحه 311-339

فایل ها

سابقه مقاله

  • تاریخ دریافت: 17 خرداد 1400
  • تاریخ بازنگری: 01 مهر 1400
  • تاریخ پذیرش: 08 آبان 1400

هم رسانی

ارجاع به این مقاله

آمار