Renewing the arrangement of radial distribution networks in order to reduce losses and improve the voltage profile in the presence of micro-networks

Number of pages: 80 File Format: word File Code: 32116
Year: 2014 University Degree: Master's degree Category: Electrical Engineering
  • Part of the Content
  • Contents & Resources
  • Summary of Renewing the arrangement of radial distribution networks in order to reduce losses and improve the voltage profile in the presence of micro-networks

    Dissertation for M.Sc.

    Inclination: Power

    Abstract

    In recent years, with the advancements made in data processing technology and its transmission, distribution companies are more and more interested in using distribution automation systems. One of the most effective users of automation is the renewal of the distribution network, which is often analyzed as an optimization problem to improve various goals. In this thesis, the problem of re-arrangement in distribution networks along with determining the size of scattered productions is done in order to optimize power losses, voltage profile, load balancing and reduce network cost. Considering that makeup renewal is a combined optimization problem, a hybrid method combining particle group and frog jump algorithms has been used. Before that, the results of this algorithm have been measured in comparison with the genetic method and the particle group algorithm. Finally, the presented framework has been evaluated on 33-base and 70-base systems. In this thesis, MATLAB software programming environment is used to model and implement the system under study.

    Key words: re-arrangement, particle swarm optimization algorithm, frog algorithm, power loss optimization, voltage profile improvement, load balancing, cost reduction

    Introduction

    Rearrangement of distribution networks is used with various goals such as reducing line losses, returning service to subscribers and minimizing areas without electricity, improving voltage profile, load balancing, reducing blackouts and increasing network security. Therefore, it can be said that the reorganization of distribution networks is an optimization problem with multiple objectives and limitations, and obtaining an absolute optimal solution for that problem is difficult. So far, several methods have been presented for the renewal of distribution networks.

    Given the extent of distribution networks and the high volume of energy distributed by them, conducting research and research in the field of effective and economic exploitation of these systems will cause huge savings. One of the modern methods of optimal utilization of distribution systems is to reorganize distribution networks in operation, that is, by changing operating conditions such as changing loads or the occurrence of a fault, we change the network arrangement in such a way that it is technically and economically optimal. There are several technical and economic goals for reorganizing distribution networks. One of the main goals of the renovation is to reduce the ohmic losses of the distribution lines. Due to the extent and low voltage in the distribution networks, the energy losses in these networks are significant, also the renewal of the arrangement may be done in order to balance the load on the feeders. In the situation where a permanent fault has been created, network reconfiguration is used to return service to subscribers and minimize areas without electricity, improving the voltage profile, load balancing, reducing blackouts of consumers and increasing network security are among other goals that have been considered so far for the reconfiguration of distribution networks. In traditional distribution systems, makeup renewal is done seasonally. Manual and automatic switches and separators are used to change the arrangement of these networks. But now, due to the increasing desire to automate (automation) distribution networks, the possibility of controlling and changing the configuration of these networks is becoming easier day by day, and therefore the renewal of the configuration may be done daily or even hourly using automatic switches and remote control. The electric power distribution network supplies various loads such as residential, commercial, industrial loads, etc., which are usually for daily load changes in a wide range. With the increase of loading and exploitation of the existing structure, the probability of voltage collapse in the distribution system increases significantly. Reconfiguration of the distribution system network is the process of changing the topology by opening and closing switches to find a radial performance structure that minimizes losses and improves voltage stability while meeting performance constraints.Considering that the number of keying combinations in the distribution system is large, reconfiguration of the network is a complex and indeterminable constrained optimization problem. In addition, the radial constraint usually increases the complexity of the problem. Distribution networks usually have a radial structure with disconnector switches that are usually closed and communication switches that are usually open to connect feeders that enable load transfer between them. In large distribution systems with many switches, there are many combinations that can meet the demand, and of course, each combination leads to its own topology and power losses. Re-arrangement in distribution systems is to find the optimal arrangement of switches, so that losses are minimized and performance limitations (operation) are maintained including voltage limitation, branch rating and radial structure.

    Network re-arrangement is an effective method to improve system efficiency without any financial investment. However, reconfiguration may not be able to meet the loss reduction and power quality limitations, so grid reconfiguration by placing capacitor or distributed generation sources (DG) [1] is used to achieve better performance. Recently, the use of distributed generation has become a promising solution to offset the challenges posed by traditional power plants such as global warming and poor air quality in urban areas. Dispersed productions are defined as electric power generation sources directly connected to loads or distribution networks (wind turbine, photovoltaic, fuel cell, biomass and micro turbine). Some of them, such as wind turbines and photovoltaics, produce clean energy that can provide environmental benefits to society. Deregulation of the electricity industry also provides enough incentives for investors to invest in DGs and sell their generated electricity in electricity markets. Since the early days of power systems, power losses have been one of the most important issues in distribution systems, and today distribution companies in an irregular (regulatory) environment are looking for optimal use of networks with less losses for more profit. Distributed production is also used to control and reduce losses in distribution systems.

  • Contents & References of Renewing the arrangement of radial distribution networks in order to reduce losses and improve the voltage profile in the presence of micro-networks

    List:

    Chapter One: Introduction

    1-1-Introduction. 2

    1-2- Necessity of research. 4

    1-3-the purpose of research and its importance. 5

    1-4-sections of the thesis. 6

    Chapter Two: Review of the research carried out in the field of renovation in distribution networks.

    2-1- Introduction. 8

    2-2- Mathematical review of the issue of make-up renewal. 8

    2-3- Weaknesses in the existing methods of radiation detection. 14

    2-4- Different methods for the problem of redecoration in distribution systems. 16

    2-4-1- The steps of the DSSHA algorithm are as follows. 18

    2-5- A new algorithm to renew the design with the aim of minimizing power losses and improving the voltage profile 21

    2-6- Redesigning the distribution system using a genetic algorithm based on connection graphs. 26- 2-7- Reorganization of the network to reduce photo loss and improve the reliability based on the improved genetic algorithm 27- 2-8- A more complete and efficient algorithm based on PSO and ACO for re-arrangement of distribution network feeders 30- 2-9 The optimal method for reconfiguration of distribution systems based on OPF and its solution by Bander's analysis method 31

    2-10- Optimum reconfiguration of balanced and unbalanced distribution network considering scattered production. 33

    Chapter three: Renewing distribution networks.

    3-3- Intuitive innovative methods. 44

    3-3-1- branch replacement method. 44

    3-3-2- ring search method, branch removal. 46

    3-3-3- The method of dividing the network into several components. 48

    3-3-4- The method of closing all the keys "in normal state" and then unlocking them 50

    4-3-5- The method of sequentially unlocking the keys 51

    Chapter four: Statement of the problem

    4-1- Introduction. 55

    4-2- Formulation of the optimization problem. 55

    4-2-1- The design variables in question. 55

    4-3- target functions. 56

    4-3-1- Resistance losses. 56

    4-3-2- Improvement of voltage profile. 56

    4-3-3- line load balancing. 56

    4-3-4- Cost. 57

    4-3-5- multi-target index. 57

    4-4- Limitations of the problem. 58

    4-4-1- The limitation of the voltage of the clamps 58

    4-4-2- The limitation of the power of the lines. 58

    4-4-3- Limitation of network arrangement. 58

    4-5- Optimization algorithm. 58

    4-5-1- Memory cluster optimization algorithm. 58

    4-5-2- proposed combination algorithm. 60

    4-5-3- The application of the presented algorithm on the problem of redecoration. 61

    Chapter five: review and analysis of simulation results.

    5-1- Introduction. 64

    5-2- Algorithm performance comparison 66

    5-3- Comparison of optimization results and other objective functions. 75

    Chapter Six: Conclusions and Proposals.

    6-1- General conclusion. 82

    8-2- Suggested solutions for future researches. 83

    Chapter Six: References

    Source:

     

    [1] Ahmadi, Hamed, and José R. Mart?. "Mathematical representation of radiality constraint in distribution system reconfiguration problem." International Journal of Electrical Power & Energy Systems 64 (2015): 293-299.

               

    [2] de Oliveira, Edimar José, et al. "New algorithm for reconfiguration and operating procedures in electric distribution systems." International Journal of Electrical Power & Energy Systems 57 (2014): 129-134.

     

    [3] Hong Ying-Yi, Ho Saw-Yu. Determination of network configuration considering multiobjective in distribution systems using genetic algorithm. IEEE Trans Power Syst 2005;20(2):1062–9.

     

    [4] Ferdavani Ali Khorasani, Zin Abdullah Asuhaimi Mohd, Khairuddin Azhar, Naeini Marjan Mortazavi. Reconfiguration of distribution system through two minimum-current neighbor-chain updating methods. IET Gener Trans Distrib 2013;7(12):1492–7.

     

    [5] Khodr HM, Martinez-Crespo J, Matos MA, Pereira J. Distribution systems reconfiguration based on OPF using benders decomposition. IEEE Trans Power Deliv 2009;24(4):2166–76.

     

    [6] Jazebi Saeed, Vahidi Behrooz. Reconfiguration of distribution networks to mitigate utilities power quality disturbances. Electr Power Syst Res 2012;91:9–17.

     

    [7] A. Mohamed Imran, M. Kowsalya" A new power system reconfiguration scheme for power loss minimization and voltage profile enhancement using Fireworks Algorithm" Electrical Power and Energy Systems 62 (2014) 312-322.

    [8] Ghasemi, S., and J.Moshtagh. "A novel codification and modified heuristic approaches for optimal reconfiguration of distribution networks considering cost losses and cost benefit from voltage profile improvement." Applied Soft Computing 25 (2014): 360-368.

    [9] Sedighizadeh, Mostafa, Masoud Esmaili, and Mobin Esmaeili. "Application of the hybrid Big Bang-Big Crunch algorithm to optimal reconfiguration and distributed generation power allocation in distribution systems." Energy 76 (2014): 920-930.

     

    [10] Tomoiag?, Bogdan, et al. "Distribution system reconfiguration using genetic algorithm based on connected graphs." Electric Power Systems Research 104 (2013): 216-225. [11] Duan, Dong-Li, et al. "Reconfiguration of distribution network for loss reduction and reliability improvement based on an enhanced genetic algorithm." International Journal of Electrical Power & Energy Systems 64 (2015): 88-95. [12] Shareef, H., et al. "Power quality and reliability enhancement in distribution systems via optimum network reconfiguration by using quantum firefly algorithm." International Journal of Electrical Power & Energy Systems 58 (2014): 160-169. [13] Niknam, Taher. "An efficient hybrid evolutionary algorithm based on PSO and ACO for distribution feeder reconfiguration." European Transactions on Electrical Power 20.5 (2010): 575-590. [14] Khodr, H. M., et al. "Optimal methodology for distribution systems reconfiguration based on OPF and solved by decomposition technique." European Transactions on Electrical Power 20.6 (2010): 730-746.

    [15] Aghaei, N. Amjady, and H. Shayanfar, "Multi-objective electricity market clearing considering dynamic security by lexicographic optimization and augmented epsilon constraint method," Applied Soft Computing, 2011.

    [16] Esmaeilian, Hamid Reza, and Roohollah Fadaeinedjad. "Distribution system efficiency improvement using network reconfiguration and capacitor allocation." International Journal of Electrical Power & Energy Systems 64 (2015): 457-468. [17] Vaidyanathan, Ramachandran, and Jerry L. Trahan, eds. Dynamic reconfiguration: architectures and algorithms. Springer, 2003.

     

    [18] Yang HuPing, Peng YunYan, Xiong Ning, "Gradual Approaching Method for Distribution Network Dynamic Reconfiguration", 2008 Workshop on Power Electronics and Intelligent Transportation System

     

    [19] Taher, Seyed Abbas, and Mohammad Hossein Karimi. "Optimal reconfiguration and DG allocation in balanced and unbalanced distribution systems." Ain Shams Engineering Journal (2014).

     

    [20] Chang, Chung?Fu. "Optimal reconfiguration and capacitor placement by robust searching hybrid differential evolution." European Transactions on Electrical Power 20.8 (2010): 1040-1057. [21] Pfitscher, L. L., et al. "Intelligent system for automatic reconfiguration of distribution network in real time." Electric Power Systems Research 97 (2013): 84-92.

     

    [22] Algorithm,” IEEE international conference on Design Using A New Shuffled Frog Leaping ] T.-H. Huynh, D.-H.Nguyen, "Fuzzy Controller industrial technology, 2009.

     

    [23] J.-P. Chiou, C.-F.Chang, and C.-T. Su, "Variable scaling hybrid differential evolution for solving network reconfiguration of distribution systems," Power Systems, IEEE Transactions on, vol. 20, pp. 668-674, 2005 [24] Merlin A., Back H., "Search for minimum-loss operating spanning tree configuration in an urban power distribution system", 5th Power System Computation Conference, 1975. [25] Shirmohammadi D, Hong H W. Reconfiguration of electric distribution networks for resistive line losses reduction [J]. IEEE Trans on Power Delivery, 1989, 4(2): 1492-1498

     

    [26] Baran, M.E., Wu, F.

Renewing the arrangement of radial distribution networks in order to reduce losses and improve the voltage profile in the presence of micro-networks