A decentralized mechanism for computing competitive equilibria in deregulated electricity markets

With the increased level of distributed generation and demand response comes the need for associated mechanisms that can perform well in the face of increasingly complex deregulated energy market structures. Using Lagrangian duality theory, we develop a decentralized market mechanism that ensures that, under the guidance of a market operator, self-interested market participants:generation companies (GenCos), distribution companies (DistCos), and transmission companies (TransCos), reach a competitive equilibrium. We show that even in the presence of informational asymmetries and nonlinearities (such as power losses and transmission constraints), the resulting competitive equilibrium is Pareto efficient.

[1]  Francisco D. Galiana,et al.  Towards a more rigorous and practical unit commitment by Lagrangian relaxation , 1988 .

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

[3]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[4]  Antonio J. Conejo,et al.  Decentralized Nodal-Price Self-Dispatch and Unit Commitment , 2002 .

[5]  Javad Lavaei,et al.  Competitive equilibria in electricity markets with nonlinearities , 2012, 2012 American Control Conference (ACC).

[6]  R. D. Christie,et al.  Load frequency control issues in power system operations after deregulation , 1995 .

[7]  S. Stoft Power System Economics: Designing Markets for Electricity , 2002 .

[8]  Felix F. Wu,et al.  Folk theorems on transmission access: Proofs and counterexamples , 1996 .

[9]  Goran Strbac,et al.  Fundamentals of Power System Economics: Kirschen/Power System Economics , 2005 .

[10]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[11]  Jonathan F. Bard,et al.  Short-Term Scheduling of Thermal-Electric Generators Using Lagrangian Relaxation , 1988, Oper. Res..

[12]  A. Conejo,et al.  On Walrasian Equilibrium for Pool-Based Electricity Markets , 2002, IEEE Power Engineering Review.

[13]  D. T. Nguyen,et al.  Walrasian Market Clearing for Demand Response Exchange , 2012, IEEE Transactions on Power Systems.

[14]  W. Ongsakul,et al.  Unit commitment by enhanced adaptive Lagrangian relaxation , 2004, IEEE Transactions on Power Systems.

[15]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[16]  P. Morgan,et al.  An Explanation of Constrained Optimization for Economists , 2015 .

[17]  Stephen P. Boyd,et al.  Subgradient Methods , 2007 .

[18]  Katta G. Murty,et al.  Nonlinear Programming Theory and Algorithms , 2007, Technometrics.

[19]  D. Kirschen,et al.  Fundamentals of power system economics , 1991 .

[20]  Olle I. Elgerd,et al.  Electric Energy Systems Theory: An Introduction , 1972 .

[21]  Stephen C. Peck,et al.  A market mechanism for electric power transmission , 1996 .

[22]  O. Alsac,et al.  Security analysis and optimization , 1987, Proceedings of the IEEE.