Dynamics of ancillary service prices in power distribution systems

This paper concerns resource allocation, and performance evaluation in large-scale electric power markets. Our ultimate goal is the integration of new approaches to dynamic control of stochastic networks, with recent results concerning the competitive market equilibrium in network industries, to obtain rigorous, yet comprehensive approaches to model reduction and control for network-level power distribution systems that share these features: (i) information is distributed; (ii) users have distributed, potentially conflicting objectives; (iii) high variability; and (iv) high costs when constraints are violated. Distributed control problems of this form arise in many current application areas, including the power distribution systems considered here. The main result shows how flow models may be constructed in analogy with queuing models for the purposes of design and analysis. Based on this model, we obtain a formula for ancillary service prices in a simple power system model. Generalizations to more complex models are also described.

[1]  David D. Yao,et al.  Fundamentals of Queueing Networks , 2001 .

[2]  Sean P. Meyn Sequencing and Routing in Multiclass Queueing Networks Part I: Feedback Regulation , 2001, SIAM J. Control. Optim..

[3]  L. F. Martins,et al.  Routing and singular control for queueing networks in heavy traffic , 1990 .

[4]  Sean P. Meyn,et al.  Value functions and performance evaluation in stochastic network models , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[5]  H. Kushner,et al.  Optimal and approximately optimal control policies for queues in heavy traffic , 1989 .

[6]  C. Zhou,et al.  The QNET Method for Re-Entrant Queueing Networks with Priority Disciplines , 1997, Oper. Res..

[7]  F. P. Kelly,et al.  Dynamic routing in open queueing networks: Brownian models, cut constraints and resource pooling , 1993, Queueing Syst. Theory Appl..

[8]  F. Kelly,et al.  Stochastic networks : theory and applications , 1996 .

[9]  R. J. Williams,et al.  Fluid model for a network operating under a fair bandwidth-sharing policy , 2004, math/0407057.

[10]  Elizabeth Schwerer A LINEAR PROGRAMMING APPROACH TO THE STEADY-STATE ANALYSIS OF REFLECTED BROWNIAN MOTION , 2001 .

[11]  D. Yao,et al.  Fundamentals of Queueing Networks: Performance, Asymptotics, and Optimization , 2001, IEEE Transactions on Automatic Control.

[12]  Mike Chen,et al.  In Search of Sensitivity in Network Optimization , 2003, Queueing Syst. Theory Appl..

[13]  Richard L. Tweedie,et al.  Markov Chains and Stochastic Stability , 1993, Communications and Control Engineering Series.

[14]  S. Meyn,et al.  Exponential and Uniform Ergodicity of Markov Processes , 1995 .

[15]  Sean P. Meyn Sequencing and Routing in Multiclass Queueing Networks Part II: Workload Relaxations , 2003, SIAM J. Control. Optim..

[16]  J. Harrison,et al.  Brownian motion and stochastic flow systems , 1986 .

[17]  Ruth J. Williams,et al.  On dynamic scheduling of stochastic networks in heavy traffic and some new results for the workload process , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[18]  Sean P. Meyn,et al.  Performance Evaluation and Policy Selection in Multiclass Networks , 2003, Discret. Event Dyn. Syst..

[19]  J. Michael Harrison,et al.  Stochastic Networks and Activity Analysis , 2002 .

[20]  Laurent Massoulié,et al.  Bandwidth sharing and admission control for elastic traffic , 2000, Telecommun. Syst..

[21]  Lawrence M. Wein,et al.  Scheduling Networks of Queues: Heavy Traffic Analysis of a Two-Station Closed Network , 1990, Oper. Res..

[22]  Mike Chen,et al.  Management of demand-driven production systems , 2004, IEEE Transactions on Automatic Control.