A Hybrid Robust Optimization Model for Day-Ahead Management of Active Distribution Networks

Document Type : Original Article

Authors

Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran

Abstract

In this paper, a hybrid day-ahead robust optimization model is presented for the optimal operation of active distribution networks subject to real-time operation. Maintaining the convex structure of the problem with load flow equations is the most important goal in how to robust modeling of uncertainties. For this purpose, the combination of the robust optimization with the worst case realization and risk-averse model of information-gap decision theory has been applied for real-time uncertainties modeling. The first approach is used for the uncertainty modeling of the real-time market price and the latter is used for modeling of the loads and renewable generations uncertainties. A new and more accurate formulation is presented for modeling of the day-ahead planning in the presence of uncertain real-time operation based on two-stage optimization of the benders decomposition. The day-ahead optimization is formulated in the first stage as a deterministic mixed integer linear programming. Initial dispatch of the generators and power exchange with the day-ahead market are determined in the first stage. At the second stage, the real-time optimization has been placed with the aim of redispatch of the generators and power exchange with the real-time market in the presence of the uncertainties and network constraints.

Keywords


[1]      S. P. Chowdhury, P. Crossley, and S. Chowdhury, Microgrids and Active Distribution Networks, the Institution of Engineering and Technology, 2009.
[2]      N. Hatziargyriou, Microgrids: Architectures and Control, John Wiley /IEEE Press, 2014.
[3]      A. J. Conejo, M. Carrión and J. M. Morales, Decision Making under Uncertainty in Electricity Markets, Springer, 2010.
[4]      J. Wu and X. Guan, “Coordinated multi-microgrids optimal control algorithm for smart distribution management system,” IEEE Transaction on Smart Grid, vol. 4, no. 4, pp. 2174–2181, 2013.
[5]      J. Wu and X. Guan, “Decentralized energy management system for networked microgrids in grid-connected and islanded modes,” IEEE Transaction on Smart Grid, vol. 7, no. 2, pp. 1097–1105, 2016.
[6]      W. Shi, X. Xie, C. C. Chu and R. Gadh, “Real-time energy management in microgrids,” IEEE Transaction on Smart Grid, vol. 8, no. 1, pp. 228–238, 2017.
[7]      W. Zheng, W. Wu, B. Zhang, H. Sun and Y. Liu, “A fully distributed reactive power optimization and control method for active distribution networks,” IEEE Transaction on Smart Grid, vol. 7, no. 8, pp. 1021–1033, 2016.
[8]      A. Safdarian, M. Fotuhi-Firuzabad and M. Lehtonen, “A stochastic framework for short-term operation of a distribution company,” IEEE Transaction on Power System, vol. 28, no. 4, pp. 4712–4721, 2013.
[9]      G. Lio, Y. Xu and K. Tomsovic, “Bidding strategy for microgrid in day-ahead market based on hybrid stochastic/robust optimization,” IEEE Transaction on Smart Grids, vol. 7, no. 1, pp. 227–237, 2016.
[10]      W. Su, J. Wang and J. Roh, “Stochastic energy scheduling in microgrids with intermittent renewable energy resources,” IEEE Transaction on Smart Grid, vol. 5, no. 4, pp. 1876–1883, 2014.
[11]      H. Pandzˇic´, J. M. Morales, A. J. Conejo and I. Kuzle, “Offering model for a virtual power plant based on stochastic programming,” Applied Energy, vol. 105, pp. 282–292, 2013.
[12]      D. T. Nguyen and L. B. Le, “Optimal bidding strategy for microgrids considering renewable energy and building thermal dynamics,” IEEE Transaction on Smart Grid, vol. 5, no. 4, pp. 1608–1620, 2014.
[13]      G. Cardosoa, M. Stadler, A. Siddiqui, C. Marnay, N. DeForest, A. Barbosa-Póvoaa and P. Ferrãoa, “Microgrid reliability modeling and battery scheduling using stochastic linear programming,” Electric Power Systems Research, vol. 103, pp. 61–69, 2013.
[14]      Z. Ding, W. J. Lee and J. Wang, “Stochastic resource planning strategy to improve the efficiency of microgrid operation,” IEEE Transactions on Industry Applications, vol. 21, no. 3, pp. 1978–1986, 2015.
[15]      علی مهدی زاده و نوید تقی زادگان کلانتری، «برنامه‌ریزی تصادفی ریزشبکه‌ جزیره‌ای در حضور سیستم ذخیره‌ساز هیدروژنی و برنامه پاسخ‌گویی بار»، مجله مهندسی برق دانشگاه تبریز، جلد ۴۷، شماره ۲، صفحات ۷۱۱-۷۲۵، ۱۳۹۶.
[16]      D. Bertsimas, E. Litvinov, X. Andy Sun, J. Zhao and T. Zheng, “Adaptive robust optimization for the security constrained unit commitment problem,” IEEE Transaction on Power System., vol. 25, no. 1, pp. 52–63, 2013.
[17]      Y. Zhang, N. Gatsis and G. B. Giannakis, “Robust energy management for microgrids with high-penetration renewables,” IEEE Transaction on Sustainable Energy, vol. 4, no. 4, pp. 944–953, 2013.
[18]      Y. Xiang, J. Liu and Y. Liu, “Robust energy management of microgrid with uncertain renewable generation and load,” IEEE Transaction on Smart Grid, vol. 7, no. 2, pp. 1034–1043, 2016.
[19]      Y. Zhang, N. Gatsis and G. B. Giannakis, “Robust energy management for microgrids with renewables,” IEEE Third International Conference on Smart Grid Communications, 2012. 
[20]      R.A. Gupta and N. K. Gupta, “A robust optimization based approach for microgrid operation in deregulated environment,” Energy Conversion and Management, vol. 93, pp. 121–131, 2015.
[21]      W. Wei, F. Liu, S. Mei, and Y. Hou, “Robust energy and reserve dispatch under variable renewable generation,” IEEE Transaction on Smart Grid, vol. 6, no. 1, pp. 369–380, 2015.
[22]      C. Zhao, J. Wang, J. P. Watson and Y. Guan, “Multi-stage robust unit commitment considering wind and demand response uncertainties,” IEEE Transaction on Power System., vol. 28, no. 3, pp. 2708–2717, 2013.
[23]      سهیل کعبه پهنه‌کلائی و مرتضی رحیمیان، «مدیریت انرژی نیروگاه مجازی بر پایه بهینه‌سازی مقاوم با پایش پیشامدهای ریزشبکه: مطالعه موردی خروجی تکی خط»، مجله مهندسی برق دانشگاه تبریز، جلد ۴۷، شماره ۱، صفحات ۲۴۹-۲۶۱، ۱۳۹۶.
[24]      B. Mohammadi-Ivatloo, H. Zareipour, N. Amjady and M. Ehsan, “Application of information-gap decision theory to risk-constrained self-Scheduling of GenCos,” IEEE Transaction on Power System, vol. 28, no. 2, pp. 1093–1102, 2013.
[25]      J. Aghaei, V. G. Agelidis, M. Charwand, F. Raeisi, A. Ahmadi, A. E. Nezhad and A. Heidari, “Optimal robust unit commitment of CHP plants in electricity markets using information gap decision theory,” IEEE Transaction on Smart Grid, vol. 8, no. 5, pp. 2296–2304, 2017.
[26]      A. Ben-Tal and A. Nemirovski, “Robust solutions of uncertain linear programs,” Operations Research, vol. 25, no. 1, pp. 1–13, 1999.
[27]      Y. Ben-Haim, Information Gap Decision Theory, Designs Under Severe Uncertainty, Academic Press, 2006.
[28]      J. Liu, H. Chen, W. Zhang, B. Yurkovich and G. Rizzoni, “Energy management problems under uncertainties for grid-connected microgrids: a chance constrained programming approach,” IEEE Transaction on Smart Grid, to be published.
[29]      Z. Wu, W. Gu, R. Wang, X. Yuan and W. Liu, “Economic optimal schedule of CHP microgrid system using chance constrained programming and particle swarm optimization,” Power and Energy Society General Meeting, 2011.
[30]      A. Ravichandran, S. Sirouspour, P. Malysz and A. Emadi, “A Chance-constraints-based control strategy for microgrids with energy storage and integrated electric vehicles,” IEEE Transaction on Smart Grid, to be published.
[31]      A. R. Malekpour and A. Pahwa, “Stochastic energy management in distribution systems with correlated wind generator,” IEEE Transaction on Power System, to be published.
[32]      S. Salinas, M. Li, P. Li and Yong Fu, “Dynamic energy management for the smart grid with distributed energy resources,” IEEE Transaction on Smart Grid, vol. 4, no. 4, pp. 2139–2151, 2013.
[33]      معصومه جوادی، موسی مرزبند و سید مازیار میرحسینی‌مقدم، «مدیریت بهینه انرژی در سیستم‌های چند-ریزشبکه‌ای در بازار خرده‌فروشی انرژی بر پایه الگوریتم سلسله‌مراتبی تعاملی»، مجله مهندسی برق دانشگاه تبریز، جلد ۴۶، شماره ۳، صفحات ۱۰۷-۱۲۰، ۱۳۹۵.
[34]      M. E. Baran and F. F. Wu, “Network reconfiguration in distribution systems for loss reduction and load balancing,” IEEE Transaction on Power Delivery., vol. 4, no. 2, pp. 1401–1407, 1989.
[35]      A. Ahmadi-Khatir, A. J. Conejo and R. Cherkaoui, “Multi-area unit scheduling and reserve allocation under wind power uncertainty,” IEEE Transaction on Power System, vol. 29, no. 4, pp. 1701–1710, 2014.
[36]      A. Nasri, S. J. Kazempour, A. J. Conejo and M. Ghandhari, “Network-constrained AC unit commitment under uncertainty: A benders’ decomposition approach,” IEEE Transaction on Power System, vol. 31, no. 1, pp. 412–422, 2016.
[37]      S. P. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004.
[38]      M. Farivar and S. H. Low, “Branch flow model: relaxations and convexification (parts I, II),” IEEE Transactions on Power Systems, vol. 28, no. 3, pp. 2554–2572, 2013.
[39]      L. Gan, N. Li, U. Topcu and S. H. Low, “Exact convex relaxation of optimal power flow in radial networks,” IEEE Transactions on Automatic Control, vol. 60, no. 1, pp. 72–87, 2015.
[40]       J. M. Arroyo and A. J. Conejo, “Optimal response of a thermal unit to an electricity spot market,” Transaction on Power System, vol. 15, no. 3, pp.1098–1104, 2000.
[41]      CVX Research Inc. CVX: The CVX Users’ Guide, Version 2.0, March 2017, http://web.cvxr.com/cvx/doc/CVX.pdf.
[42]      M. Moradi-Dalvand, B. Mohammadi-Ivatloo, N. Amjady, H. Zareipour and M. Mazhab-Jaferi, “Self-scheduling of wind producer based on information gap decision theory,” Energy, vol. 81, pp. 588–600, 2015.
[43]      R. T. Rockafellar and S. Uryasev, “Conditional value-at-risk for generallossdistributions,” Journal of Banking & Finance, vol.26, no.7, pp.1443–1471, 2002.
[44]      North Dakota Agriculture Weather Network, http://ndawn.ndsu.nodak.edu/wind-speeds.html.