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Optimal reconfiguration and scheduling of a smart distribution network with uncertain renewables and price-responsive demand

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Języki publikacji
EN
Abstrakty
EN
A new two-stage operation scheduling framework is proposed in this paper to optimize day-ahead (DA) operation of a reconfigurable smart distribution network (SDN). The SDN contains wind farm as uncertain renewable generation as well as responsive demand and is operated by a distribution company (DisCo). The DisCo implements nodal hourly pricing as a pricebased demand response program (DRP) to modify consumers’ demand profile. Retail prices are determined in the first stage of the proposed scheduling framework, while the best network topology and the bidding strategy of the DisCo in the DA energy market are determined in the second stage. The two point estimate method (TPEM) is implemented in this paper to model the intrinsic uncertainty of wind farm power generation and responsive demand. Finally, the effectiveness of the proposed framework is evaluated in several case studies.
Rocznik
Strony
183--193
Opis fizyczny
Bibliogr. 21 poz., rys., tab., wykr.
Twórcy
autor
  • Department of Electrical and Computer Engineering, Jundi-Shapur University of Technology, Dezful, Iran
Bibliografia
  • [1] Gams webpage. URL http://www.gams.com.
  • [2] Ontario energy board (oeb). regulated price plan manual. URL http://www.ontarioenergyboard.ca/OEB/Documents/EB-2004-0205/RPP-Manual pdf.
  • [3] http://www.ee.washington.edu/research/pstca/pf30/pg_tca30bus.html.
  • [4] H. A. Aalami, M. ParsaMoghaddam, and G. R. Yousefi. Demand response modeling considering interruptible/curtailable loads and capacity market programs. Applied Ener, 87:243–250, 2010.
  • [5] G. Angevine and D. Hrytzak-Lieffers. Ontario industrial electricity demand responsiveness to price. The Fraser Institute, 2007.
  • [6] M. Doostizadeh and H. Ghasemi. A day-ahead electricity pricing model based on smart metering and demand-side management. Energy, 46:221–230, 2012.
  • [7] FERC. Staff report.: Assessment of demand response and advanced metering. URL http://www.FERC.gov.
  • [8] A. Ghasemi, S. S. Mortazavi, and E. Mashhour. Integration of nodal hourly pricing in day-ahead sdc (smart distribution company) optimization framework to effectively activate demand response. Energy, 86: 649–660, 2015.
  • [9] S. Golshannavaz, S. Afsharnia, and F. Aminifar. Smart distribution grid: Optimal day-ahead scheduling with reconfigurable topology. IEEE Transactions on Smart Grid, 5(5):2402–2411, 2014.
  • [10] R. Jabbari-Sabet, S. M. Moghaddas-Tafreshi, and S. S. Mirhoseini. Microgrid operation and management using probabilistic reconfiguration and unit commitment. International Journal of Electrical Power & Energy Systems, 75:328–336, 2016.
  • [11] A. Kumar, S. C. Srivastava, and S. N. Singh. A zonal congestion management approach using real and reactive power rescheduling. IEEE Transactions on Power Systems, 19(1):554–562, 2004.
  • [12] M. Moeini-Aghtaie, A. Abbaspour, and M. Fotuhi-Firuzabad. Incorporating large-scale distant wind farms in probabilistic transmission expansion planning—part i: Theory and algorithm. IEEE Transactions on Power Systems, 27(3):1585–1593, 2012.
  • [13] B. Moradzadeh and K. Tomsovic. Mixed integer programming-based reconfiguration of a distribution system with battery storage. In Proceedings of North American Power Symposium, Champaign, IL, USA, sep 2012.
  • [14] L. Nikonowicz and J. Milewski. Virtual power plants - general review: structure, application and optimization. Journal of Power Technologies, 92(3):135–149, 2012.
  • [15] M. Parastegari, R. A. Hooshmand, A. Khodabakhshian, and A. H. Zare. Joint operation of wind farm, photovoltaic, pump-storage and energy storage devices in energy and reserve markets. International Journal of Electrical Power & Energy Systems, 64:275–284, 2015.
  • [16] S. Rahimi, Niknam T., and F. Fallahi. A new approach based on benders decomposition for unit commitment problem. World Applied Science Journal, 6(12):1665–1672, 2009.
  • [17] A. Safdarian, M. Fotuhi-Firuzabad, and M. Lehtonen. Integration of price-based demand response in DisCos’ short-term decision model. IEEE Transactions on Smart Grid, 5(5):2235–2245, 2014.
  • [18] F. C. Schweppe, M. C. Caramanis, R. D. Tabors, and R. E. Bohn. Spot pricing of electricity, 1989.
  • [19] M. Shahidehpour and Y. Fu. Benders decomposition: applying benders decomposition to power systems. IEEE Power and Energy Magazine, 3(2):1–2, 2005.
  • [20] T. Sousa, H. Morais, Z. Vale, P. Faria, and J. Soares. Intelligent energy resource management considering vehicle-to-grid: A simulated annealing approach. IEEE Transactions on Smart Grid, 3(1):535–542, 2012.
  • [21] G. Verbic and C. A. Canizares. Probabilistic optimal power flow in electricity markets based on a two-point estimate method. IEEE Transactions on Power Systems, 21:1–11, 2006.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-fe6b837a-2d85-4518-b390-df7ce87938cf
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