مدیریت سمت تقاضا در یک ریز‌شبکه هوشمند با حضور منابع تجدیدپذیر و بارهای پاسخ‌گو

محورهای موضوعی : انرژی های تجدیدپذیر

غلامرضا اقاجانی ¹ , داور میرعباسی ² , بهروز الفی ³ , هادی سید حاتمی ⁴

1 - استادیار - گروه برق قدرت، واحد اردبیل، دانشگاه آزاد اسلامی، اردبیل، ایران
2 - استادیار - گروه برق قدرت، واحد اردبیل، دانشگاه آزاد اسلامی، اردبیل، ایران
3 - مربی - گروه برق قدرت، واحد اردبیل، دانشگاه آزاد اسلامی، اردبیل، ایران
4 - استادیار - گروه برق قدرت، واحد اردبیل، دانشگاه آزاد اسلامی، اردبیل، ایران

تاریخ دریافت : 1395/10/11 تاریخ پذیرش : 1396/07/18 تاریخ انتشار : 1396/05/01

کلید واژه: منابع تجدیدپذیر, ریز‌شبکه هوشمند, مدیریت سمت مصرف, بهره‌برداری چندهدفه,

چکیده مقاله :

در این مقاله خطای حاصل از پیش‌بینی سرعت باد و تابش خورشید به وسیله توابع چگالی احتمال مدل‌سازی شده و یک مدل برنامه‌ریزی احتمالاتی به‌منظور بهینه‌سازی عملکرد ریزشبکه هوشمند در کوتاه‌ مدت جهت حداقل‌سازی هزینه بهره‌برداری و آلایندگی با حضور منابع تجدیدپذیر پیشنهاد می‌شود. بطوریکه در آن استفاده از برنامه‌های پاسخگویی بار که توسط شرکت کنندگان خانگی، تجاری و صنعتی صورت می‌گیرد، جهت پوشش عدم قطعیت توان تولیدی حاصل از منابع تجدیدپذیر پیشنهاد می‌شود. جهت اجرای برنامه‌های پاسخگویی بار از روش پرداخت تشویقی به‌صورت بسته‌های پیشنهادی قیمت و میزان انرژی که به‌ وسیله فراهم‌کنندگان پاسخ‌گویی بار جمع‌آوری می‌شود، پیشنهاد گردیده است. نتایج شبیه‌‌سازی در سه حالت مختلف برای بهینه‌سازی هزینه بهره‌برداری و آلایندگی با مشارکت و عدم مشارکت بارهای پاسخ‌گو در نظر گرفته ‌شده است. برای حل مساله پیشنهادی روش چندهدفه حرکت ازدحام ذرات پیشنهاد شده است؛ بطوریکه سیستم مرتب‌سازی غیرخطی و مکانیزم فازی برای تعیین بهترین پاسخ با توجه به مجموعه پاسخ‌های حاصل از فضای پارتو توصیه می‌گردد. جهت راست‌آزمایی، مدل پیشنهادی بر روی یک ریزشبکه هوشمند نمونه بکار برده شده و نتایج عددی حاصل به‌طور واضح نشان دهنده تأثیر مدیریت سمت تقاضا در کاهش اثر عدم قطعیت حاصل از توان تولیدی و پیش‌بینی‌ شده توربین بادی و سلول خورشیدی می‌باشد.

چکیده انگلیسی:

In this study, a stochastic programming model is proposed to optimize the performance of a smart micro-grid in a short term to minimize operating costs and emissions with renewable sources. In order to achieve an accurate model, the use of a probability density function to predict the wind speed and solar irradiance is proposed. On the other hand, in order to resolve the power produced from the wind and the solar renewable uncertainty of sources, the use of demand response programs with the participation of residential, commercial and industrial consumers is proposed. In this paper, we recommend the use of incentive-based payments as price offer packages in order to implement demand response programs. Results of the simulation are considered in three different cases for the optimization of operational costs and emissions with/without the involvement of demand response. The multi-objective particle swarm optimization method is utilized to solve this problem. In order to validate the proposed model, it is employed on a sample smart micro-grid, and the obtained numerical results clearly indicate the impact of demand side management on reducing the effect of uncertainty induced by the predicted power generation using wind turbines and solar cells.

منابع و مأخذ:

[1] Q. Sun, X. Ge, L. Liu, X. Xu, Y. Zhang, R. Niu, Y. Zeng, "Review of smart grid comprehensive assessment systems", Energy Procedia, Vol. 12, pp. 219-29, 2011.

[2] K. Mazlumi, "Presenting a new method based on branch placement for optimal placement of phasor measurement units", Journal of Operation and Automation in Power Engineering, Vol. 2, No. 2, pp. 113-120, 2014.

[3] Z. Li, F. Yang, S. Mohagheghi, Z. Wang, J. Tournier, Y. Wang, "Toward smart distribution management by integrating advanced metering infrastructure", Electric Power Systems Research, Vol. 105, pp. 51-56, 2013.

[4] G. López, J. Moreno, H. Amarís, F. Salazar, "Paving the road toward smart grids through large-scale advanced metering infrastructures", Electric Power Systems Research, Vol. 120, pp. 194-205, 2015.

[5] R.R. Mohassel, A.S. Fung, F. Mohammadi, K. Raahemifar, "A survey on advanced metering infrastructure and its application in smart grids", Proceeding of the IEEE/CCECE, pp. 1-8, Toronto, ON, Canada, May 2014.

[6] F. Bouhafs, M. Mackay, M. Merabti. "Communication challenges and solutions in the smart grid", Springer, 2014.

[7] P.K. Verma, H. Sharma, "Application of cloud computing in smart grid: A review", Proceeding of the ICAR, pp. 404-409, 2015.

[8] A. Sheikhi, M. Rayati, S. Bahrami, A.M. Ranjbar, S. Sattari, "A cloud computing framework on demand side management game in smart energy hubs", International Journal of Electrical Power and Energy Systems, Vol. 64, pp. 1007-1016, 2015.

[9] M.H. Albadi, E. El-Saadany, "A summary of demand response in electricity markets", Electric Power Systems Research, Vol. 78, No. 11, pp. 1989-1996, Nov. 2008.

[10] A.A. Khan, S. Razzaq, A. Khan, F. Khursheed, "HEMSs and enabled demand response in electricity market: An overview", Renewable and Sustainable Energy Reviews, Vol. 42, pp. 773-785, Feb. 2015.

[11] D.G. Hart, "Using AMI to realize the smart grid", Proceeding of the IEEE/PES, pp. 1-2, Pittsburgh, PA, USA, July 2008.

[12] T. Broeer, J. Fuller, F. Tuffner, D. Chassin, N. Djilali, "Modeling framework and validation of a smart grid and demand response system for wind power integration", Applied Energy, Vol. 113, pp. 199-207, Jan. 2014.

[13] S.A. Alavi, A. Ahmadian, M. Aliakbar-Golkar, "Optimal probabilistic energy management in a typical micro-grid based-on robust optimization and point estimate method", Energy Conversion and Management, Vol. 95,pp. 314-325, May 2015.

[14] A. Zakariazadeh, S. Jadid, P. Siano, "Stochastic multi-objective operational planning of smart distribution systems considering demand response programs", Electric Power Systems Research, Vol. 111, pp. 156-168, June 2014.

[15] A. Zakariazadeh, S. Jadid, P. Siano P. "Economic-environmental energy and reserve scheduling of smart distribution systems: A multiobjective mathematical programming approach", Energy Conversion and Management, Vol. 78, pp. 151-64, Feb. 2014.

[16] F.A. Mohamed, H.N. Koivo, "System modelling and online optimal management of microgrid using mesh adaptive direct search", International Journal of Electrical Power and Energy Systems, Vol. 32, No. 5, pp. 398-407, June 2010.

[17] N. Rezaei, M. Kalantar, "Smart microgrid hierarchical frequency control ancillary service provision based on virtual inertia concept: An integrated demand response and droop controlled distributed generation framework", Energy Conversion and Management,Vol. 92, pp. 287-301, March 2015.

[18] N. Cicek, H. Delic, "Demand Response Management for smart grids with wind power", IEEE Trans. on Sustainable Energy, Vol. 6, No. 2, pp. 625-634, April 2015.

[19] K. Afshar, A.S. Gazafroudi, "Application of stochastic programming to determine operating reserves with considering wind and load uncertainties", Journal of Operation and Automation in Power Engineering,Vol. 1, No. 2, pp. 96-109, Nov. 2013.

[20] G. Boyle, Renewable energy: OXFORD university press; 2004.

[21] B. Gen, Reliability and cost/worth evaluation of generating systems utilizing wind and solar energy. 2005.

[22] Owner’s manual of the AIR403 wind turbine made by Southwest Wind power Inc. <www.nooutage.com/pdf/swwp_air403_landman.pdf>.

[23] Y. Atwa, E. El-Saadany, M. Salama, R. Seethapathy, "Optimal renewable resources mix for distribution system energy loss minimization", IEEE Trans. on, Power Systems,Vol. 25, No. 1, pp. 360-370, Feb. 2010.

[24] F.Y. Ettoumi, A. Mefti, A. Adane, M. Bouroubi, "Statistical analysis of solar measurements in algeria using beta distributions", Renewable Energy, Vol. 26, No. 1, pp. 47-67, 2002.

[25] M. Deshmukh, S. Deshmukh, "Modeling of hybrid renewable energy systems", Renewable and Sustainable Energy Reviews, Vol. 12, No. 1, pp. 235-249, 2008.

[26] G. Tina, S. Gagliano, S. Raiti, "Hybrid solar/wind power system probabilistic modelling for long-term performance assessment", Solar Energy, Vol. 80, No. 5, pp. 578-88, 2006.

[27] S. Chowdhury, S.P. Chowdhury, P. Crossley, Microgrids and active distribution networks, The Institution of Engineering and Technology; 2009.

[28] H. Kanchev, D. Lu, F. Colas, V. Lazarov, B. Francois, "Energy management and operational planning of a microgrid with a PV-based active generator for smart grid applications", IEEE Trans. on Industrial Electronics, Vol. 58, No. 10, pp. 4583-92, Oct. 2011.

[29] A.A. Moghaddam, A. Seifi, T. Niknam, M.R.A. Pahlavani, "Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source", Energy, Vol. 36, No. 11, pp. 6490-6507, Nov. 2011.

[30] J. Kennedy, "Particle swarm optimization", Encyclopedia of Machine Learning, Springer, pp. 760-766, 2011.

[31] C.A. Coello Coello, M.S. Lechuga, "MOPSO: A proposal for multiple objective particle swarm optimization", Proceeding of the IEEE/CEC, Honolulu, HI, USA, USA, May 2002.

[32] S. Papathanassiou, N. Hatziargyriou, K. Strunz, "A benchmark low voltage microgrid network", Proceedings of the CIGRE Symposium: Power Systems with Dispersed Generation, Jan. 2005.

[33]Apx power spot exchange. Accessted at, https://www.apxgroup.com/trading-clearing/apx-power-uk/.

[34] F. Bouffard, F.D. Galiana, A.J. Conejo, "Market-clearing with stochastic security-part I: Formulation", IEEE Trans. on, Power Systems,Vol. 20, No. 4, pp.1818-1826, 2005.

[35] Willy Weather. Available online; http://wind.willyweather.com.au.

[36] The Solar Power Group Company. Accessed at, http://thesolarpowergroup.com.au.

[37] Reconstruction and Short-term Forecast of the Solar Irradiance. Accessed at, http://lpc2e.cnrs-orleans.fr/soteria/.

[38] A.S. Bouhouras, D.P. Labridis, A.G. Bakirtzis, "Cost/worth assessment of reliability improvement in distribution networks by means of artificial intelligence", International Journal of Electrical Power and Energy Systems, Vol.32, No.5,pp.530-538, 2010.

[39] C. Chen, S. Duan, T. Cai, B. Liu, G. Hu, "Smart energy management system for optimal microgrid economic operation", IET Renewable Power Generation, Vol.5, No.3, pp.258-67, 2011.

[40] K. Clement-Nyns, E. Haesen, J. Driesen, "The impact of charging plug-in hybrid electric vehicles on a residential distribution grid", IEEE Trans. on, Power Systems, Vol.25, No.1, pp.371-380, 2010.