Konvansiyonel ve mikro şebeke içeren güç sistemlerinde dinamik ekonomik yük ve emisyon dağıtımının sezgisel yöntemlerle analizi
Dynamic economic emission dispatch in power systems with and without microgrids by using heuristic algorithms
Yazar: ESRA AYDIN
Danışman: PROF. DR. BELGİN TÜRKAY
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / Elektrik Mühendisliği Ana Bilim Dalı / Elektrik Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2022
Konu: Elektrik ve Elektronik Mühendisliği = Electrical and Electronics Engineering
Dizin: Ekonomik yük = Economic burden ; Elektrik güç sistemleri = Electric power systems ; Emisyon = Emission ; Güç sistemleri = Power systems ; Konvansiyonel sistemler = Conventional systems

Özet:
Yıllar içerisinde yaşanan nüfus artışı ve teknolojik gelişmeler ile birlikte enerji talebinde artış yaşanmaktadır. Bu artış ile birlikte elektrik enerjisi üretim sistemlerinin sayısında artış yaşanmakta ve güç sistemleri, daha büyük ve daha karmaşık bir hale gelmektedir. Talebin artması ile güç sistemlerinde yaşanan büyüme, bu sistemlerin ekonomik olarak işletilmesi konusuna büyük önem kazandırmaktadır. Bu hususta, güç sistemlerinin optimizasyon planlamalarından biri olan Ekonomik Yük Dağıtımı problemi oldukça önemli bir hale gelmiştir.
Ekonomik yük dağıtımı, termik santrallerde yakıt maliyetinin en aza indirgenmesinin amaçlandığı ekonomik bir planlamadır. Bu kapsamda, güç ünitelerinin çıkış güçleri talep gücü karşılayacak şekilde yakıt maliyetinin minimum olması için optimum planlama yapılır. Bu planlama yapılırken sistemin kısıtları göz önünde bulundurulmalıdır. Güç denge kısıtları, generatör kısıtları ve rampa oranı kısıtları dahilinde en optimum planlama yapılmalıdır.
Fosil yakıtların kullanıldığı güç ünitelerinde atmosfere emisyon gazları salınır. Sera gazı olarak da bilinen bu gazlar, atmosferde sera etkisine sebep olarak dünyadaki yaşamı pek çok açıdan tehdit etmektedir. Atmosferdeki emisyon gazı yoğunluğunu azaltmaya yönelik çalışmalar küresel bir boyuta ulaşmıştır. Güç sistemlerinin, emisyon yoğunluğuna en fazla sebep olan birimlerden biri olduğu düşünüldüğünde, emisyon yoğunluğunun minimuma indirilmesinin amaçlandığı ekonomik emisyon dağıtımı, önemli bir konu haline gelmiştir.
Ekonomik emisyon dağıtımında, emisyon yoğunluğunun minimuma indirgenmesi amaçlanır, yakıt maliyetinden bağımsızdır. Ekonomik yük dağıtımı probleminde ise yakıt maliyetinin minimum olması amaçlanır, emisyon yoğunluğu önemsenmez. Ekonomik yük dağıtımı ve emisyon dağıtımının birlikte ele alındığı durumda ise birleşik ekonomik emisyon-yük dağıtımı fonksiyonu oluşturulur ve hem yakıt maliyetinin hem de emisyon yoğunluğunun en aza indirilmesi amaçlanır.
Güç sistemlerinin ekonomik yük ve emisyon dağıtımı problemlerinin çözümünde çeşitli optimizasyon yöntemleri kullanılmaktadır. Bu yöntemler klasik ve sezgisel yöntemler olarak ikiye ayrılır. Sistemlerin büyük boyutlu olması sebebi ile klasik yöntemlerden ziyade sezgisel yöntemlerin kullanımı daha uygun olmaktadır. Sezgisel yöntemlerin karmaşık problemlere uygulanabilirliği, çözüm süresinin hızlı olması gibi sağladığı avantajlar popülerliğini arttırmıştır. Genetik Algoritma, Parçacık Sürü Optimizasyonu, Tabu Araştırma ve Yapay Sinir Ağları günümüzde uygulamalarda en çok tercih edilen sezgisel yöntemlerdendir. 
Bu tez çalışmasında, güç sistemlerinin dinamik ekonomik yük dağtımı ve emisyon dağıtımı gerçekleştirilmiştir. Problemlerin analizi için sezgisel algoritma yöntemlerinden olan Genetik Algoritma (GA) ve Parçacık Sürü Optimizasyonu (PSO) yöntemleri kullanılmıştır. Algoritmalar, 5 ve 10 üniteli sistemler ile mikro şebeke içeren sisteme uygulanmıştır. Algoritmalara ait kodlamalar MATLAB programında oluşturulmuştur. 5 ve 10 üniteli sistemlerin dinamik ekonomik yük dağıtımı, emisyon dağıtımı ve dinamik ekonomik emisyon-yük dağıtımı gerçekleştirilmiştir. Mikro şebeke içeren sistem için ekonomik yük dağıtımı gerçekleştirilmiştir. Uygulamada güç denge kısıtı, generatör limitleri, hat kayıpları, rampa oranı kısıtları ve valf nokta etkisi dikkate alınmıştır. Analiz sonuçları literatürde yapılan çalışmaların bulguları ile karşılaştırılmış, GA ve PSO yöntemleri ile daha optimum sonuçlar elde edildiği görülmüştür. Ayrıca bu yöntemler kendi arasında karşılaştırıldığında ise PSO algoritmasının daha uygun sonuçlar verdiği görülmüştür.

Abstract:
Depending on the technological developments, urbanization, industrialization and the increase of the population energy consumption is increasing over the years. Increasing in the power consumption triggers an augmentation in the number of electrical power generation systems and power systems become larger and more complex. The growth in power systems gives great importance to the economic operation of these systems. In this regard, the Economic Load Dispatch, which is one of the optimization planning of power systems, has become very important. 
Large part of power demand is met by thermal power units. Economic load dispatch is an economic planning aim to minimizing the fuel cost in thermal generating units. The most optimum planning is made in order to minimize the fuel cost so that the output power of the power units meets the demand power. The constraints of the system should be considered while scheduling this planning. Optimum operation should be done within the power balance, generator capacity and ramp rate constraints. Power balance constraint of the system is the meeting of the demand power and transmission losses of the system by the power output of the generating units. Each generation unit has generation limits and the power output of the units should be within these limits. This circumstance is called as generation capacity limits. In dynamic economic dispatch problem when increasing or decreasing the power outputs of the generating units, the variation of the power outputs should not surpass a certain rate. This rate called as the ramp rate constraints. Economic load dispatch can be solved for cases where transmission losses considered or neglected. The case where the transmission losses are neglected is relatively easier to solve because the total power output of the generating units only needs to meet demand. When the transmission losses are considered, the total power output of the generating units should meet the sum of demand power and the transmission losses.  In this case, the B coefficients matrix of the system is also needed and the loss calculation is made according to this matrix. The case where the losses are neglected is easier to solve, but the case where the losses are included will give more realistic results. Alongside the transmission losses, the valve point effect is mentioned for power systems with multi-valve generating units. Large steam turbine power units have a multi-valve structure that opens sequentially and causes a sudden increase in losses when opened. Due to the valve-point effect, the total fuel cost function includes nonlinear higher components. For this reason, a non-convex function is used when the valve point effect is considered.
Energy resources are classified as non-renewable energy resources (fossil fuels) and renewable energy resources (solar, wind, geothermal). Fossil fuels are generally used for most of the electric energy production systems. Since the fossil fuels are used in the thermal generation units, emission gases (such as 〖NO〗_x,〖SO〗_2,〖CO〗_2) are released into the atmosphere. The increased greenhouse density in the atmosphere caused by gas emission in the thermal units and the other factors causes the greenhouse effect. Greenhouse effect provokes many global negative consequences such as global warming, drought, climate change and soil degradation. These negative circumstances threaten life in the world in many ways therefore it is very important the controlling the density of emission gases. Also considering that power systems are one of the plants that cause the most emission intensity, emission dispatch in power systems has become an important issue. Emission dispatch in power systems aims minimizing the emission intensity independent of fuel cost. 
In economic load dispatch, the goal is minimizing the cost and the amount of emissions released is not considered. In emission dispatch, the goal is minimizing the amount of emission released and the fuel cost of the system is not considered. In order to examine both economic load dispatch and emission dispatch together, a combined economic load-emission dispatch function is created. In the combined economic load-emission dispatch, the results of the economic load dispatch and the emission dispatch are examined interdependently. In this way, both less emission density is provided and more economical planning is made for thermal generating units.
The use of fossil fuels in large-scale power plants also causes a significant reduction in fossil fuel reserves with increasing energy consumption. And also the usage of fossil fuels has a negative impact on the environment. In order to reduce these negative effects, the orientation towards renewable energy sources has increased as well as emission dispatch arrangements. The usage of renewable energy sources is becoming widespread with the advantages of being environmentally friendly and the resources are free of charge and easily accessible. In this case, distributed generations, which are small-scale power generators such as wind turbines and solar panels, technology has gained great importance for the grids to fully benefit from renewable energy resources. Microgrids including distributed generations can operate islanded mode or directly connnected to the distribution network. Thus, the power losses are reduced and the system has high reliability and efficiency.
Many effective optimization techniques are used to solve the economic load and emission dispatch problems of power systems. These techniques are divided into classical and heuristic methods. Over the years, the interest in classical methods has decreased with the developments in heuristic methods. The advantages of heuristic methods such as applicability to complex problems and fast solution time have increased its popularity. Genetic Algorithm and Particle Swarm Optimization are the most preferred heuristic methods in applications today.
Genetic Algorithm is a population-based heuristic method developed by John Holland inspired from the Darwin's evolution theory. It is an iteration-based evolutionary algorithm that searches for the best solution in a complex, multi-dimensional search space. The genetic algorithm allows to obtain better results by iteratively renewing the individuals, each of which carries the probable outcome information of the problem. In the genetic algorithm, there is a randomly generated population, and in each iteration, new individuals are created from the individuals belonging to the population by applying random genetic operators. Thus, by providing a genetic diversity, the search space is expanded, individuals with low quality are expelled from the population, and the search continues with high quality individuals. Thus, the optimum result is achieved.
Particle swarm optimization is a meta-heuristic method developed by Dr. Eberhart and Dr. Kennedy in 1995. It is designed for solving complex and non-linear optimization problems. Particle swarm optimization is inspired from the bird and fish flocks. Fish and bird flocks have a simple social behavior to reach for food. According to this behavior, individuals try to reach the food by following the successful movements of the herd. Randomly generated individuals are influenced by successful movements already achieved in the swarm and updated according to the best experiences obtained by the herd and the best experiences of the individuals in each iteration. Thus, it is aimed to achieve the best result. Particle swarm optimization has more advantageous such as ease of implementation, fast solution time and fast convergence.
In this thesis, dynamic economic load dispatch and emission dispatch of power systems were operated. For the analysis of the problems, Genetic Algorithm and Particle Swarm Optimization methods, which are the heuristic algorithm methods, were used. Algorithms were applied to the 5-unit and 10-unit test systems and the multi-microgrid system consisting of four generating units and two microgrids. Dynamic economic load dispatch, emission dispatch and dynamic economic emission-load dispatch of 5-unit and 10-unit systems were examined. Power balance constraints, generator capacity constraints, ramp rate constraints, transmission losses and valve point effect were considered. Economic load dispatch was examined for multi-microgrid system and power balance constraint and generator capacity constraints were taken into account. 5 cases were examined for multi-microgrid system. In case 1, microgrids were neglected and the systems economic load dispatch problem was solved. In case 2, microgrids and generating units were considered and the generating units were considered. In case 3, microgrid 1 was neglected and the microgrid 2 and the generating units were considered. In case 4, microgrid 2 was neglected and the microgrid 1 and the generating units were considered. Finally in case 5, all system and the interaction between microgrid 1 and microgrid 2 were considered. MATLAB program was used in the development of genetic algorithm and particle swarm optimization algorithms and their implementation.
The analysis results were compared with the findings of the studies in the literature, and it was seen that more optimum results were obtained with the genetic algorithm and particle swarm optimization methods. In addition, when these methods are compared among themselves, it has been seen that the particle swarm optimization gives best results.