Optimal combined scheduling of generation and demand response with demand resource constraints

Demand response (DR) extends customer participation to power systems and results in a paradigm shift from simplex to interactive operation in power systems due to the advancement of smart grid technology. Therefore, it is important to model the customer characteristics in DR. This paper proposes customer information as the registration and participation information of DR, thus providing indices for evaluating customer response, such as DR magnitude, duration, frequency and marginal cost. The customer response characteristics are modeled from this information. This paper also introduces the new concept of virtual generation resources, whose marginal costs are calculated in the same manner as conventional generation marginal costs, according to customer information. Finally, some of the DR constraints are manipulated and expressed using the information modeled in this paper with various status flags. Optimal scheduling, combined with generation and DR, is proposed by minimizing the system operation cost, including generation and DR costs, with the generation and DR constraints developed in this paper.

[1]  C. Senabre,et al.  Methods for customer and demand response policies selection in new electricity markets , 2007 .

[2]  L. Goel,et al.  Reliability enhancement of a deregulated power system considering demand response , 2006, 2006 IEEE Power Engineering Society General Meeting.

[3]  Jose M. Yusta,et al.  Optimal pricing of default customers in electrical distribution systems: Effect behavior performance of demand response models , 2007 .

[4]  D. Kirschen,et al.  Factoring the elasticity of demand in electricity prices , 2000 .

[5]  Hadi Saadat,et al.  Power System Analysis , 1998 .

[6]  Tsung-Ying Lee Short term hydroelectric power system scheduling with wind turbine generators using the multi-pass iteration particle swarm optimization approach , 2008 .

[7]  E. Bompard,et al.  The Demand Elasticity Impacts on the Strategic Bidding Behavior of the Electricity Producers , 2007, IEEE Transactions on Power Systems.

[8]  Qiaozhu Zhai,et al.  A new method for unit commitment with ramping constraints , 2002 .

[9]  In-Keun Yu,et al.  Application of the ant colony search algorithm to short-term generation scheduling problem of thermal units , 1998, POWERCON '98. 1998 International Conference on Power System Technology. Proceedings (Cat. No.98EX151).

[10]  Mohammad Shahidehpour,et al.  The IEEE Reliability Test System-1996. A report prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee , 1999 .

[11]  M. Parsa Moghaddam,et al.  Security-based demand response allocation , 2009, 2009 IEEE Power & Energy Society General Meeting.

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

[13]  Peng Wang,et al.  Technique to evaluate nodal reliability indices and nodal prices of restructured power systems , 2005 .

[14]  D. Kirschen,et al.  Quantifying the Effect of Demand Response on Electricity Markets , 2007, IEEE Transactions on Power Systems.

[15]  Roy Billinton,et al.  Reliability Evaluation of Engineering Systems , 1983 .

[16]  Mohammed H. Albadi,et al.  A summary of demand response in electricity markets , 2008 .

[17]  M. Parsa Moghaddam,et al.  Modeling and prioritizing demand response programs in power markets , 2010 .