A security‐constrained flexible demand scheduling strategy for wind power accommodation

Summary The rapid development of wind power integration brings significant challenges to the regulation of power systems. Flexible demand scheduling is an effective complement to traditional generation scheduling that can be employed to accommodate wind power. Meanwhile, the transmission power flow and the node voltages will change because of the variability of wind power, which makes the security constraints of the power network one of the key factors for the accommodation of wind power. Given this background, a security-constrained flexible demand scheduling strategy for the accommodation of wind power is proposed in this paper to minimize the scheduling cost of flexible demand. The proposed scheduling strategy first considers the security constraints of the transmission interfaces, the constraints of flexible demand response, and the constraints of response benefit satisfaction of flexible demand. Then, the sensitivity analysis method and an interior-point method are employed to solve the scheduling model. Finally, the performance of the proposed strategy is tested using a case based on a provincial power grid in China. The results verify that the modeling and solution method can effectively accommodate the uncertainties of wind power output and that the transmission interface power is controlled within the scope of the security constraints. Copyright © 2015 John Wiley & Sons, Ltd.

[1]  S. Oren,et al.  Large-Scale Integration of Deferrable Demand and Renewable Energy Sources , 2014, IEEE Transactions on Power Systems.

[2]  Vladimiro Miranda,et al.  Demand Dispatch and Probabilistic Wind Power Forecasting in Unit Commitment and Economic Dispatch: A Case Study of Illinois , 2013 .

[3]  A. Bagchi,et al.  Economic dispatch with network and ramping constraints via interior point methods , 1998 .

[4]  Hau-Ren Lu,et al.  A Two-Way Direct Control of Central Air-Conditioning Load Via the Internet , 2009, IEEE Transactions on Power Delivery.

[5]  M. P. Moghaddam,et al.  Flexible demand response programs modeling in competitive electricity markets , 2011 .

[6]  Yann-Chang Huang,et al.  Integrating direct load control with interruptible load management to provide instantaneous reserves for ancillary services , 2004 .

[7]  Gerald B. Sheblé,et al.  Direct load control-A profit-based load management using linear programming , 1998 .

[8]  Yann-Chang Huang,et al.  A model reference adaptive control strategy for interruptible load management , 2004 .

[9]  Carlos Henggeler Antunes,et al.  A multiple objective decision support model for the selection of remote load control strategies , 2000 .

[10]  Ian A. Hiskens,et al.  Achieving Controllability of Electric Loads , 2011, Proceedings of the IEEE.

[11]  C. H. Antunes,et al.  A multiple objective evolutionary approach for the design and selection of load control strategies , 2004, IEEE Transactions on Power Systems.

[12]  D. Chattopadhyay,et al.  Interruptible load management using optimal power flow analysis , 1996 .

[13]  Elias Kyriakides,et al.  Recent methodologies and approaches for the economic dispatch of generation in power systems , 2013 .

[14]  K. Cheung,et al.  Evolution toward standardized market design , 2003 .

[15]  J. Watson,et al.  Multi-Stage Robust Unit Commitment Considering Wind and Demand Response Uncertainties , 2013, IEEE Transactions on Power Systems.

[16]  G. Strbac,et al.  Decentralized Participation of Flexible Demand in Electricity Markets—Part I: Market Mechanism , 2013, IEEE Transactions on Power Systems.

[17]  Babak Mozafari,et al.  Reliability assessment of incentive- and priced-based demand response programs in restructured power systems , 2014 .

[18]  Mohammad Shahidehpour,et al.  SCUC With Hourly Demand Response Considering Intertemporal Load Characteristics , 2011, IEEE Transactions on Smart Grid.

[19]  Duncan S. Callaway Tapping the energy storage potential in electric loads to deliver load following and regulation, with application to wind energy , 2009 .

[20]  Andrej F. Gubina,et al.  An assessment of the effects of demand response in electricity markets , 2013 .

[21]  Ross Baldick,et al.  Dynamic Demand Response Controller Based on Real-Time Retail Price for Residential Buildings , 2014, IEEE Transactions on Smart Grid.

[22]  D. Brandt,et al.  A linear programming model for reducing system peak through customer load control programs , 1996 .