Network reactive power planning considering load uncertainty using an evolutionary method

Number of pages: 102 File Format: word File Code: 31367
Year: 2014 University Degree: Master's degree Category: Electronic Engineering
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  • Summary of Network reactive power planning considering load uncertainty using an evolutionary method

    Continuous Master's Dissertation in the field of Power Electricity

    Abstract:

    In electrical networks, the costs caused by system losses and defects caused by voltage deviations from the permissible limits are among the biggest problems that hinder the production, transmission and distribution of power. Therefore, reducing the costs of planning and operating power systems, and at the same time, observing its limits and restrictions has been one of the main goals of power system designers. Using parallel capacitors and changing the ratio of Tap Changers are considered to be the most economical methods to provide reactive load and adjust voltage limits. Capacitors can reduce the size of reactive generators by reducing their load demand. Also, capacitors can reduce the flow of lines from the capacitor location to the power plant, and as a result, reduce losses and load on lines, transformers, and transmission lines. Using the capacitor at the same time as changing the ratio of the tap changer, in addition to the mentioned cases, causes delay or elimination of capital for the development of the power network. This thesis examines how to plan capacitors by considering the uncertainties affecting the transmission network level. In this regard, the owner of the transmission network is considered as an owner-operator who seeks to minimize his costs. In order to minimize costs, the owner-operator is faced with two types of decision variables. The first category is the decision-making variables related to capacitors at the level of the transmission network, which are effective at the beginning of the planning period. Other types of control variables at the operator's disposal include tap trans adjustment, active and reactive power dispatching of generators, which will be considered during the operation period. These two categories of variables are included as variables at the disposal of the owner-operator. In other words, the owner-operator of the network seeks to minimize his investment and operation costs by making decisions related to these variables. On the other hand, parameters play a fundamental role in shaping the definition of the owner-operator planning problem. These parameters include the price, amount of consumption, price of active and reactive power and cost of capacitors, which are associated with uncertainty and their impact can be evaluated with the help of risk modeling in random planning and scenario tree on owner-operator decisions. In this thesis, the accuracy of the programmed model has been evaluated by simulating the random programming designed on IEEE 30, 57 and 118 bus networks.

    Key words: capacitor programming, uncertainty, random programming

    Introduction

    The importance of electric energy today is not hidden from anyone. Due to the simplicity of conversion to other types of energy, ease of transfer, easy control and environmental considerations, electric energy has been used more than other types of energy. Supplying electrical energy needed by customers with the lowest price and the best possible quality is the main goal of a power system. In electrical networks, losses are one of the biggest problems that plague power generation, transmission and distribution. Therefore, reducing losses and improving the voltage profile have been one of the main goals of power system designers, and one of the proposed solutions to achieve these goals is to use parallel capacitors and change the ratio of Tap Changers in the network [1]. Real power is produced in power plants, while reactive power is provided in the power plant (synchronous condensers) or by installing capacitors and changing the ratio of Tap Changers. Using parallel capacitors and changing the ratio of Tap Changers are considered to be the most economical methods to provide reactive load. Capacitors can reduce the size of reactive generators by reducing their load demand. Also, capacitors can reduce the current of the lines from the capacitor to the power plant and thus reduce the losses and load on the lines, transformers and transmission lines. Using the capacitor at the same time as changing the ratio of the tap changer, in addition to the mentioned cases, causes delay or elimination of capital for the development of the power network. In this thesis, by using capacitors in the role of parallel admittances and changing the ratio of TapChanger, the above objectives are achieved [2-4].Therefore, in order to control the bus voltage within the minimum and maximum permissible range, in the conditions of feeding different loads, parallel capacitors are used with a change in Tap ratio [5].

    Despite extensive studies of capacitor displacement in power networks, there is a lack of in-depth investigation related to two basic issues. First, the simultaneous investigation of other variables at the operator's disposal, such as the adjustment of tap trans and capacitors in power systems, and the relationship of these activities with other parameters at the disposal of the system operator, deserve more attention. Second, the economic review and analysis based on it as a link connecting technical and economic decisions requires more attention. In addition, considering the uncertainty in the parameters affecting the anchoring is also a factor that brings a wider and more complete range of choices to the network decision makers. For this purpose, the basic question of this research is how to plan the reactive power by considering other variables and parameters available to the network decision maker. In this regard, taking into account the effect of load uncertainty by observing the limitations of the power range of reactive generators, the voltage range of buses, the range of Tap changes and the range of shunt admittance size changes has been analyzed and investigated.

    Taking into account the uncertainty in the parameters affecting the decision-making activities of the network, the reactive power planning structure takes a random image and requires reviews based on economic risk factors and scenario creation. It should be mentioned here that in order to have a suitable analysis, the planning structure requires a suitable problem solving solution. Therefore, reducing the dimensions of the problem and at the same time having accurate answers using common techniques in the literature on reducing the dimensions of the problem such as scenario reduction techniques are included in the theme of this research. In the literature, the use of intelligent optimization methods has been proposed to solve problems of such dimensions. For this purpose, in this research, the particle swarm algorithm has been used to solve the problem and have the optimal answer for network decision-making activities.

    1-3 report structure

    In the second chapter, general information about reactive power equipment and the background of the upcoming research has been discussed. The third chapter of Binary Particle Aggregation Algorithm is introduced as an innovative intelligent method in solving optimization problems. In the fourth chapter, the issue of scenario reduction and reactive power planning modeling is discussed. In the rest of this chapter, the simulation results are presented along with the economic and technical analysis of the network decision maker. In the fifth chapter, conclusions and suggestions for further work are presented.

     

     

     

     

     

     

     

     

     

     

     

     

     

    The second chapter of reactive power, its supply equipment and planning

    2-1 general definition of reactive power

    The load with real (active) power expressed in kilowatts or megawatts is provided by power plants. All the actions that are performed in a power system are for the purpose of supplying the load. Also, in an alternating current (AC) system, virtual (reactive) power expressed as kilowatts or megawatts forms an important part. In other words, the sum of real power and virtual power is called apparent power. The demand for reactive power is increased by the electromagnetic circuits of motors and transformers and lines and electric furnaces and other industrial uses. In the case that the ratio of the real power that is transmitted through the lines. If the apparent power is small, it is said that the power factor of the system is low. Power factor means the ratio of real power to apparent power for a specific amount of real power. If the power factor is low, it increases in transmission lines, transformers, and generators due to the high apparent power of the current, which results in an increase in losses in the system, which is proportional to the square of the current. This issue also causes a voltage drop in the network and as a result for the consumer [6-9].

    2-2 Reactive power generation devices [10-19]

    Often, the equipment and devices that are connected to the electrical energy network require not only active power but also a certain amount of reactive power.

  • Contents & References of Network reactive power planning considering load uncertainty using an evolutionary method

    List:

    Table of Contents

    Abstract:.. 1

    Chapter One.. 2

    1-1 Introduction.. 2

    1-2 Topic Plan.. 3

    1-3 Report Structure.. 3

    Chapter Two Reactive Power, Supply Devices and Its Planning. 5

    2-1 General definition of reactive power.. 5

    2-2 Reactive power generation devices.. 5

    2-2-1 Synchronous generators.. 6

    2-2-2 Synchronous condensers.. 6

    2-2-3 Synchronous motors.. 6

    2-2-4 Capacitor.. 6

    2-2-5 Capacitor installation location.. 10

    2-2-6 Capacitor placement to reduce losses. 11

    2-3 Reasons for increasing the need for capacitors in Iran's networks. 12

    2-4 Background of reactive power planning. 13

    2-5 methods used to solve the reactive power planning problem. 19

    2-5-1 Analytical methods (AM).. 20

    2-5-2 Numerical programming methods (NP). 20

    2-5-3 heuristic methods (HM). 24 2-6-1 Algorithm (PSO) Leaping.. 28

    2-6-2-5 Decoding.. 28

    2-7 Training-learning based optimization algorithm (TLBO). 29

    2-7-1 Teacher phase.. 29

    2-7-2 Learner phase.. 30

    2-8 Classification of presented works.. 30

    The third chapter of PSO algorithm.. 33

    3-1 Overview of PSO algorithm.. 33

    3-2 Types of particle topology.. 34

    3-2-1 star topology.. 34

    3-2-2 ring topology.. 34

    3-2-3 wheel topology.. 35

    3-3 process of PSO algorithm.. 35

    3-4 steps of PSO algorithm implementation.. 37

    3-5 checking the effects of parameters PSO.. 38

    3-5-1 Acceleration constants.. 38

    3-5-2 Number of particles.. 38

    3-5-3 Maximum speed.. 39

    3-5-4 Inertia weight.. 39

    Chapter four.. 41

    4-1 Introduction.. 41

    4-2 Performance framework of beneficial owner in capacitor planning problem. 41

    4-3 scenario reduction.. 46

    4-4 scenario reduction regression algorithm.. 48

    4-5-Mathematical formulation of the problem of stochastic planning of capacitors. 49

    4-6 analytical studies.. 58

    Chapter five:.. 85

    5-1 conclusion.. 85

    5-2 suggestions.. 87

    English sources.. 88

    English abstract.. 94

    List of tables

    Table (2-1) classification of articles.. 7

    List of forms

    Figure (2-1) Vector representations for a circuit with delayed power factor. 9

    Figure (2-2) a general view of the methods used to solve the reactive power planning problem. 23

    Figure (2-3) the movement path of a particle in two consecutive repetitions. 25

    Figure (2-4) genetic algorithm flowchart.. 27

    Figure (4-1) owner-operator and future decision variables. 42

    Figure (4-2) structure of capacitor planning problem. 43

    Figure (4-3) structure of random programming problem. 44

    Figure (4-4) Random programming scenario tree. 45

    Figure (4-5) cost of three-stage capacitor.. 50

    Figure (4-6) schematic of tap trans model.. 52

    Figure (4-7) 30 bus standard network.. 58

    Figure (4-8) 57 bus standard network.. 59

    Figure (4-9) 118 standard network Base.. 60

    Figure (4-10) Expected cost according to the number of scenarios in the 30 base network. 61

    Figure (4-11) Expected cost according to the number of scenarios in the 57 bus network. 61

    Figure (4-12) Expected cost according to the number of scenarios in the 118 bus network. 62

    Figure (4-13) scenario tree of active load of 30 buses. 63

    Figure (4-14) reduced scenario tree of active load of 30 buses. 63

    Figure (4-15) active load scenario tree of 57 buses. 64

    Figure (4-16) reduced scenario tree of active load of 57 buses. 64

    Figure (4-17) 118 bus active load scenario tree. 65

    Figure (4-18) reduced scenario tree of active 118 bus load. 65

    Figure (4-19) Reactive load scenario tree of 30 bases. 66

    Figure (20-4) reduced scenario tree66

    Figure (20-4) reduced scenario tree of reactive 30-base load. 66

    Figure (4-21) Reactive load scenario tree 57 Basse. 67

    Figure (4-22) reduced scenario tree of Reactive 57 Basse load. 67

    Figure (4-23) Reactive load scenario tree of 118 bases. 68

    Figure (4-24) reduced scenario tree of 118-base reactive load. 68

    Figure (4-25) scenario tree of active power price. 69

    Figure (4-26) reduced scenario tree of active power price. 69

    Figure (4-27) reactive power price scenario tree. 70

    Figure (4-28) reduced scenario tree of reactive power price. 70

    Figure (29-4) Expected cost according to the 30-basis risk criterion. 72

    Figure (30-4) Expected cost according to the risk criteria of 57 Basse. 74

    Figure (4-31) Expected cost according to the risk criteria of 118 bases. 74

    Figure (4-32) capacitor capacity according to the risk criteria in the 30 bus network. 75

    Figure (4-33) capacitor capacity according to the risk criteria in the 57 bus network. 76

    Figure (4-34) capacitor capacity according to the risk criteria in the 118 bus network. 77

    Figure (4-35) Expected reactive power according to 30-basis risk criteria. 78

    Figure (4-36) Expected reactive power according to the risk criteria of 57 Basse. 79

    Figure (4-37) Expected reactive power according to the risk criterion of 118 bases. 80

    Figure (38-4) 30 bus network voltage fluctuations according to value. 82

    Figure (4-39) voltage fluctuations of the 57-base network in terms of value. 83

    Figure (4-40) voltage fluctuations of 118 bus network according to value. 84

    Source:

    English sources

    [1] K.R.C. Mamundar, R.D. Chenweth, "Optimal control of reactive power flow for improvements in voltage profiles and for real power loss minimization", IEEE Trans. Power App. and Systems, vol. 7, pp. 3185-3194, Jul. 1981.

    [2] 2012-18 - IEEE Standard for Shunt Power Capacitors, Revision of IEEE Std 18-2002 (Revision of IEEE Std 1992-18), Feb. 2013.

    [3] 2004-824 - IEEE Standard for Series Capacitor Banks in Power Systems, Revision of IEEE Std 824-1994, 01 August 2005.

    [4] 2004-824 - IEEE Standard for Series Capacitor Banks in Power Systems, Revision of IEEE Std 824-1994, 22 May 2012.

    [5] J. Fang, Z. Bo, "Modeling of on-load tap-changer transformer with variable impedance and its application", Proceedings of Energy Management and Power delivery, 1998.

    [6] R. Marconato, “Electric Power Systems – Vol.1: Background and Basic Components”, Second Edition, 2002, Edited by: CEI–Italian Electrotechnical Committee, Milan, Italy.

    [7] Franco JF, Rider MJ, Lavorato M, Romero R. A mixed-integer LP model for the optimal allocation of voltage regulators and capacitors in radial distribution systems. Int J Elect Power Energy Syst 2013;48:123–30.

    [8] Goswami SK, Ghose T, Basu SK. An approximate method for capacitor placement in distribution system using heuristics and greedy search technique. Elect Power Syst Res 1999;51(1):143–51.

    [9] Hogan PM, Rettkowski JD, Bala JL. Optimal capacitor placement using branch and bound. In: Proceedings of the 37th annual North American Power, symposium 2005; 23rd–25th October. p. 84-9.

    [10] IEEE Guide for the Application of Shunt Power Capacitors, 978-0-7381-6492-2, 30 March 2012.

    [11] EN 50160 standard, "Voltage characteristics in public distribution networks".

    [12] Khodr HM, Yusta JM, Vale Z, Ramos C. An efficient method for Optimal location and sizing of fixed and switched shunt capacitors in large distribution systems. In: IEEE power and energy society general meeting 2008; 20th–24th July. p. 1–9

    [13] Grainger JJ, Lee SH. Optimum size and location of shunt capacitors for reduction of losses on distribution feeders. IEEE Trans Power Appar Syst 1981;100 (3):1105–18.

    [14] Kaur D, Sharma J. Multiperiod shunt capacitor allocation in radial distribution systems. Int J Elect Power Energy Syst 2013;52:247–53.

    [15] Khodr HM, Olsina FG, Jesus DOD, Yusta JM. Maximum savings approach for location and sizing of capacitors in distribution systems.

Network reactive power planning considering load uncertainty using an evolutionary method