Price-Based Commitment Decisions in the Electricity Market

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The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology impacts all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies . . . , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Deregulation of electricity markets is a worldwide activity with leading and distinctive developments occurring in the European and US markets. The immediate impact of these changes is felt in the higher levels of the power production hierarchy where tools to aid short-term economic decision making are needed. At the lower levels of the production control hierarchy the impact is to demand much more flexible unit operation so that the determined short-term goals can be met. Allen and Illic have produced this useful monograph on the decision algorithms needed to determine unit commitment proflles over the shorter time frame of hours and days. The method of dynamic programming has been used and the work covers modelling, cost function and construction, an introduction to dynamic programming and a detailed assessment of its use. The monograph comes complete with programs in Appendix D and with other appendices giving the necessary supporting theory.

Offers a re-evaluation of the electric power industry

1 Introduction.- 2 The Unit Commitment Problem.- 2.1 Unit Commitment in a Regulated Industry.- 2.2 Present Unit Commitment Solution Methods.- 2.3 Interruptible Service Contracts.- 3 Unit Commitment in a Deregulated Environment.- 3.1 Possible Formats for the Electricity Market.- 3.1.1 The Poolco Marketplace.- 3.1.2 The Market of Bilateral Contracts.- 3.2 Unit Commitment for an Individual Supplier.- 3.2.1 Bilateral Market.- 3.2.2 Poolco Market.- 3.3 Multiple Generation.- 3.4 Secondary Market for Reliability.- 3.5 Other Issues.- 4 Survey of the Dynamic Programming Formulation.- 4.1 Finite Horizon Problems.- 4.2 Infinite Horizon Problems with a Discount Factor.- 4.3 Stochastic Shortest Path Problems.- 4.4 Average Cost per Stage Problems.- 5 Unit Commitment for an Individual Power Producer.- 5.1 Unit Commitment without Generation Limits.- 5.2 Unit Commitment with Generation Limits.- 5.2.1 Truncated Normal Distributions.- 5.2.2 Truncated Lognormal Distributions.- 5.2.3 Expected Profit of Generation.- 6 Price Process of Electricity.- 6.1 Price Process Models.- 6.1.1 Long Term Model Behavior.- 6.1.2 Price Models using Logarithms.- 6.1.3 Unit Root Tests.- 6.1.4 Application of Unit Root Test to PJM Electricity Data.- 6.2 Correlation of Price with Load.- 6.2.1 Expanded Price Process Models.- 6.2.2 Test for Mean Reversion.- 6.2.3 Price Prediction Algorithms.- 6.3 Correlation of Load with Date.- 6.4 Correlation of Load with Temperature.- 6.4.1 Overview of Linear Regression Analysis.- 6.4.2 Application of Regression to Temperature/Load Data.- 6.4.3 Conclusions of Regression Analysis for Load vs. Temperature.- 6.5 Effects of Variance of Load Estimation.- 7 Computational Complexity of Unit Commitment.- 7.1 Dynamic Programming Algorithm.- 7.2 Heuristic Simplifications.- 7.3 Ordinal Optimization.- 7.3.1 Overview of Ordinal Optimization.- 7.3.2 Application to Unit Commitment.- 7.4 Example.- 7.4.1 Solution by Dynamic Programming.- 7.4.2 Solution by Ordinal Optimization.- 8 Forward Contracts and Futures.- 8.1 Forward Contracts.- 8.2 Producer Profits with Forward Contracts.- 8.3 Forward Contract Strategies.- 8.3.1 One Hour Forward Contracts.- 8.3.2 Multi-Hour Forward Contract.- 8.3.3 Example.- 8.4 Temporal Forward Contract Problem.- 9 Reserve Markets for Power System Reliability.- 9.1 General Form of a Reserve Market.- 9.1.1 Payment for Power Delivered.- 9.1.2 Payment for Reserve Allocated.- 9.1.3 Price Process for Reserve Price.- 9.2 Individual Power Producer Strategies for Selling Reserve.- 9.2.1 Payment for Power Delivered.- 9.2.2 Payment for Reserve Allocated.- 9.3 Provision of Reserve for Transactions.- 9.4 Effect of Reserve Market on Unit Commitment.- 9.5 Example.- 10 Unit Commitment in Congested Transmission Systems.- 10.1 Probabilistic Model of Congestion.- 10.1.1 Modifications to Price Model.- 10.1.2 Congestion Model with Limits.- 10.2 Producer Strategy under Congestion.- 10.3 Example.- 10.4 Solution of Unit Commitment under Congestion and Reserve.- 11 Conclusions.- A Calculation of Parameters for Price Processes.- A.1 Exponential of a Normally Distributed Variable.- A.2 Nonlinear Least Squares Regression.- A.3 Recursive Least Squares Algorithm.- B Results of Regression of Load vs. Temperature.- B.1 Regression on Same Day High Temperature.- B.2 Regression using Previous Day's High Temperature.- C Derivation of Formulas for Truncated Random Variables.- C.1 Truncated Normal Distributions.- C.2 Truncated Lognormal Distributions.- D Software for Unit Commitment.- D.1 Dynamic Programming Software.- D.2 Ordinal Optimization Software.- D.3 Reserve and Congestion DP Software.- D.4 Source Code for uci2. c.- D.5 Source Code for ucool. c.- D.6 Source Code for uci2rs. c.- D.7 Source Code for uci2con. c.- D.8 Source Code for ucerr.h.- D.9 Source Code for ucerroo. h.- E Stochastic Unit Commitment in a Regulated Industry.- E.1 Unit Commitment with a Finite Horizon.- E.2 Solution of Optimal Power Flow.- E.2.1 Uncongested, Lossless Optimal Power Flow without Generation Limits.- E.3 Expected Value of the Generation Cost.- E.3.1 Expected Cost over Load Power.- E.3.2 Expected Cost over Generation Failures.

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Price-Based Commitment Decisions in the Electricity Market
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Springer London
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H235mm x B155mm x T14mm
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