Study and analysis of wind curtailment situations and developing an appropriated methodology for its management

Recent energy policies in several countries around the world, including in Europe, points the need to integrating growing amounts of distributed generation in electric power systems, namely at distribution networks level. Such resources are mainly of a distributed, non-dispatchable, and natural sources based nature (including wind power). With this, several changes in the operation and planning of power systems have occurred. When facing a situation of excessive non-dispatchable generation, demand response programs and water pumping from reservoirs may be applied to encourage the increase of consumption so that wind curtailment is minimized. The methodology proposed in this paper aims to be used by a Virtual Power Player, who is able to manage the available energy resources optimizing its costs. The case study includes 2223 consumers and 47 distributed generators units. The implemented scenario corresponds to a real day in Portuguese power system, 9th March 2014.

[1]  Ziad Shawwash,et al.  Assessing the benefits of wind power curtailment in a hydro-dominated power system , 2009, 2009 CIGRE/IEEE PES Joint Symposium Integration of Wide-Scale Renewable Resources Into the Power Delivery System.

[2]  Peter Palensky,et al.  Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads , 2011, IEEE Transactions on Industrial Informatics.

[3]  Antonio J. Conejo,et al.  Short-Term Trading for a Wind Power Producer , 2010 .

[4]  H. Morais,et al.  Energy resource scheduling in a real distribution network managed by several virtual power players , 2012, PES T&D 2012.

[5]  F. D. Galiana,et al.  The impact of wind power variability and curtailment on ramping requirements , 2010, 2010 IEEE/PES Transmission and Distribution Conference and Exposition: Latin America (T&D-LA).

[6]  Johanna L. Mathieu,et al.  Examining uncertainty in demand response baseline models and variability in automated responses to dynamic pricing , 2011, IEEE Conference on Decision and Control and European Control Conference.

[7]  W. D. Rosehart,et al.  Long-Term Market Equilibrium Model With Strategic, Competitive, and Inflexible Generation , 2012, IEEE Transactions on Power Systems.

[8]  Ganesh K. Venayagamoorthy,et al.  Dynamic, Stochastic, Computational, and Scalable Technologies for Smart Grids , 2011, IEEE Computational Intelligence Magazine.

[9]  Yingzhong Gu,et al.  Congestion-induced wind curtailment: Sensitivity analysis and case studies , 2011, 2011 North American Power Symposium.