Akıllı Şebekelerde Depolama Sistemlerine Sahip Rüzgar Enerji Santrallerinde Üretim Tahmini Ve Sisteme Katılım Miktarının Belirlenmesi

Abstract

Çalışmada, 10 adet 900 kW rüzgar türbinlerine sahip 9 MW kurulu gücü olan rüzgar santralinin üreteceği enerjinin tahmini yapılmıştır. Üretileceği tahmin edilen enerjiden şebekeye dahil olamayan enerji miktarı elde edilmiştir. Bu miktar ise kullanılması gereken ve yenilenebilir enerji ile uyumlu çalışan, markette ulaşılabilen enerji depolama sistemlerini belirlemek amacıyla kullanılmıştır. Depolama sistemleri rüzgar santrali ile uyumlu çalışacak çeşitlerinin belirlenmesi, boyutunun ve maliyetinin yıllık bazda ne olacağının hesaplanması ile bulunmuştur. Bu amaç doğrultusunda rüzgar enerjisi tahmini yapmak için adaptif ağ tabanlı bulanık çıkarım sistemi kullanılmıştır. MATLAB programı içerisinde bulunan ve anfisedit komutu ile kullanılan bu modelin girdileri ve çıktıları belirlenmiştir. Rüzgar santralinden üretilecek enerjinin hesaplanabilmesi için rüzgar hızı, hava yoğunluğu, türbin kanatlarının süpürdüğü alan ve bölgeye göre değişen kapasite faktörü göz önünde bulundurulmuştur. Rüzgar hızı Urla’da 2008 ile 2011 yılları arasında on dakikada bir alınan kayıtlar aracılığı ile sağlanmıştır. Rüzgar hızı modellemenin ilk girdisidir. Diğer bir girdi olan hava yoğunluğu ise yine aynı tarihler arasında Çeşme, Seferihisar ve İzmir bölgeerine ait Meteoroloji Genel Müdürlüğü’nden alınan hava sıcaklığı ve nem değerleri ile bulunmuştur. Modelin çıktısı ise elimizde bulunan veriler ile teorik olarak hesaplanan rüzgar gücüdür. Bu değerler modellemede test ve eğitim için kullanılmıştır. Hat kapasiteleri, gerilim limitleri gibi şebeke fiziksel kısıtlamaları sebebi ile üretilen rüzgar enerjisinin tamamı sisteme verilememektedir. Bu nedenle sisteme katılabilecek enerji miktarı Power System Analysis Toolbox ile hesaplanmıştır. Şebekenin teknik limitleri Power System Analysis Toolbox’ta gömülü bulunan IEEE 14 baralı sisteme entegre edilmiştir. Türkiye Elektrik Dağıtım Anonim Şirketi’nin belirlediği elektrik tarifeleri ve Türkiye Elektrik İletim Anonim Şirketi’nden elde edilen birim enerji üretim maliyetleri de sistemde göz önünde bulundurulmuştur. Rüzgar enerjisi kullanıldığında üretilen birim enerji maliyeti düşmektedir. Karşılaştırma yaparken birim enerji maliyeti de modele dahil edilmiştir. Şebekeye dahil edilemeyen enerji başlı başına bir maliyettir ve bu yöntemle bu maliyet belirlenmiştir. Dahil edilemeyen enerjinin depolanması bu maliyet, düşürmekle beraber depolama sisteminin maliyeti de düşünülmedir. Bu nedenle çalışmada yenilenebilir enerji kaynaklarında kullanılan depolama sistemleri belirlenmiştir. Paranın bugünkü değeri, gelecek değeri ve yıl bazında hesaplanmış değerleri finansal analizle hesaplanarak enerji depolama sistemlerinin yıllık kurulum ve kullanım maliyetleri hesaplanmıştır. Hesaplamalarda ilk yatırım, işletme, bakım maliyeti ve yenileme maliyetleri de eklenmiştir. Enerji depolama sistemlerinin kurulumdan kaynaklanan hat kayıplarında azalma ve sisteme verilemeyen rüzgar enerjisinin depolanmasıyla birlikte ortaya çıkan yıllık kazanç, enerji depolama sisteminin kurulumunun yıllık maliyeti ile karşılaştırılmıştır. Çalışma sonucunda 9 MW sistem de maliyeti yüksek olduğu için depolama sisteminin uygun olmadığı görülmüştür. Ancak rüzgar türbinin gücü arttıkça depolama maliyeti azaldığı için sistem uygun olduğu tespit edilmiştir.

In this study, expected wind energy of 9 MW installed power is determined. The proper energy storage system (ESS) is find out by considering power rate, capacity and cost. As a result, annual cost of energy storage systems and spilled wind energy are compared. Wind energy is a renewable energy source but intermittence is a drawback. Energy storage systems are a solution to provide stability both on grid and off grid. ESS is an important issue and nowadays studies on ESS are increasing. By modeling renewable energy sources, capacity of storage systems is calculated in some studies. In another studies, in aspect of power electronics, storage systems are compared for different kinds of distributed energy systems. Comparing storage systems according to their advantages and disadvantages is another subject for the studies. Another important issue is reliability of storage systems. So reliability of a grid by adding ESS and wind energy are mentioned. Not only for wind energy but also for other renewable energy sources, types of ESS and capacity calculations are common. But in these studies, missing subject is cost of ESS, benefit analysis for customers and system owners. The study also improves conditions for customers and prompt potential investors to invest on renewable energy projects. So in this study, some tools are used to estimate wind energy production, calculate spilled energy and also financial analysis is performed for both customers and producers. For the study, different methods are used in each steps. First step is to predict amount of wind energy. In literature, there are many methods that are used such as Weibull distribution, neural networks, classic linear models. In the study, Adaptive Neuro-Fuzzy Inference System is used for estimation wind energy because it has advantages of both artificial neural networks and fuzzy logic. The program includes nodes which have different functions. In this program, there are inputs and outputs, these are used to train and determine rules. Two inputs, one output and two rules Sugeno model is commonly used in literature. According to the data, the proper wind turbine was selected and power related with wind speed was specified in the wind turbine datasheet. In the study, program is prepared by considering air density, wind speed, swept area and capacity factor as inputs and wind power calculated by wind power equation is used as outputs in program and all data are normalized. Wind speed is provided from a study in Urla, İzmir between 2008 – 2011. Other inputs are provided from Turkish State Meteorological Service for same time interval but for Çeşme, İzmir and Seferhisar since meterological station is broken in Urla. ANFIS is used for wind energy prediction. In the program, 66% of wind speed data (5187) were used for training and 34% of data (3573) were used for testing. Subtractive clustering was selected in the tool. This selection is effective when clustering of data is not accurately known. In the process, the epoch number was three and the margin of error 3.74%. In the model, there are three rules. As a result of the program, prediction of wind energy by ANFIS for each hour in typical season day and theoretical results of wind power are compared. The coefficient of determination R2 shows that the difference between measurement of fit prediction and statistical analysis was 98.84%, which is acceptable. After prediction of wind energy production, the electrical network and loads were specified. Next step is determine amount of accomated wind energy to the network. Because of physical limits such as line capacities, voltage limits, all amount of expected energy cannot penetrate grid. Spilled wind energy is determined by Power System Analysis Toolbox. PSAT is a power analysis and control program and used for power flow analysis and control. It can perform continuous power flow, optimal power flow, small signal stability analysis, and time domain simulation. There are embedded test networks of IEEE in PSAT. In network analysis, optimal power flow is performed on IEEE 14 bus system. In this analysis, control equations can be generator active powers except for slack bus, voltage magnitude of generator bus, reactive powers of reactive generators, tap changers and angle of phase transformations. Constraints are active and reactive power flow in branches, voltage limits of branches except for slack, and feeder capacity limit. Objective function is determine maximum wind energy by considering technical limits and maximize cost of using wind energy in network. Wind power plant is connected to bus 12. Since unit price of electricity generation by wind energy is known, cost of the spilled wind energy is calculated. Electricity tariffs in distribution system is provided from Turkish Electricity Distribution Company and electricity prices for day, night and peak hours and the cost of energy production methods were considered. In the study, data which is hour by hour wind energy prediction for typical days of each season is entered into the program and also for same hours, electricity prices with regard to wind energy penetration are added. Unit cost of energy generation for different sources is provided from Turkish Electricity Transmission Company since common energy sources are coal, natural gas and hydropower, in the study, these sources are considered. Then, energy storage systems are researched. There are many energy storage systems such as supercapacitors, fuel cells, lithium-ion, flywheels but not all them are suitable for renewable energy systems. Energy storage systems which can be used for renewable energy and common in markets are lead acid, valve-regulated lead acid, sodium sulphur and vanadium redox. In specific hour, maximum spilled power specifies power rating of ESS and maximum spilled energy during a day specifies capacity of ESS. To calculate annual cost of ESS, all possible investments such as unit costs of ESS, power electronics, balance of plant and fixed operation and maintenance cost are considered and annual costs of installation of each ESS are calculated by using financial equations related with net present, future and annual values. Also, it is figured out that ESS leads to decrease in line losses so profits of line losses and spilled energy are compared with cost of installed ESS. Since the production cost of wind energy is lower than that of the energy sources already installed, the production cost of energy decreases. This provides benefits to customers because electricity prices decrease. For the producer, it is also beneficial because of lower production costs. There is also another benefit for the producer because spilled energy will be stored and then penetrate the system. Profits of producers and consumers for each year are compared with annual cost of installing different kind of ESS technologies. Results of the study showed that for 9 MW wind energy system, ESS is not feasible but it is obviously seen that ESS will be feasible for penetration of bigger power ratings wind energy. In today’s technology, annual cost of ESS is extremely expensive. Also, as growing technology, the cost of energy storage systems will decrease and investments in storage systems will become more feasible and for smart grids, energy storage systems will be necessary. The study can be improved by exploring more wind energy operations and by finding the optimum point for installing energy storage systems by using more than one wind power plant and ESS.