Project Materials




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Chapter One:


1.1 General Background

Because of ongoing economic growth and development, load demand in distribution networks is prone to sudden increases. As a result, most developing countries, including Nigeria, have distribution networks that operate extremely close to voltage instability boundaries. The loss in voltage stability margin is one of the major issues limiting the increase in loads served by distribution firms (Jain et al., 2014).

The constantly expanding demand for electrical power, as well as the difficulty in meeting capacity requirements using traditional solutions such as transmission network expansions and substation modifications, motivates the use of Distributed Generation (DG).

DG can be integrated into distribution systems to improve voltage profiles, power quality, and overall system performance (Muttaqi et al., 2014). When DG units are linked into distribution networks

they provide ancillary services such as spinning reserve, reactive power support, loss compensation, and frequency management. On the other side, poorly designed and operated DG units can result in reverse power flows, high power losses, and consequent feeder overloads (Atwa et al., 2010).

The DG solution may be more cost effective because it offers the system with increased supply capacity and a power reserve. It provides an alternate source that can assist meet expanding power demand, enhance power supply dependability and efficiency, and lower electricity costs during peak hours (Leite da Silva et al., 2012).

According to Georgilakis and Hatziargyriou (2013), DG placement has a significant impact on how the distribution network operates. Inappropriate DG placement can raise system losses, network capital, and operating expenses.

Optimal DG placement (ODGP) can enhance network performance by improving voltage profile, reducing power flows and losses in distribution lines, and improving power quality and supply reliability.

Soudi’s studies (Soudi, 2013) demonstrated that identifying the right position and size of DG is critical to maximising the benefits of DG deployment in distribution systems.

The findings of his research revealed that the appropriate use of DG may cut system losses by up to 47%, power purchase costs by up to 92%, and energy not supplied costs by 40%. The voltage profile of the distribution system has also significantly improved.

Because the integration of DGs into a distribution network can either improve or degrade network performance, distribution businesses need ways to measure the capacity and location of these new DGs that may be connected to distribution networks.

This work has generated significant interest because to its diverse methodologies, objectives, and constraints (Gomez-Gonzalez et al., 2012).

Different researchers offered various methodologies, objectives, and limits. Methods used include the classical or numerical method presented by Atwa et al., (2010), Ochoa and Harrison (2011), and Rau and Wan (1994); and the analytical approach presented by Wang and Nehrir, (2004), Acahrya et al., (2006), Gözel and Hocoaglu, (2009), Hung et al., (2010), Hung et al., (2013), and Hung et al., (2014).

Another method utilised is the heuristic approach, which was proposed by Abou El-Ela et al. (2010), Soroudi and Ehsan (2011), Akorede et al. (2011), Vinothkumar and Selvan (2011), and Vinothkumar and Selvan (2012).

Some researchers have also employed integrated solution methods, which include employing multiple approaches, as demonstrated by Afzalan and Taghikhani (2012) and Moradi and Abedini (2012).

These methods also introduced several sorts of objective functions, ranging from single to multiple objectives, as well as different types of restrictions.

This dissertation introduces a hybrid strategy that combines analytical and meta-heuristic algorithms to optimise the location and size of DGs in radial distribution networks.

1.2 Aims and Objectives

The goal of this project is to create a hybridised strategy for appropriate DG siting and sizing in radial distribution networks in order to reduce total actual power loss and improve the network’s voltage profile. The aims include creating a common analytical procedure for determining the appropriate location and size of DGs.

2. Creation of a firefly algorithm for the best placement of a DG.

3. Hybridization of objectives 1 and 2 to incorporate the analytical solution for sizing only before searching for the ideal location of the DG using the metaheuristic method.

4. Validation of the hybridised solution technique using standard IEEE-33 and 69 radial test buses.

1.3 Statement of Problem

The most common challenge encountered with integrating DGs into a distribution system is determining how to integrate them in order to increase the system’s performance. It can be difficult to find the best placement and size for DGs.

Analytical and heuristic methods are most commonly employed for locating and sizing DGs in a distribution network. For the problem of optimal DG allocation, the analytical method appears simple to construct and execute, but it is computationally exhaustive and time expensive, whereas most meta-heuristic methods appear robust but rarely offer optimal solutions.

Integrating an analytical concept into a meta-heuristic algorithm can address the shortcomings of both methods, including computational exhaustion and time consumption, as well as non-optimal results.

This approach can lead to an optimal time-saving solution (4). This will use the analytical method’s precision as well as the meta-heuristic algorithm’s flexibility and robustness.

1.4 Motivation.
A substantial increase in energy demand has prompted energy companies to seek a faster and less expensive method of restoring the diminishing dependability and stability of power distribution networks.

This has prompted the decision to employ Distributed Generation as an emergency solution to this situation because it is less expensive than traditional transmission extension or network reconfiguration.

The next challenge was how to successfully integrate these DGs into the distribution network in order to meet the goal of boosting distribution network performance rather than the opposite. This served as impetus for this study.

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