Responsive demand in networks with high penetration of wind power

The value of renewables is significantly affected by their penetration, concentration and location. Value is further affected by the responsiveness of demand which will reduce the need for back up power through non-renewable sources. By increasing the penetration of renewables in power systems, demand side participation become more important. Demand side management (DSM) programs have been studied for a long time and among all DSM programs responsive demand seems to be the most applicable type of DSM for a system with significant intermittent generation. It mitigates issues such as required reserve, network congestions and higher/lower voltage profiles and thus results in less operation cost although little attention has been made to quantify the benefits of responsive demand. In this paper, the value of wind generation without responsive demand is quantified first, by introducing responsiveness in the demand side, the reduction in operation cost is calculated and the additional benefits are quantified. The quantification was evaluated on the IEEE 30 busbar system through security constraint unit commitment (SCUC) and the results indicate the benefits of responsive demand on operational and environmental characteristics in power system.

[1]  B. Fox,et al.  Novel voltage control for embedded generators in rural distribution networks , 2000, PowerCon 2000. 2000 International Conference on Power System Technology. Proceedings (Cat. No.00EX409).

[2]  P. Glendinning.  Potential solutions to voltage control issues for distribution networks containing independent generators , 2001 .

[3]  X.-P. Zhang,et al.  Congestion Management of Transmission Systems Using FACTS , 2005, 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific.

[4]  G. Dany.  Power reserve in interconnected systems with high wind power production , 2001, 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502).

[5]  B. Kuri,et al.  Generation Scheduling in a system with Wind Power , 2005, 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific.

[6]  A. Mahmoud,et al.  Load Forecasting Bibliography Phase II , 1981, IEEE Transactions on Power Apparatus and Systems.

[7]  Hong Xu,et al.  Online daily load forecasting based on support vector machines , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[8]  Wei-Jen Lee,et al.  Defining the must-run and must-take units in a deregulated market , 2002 .

[9]  J. Lopes,et al.  Barriers (and Solutions...) to Very High Wind Penetration in Power Systems , 2007, 2007 IEEE Power Engineering Society General Meeting.

[10]  Goran Strbac,et al.  Maximising penetration of wind generation in existing distribution networks , 2002 .

[11]  A.I. Cohen,et al.  Optimization-based methods for operations scheduling , 1987, Proceedings of the IEEE.

[12]  Ray Zimmerman,et al.  Testing the effects of price responsive demand on uniform price and soft-cap electricity auctions , 2002, Proceedings of the 35th Annual Hawaii International Conference on System Sciences.

[13]  B.J. Kirby.  Load Response Fundamentally Matches Power System Reliability Requirements , 2007, 2007 IEEE Power Engineering Society General Meeting.

[14]  A. Tuohy,et al.  Experience From Wind Integration in Some High Penetration Areas , 2007, IEEE Transactions on Energy Conversion.

[15]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .