Abstract
The primary goal of this thesis is to develop an optimal placement model for various types of Flexible AC Transmission System (FACTS) devices within power grid networks to increase the loadability of the network while considering budget constraints as a critical factor. FACTS devices play a critical role in today's power grids by improving manageability and stability and enhancing grid efficiency. Through power flow control capabilities provided by these devices, they can effectively boost the load capacity of the transmission system by directing power along congested and more effective pathways. This also aids in reducing blockages resulting from power flow patterns. It further boosts the efficiency of energy distribution within the grid system.
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This thesis tackles the optimal placement problem by focusing on determining the locations, quantity and settings for deploying FACTS devices in a transmission network while adhering to budget limitations. The thesis introduces two situations: the problem of placing a single type and the problem of identifying multiple types. In the single-type scenario, the model decides on the location quantity and parameters for a FACTS device in the placement situation involving types of devices to enhance system loadability.
The thesis investigates four kinds of FACTS devices known as TCSC (Thyristor Controlled Series Capacitor), TCPST (Thyristor Controlled Phase Shifting Transformer), STATCOM (Static Compensator) and ST (Sen Transformer). Each of these devices possesses features that enhance power flow efficiency, system loadability and other power system benefits. The combination of device types offers an adaptable and thorough optimization strategy by exploring the possible synergies between them.
The optimization model's complexity stemmed from the significant number of variables involved, and the presence of non-linear relationships, alongside financial limitations, necessitated the use of a genetic algorithm to tackle the issue at hand effectively. Genetic algorithms are particularly adept at addressing multi-objective optimization challenges like this one since they can navigate solution spaces efficiently and pinpoint near-optimal solutions. The algorithm progresses by evolving a population of solutions through rounds where top-performing candidates are chosen, and enhancements are made with each subsequent generation.
The results of the model show some findings: the system's ability to handle increased load (referred to as system loadability) goes up as more FACTS devices are installed in the network. Yet this improvement starts to diminish after a point, meaning that adding more FACTS devices beyond that threshold doesn't bring about enhancements in system loadability. When the budget goes up in a project system like this one, it does improve at first, but only to an extent before hitting a plateau where more money doesn't really make a difference in performance anymore. It seems like the best approach is to find the right balance between what you spend and what you gain from adding more FACTS
devices. The results also shows that it is better to use a combination of devices than to use a single type FACTS devices. During the optimal placement FACTS devices the introduction of renewable energy resources increase the loadability of the system.
This study has an impact on power systems engineering as it offers an affordable way to enhance power system efficiency especially system loadability by strategically situating various types of FACTS devices. The model gives information on balancing system performance with limitations and equips grid operators and planners with a means to manage power effectively while increasing efficiency and maintaining system stability. By tackling the real-world constraints posed by resources, this research work connects the dots between theoretical optimization models and actual practical use cases , setting the stage for future power networks that are adaptable and robust.
Based on the results and the insights derived from the Optimal Placement Model of Multi-type FACTS Devices, the development of such models is essential for enhancing power system network functionality and efficiency improvement plans. It demonstrates how such FACTS technology impacts system performance to overcome budget constraints. The findings of this research further contribute to the advancements of creating more innovative and more dependable power grids for modern energy systems' evolving needs.