Zuhusna Adilla Binti IbrahimB011110121

Supervisor : Encik Mohamad Fani bin Sulaima

Distribution Network Reconfiguration (DNR) Using Improved Artificial Bee Colony (IABC) For Energy Saving

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Motivation

 In Malaysia, the growing industrialization and increasing standard of living has considerably increased the usage of energy. The increasing demand of the electrical energy is quietly related to the power demand. In order to cope the demand of the electricity, the distribution system has become more complex and causing power loss always occurred while distributing the electric. To reduce the power loss, the network distribution system needs to be reconfigured.

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• The demand for the electricity is rising due to the increasing population group.

• The distribution system has become more complex.• The current drawn increasing during the distribution of

electricity which lead to the instability.• As the system unstable, the power losses will occur.

Problem Statements

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Research BackgroundPower System

Generation Transmission Distribution

LoopMeshRadial

DNRAct of

opening and closing

switches

Easy to analyze and isolate fault

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Research BackgroundOptimization Technique

Heuristic Artificial Intelligence

ABCGAANN

Works by mimicking bee

behavior of finding food

source

Optimal Flow

Pattern (OFP)

Branch Exchange Method (BEM)

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Scope

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Previous WorkAuthor Project Title Method Used Description Comment

R.J Safri, M.M.A Salama, A.Y Chikhani

Distribution System Reconfiguration for Loss Reduction : A New Algorithm based on a set of Quantified Heuristic Rules

Quantified Heuristic Rules

Aim to reduce power losses

The method serves as pre-processor by removing the undesirable switching

Does not perform the complex analysis load flow.

This proposed method does not perform the load flow analysis

A new artificial intelligence technique is proposed

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Author Project Title Method Used

Description Comment

S. Ganesh Network Reconfiguration of Distribution System Using Artificial Bee Colony Algorithm

ABC algorithm technique

Aim to minimize power losses

The ABC is tested on the 33-bus system

Compared with Refined Generic Algorithm (RGA) and Tabu Search Algorithm (TSA)

ABC has the best performance in minimizing power losses.

Does not apply the improved ABC algorithm

Does not improve the voltage profile

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Author Project Title Method Used

Description Comment

M. Assadian, M.M Farsangi, Hossein

GCPSO in cooperation with graph theory to distribution network reconfiguration for energy saving

Guaranteed Convergence Particle Swarm Optimization (GCPSO) and Particle Swarm Optimization (PSO)

Objectives are to reduce power loss and enhancement of voltage profile

Compared with applied GA + GCPSO

Results show that the GA and GCPSO are better than conventional PSO in term of energy saving.

The paper does not show the cost saving

The proposed method does not show the value of energy saved.

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METHODOLOGY

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MethodologyStart

Initialization Phase

Employed Bee Phase

Onlooker Bee Phase

Scout Bee Phase

Memorize the best solution

Exceed maximum

cycle?

Stop

No

Yes

Flowchart of ABC

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Improved Artificial Bee Colony (IABC) Technique

• Inspired by the improved strategies of Particle Swarm Optimization (PSO)• An inertial weight w inspired by PSO evolution equation and its improving

strategies are added.• The benefits of using this technique are: Maximize the exploitation capacity Balanced the exploitation and exploration phase

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Start

Initialization Phase

Employed Bee Phase

(Weight is added here)

Onlooker Bee Phase

Scout Bee Phase

Memorize the best solution

Exceed maximum

cycle?

Stop

No

Yes

Flowchart of IABC

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Energy Saving Formulation

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RESULTS AND DISCUSSION

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Test System Analysis

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• In this system, the 33-bus initial configuration are consists of:

• 1 feeder, 32 normally closed tie line and 5 normally open tie lines.

• The normally open tie lines are represented by 33, 34, 35, 36 and 37 branches.

Sectionalizing Switch

Tie Switch

Figure 1: IEEE 33-bus radial original network configuration

Test System Analysis

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• The IABC algorithm is tested on 33-bus network system for 30 times.

• From the 30 run times, only 12 of them are radial.

• The best combination of switches that has been chosen is at 20 because value of power loss at this 20th running times is the lowest which is 107.1 kW and has the fastest computational time (1222.6623s).

• The best combination switches are opened at S31, S6, S21, S13 and, S37

Test System Analysis

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Figure 4.2: The Power Loss after IABC Network Reconfiguration

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Power Losses

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Test System Analysis

Figure 5.1: Power Loss (kW) Comparison between the Network Reconfiguration

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Figure 5.2: Loss Reduction Comparison between the Network Reconfiguration

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Voltage Profile

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Figure 4.5: Voltage Profile of the Three Network Reconfiguration System25

Energy Saving & Cost Saving

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Company SAIDI (Minute)2008 2009 2010 2011 2012 2013

 TNB

 

 68.31

 56.72

 88.1

 63.25

 49.30

 56.20

Data from SAIDI (TNB)

Table 4.3: The Average SAIDI data in Peninsular Malaysia [22]

Region Electricity Average Selling Price (sen/kWh)

Peninsular Malaysia 33.88

Table 4.4: The Electricity Average Selling Price (sen/kWh) [22]

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Energy Saving

Network Reconfiguration

Initial Network ABC IABC

Total Power Loss (kW)

202.71 134.26 107.10

Energy (kWh) 4 833.82 3201.56 2553.90Total loss Cost for

one day (RM)1 637.70 1084.69 865.26

Table 5.2: The total energy and total cost loss in one day

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Total Cost Loss

Figure 5.3: The Monthly Cost Loss of the Network Reconfiguration

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Figure 5.4: Total Cost Loss for a Year

Total Cost Loss

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Conclusion

• IABC algorithm technique has shown a good performance in minimizing the power loss when it is compared to the ABC and other optimization method

• Succeeded in reducing the energy losses in the distribution network system• The objectives of this study have been achieved successfully

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Recommendation

• Tested on 14-kV and 69-kV IEEE test bus system in order to get better outcomes and analysis.

• To consider the Distribution Generators (DGs) in the future.

• To consider the power quality.

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References[1] R.J Safri, M.M.A Salama and A.Y Chickani, “Distribution system reconfiguration for loss reduction: a new algorithm based on a set of quantified heuristic rules”, Proceedings of Electrical and Computer Engineering, Vol. 1, Canada , pp. 125-130,1994.[2] S. Ganesh, “Network Reconfiguration of Distribution System Using Artificial Bee Colony Algorithm”, International Journal of Electrical, Robotics, Electronics and Communication Engineering, Vol.8, No. 2, pp. 403-409, 2014.[3] M. Assadian, M. M. Farsangi, Hossein Nezamabadi, “GCPSO in cooperation with graph theory to distribution network reconfiguration for energy saving”, Energy Conversion and Management vol. 51,pp. 418-417, 2010. [22] Suruhanjaya Tenaga, Performance and Statistical Information on Electricity Supply Industry in Malaysia, pp. 22-24, 2013.[14] M. Rohani, H. Tabatabaee & A. Rohani, “Reconfiguration Optimization for Loss reduction in Distribution Networks using Hybrid PSO Algorithm and Fuzzy Logic”, MAGNT Research Report, Vol. 2(5), pp. 903-911, 2011

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