Kuzeybatı Anadolu güç sisteminde yenilenebilir enerji kaynaklarının gerilim kararlılığına etkileri = The effects of renewable energy sources on voltage stability in Northwest Anatolia power system

Abstract

Elektrik enerjisine olan ihtiyacın artmasına bağlı olarak güç sistemlerinin büyümesiyle kararlılık konusunun değerlendirilmesi önem kazanmıştır. Yük tarafındaki enerji talebi artışı ve sınırlı üretim kaynakları güç sisteminin gerilim kararlılığı açısından kritik değerlerini etkilemektedir. Yük talebi artışına bağlı olarak enerji talebini karşılamak için içerisinde yenilenebilir enerji kaynakları (YEK) ve çeşitli teknolojiler barındıran dağıtık üretim birimleri (DÜ) güç sistemine farklı konumlarda ve boyutlarda eklenmektedir. Bu birimler son yıllarda güç sistemlerinde elektrik enerjisi üretiminde önemli ölçekte pay sahibi haline gelmiştir. Dağıtık üretimlerin sisteme eklenmesi, güç sisteminin üretim kapasitesini arttırırken ayrıntılı araştırma yapılmadan kapasite ve yerleşim yeri seçimi durumunda sistemde güç kayıplarını arttırdığı gibi sistem kararlılığını da olumsuz etkileyebilmektedir. Bu nedenle ortaya çıkabilecek olumsuzlukları engellemek için, dağıtık üretim birimlerinin sisteme en uygun yer ve güç değerinde seçilmesi gerekmektedir. Ayrıca enerji sistemlerinde gerilim çökmelerinden kaçınmak için gerilim kararlılığını değerlendirmek bir gereklilik olmuş ve gerilim kararlılığı indeksleri önerilmiştir. İndeksler gerilim kararlılığı değerlendirilmesi yapılacak bara hakkında bilgi vermektedir. Bu çalışmada, Türkiye enerji iletim sisteminin önemli bölgelerinden olan Kuzeybatı Anadolu (KBA) 114 baralı güç sistemi örnek alınmış ve bu sisteme eklenecek dağıtık üretim kaynaklarının yer ve güç değerlerinin belirlenmesi problemi, genetik algoritmalar (GA) optimizasyon yöntemi kullanılarak çözülmüştür. Amaç fonksiyonu olarak aktif güç kayıplarının azaltılması, gerilim profilinin iyileştirilmesi ve gerilim kararlılık indeksinin maksimize edilmesi olmak üzere üç farklı fonksiyon kullanılmıştır. Yük tarafındaki enerji talebi artışı göz önüne alınarak mevcut Kuzeybatı Anadolu güç sisteminde yük artışı yapılmıştır. Yük artışı ve amaç fonksiyonlarına göre belirlenen 4 farklı senaryo oluşturulmuş, 114 baralı Kuzeybatı Anadolu güç sistemine uygulanmıştır. Örnek alınan 114 baralı Kuzeybatı Anadolu güç sistemine dağıtık üretim birimleri eklenmeden önce ve oluşturulan 4 senaryo ile dağıtık üretim birimleri eklendikten sonraki gerilim profili, aktif güç kayıpları ve gerilim kararlılığı indeks değerleri karşılaştırılmıştır. Önerilen yaklaşım ile güç sisteminin ağ topolojisini değiştirmeksizin belirlenen yer ve kapasitede dağıtık üretim kaynaklarının eklenmesi durumunda; aktif güç kayıplarının azaldığı, bara gerilim profilinin iyileştiği ve gerilim kararlılığı indeksinin arttığı gözlemlenmiştir.

Demand for electrical energy has increased with population growth and technological developments, and electrical energy has become an indispensable part of our lives. In meeting the rapidly increasing energy demand, it is important that the cost of energy is low, sustainable and at the same time reliable. Increasing energy competitiveness, energy efficiency and the share of renewable energy sources in the market are among the energy policies of countries. Sustainable renewable energy, which does not harm the environment, is becoming widespread both in our country and in the world, instead of fossil-sourced power plants with carbon emissions. In addition, renewable energy sources are preferred because they can be used together with other production sources. The share of renewable energy in energy production is increasing. However, renewable energy sources-based power generation units are less reliable compared to traditional fossil fuel-based power generation systems due to their intermittent nature. Although having generation units close to the loads in the network prevents voltage drops, the constraints on the output current of renewable energy sources are an important factor on the voltage instability of the network. Microgrids are electrical distribution systems that include loads and distributed energy sources (such as distributed generators, storage devices, or controllable loads) that can be operated in a controlled, coordinated manner while connected to the main grid or the island grid. A microgrid generates dynamics that affect the direction and amplitude of the current. Renewable energy sources have low short-circuit capacities compared to synchronous generators, which visibly reduces the fault levels of the grid. Low short-circuit power limits the ability of asynchronous motors to provide inrush current. At the same time, it creates bus with lower power compared to the buses connected to the synchronous generator. The evaluation of stability has gained importance with the growth of power systems due to the increase in the need for electrical energy. The increase in energy demand on the load side and limited generation resources affect the critical values of the power system in terms of voltage stability. To meet the energy demand depending on the increase in load demand, distributed generation units which contain renewable energy sources (RES) and various technologies are added to the power system in different locations and sizes, and these units have become a significant shareholder in electrical energy production in power systems in recent years. In electrical energy systems, Distributed Generation (DG) is electricity generation facilities that generally consist of renewable energy sources and can be integrated close to the loads. Distributed generation contributes to the power system by improving voltage profile, but this is only possible with the use of appropriate optimization algorithms for optimal sizing and placement of resources. The general criterion for a bus to contribute to voltage stability is that in case of any fault, the bus responds to the fault with small voltage changes and is a strong bus. Sudden changes in load flow or capacitances in load flow are also causes of voltage instability. For these reasons, when adding DG units to the power system, the issue of which bus to be added and which power value should be selected is important. As a result of the wrong addition of distributed generation to the power system, voltage instability or even voltage sags can occur. While adding distributed generation to the system increases the generation capacity of the power system, it also increases the power losses in the system in case of stage and location selection without detailed research, as well as negatively affecting the stability of the system. For this reason, to prevent possible problems distributed generation units should be chosen at the most suitable location and power value for the system. In this thesis, general information about genetic algorithm is given and genetic algorithm default features in Matlab Global Optimization Toolbox are used. Genetic algorithm (GA) is a metaheuristic search and optimization algorithm proposed by John Holland in 1975, based on Darwin's principle of natural selection, the basic principle of which is survival of the fittest. They are algorithms that try to find the most suitable one among many possible solutions to a problem. Organisms adapt to optimize their chances through the process of natural selection to survive in a given environment. Each solution candidate to the problem is called a chromosome or genotype. Chromosomes are made up of many genes. The population is the collection of solutions in the current generation. With each new generation, a new population is formed. It is assumed that the population reaches its local minimum or local maximum as the number of new generations increases, depending on the problem type. The fitness value shows the performance of the individual in the problem. An individual with a high fitness value means a good solution to the problem. The solution space is the combination of all possible solution candidates to the problem. The main reason why GA is preferred in optimization problems is the multiplicity of solution candidates in the solution space. By using GA and its operators, only a small part of the solution space is evaluated and the best or near-best solutions are reached. As the population evolves from generation to generation, bad solutions tend to disappear and good solutions tend to be used to create better solutions. In addition, it has become a necessity to evaluate voltage stability in order to avoid voltage collapses in energy demands and voltage stability indices have been proposed. The indexes provide information about the bus whose voltage stability evaluation will be made. If used appropriately, indices can provide information about the nature of the problem and contributing factors. As described in the PV and QV curves, the point corresponding to the critical voltage is the critical power value point, which expresses the maximum loadability limit, and this point is called the voltage collapse point. In the Thevenin equivalent circuit in the maximum power value to be drawn from the bus is calculated with the help of the maximum power theorem and the critical point of the relevant bus is obtained. For voltage stability, the bus voltage must be greater than the critical voltage. By using the bus voltage and critical voltage of the critical bus selected according to the power flow, information about the bus voltage stability is obtained with the voltage stability index (〖VSM〗_v). The mentioned critical voltage value is obtained with the help of Thevenin equivalent circuit provided that the amplitudes of the load impedance and Thevenin impedance are the same. The transmission systems are divided into regions and each region is controlled from its own center, and the Northwest Anatolian region, which is taken as an example, is one of these regions. There are 12 380 kV and 102 154 kV buses (substation) in the Northwest Anatolia region with operating voltages of 380 kV and 154 kV. In this study, the Northwest Anatolian (KBA) 114 bus power system, which is one of the important regions of Turkey's energy transmission system, was taken as an example and the problem of determining the location and power values of the distributed generation resources to be added to this system was investigated using genetic algorithms (GA) optimization method. KBA 114 bus power system modeled using MATLAB MATPOWER. The size of DG units to be added to the power system is determined to be between %20-%30 of the total production and the number is determined as 10. Three different functions were used as the objective function: reducing active power losses, improving the voltage profile, and maximizing the voltage stability index. Considering the increase in energy demand on the load side, the load increase has been made in the existing Northwest Anatolian power system. 4 different scenarios which determined according to load increase and objective functions were created and applied to the Northwest Anatolian power system with 114 bus. In order to prevent instability problems that may occur in the power system, analyzes were made by creating four different scenarios. Against the instability problems that may occur due to the load growth in the power system, the analysis was made by increasing the load in scenario 3 and scenario 4. A total of 10 DG units, whose power values were determined by the GA optimization method, were added to the KBA 114 bus power system with the proposed solution method. The voltage profile, active power losses and voltage stability index values were compared before adding distributed generation units to the 114 bus Northwest Anatolian power system, which was taken as an example, and after adding distributed generation units with the 4 scenarios which are created. For scenario 1, the objective functions of minimizing the active power losses and improving the voltage profile were used and the load values were taken as given in APPENDIX-1 A without changing the load values of the KBA 114 bus power system. If DG units are added at the position and power values determined according to scenario 1, it has been observed that the voltage profile for the buses and other buses with DG units increased and the active power losses decreased by %27.85. For scenario 2, the objective function of minimizing the active power losses, improving the voltage profile and maximizing the voltage stability index is used. The load values of the KBA 114 bus power system are taken as given in APPENDIX-1 A without changing the load values. The bus and power values to which DG units will be added have changed according to scenario 1. If DG units are added at the position and power values determined according to scenario 2, it is seen that the voltage profile for the buses and other buses with DG unit is improved and the active power losses are reduced by %27.94. Index values were compared for the pre-optimization and post-optimization situation. It was observed that the index values increased after optimization, so the system worked more stable. For scenario 3, the objective functions of minimizing power losses and improving the voltage profile are used. The load values of the KBA power system have been increased by %8. The buses and power values to which DG units will be added have changed according to the other 2 scenarios. If DG units are added at the position and power values determined according to scenario 3, it is seen that the voltage profile increases and the active power losses decrease by %33.63 for the buses and other buses with the DG unit added. For scenario 4, the objective functions of minimizing power losses, improving the voltage profile and maximizing the voltage stability index are used. The load values have been increased by %8 compared to the 114 bus KBA power system load values. The buses to which DG units will be added have changed according to scenarios 1, 2 and 3. If DG units are added at the position and power values determined according to scenario 4, it is seen that the voltage profile improves and the active power losses decrease by %32.74. Index values were compared for the pre-optimization and post-optimization situation. It was observed that the index values increased after optimization. With the proposed approach, in the case of adding distributed generation resources at the determined location and capacity without changing the network topology of the power system; It was observed that active power losses decreased, bus voltage profile improved and voltage stability index increased.