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Application Of Bat Algorithm-Based Method For Multi-Objective Optimal Network Reconfiguration And Distributed Generation Placement In Radial Distribution Network

ABSTRACT

This dissertation presents an Application of Bat Algorithm-Based Method (BA) forMulti-Objective Optimal Network Reconfiguration and distributed Generation placement in Radial Distribution Network. The BA approach presented in this work enables the reconfiguration of the network as well as the optimal distributed generation(DG) placement in one seamless algorithm. This would minimize errors encountered in using analytical approaches and also improve on the accuracy of the results obtained. In the developed method, the base case active and reactive power losses for the standard IEEE 33-bus were first determined using forward-backward algorithm as 208.46kW and 111.67kW respectively. Then, the developed BA method was applied on the IEEE-33 to determine the optimal DG sizes and the location as well as the optimal reconfiguration of the network.The DGssizes and locations were determined as 957kW, 870kW, 822kW as well as 593kVar, 539kVar, 509kVar at buses 29, 31 and 12 respectively. The total active and reactive power loss obtained after the DG placement as well as network reconfiguration were 15.2353kW and 12.0593kVAr respectively.Thus, the developed method recorded a loss reduction of 92.6915% and 53.56% for active and reactive power loss respectively over the base case, while the voltage profile of 0.91075pu for base casewas improved to 0.9918pu.Furthermore, the results were compared with the work of Syahputraet al., wherethe developed method recorded an improvement of 6.32% on active power loss reduction and 1.04% on voltage profile improvement over the results of Syahputra. The developed method was also implemented on a simulated feeder of Sabon-Gari at Zaria distribution network with the view to optimize the synchronous DG placement as well as network reconfiguration. The results indicated that active and reactive power losses were reduced by 88.77% and 88.18% respectively, while the voltage profile has beenimproved to 4.1% over the base case.All simulations were implemented on MAT LAB 2013b environment.

CHAPTER ONE

INTRODUCTION 1.1 Background Information Distribution network (DN) is the final stage of electric power delivery. The system conveys electrical energy from transmission system to electrical energy consumers. Generally, distribution networks are configured in a radial pattern against meshed configuration used in transmission networks, which makes power flow unidirectional. This often results in power and voltage reduction delivered at the consumer load points where the demand is high. Distribution networks normally have much power loss and poor voltage regulation due to high load current and low voltage in the distribution network (Nguyen et al.,2016) Thus, the performance improvement of the radial distribution network is usually premised on minimization of power loss and voltage deviation. Many efforts have been put in place to mitigate power losses and improve voltage stability in distribution network such as distribution synchronous static compensator (DSTATCOM), voltage source converter (VSC) such DC link Capacitors placement, network reconfiguration, DG placement and many others (Shamsuddin et al., 2014) However, excessive power loss and voltage deviation in the distribution network may further require combination of network reconfiguration and placement of distributed generation, so as to reduce high power losses, poor voltage regulation and improve loading capacity margin (Ali et al., 2015)
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Distribution Network Reconfiguration (DRN) is the process of varying the topology of distribution network by changing the closed/open condition of sectionalizing and tie switches while abiding by distribution system constraints (Rao et al., 2013). On the other hand, Distributed Generation (DG) as defined by the International Energy Association (IEA), as a generating plant serving customers on-site or providing support to distribution network connected to the grid at distribution voltage level (Kansal et al., 2011) The current drive towards power loss reduction, voltage deviation minimization and load balance enhancement tend to favor the introduction of distributed generations (DGs) at distribution load centers, in view of the advantages of the DGs over the expansion of large central power generation plants. Some of these advantages include reduction in line losses, reduction in overall operation costs due to improved efficiency, peaking shaving, improvement of voltage profile, system reliability, ease of finding sites for smaller generators, availability of modular generating plant, proximity to heavy loads and security (Prakash & Khatod, 2016)
There are several types of DG technologies in modern power system generation. These include, solar PV, wind power, small hydro, pumped hydro and synchronous diesel or gas generators. Because solar PV and wind generate power intermittently and require expensive energy storage systems, they are often employed as off-grid DGs, since their unit cost of electricity generation is much higher than the central grid system. The hydro and diesel/gas synchronous generators on the other hand, have been preferred as grid connected DGs due to their ability to generate fairly constant power as well as their ability to provide both active and reactive power at relatively comparative cost with the central grid system. In addition, the synchronous DGs ensure local generation of reactive power and reduces the import of reactive power from the feeder, thus
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reduces the associated losses and improves the voltage profile. As a result, voltage security is improved (Prakash & Sujatha, 2016) The placement of the DG in the distribution network, however, is often constrained by the size and location of the DG on the distribution network, which if not optimally located and sized, their deployment in distribution network could pose serious technical challenges that may affect the stability of the power system as well as its quality (Buaklee & Hongesombut, 2014). Similarly, the distribution network reconfiguration requires some optimization schemes to help fashion out the optimal network topology that would ensure minimum power loss and voltage deviation in the network. The recent approaches to network reconfiguration involved numerical methods as in (Merlin & Back, 1975), analytical methods as in (Baran & Wu, 1989) and heuristics methods as in (Li et al., 2016). On the other hand, the recent approaches to network reconfiguration and DG placement in the distribution network have been mostly meta-heuristics approaches as in (Syahputra et al., 2015) where extended particle swarm optimization (PSO) was employed (Hivziefendić et al., 2016) In this research work, an application of Bat Algorithm (BA) based method is presented for multi-objective optimal network reconfiguration and distributed generation placement in radial distribution network considering Sabo-Feeder in Zaria distribution network as a case study.
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1.2 Motivation The incessant power loss and poor voltage level due to varying loads in the distribution network has prompted the power utility to search a quicker and relatively less expensive approach for reducing power loss, improve voltage profile as well as relieving the load among the distribution feeders. This has brought about the choice of distribution network reconfiguration with DG deployment technique to solve the stated problem, hence a motivation for this research work 1.3 PROBLEM DEFINITION Minimum power loss and voltage deviation in conventional radial distribution network could not achieved for a fixed network configuration due to varying loads, which increases load current drawn from the network .This effect makes the network inefficient, owing to voltage magnitude reduction and increase in network losses. Hence, there is a need for reconfiguration of the network from time to time for optimal network performance. In addition, the total load is more than the system generation capacity due to dynamic nature of the load as such eliminating the load on the feeder could not be possible, hence DG units could be further installed to meet the required level of voltage profile improvement and power loss minimization for overall distribution network performance improvement. The recent approaches to network reconfiguration involved heuristics as in Li et – al, (2016) and Meta heuristics methods as in Kunya et – al (2016), On the other hand, the recent approaches to DG placement in the distribution network have been mostly Meta heuristics approaches as in Prakash & Sujatha.
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In most of the past research efforts, the framework for optimal network reconfiguration of the distribution network and DG placement considered the two optimization problems independently. However, attempts were made recently to consider the two optimization problems simultaneously by employing multi-objective modeling framework using extended particle swarm optimization (PSO) algorithm as in Syahputraet al and a multi-objective approach using a harmony search algorithm as in Rao et al. However, in view of the need to further improve accuracy and speed of computation, other meta-heuristic approaches, such as bat algorithm could be explored to achieve better DNR and DG deployment. Hence, this research work, presented an application of multi-objective bat algorithm-based method for optimal network reconfiguration and DG placement in a radial distribution network. The results obtained from this work have been compared with works of Syahputra 2015 to validate the research work. 1.4 Aim and Objectives The aim of this dissertation is the application of Bat algorithm-based method for multi-objective optimal network reconfiguration and distributed generation placement in radial distribution network to minimize total power loss, voltage deviation and enhance load balancing in distribution feeders, considering Zaria (Sabo-Feeder) distribution network as a case study.
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To attain the aim, the set objectives are given as follows, 1. Determination of Base Case Network Parameters Using Backward-Forward Algorithm 2. Application of Bat Algorithm (BA) Method for Multi-Objective Optimal Network Reconfiguration and DG Placement 3. Validation of the developed method on standard IEEE-33 bus system by comparing with the Work of Syahputraet al., 2015 on MATLAB Platform. 4. Implementation of the developed method on Sabo feeder in Zaria distribution network 1.5 Methodology The following approach was adopted in order to achieve the stated aim and objectives. 1. Adoption of IEEE-33 bus data and Zaria distribution network data The following data have been obtained and analyzed:
i. Line data; impedance data (resistance and reactance) of each line
ii. Bus data ; active and reactive power demand at each bus with the exception of slack bus
iii. Network base voltage
iv. Sending and receiving end buses data
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2. Determination of Base Case Network Parameters using backward-forward algorithm The following steps could be followed; i. Formulation of forward-backward sweep equations ii. Loading of the line and bus data for base case iii. Computation of the load current and bus voltages at each bus 3. Application of BA for optimal reconfiguration and DGs placement The following steps were followed for optimal DNR and DG Deployment i. Initialization of the Bat algorithm ii. Initialization of the population iii. Selecting the appropriate tuning parameters 4 Validation of the Bat algorithm on IEEE-33 Bus System
i. Loading of line and bus data of IEEE-33 bus
ii. Analysis of base power flow
iii. Initialization the Bat parameters
iv. Application of bat algorithm with the formulated objective functions
v. Analysis of final power flow for reconfiguration and DGs at critical buses
vi. Validation of the developed Method by comparing with the work of Syahputraet al., (2015)
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5 Implementation of the Developed Method on Zaria (Sabo-feeder) Distribution Network
i. Load the line and bus data of Zaria distribution network
ii. Analysis of base power flow for Zaria distribution network
iii. Initialization of the Bat parameters for Zaria distribution network
iv. Application of the BA with the formulated objective functions
v. Simulation of final power flow analysis for reconfiguration and DGs incorporated at the optimum buses of Zaria distribution network.
1.6 Dissertation Organization In chapter one, a general background introduction of distribution network reconfiguration and DG placement concepts were presented accordingly. Chapter two provides a concise review of the fundamental concepts and review of similar works on distribution network reconfiguration and DG placement. The research methods and materials, which include determination of radial network reconfiguration, placement and sizing of DGs were presented in chapter three. In chapter four, the results and discussions based on network reconfiguration and DG deployment were presented, while chapter five discussed on the significance contribution of the research work, conclusion, limitations of the research work and recommendation for further work.

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