Development of an Optimal Reconfiguration Algorithm for Radial Distribution Electrical Power Networks (a Case Study of Zaria Distribution Network)

Development of an Optimal Reconfiguration Algorithm for Radial Distribution Electrical Power Networks (a Case Study of Zaria Distribution Network)

Chapter One

Aim and Objectives

The aim of this research is to develop an optimal reconfiguration algorithm for radial distribution networks, using non-dominated sorting genetic algorithm (NSGA-II) with a view to improving the performances of a radial distribution system through minimizing its active power loss and reduce total voltage deviation.

The objectives of this research are itemized as follows:

Development of an optimal reconfiguration algorithm using fast and elitist non-dominated sorting genetic algorithm (NSGA-II) to determine the optimal location of tie and sectionalizing
Determination of the location and size distributed generation for the distribution
Investigation of the effect of distributed generation placement on the power quality for the distribution
Comparison of the results obtained from the normal and reconfigured network in order to determine the extent of reductions in both active power loss and total voltage deviation
Comparison of the efficiency of the proposed model with existing model as a means of validations.

CHAPTER TWO

LITERATURE REVIEW

Introduction

This chapter is divided into two parts. The first part discussed the fundamental concepts relevant to the study and the second part provides a review of related works.

Conventional Approach

This section presents an overview of some of the conventional approaches and fundamental concept critical to the concept of network reconfiguration.

Determination of Radial Configuration

The number of possible radial structure for a distribution network can be obtained for a given configuration. The number of branches needed to maintain a radial network with redundant connections is fixed and can be obtained using (Chicco, 2013):

A complete distribution network (graph), with 14 nodes, the number of possible radial configurations are 1.07× 10¹⁶. A practical distribution system has a number of branches that is far less than the complete graph, as such the number of possible configuration can be obtained using Kirchhoff matrix tree theorem.

Kirchhoff Matrix Tree Theorem

Kirchhoff matrix theorem can be applied to determine the number of radial configuration extracted from a weakly structure of an electrical distribution system (Chicco, 2013). The number of possible radial configuration can be obtained by using the determinant of the Laplacian matrix. The Laplacian matrix is a square matrix and can be obtained using (Chicco, 2013):

ì no of branches connected to node. for i = j

Iij

= ï- 1 if (i ¹ j) and node i adjacent to node j

ï otherwise

The number of possible radial configuration can be obtained using:

f = det( I ij )

For real network, the exploration can be quite complex and time consuming or even impossible reasonable computational times. As such, the need to discuss other possible approaches to distribution network reconfiguration.

Heuristic Approach to Distribution Network Reconfiguration

This section presents an overview of some of the heuristic approaches used in network reconfiguration.

Branch-and-Bound Technique

Branch-and-Bound technique is a general algorithm for finding optimal solutions of various optimization problems, especially in discrete optimization. It consists of a systematic enumeration of all candidate solutions, while discarding infeasible solution based on the upper and lower bound of the optimized quantities. The concept of branch-and-bound technique is to search into the graph of the distribution network, with a view to obtaining the minimal loss configuration, while satisfying radial constraint by using:

PT = Min.å Pj x j

j =1

Where:

ì1

x j = í

Status of the branch

Pj : Branch power loss

The basic idea of the heuristic branch exchange is to compute the change of power losses by operating a pair of switches with the aim of achieving reduction in system active power loss (Zhu, 2009). The main advantage of the Branch-and-bound is its simplicity and ease of comprehension, while its disadvantages are:

Final system configuration depends on the initial
It is time consuming for selecting and operating each pair of switches 2ⁿ is the possible switching combinations.
The solution is a local optimal, rather than a global optimal

Optimal Flow Pattern

If the impedance of all branches in the loop network is replaced by their branch resistances, the load flow distribution that satisfies the Kirchhoff current law and Kirchhoff voltage law is called an optimal flow pattern. When the load flow distribution in a loop is an optimal flow, the corresponding network power losses will be minimal. The steps of the heuristic algorithm based on optimal flow pattern are itemized as follows (Zhu, 2009):

Compute load flow of the initial network.
Close all normal open switches to produce loop
Compute the equivalent currents at all
Replace all branch impedances by their corresponding branch resistance and run a power
Open a switch of the branch that has minimal current value in the Recompute the load flow for the remaining network.

Ruled-Based Comprehensive Approach

This method combines both the branch and exchange methods and a set of rules. The rules used to select the optimal reconfiguration of the distribution network are formed based on system operation experiences (Zhu, 2009). The rule based method can be expressed by the use of the following (Zhu, 2009):

CHAPTER THREE

MATERIALS AND METHODS

Introduction

This chapter present a details mathematical model equation for computing parameters of the distribution network, load model, model of the distributed generation, problem formulation and a detail of the developed non-dominated sorting genetic algorithm (NSGA II).

Model Equation of the Two Bus Distribution Network

Considered a n bus distribution network, reduced to a two bus distribution network as contained in Figure 3.1.

Modelling of Distributed Generation

The inclusion of a distributed generation to the radial system results in a network that may not be strictly radial (weakly meshed network) and their feeders may carry power whose direction changes as a function of the loading and distributed generation levels. In distribution system power flow analysis, distributed generation are model as either constant active and reactive power (PQ) or constant active power and voltage (PV) model.

Constant Active and Reactive Power Model

The constant active and reactive power models are identified with a constant power load model except that the current is injected into the bus. (Eminoglu and Hocaoglu, 2008). In this model the distributed generation is modelled as a negative load which alters the direction of flow of current in the radial system (acting as generator). In this model the total load at the bus where the distributed generation is situated is increased, but its challenge is its ability to handle or control their reactive power output in order to maintain a specific terminal voltage. The constant active and reactive power models are expressed using:

CHAPTER FOUR

RESULTS AND DISCUSSIONS

Introduction

This section discusses the results obtained from the standard IEEE network before and after reconfiguration. The results obtained after application of reconfiguration model on the Zaria distribution network are also analyzed and discussed.

Comparative Study Before and After the Application of Reconfiguration

The developed reconfiguration model was validated using a standard 33 Bus distribution network with line and load data as contained the works of Baran and Wu (1989) and Srinivas et al., (2009). The 33 IEEE bus test feeder comprises of 33 nodes, with 5 tie switches, 32 sectionalizing switches as shown in Figure 4.1.

CHAPTER FIVE

CONCLUSION AND RECOMMENDATIONS

Introduction

This section present the limitation, conclusion and recommendation for further work.

Conclusion

The research has developed an approach to distribution network reconfiguration using enhance non-dominated sorting genetic algorithm (NSGA-II) multi-objective based, considering active power loss and total voltage deviation as the main objective function. The developed algorithm was tested using sample of data extracted from Zaria distribution network. The result revealed several feasible switching state for the various distribution system as compared to the normal network. Optimal locations of tie switches (open branches) were found to be at branches 12, 14 and locations 14, 16, 18 and 25, 20, 10 and 23, 16, 43 for Gaskiya, Railway, Sabo and Canteen distribution network respectively. An improvement in active power loss reduction of 37.14%, 18.22%, 39.21% and 23.42% as compared to the active power loss of the normal network (55.32kW, 17.22kW, 120.08kW and 508.3kW) for Gaskiya, Railway, Sabo and Canteen distribution network respectively. While a reduction in total voltage deviation of 9.43%, 9.02%, 37.81% and 10.72% as compared to the total voltage deviation of the normal network (0.2672V, 0.2340V, 0.9949V and 4.7482V) for Gaskiya, Railway, Sabo and Canteen distribution network respectively. Based on the result obtained, it can be concluded that the total active power and total voltage deviation has been estimated for different switching state, using non-dominated sorting genetic algorithm (NSGA II). Also a noticeable reduction in active power loss and improvement voltage profile were recorded for all the sample of distribution network, with the introduction of distributed generation.

Limitations

During the course of this work, certain limitation were observed which are itemized as follows:

The scope of this work was limited to a balanced network, hence the effect of unbalanced nature of the distribution network were not captured.
The enhance dominated sorting genetic algorithm explore the search space and performs best at a high generation, as such increase the computation burden consequently affecting the convergence
The dynamic nature of load for a typical distribution network was not considered.

Recommendation for Further Work

Future works should consider the following areas:

This algorithm can be developed and extended to an unbalanced distribution network, so as to capture the exact nature of a distribution system.
The use of hybrid algorithm can be adopted to enhance the convergence time, while simultaneously exploring the search space.

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