On Monte Carlo Simulation and Analysis of Electricity Markets

This dissertation is about how Monte Carlo simulation can be used to analyse electricity markets. There are a wide range of applications for simulation; for example, players in the electricity market can use simulation to decide whether or not an investment can be expected to be profitable, and authorities can by means of simulation find out which consequences a certain market design can be expected to have on electricity prices, environmental impact, etc. In the first part of the dissertation, the focus is which electricity market models are suitable for Monte Carlo simulation. The starting point is a definition of an ideal electricity market. Such an electricity market is partly practical from a mathematical point of view (it is simple to formulate and does not require too complex calculations) and partly it is a representation of the best possible resource utilisation. The definition of the ideal electricity market is followed by analysis how the reality differs from the ideal model, what consequences the differences have on the rules of the electricity market and the strategies of the players, as well as how non-ideal properties can be included in a mathematical model. Particularly, questions about environmental impact, forecast uncertainty and grid costs are studied. The second part of the dissertation treats the Monte Carlo technique itself. To reduce the number of samples necessary to obtain accurate results, variance reduction techniques can be used. Here, six different variance reduction techniques are studied and possible applications are pointed out. The conclusions of these studies are turned into a method for efficient simulation of basic electricity markets. The method is applied to some test systems and the results show that the chosen variance reduction techniques can produce equal or better results using 99% fewer samples compared to when the same system is simulated without any variance reduction technique. More complex electricity market models cannot directly be simulated using the same method. However, in the dissertation it is shown that there are parallels and that the results from simulation of basic electricity markets can form a foundation for future simulation methods. Keywords: Electricity market, Monte Carlo simulation, variance reduction techniques, operation cost, reliability.

[1]  Benjamin F. Hobbs,et al.  A bounding approach to multiarea probabilistic production costing , 1995 .

[2]  D. Streiffert,et al.  Multi-area economic dispatch with tie line constraints , 1995 .

[3]  Thomas L. Magnanti,et al.  Applied Mathematical Programming , 1977 .

[4]  F. T. Sparrow,et al.  Market gaming and market power mitigation in deregulated electricity markets , 1999, IEEE Power Engineering Society. 1999 Winter Meeting (Cat. No.99CH36233).

[5]  J. Huang,et al.  Sample size reduction in stochastic production simulation , 1990 .

[6]  Lennart Söder,et al.  A fast multi-area economic hydro-thermal power system model , 1999 .

[7]  Olof Nilsson Short Term Scheduling of Hydrothermal Power Systems With Integer Hydro Constraints , 1997 .

[8]  Laureano F. Escudero,et al.  Hydropower generation management under uncertainty via scenario analysis and parallel computation , 1995 .

[9]  Magnus Hindsberger,et al.  Co-existence of electricity, TEP, and TGC markets in the Baltic Sea Region , 2003 .

[10]  Michael Caramanis Investment Decisions and Long-Term Planning Under Electricity Spot Pricing , 1982, IEEE Transactions on Power Apparatus and Systems.

[11]  Benjamin F. Hobbs,et al.  Understanding how market power can arise in network competition: a game theoretic approach , 1999 .

[12]  Arve Halseth,et al.  Market power in the Nordic electricity market , 1999 .

[13]  J. Pang,et al.  Oligopolistic Competition in Power Networks: A Conjectured Supply Function Approach , 2002, IEEE Power Engineering Review.

[14]  S. R. Huang,et al.  Emission control research of spot markets for separate generation systems , 2000 .

[15]  D.W. Lane,et al.  Modeling and evaluating electricity options markets with intelligent agents , 2000, DRPT2000. International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. Proceedings (Cat. No.00EX382).

[16]  C. Mamay,et al.  Effectiveness of Antithetic Sampling and Stratified Sampling in Monte Carlo Chronological Production Cost Modeling , 1991, IEEE Power Engineering Review.

[17]  Lennart Söder,et al.  Validity of a Linear Model of a Thyristor-Controlled Series Capacitor for Dynamic Simulations , 2002 .

[18]  Richard D. Tabors,et al.  Optimal operating arrangements in the restructured world: economic issues , 1998 .

[19]  M. J. Short,et al.  Neural networks approach for solving economic dispatch problem with transmission capacity constraints , 1998 .

[20]  N. Growe-Kuska,et al.  Scenario reduction and scenario tree construction for power management problems , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[21]  R. R. Booth,et al.  Power System Simulation Model Based on Probability Analysis , 1972 .

[22]  E. Henley,et al.  Dagger-Sampling Monte Carlo For System Unavailability Evaluation , 1980, IEEE Transactions on Reliability.

[23]  Benjamin F. Hobbs,et al.  An improved bounding-based method for multiarea probabilistic production costing , 1996 .

[24]  Suhartono,et al.  The evaluation of confidence limit on LOLP for multi area system , 2000, DRPT2000. International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. Proceedings (Cat. No.00EX382).

[25]  J.R. Saenz,et al.  Allocating distribution losses to customers using distribution loss factors , 2001, 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502).

[26]  Chao-Ming Huang,et al.  A novel approach to real-time economic emission power dispatch , 2003 .

[27]  H. Stoll Least-Cost Electric Utility Planning , 1989 .

[28]  A.D.R. Medeiros,et al.  Reviewing strategies for active power transmission loss allocation in power pools , 2004 .

[29]  Antonio J. Conejo,et al.  Incremental Transmission Loss Allocation under Pool Dispatch , 2002 .

[30]  David G. Luenberger,et al.  Linear and Nonlinear Programming: Second Edition , 2003 .

[31]  S. Nash,et al.  Linear and Nonlinear Programming , 1987 .

[32]  Chanan Singh,et al.  Composite system reliability evaluation using state space pruning , 1997 .

[33]  Roy Billinton,et al.  Variance reduction techniques for use with sequential Monte Carlo simulation in bulk power system reliability evaluation , 1996, Proceedings of 1996 Canadian Conference on Electrical and Computer Engineering.

[34]  E. Bompard,et al.  Congestion-management schemes: a comparative analysis under a unified framework , 2003 .

[35]  Francesco Torelli,et al.  Nondiscrinilnatory System Losses Dispatching Policy in a Bilateral Transaction-Based Market , 2002, IEEE Power Engineering Review.

[36]  S. R. Huang,et al.  Effectiveness of optimum stratified sampling and estimation in Monte Carlo production simulation , 1997 .

[37]  Achla Marathe,et al.  ASSESSING THE EFFICIENCY OF US ELECTRICITY MARKETS , 2001 .

[38]  Mikael Amelin The value of transmission capability between countries and regions , 2000 .

[39]  H. Rudnick,et al.  Hydrothermal Market Simulator Using Game Theory: Assessment of Market Power , 2002, IEEE Power Engineering Review.

[40]  E. Read Transmission pricing in New Zealand , 1997 .

[41]  Poul Erik Morthorst,et al.  The development of a green certificate market , 2000 .

[42]  T. Groves,et al.  Optimal Allocation of Public Goods: A Solution to the 'Free Rider Problem' , 1977 .

[43]  John R. Birge,et al.  Introduction to Stochastic Programming , 1997 .

[44]  Roy Billinton,et al.  A hybrid model for quantifying different operating states of composite power systems , 1992 .

[45]  Poul Erik Morthorst,et al.  A green certificate market combined with a liberalised power market , 2003 .

[46]  T. Gomez,et al.  Multi-area decentralized optimal hydro-thermal coordination by the Dantzig-Wolfe decomposition method , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

[47]  Jacob Lemming,et al.  Financial risks for green electricity investors and producers in a tradable green certificate market , 2003 .

[48]  Stein W. Wallace,et al.  Generating Scenario Trees for Multistage Decision Problems , 2001, Manag. Sci..

[49]  Joanna Isobel House,et al.  Climate change 2001 : synthesis report , 2001 .

[50]  Hugh Outhred The competitive market for electricity in Australia: why it works so well , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[51]  M. R. Gent,et al.  Minimum-Emission Dispatch , 1971 .

[52]  J. Krawczyk,et al.  Numerical solutions to Nash-Cournot equilibria in coupled constraint electricity markets , 2004, IEEE Transactions on Power Systems.

[53]  Wenyuan Li,et al.  Reliability Assessment of Electric Power Systems Using Monte Carlo Methods , 1994 .

[54]  D. Kirschen Market power in the Electricity Pool of England and Wales , 2001, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[55]  L. J. De Vries,et al.  Capacity allocation in a restructured electricity market: technical and economic evaluation of congestion management methods on interconnectors , 2001 .

[56]  Mokhtar S. Bazaraa,et al.  Nonlinear Programming: Theory and Algorithms , 1993 .

[57]  Gerard Doorman,et al.  Peaking Capacity in Restructured Power Systems , 2000 .

[58]  G. J. Schaeffer,et al.  Renewable Electricity in a Liberalised Market – The Concept of Green Certificates , 2000 .

[59]  S. H. F. Cunha,et al.  A Technique for Reducing Computational Effort in Monte-Carlo Based Composite Reliability Evaluation , 1989, IEEE Power Engineering Review.

[60]  A. Venturini,et al.  Day-ahead market price volatility analysis in deregulated electricity markets , 2002, IEEE Power Engineering Society Summer Meeting,.

[61]  Z. Yu,et al.  A market power model with price caps and compact DC power flow constraints , 2003 .

[62]  Janusz Bialek,et al.  Topological generation and load distribution factors for supplement charge allocation in transmission open access , 1997 .

[63]  L. Soder,et al.  On Monte Carlo simulation of electricity markets with uncertainties in precipitation and load forecasts , 2001, 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502).

[64]  Stefan Feltenmark On optimization of power production , 1997 .

[65]  A. Conejo,et al.  Transmission Loss Allocation: A Comparison of Different Practical Algorithms , 2002, IEEE Power Engineering Review.

[66]  Roy Billinton,et al.  Reliability evaluation of power systems , 1984 .

[67]  A. Ott,et al.  Experience with PJM market operation, system design, and implementation , 2003 .

[68]  Janusz Bialek,et al.  Average zonal transmission losses , 2003 .

[69]  M. Pereira,et al.  Stochastic Optimization of a Multireservoir Hydroelectric System: A Decomposition Approach , 1985 .

[70]  X. Wang,et al.  Modern power system planning , 1994 .

[71]  A. T. Bharucha-Reid,et al.  The Theory of Probability. , 1963 .

[72]  Jorge Valenzuela,et al.  Monte Carlo Computation of Power Generation Production Costs under Operating Constraints , 2001 .

[73]  S. Granville,et al.  The Stackelberg equilibrium applied to AC power systems-a noninterior point algorithm , 2003 .

[74]  Lina Bertling,et al.  Reliability-centred maintenance for electric power distribution systems , 2002 .

[75]  D. B. Das,et al.  New multi-objective stochastic search technique for economic load dispatch , 1998 .

[76]  Reuven Y. Rubinstein,et al.  Simulation and the Monte Carlo method , 1981, Wiley series in probability and mathematical statistics.

[77]  P. Klemperer,et al.  Supply Function Equilibria in Oligopoly under Uncertainty , 1989 .

[78]  P. Basagoiti,et al.  Spanish power exchange market and information system design concepts, and operating experience , 1999, Proceedings of the 21st International Conference on Power Industry Computer Applications. Connecting Utilities. PICA 99. To the Millennium and Beyond (Cat. No.99CH36351).