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Title of Thesis

Unit Commitment using Hybrid Approaches

Author(s)

Aftab Ahmad

Institute/University/Department Details
Department Of Electrical Engineering / University Of Engineering And Technology, Taxila
Session
2010
Subject
Engineering Electrical
Number of Pages
196
Keywords (Extracted from title, table of contents and abstract of thesis)
Transmission, Combinatorial, Control, Commitment, Optimization, Approaches, Amount, Peak, Unit, Hybrid, Functions, Forecasted

Abstract
Unit Commitment is an important and vital optimization task in a power control centre.After load forecasting it is the second step in the planning process. It consists of two linked optimization problems. It comprises unit on/off scheduling problem and the economic dispatch sub-problem.The on/off scheduling problem is a 0-1 combinatorial problem with equality and inequality constraints, while the economic dispatch sub-problem is a nonlinear constrained optimization problem.
Unit commitment is a nonlinear, large scale, combinatorial, constrained optimization problem.The complete unit commitment optimization problem is to minimize the total production cost (TPC) of utility in such a way that the constraints such as load demand, spinning reserve, minimum and maximum power limits of units, minimum up (MUT) and minimum down times (MDT) are satisfied.Therefore, based on the forecasted load demand, preparing proper on/off schedule of generators can result in cost saving for utility.It is much more difficult problem to solve due to its high dimensionality.
The present work is based on the scheduling of thermal units. The generation of initial feasible UC schedules is much important, for the UCP. When initial feasible schedules (generation > load + spinning reserve) are generated randomly, it is difficult to get feasible schedules for the whole daily forecasted load curve.
In this work initial schedule is generated by considering the peak, off peak load of the forecasted load curve, must run and must out units based on a new priority list method.The proposed method is very efficient and fast in generating initial unit commitment schedules. In this work the MUT and MDT constraints are checked and repaired by using bit change operator.The trial solutions were generated by taking upper four units in the priority list at each hour to avoid entrapment in local minimum.
In the unit commitment problem, the economic load dispatch (ELD) sub-problem is an intensive part and its calculations consume a large amount of time. Convex economic dispatch using load to efficient unit and incremental cost criterion methods have been solved. In this work, the ELD calculation for non convex problem has been solved using genetic algorithm (GA) based on real power search method.
In the present work, three hybrid approaches have been developed for the convex and non-convex cost functions and applied first time to solve the unit commitment problem. To implement these algorithms a flexible and extensible computational framework has been developed to run in visual C++ environment. The proposed algorithms are (i) “hybrid of dynamic programming, particle swarm optimization and artificial neural network algorithms (DP-PSOANN) for convex cost function, (ii) “Neuro-Genetic hybrid approach for non-convex cost function” and (iii) “hybrid of full load average production cost and maximum power output, for convex and non convex cost functions”. For comparison the neural network trained with back propagation learning rule has also been developed. The proposed models have been tested onI EEE 3 and 10 units standard test systems.The significant improvement in the total productioncost shows the promise of these hybrid models.
National utility system, National Transmission and despatch Company (NTDC) has been reviewed with reference to its operation problems. Four test systems consisting of 12, 15, 25 and 34 units of NTDC system have been tested.

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S. No. Chapter Title of the Chapters Page Size (KB)
1 0 CONTENTS

 

 
102 KB
2

1

INTRODUCTION

1.1 General
1.2 Problem statement
1.3 Objectives
1.4 Scope of the work
1.5 Thesis Organization

1
118 KB
3 2 UNIT COMMITMENT PROBLEM --- A BRIEF LITERATURE SURVEY

2.1 Introduction
2.2 Power System Operational Planning
2.3 Unit commitment --- Literature Survey
2.4 Single Classical/Deterministic Approaches
2.5 Non classical approaches
2.6 Hybrid approaches
2.7 Unit Commitment --- Issues and Bottlenecks
2.8 Discussion

7
170 KB
4 3 OPTIMIZATION TOOLS FOR UNIT COMMITMENT

3.1 Introduction
3.2 Particle swarm optimization (P.S.O)
3.3 Artificial Neural Networks (ANN)
3.4 Dynamic Programming (DP) or Recursive Optimization
3.5 Genetic Algorithm (GA)

29
191 KB
5 4 UNIT COMMITMENT --- PROBLEM FORMULATION AND SINGLE SOLUTION APPROACHES

4.1 Introduction
4.2 Characteristics of Power Generation Units
4.3 Unit Commitment Problem (UCP)
4.4 Constraints

4.5 Unit Commitment mathematical formulation as an optimization problem
4.6 Generation of initial feasible unit commitment schedules
4.7 Minimum up and minimum down Time Constraint Handling
4.8 Minimum up and down time constraint repairing by using bit change operator
4.9 Algorithm for the construction of initial unit commitment schedule and M.U.T and M.D.T constraint handling
4.10 Unit commitment schedule and determination of number of units to be operated
4.11 Economic dispatch Problem (Allocation of Generation)
4.12 Economic Dispatch versus Unit Commitment
4.13 Conventional/Classical Single Approaches for convex fuel cost function
4.14 Case studies --- Convex cost function

39
690 KB
6 5 PROPOSED NEW HYBRID MODELS FOR UNIT COMMITMENT PROBLEM BASED ON CONVEX AND NON-CONVEX COST FUNCTIONS

5.1 Introduction
5.2 Hybrid Model – I: A hybrid of particle swarm optimization (PSO), artificial neural network (ANN) and dynamic programming (DP)
5.3 Case Studies---Convex cost function
5.4 Hybrid Model-II: Neuro-Genetic Hybrid Approach
5.5 Case Studies --- non convex cost function
5.6 Hybrid Model –III: Scaleable deterministic hybrid approach
5.7 Case Studies ---Convex fuel cost function
5.8 Case Studies: Hybrid model-III ---Non-Convex fuel cost function

80
435 KB
7 6 UNIT COMMITMENT OF NATIONAL TRANSMISSION & DESPATCH COMPANY LIMITED (NTDC)

6.1 Introduction
6.2 WAPDA --- Brief Overview
6.3 National Transmission and Despatch Company
6.4 Operational Constraints in NTDC System
6.5 Test systems for NTDC system
6.6 Case Studies
6.7 Numerical results

143
119 KB
8 7 CONCLUSIONS AND SUGGESTIONS

 

152
110 KB
9 8 REFERENCES

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154 KB