Price-Based Unit Commitment. PBUC FORMULATION  maximize the profit.

Slides:



Advertisements
Similar presentations
Solve a System Algebraically
Advertisements

Winter 2013/2014 Reliability Solution Including LNG MWHs N. Jonathan Peress Conservation Law Foundation Greg Lander Skipping Stone NEPOOL Markets & Reliability.
1 LP Duality Lecture 13: Feb Min-Max Theorems In bipartite graph, Maximum matching = Minimum Vertex Cover In every graph, Maximum Flow = Minimum.
Electrical and Computer Engineering Mississippi State University
Optimization of thermal processes2007/2008 Optimization of thermal processes Maciej Marek Czestochowa University of Technology Institute of Thermal Machinery.
Power Station Control and Optimisation Anna Aslanyan Quantitative Finance Centre BP.
Separating Hyperplanes
Applications of Stochastic Programming in the Energy Industry Chonawee Supatgiat Research Group Enron Corp. INFORMS Houston Chapter Meeting August 2, 2001.
Numerical Optimization
Linear Programming Special Cases Alternate Optimal Solutions No Feasible Solution Unbounded Solutions.
Duality Lecture 10: Feb 9. Min-Max theorems In bipartite graph, Maximum matching = Minimum Vertex Cover In every graph, Maximum Flow = Minimum Cut Both.
Constrained Maximization
Economics 214 Lecture 37 Constrained Optimization.
Support Vector Machines Formulation  Solve the quadratic program for some : min s. t.,, denotes where or membership.  Different error functions and measures.
Design Optimization School of Engineering University of Bradford 1 Formulation of a design improvement problem as a formal mathematical optimization problem.
Reformulated - SVR as a Constrained Minimization Problem subject to n+1+2m variables and 2m constrains minimization problem Enlarge the problem size and.
Optimal Clearing of Supply/Demand Curves Ankur Jain, Irfan Sheriff, Shashidhar Mysore {ankurj, isheriff, Computer Science Department.
EE 685 presentation Optimization Flow Control, I: Basic Algorithm and Convergence By Steven Low and David Lapsley Asynchronous Distributed Algorithm Proof.
THE MATHEMATICS OF OPTIMIZATION
Market Overview in Electric Power Systems Market Structure and Operation Introduction Market Overview Market Overview in Electric Power Systems Mohammad.
Linear Programming Digital Lesson. Copyright © by Houghton Mifflin Company, Inc. All rights reserved. 2 Linear programming is a strategy for finding the.
LP formulation of Economic Dispatch
Introduction to Optimization (Part 1)
Linear Programming Operations Research – Engineering and Math Management Sciences – Business Goals for this section  Modeling situations in a linear environment.
1 Least Cost System Operation: Economic Dispatch 2 Smith College, EGR 325 March 10, 2006.
Optimization Techniques Methods for maximizing or minimizing an objective function Examples –Consumers maximize utility by purchasing an optimal combination.
Column Generation Approach for Operating Rooms Planning Mehdi LAMIRI, Xiaolan XIE and ZHANG Shuguang Industrial Engineering and Computer Sciences Division.
Optimization for Operation of Power Systems with Performance Guarantee
EE/Econ 458 Introduction to Linear Programming J. McCalley 1.
Operations Research Assistant Professor Dr. Sana’a Wafa Al-Sayegh 2 nd Semester ITGD4207 University of Palestine.
Stevenson and Ozgur First Edition Introduction to Management Science with Spreadsheets McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies,
Opener. Notes: 3.4 Linear Programming Optimization  Many real-life problems involve a process called optimization.  This means finding a maximum or.
1 Optimization Based Power Generation Scheduling Xiaohong Guan Tsinghua / Xian Jiaotong University.
Integer Programming Each year CrossChek decides which lines of golf clubs and clothing it will market. Consider that each line of golf clubs is expected.
Market Evolution Program Day Ahead Market Project How the DSO Calculates Nodal Prices DAMWG October 20, 2003.
1 THE MATHEMATICS OF OPTIMIZATION Copyright ©2005 by South-Western, a division of Thomson Learning. All rights reserved. Walter Nicholson, Microeconomic.
Optimal Fueling Strategies for Locomotive Fleets in Railroad Networks Seyed Mohammad Nourbakhsh Yanfeng Ouyang 1 William W. Hay Railroad Engineering Seminar.
D Nagesh Kumar, IIScOptimization Methods: M2L4 1 Optimization using Calculus Optimization of Functions of Multiple Variables subject to Equality Constraints.
EE 685 presentation Optimization Flow Control, I: Basic Algorithm and Convergence By Steven Low and David Lapsley.
Optimization unconstrained and constrained Calculus part II.
Branch-and-Cut Valid inequality: an inequality satisfied by all feasible solutions Cut: a valid inequality that is not part of the current formulation.
1 System planning 2013 Lecture L9: Short term planning of thermal systems Chapter Contents: –General about thermal power –Production cost –Start.
PJM©2013www.pjm.com Demand Side Working Group Loads in SCED Angelo Marcino Real-Time Market Operations – PJM April 14, 2014.
A Simple Unit Commitment Problem Valentín Petrov, James Nicolaisen 18 / Oct / 1999 NSF meeting.
Optimization and Lagrangian. Partial Derivative Concept Consider a demand function dependent of both price and advertising Q = f(P,A) Analyzing a multivariate.
Calculus-Based Optimization AGEC 317 Economic Analysis for Agribusiness and Management.
Economics 2301 Lecture 37 Constrained Optimization.
Searching a Linear Subspace Lecture VI. Deriving Subspaces There are several ways to derive the nullspace matrix (or kernel matrix). ◦ The methodology.
1 Chapter 6 Reformulation-Linearization Technique and Applications.
1 Chapter 5 Branch-and-bound Framework and Its Applications.
Part 3 Linear Programming 3.3 Theoretical Analysis.
0 Integer Programming Introduction to Integer Programming (IP) Difficulties of LP relaxation IP Formulations Branch and Bound Algorithms Reference: Chapter.
Linear Programming: Sensitivity Analysis and Duality
Ardavan Asef-Vaziri Systems and Operations Management
Digital Lesson Linear Programming.
Digital Lesson Linear Programming.
EMGT 6412/MATH 6665 Mathematical Programming Spring 2016
6.5 Stochastic Prog. and Benders’ decomposition
Chap 9. General LP problems: Duality and Infeasibility
3-3 Optimization with Linear Programming
Integer Linear Programming
Linear Programming Example: Maximize x + y x and y are called
LINEARPROGRAMMING 4/26/2019 9:23 AM 4/26/2019 9:23 AM 1.
6.5 Stochastic Prog. and Benders’ decomposition
Example 2B: Solving Linear Systems by Elimination
1.6 Linear Programming Pg. 30.
Calculus-Based Optimization AGEC 317
Constraints.
EE/Econ 458 Introduction to Linear Programming
Presentation transcript:

Price-Based Unit Commitment

PBUC FORMULATION

 maximize the profit

PBUC FORMULATION  System Constraints  These constraints represent a GENCO’s special requirements.  For example, a GENCO may have minimum and maximum generation requirements in order to play the game in the energy market.  Because of reliability requirements, a GENCO may pose lower and upper limits on its spinning and no-spinning reserves.  These constraints can be relaxed otherwise.

PBUC  System Fuel Constraints (For a “FT” type of fuel)  System Emission Constraint

PBUC  Unit Constraints

PBUC  Unit Minimum ON/OFF Durations  Unit Ramping Constraints  Unit Fuel Constraints

PBUC SOLUTION  Lagrangian relaxation is used to solve PBUC.  The basic idea is to relax coupling constraints (i.e., coupling either units, time periods, or both) into the objective function by using Lagrangian multipliers.  The relaxed problem is then decomposed into subproblems for each unit.  The dynamic programming process is used to search the optimal commitment for each unit.  Lagrangian multipliers are then updated based on violations of coupling constraints

Solution without Emission or Fuel Constraints  Using Lagrangian multipliers to relax system constraints (i.e., energy and reserve), we write the Lagrangian function as

Single-Unit DP  The Lagrangian term for one unit at a single period is given as follows  The separable single-unit problem is formulated as

Optimality Condition  When the unit is ON, the derivatives of the Lagrangian function with respect to P, R, and N are

optimality condition  when the unit is ON, the optimality condition is

Optimality Condition when the Unit is OFF

Multipliers Update

Economic Dispatch  Once the unit commitment status is determined, an economic dispatch problem is formulated and solved to ensure the feasibility of the original unit commitment solution.  subject to energy, reserve, and unit generation limits  quadratic or linear programming can be applied to solve this problem

Economic Dispatch for Non-spinning Reserve

Economic Dispatch for Spinning Reserve

Economic Dispatch for Energy

Convergence Criterion  Suppose that the solution from unit commitment is SU and the solution from economic dispatch is SE  Substituting SU into the Lagrangian function, we would get the Lagrangian value, LU.  Substituting SE into the Lagrangian function we would get the Lagrangian value, denoted as LE  The relative duality gap (RDG)

Solution with Emission and Fuel Constraints

Optimality Condition

Multipliers Update for Emission and Fuel Constraints

Economic Dispatch

Energy Purchase

Derivation of Steps in Update of Multipliers

Optimality Condition

Bidding Strategy Based on PBUC

Bidding Strategy

Case Study of 5-Unit System

Case 1: Impact of the Energy Market Price

Case 2: Impact of Ramp Rates

Case 3: Impact of Fuel Price Variations

Case 5: Impact of Different LMPs