Küresel nüfus artışına bağlı olarak elektrik enerjisi tüketimindeki büyük artış, elektrik enerjisinin merkezi olarak tedarik edilmesinde zorluklar yaratmıştır. Geleneksel olarak elektrik, farklı gerilim seviyelerinde birbirine bağlı iletim sistemlerinden oluşan geniş bir şebeke aracılığıyla elektrik santrallerinden tüketim merkezlerine aktarılmaktadır. Ancak bu durum büyük güç kayıplarına ve gerilim düşümü sorunlarına yol açmıştır. Bu sorunlar güç sisteminin hem performansını hem de güvenilirliğini olumsuz etkilemektedir. Güneş ve rüzgâr enerjisi gibi yenilenebilir enerji teknolojilerindeki son gelişmeler sayesinde, artan yük taleplerini Dağıtık Üretim (DÜ) kaynaklarını kullanarak karşılamak artık mümkün hale gelmiştir. Dağıtık üretimin dağıtım şebekeleri üzerinde olumlu etkileri vardır. DÜ sahaları tasarlanırken yatırım getirisini en üst düzeye çıkarmak için DÜ ünitelerinin boyutu ve konumu gibi parametrelerin seçimi dikkatle değerlendirilmelidir. Bu ünitelerin etkinliği, dağıtım şebekesine optimal şekilde yerleştirilmelerine ve boyutlandırılmalarına bağlıdır. Dağıtım şebekelerinin karmaşıklığı nedeniyle planlama karmaşık bir görev haline gelmektedir. Bu nedenle, dağıtım şebekelerinde DÜ'lerin optimum şekilde yerleştirilmesi ve boyutlandırılması konusunda şebeke planlamacılarına yardımcı olacak yeni teknikler geliştirilmelidir. Bu tezde, sezgisel optimizasyon algoritmaları kullanılarak üç fazlı dengesiz dağıtım güç sistemlerinin enerji kaybını minimize etmek ve tüm bara gerilimleri belirlenen sınırlar içinde tutmak için DÜ'nün optimal yerleşimi ve boyutlandırılması probleminin incelenmesi amaçlanmıştır. Birim güç faktöründe ve optimal güç faktöründe çalışan DÜ sistemlerinin optimal konumunu ve boyutunu bulmak için Genetik Algoritma (GA), Hibrit Gri Kurt Optimizasyonu ve Guguk Kuşu Araması Algoritması (GWOCS), Parçacık Sürü Optimizasyonu (PSO), Hibrit Arttırılmış Gri Kurt Optimizasyonu ve Guguk Kuşu Araması Algoritması (AGWOCS), Gri Kurt Optimizasyon (GWO) algoritmaları kullanılmıştır. Bu çalışma kapsamında, önerilen yöntemler MATLAB ve OpenDSS programları kullanılarak dengesiz yüklenme ve hatlara sahip IEEE 34 baralı test sistemine uygulanmıştır. Dağıtım sistemindeki yükleri doğru bir şekilde modellemek için gerilime bağlı yük modeli olan ZIP yük modeli kullanılmıştır. Gerçeğe yakın sonuçlar elde etmek için ticari, konutsal ve endüstriyel tüketicilerin ZIP katsayıları kullanılmıştır. Ayrıca, literatürdeki çalışmalarla karşılaştırmak için IEEE 34 bara sisteminin orijinal yükleri de kullanılarak benzetimler gerçekleştirilmiştir. Elde edilen sonuçlar, optimum yerleştirilmiş ve boyutlandırılmış DÜ'lerin yalnızca güç kaybını azaltmakla kalmayıp aynı zamanda sistemin gerilim profilini ve karalılığını da iyileştirdiğini göstermektedir. Ayrıca, DÜ'lerin reaktif güç kapasitesinin kullanılmasıyla güç kaybındaki azalmanın ve gerilim profilleri iyileştirmelerinin daha fazla olduğu tespit edilmiştir.
Energy demand in the world is growing exponentially and traditional energy sources are exhaustible and limited in supply. Therefore, there is an urgent need to conserve the remaining energy resources and explore alternative energy sources. Renewable energy sources are increasingly being used to meet energy needs and are seen as potential solutions to tackle serious energy crises and environmental concerns. The massive increase in electrical energy consumption due to global population growth has also created challenges in the centralized supply of electrical energy. Traditional electricity systems are built according to a centralized model. Electric energy is generated in large power plants located far away from consumers. Transmission lines are used to deliver electrical energy to consumers. Most of the time, these transmission lines are constructed over long distances and have high voltage. Once the electrical energy is transported close to the consumption centers, it must be distributed through a larger number of short length distribution lines with low nominal power. From a technical and economical point of view, it is desirable to reduce voltage levels. All these networks and substations together form what is called the distribution system. Traditionally, this is a passive network as there are no Distributed Generation (DG) units connected to the distribution network. Thus, the power flow is unidirectional from the transmission to the distribution network. However, this centralized model has led to high power losses and voltage drop problems. These problems adversely affect both the performance and reliability of the power system. Among the various renewable energy sources, solar and wind energies are the most promising energy sources for humanity. Due to their abundant availability, renewable energy sources will form the backbone of the future energy system. These sources will gradually replace coal, oil, and gas in energy consumption schemes over time. To integrate large amounts of renewable energy into the power system requires a restructuring of existing energy systems. The smart grid is the key to this transformation. In the future, smart grid systems will consist of various elements such as distributed renewable energy sources, a robust electricity grid, flexible consumption, and an intelligent power control system. Distributed renewable energy sources and energy storage devices are expected to play a vital role in meeting the future energy demand of the smart grid system. Thanks to recent advances in renewable energy technologies, it is now possible to meet increasing load demands using DG resources. DGs has positive impacts on distribution networks. When designing Distributed Generation sites, the choice of parameters such as the size and location of DG units should be carefully considered to maximize the return on investment. The effectiveness of these devices depends on their optimal placement and sizing within the distribution network. Traditional power systems are designed to be radial and unidirectional, so adding DG units to distribution systems can cause some problems. These problems are related to how to choose the size and location of DG units. Many benefits can be obtained from integrating DG units into distribution systems, such as reducing power losses and improving voltage profiles. These benefits can be maximized if the size and location of DG units are optimized. Many researchers have investigated different aspects of this problem using various optimization methods, such as heuristic methods, analytical approaches, and computational artificial intelligence. However, there are still some limitations in both the problem formulations and the methods used. Due to the complexity of distribution networks, planning becomes a complex task. Therefore, new techniques should be developed to assist network planners in the optimal placement and sizing of distributed generation in distribution networks. This thesis aims to investigate the problem of optimal placement and sizing of Distributed Generation in three-phase unbalanced distribution systems to minimize energy loss and keep voltage magnitudes within specified limits using heuristic optimization algorithms. In this thesis, Genetic algorithm (GA), Hybrid Grey Wolf Optimization and Cuckoo Search algorithm (GWOCS), Particle Swarm Optimization (PSO), Hybrid Augmented Grey Wolf Optimization and Cuckoo Search algorithm (AGWOCS), Grey Wolf Optimization and Cuckoo Search algorithm (GWO) are used to find the optimal location and sizing of DG systems. Due to the unbalanced nature of distribution systems, the IEEE 34-bus test system, which is three-phase unbalanced, is used in the simulations. The effectiveness of the proposed algorithms is tested on the IEEE 34-bus test system. An open-source software called OpenDSS is used for the three-phase unbalanced power flow solution. Optimization algorithms for the placement of DGs in the distribution system are developed in MATLAB. To analyze the power system, loads should be modeled such that they closely represent the actual system loads. No load consists entirely of constant power, constant impedance, or constant current. Each of the loads in the power system has its own characteristics that can be represented by ZIP (constant impedance, constant current, constant power) coefficients. For this reason, exponential and polynomial load models have started to be preferred since constant power, constant impedance and constant current load models do not reflect the real loads. Therefore, in this thesis, ZIP load model, which is a voltage dependent load model, is used to model the loads in the distribution system accurately. The ZIP coefficients of commercial, residential, and industrial customers are used to obtain accurate results. In addition, simulations are also performed using the original loads of the IEEE 34 bus system to compare with the studies in the literature. In this thesis, six scenarios have been designed. By creating different scenarios, the optimal sizing and placement of a DG operating at unit power factor and a DG operating at optimal power factor and their impact on the grid are also analyzed. Simulations have been performed to compare with the results obtained in the literature. DG placement has been done with and without voltage regulator in the distribution system. The results show that the proposed methods provide better results in terms of power losses in finding the optimal location and size of the DGs compared to other existing studies due to their robustness and efficiency. The results also show that optimally located and sized DGs not only reduce power loss but also improve the voltage profile of the distribution system. It is found that the power loss reduction and voltage profile improvements are further enhanced by utilizing the reactive power capacity of the DGs. By comparing the results of DGs operating at unit power factor and DGs operating at optimum power factor, the best results are obtained when DGs are operated at optimum power factor. The location, size, and power factor of DGs are very important in the placement of DGs in distribution networks, and when they are properly placed, they can reduce losses and consequently carbon emissions.
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/
