Optimal placement of SVC & TCSC devices in order to reduce losses in transmission systems using genetic algorithm

Dissertation for Master's Degree (M.S)

Trend of Power Electricity

Abstract

With the ever-increasing expansion of energy consumption in the world, the development of power networks is essential. But the creation of new transmission lines requires time and high costs, so if it is possible to use the same lines with a higher transmission capacity, it is very cost-effective. Today, the power grid system is facing problems such as high-risk voltage instability and power losses. This phenomenon is caused by the lack of reactive power control in heavy operating conditions due to the increase in load demand and the development of power systems in the world. In general, the reactive power control problem can be investigated from two perspectives: load compensation and voltage support. For this purpose, in order to reduce the power loss of the whole system or voltage deviation as a goal, by calculating the optimal settings of reactive power output and terminal voltage of power plants, tap transformer and output settings of other compensating devices such as capacitor bank and synchronous generator are used. In this regard, the settings of FACTS flexible alternating current devices have been considered as added controllers so that by applying the parameters of the transmission system, we can see its effect on reducing transmission losses in the power system.  From the static models of compensators, two types of FACTS devices including: reactive power parallel compensator (SVC) and thyristor controlled series capacitor compensator (TCSC), are included in the problem solving formula. In this thesis, by optimal placement of these devices in the power network by genetic algorithm, by controlling reactive power, it will help to reduce the losses and increase the energy transfer capacity in the lines, and at the end, this will be proven by simulating the test network of IEEE 6 and 24 buses and putting FACTS tools in it.

Key words: FACTS tools, SVC, TCSC, placement, genetic algorithm (GA)

One of the most important concerns of mankind today is energy supply. Energy is one of the main pillars of the economy, and electricity has a special place as the highest type of energy. Today, the production and consumption of electricity is considered as an indicator of the economic growth of societies and one of the criteria for measuring well-being. On the other hand, the decrease in oil and gas reserves and the increase in energy consumption in the world, on the other hand, prompted researchers to pay serious attention to the optimization of energy production and transmission in power networks in order to increase the efficiency and improve the efficiency of power plants, reduce losses and protect the environment. Along these lines, in recent years, with the expansion of the power electronics industry, flexible alternating current transmission systems (FACTS[1]) and due to their many capabilities and wide applications, were quickly used in power transmission networks and created a change in transmission systems so that it provides the possibility of improving electric current transmission. The technology of these systems provides a higher speed than old electronic devices. The choice of FACTS devices is used to control the reactive power of the system to control the voltage in the load buses. Many studies show that the technology of FACTS devices, by maintaining the voltage profile and affecting the transmission parameters, increases the transmission capacity and reduces the power system losses, and reduces the load density of the lines, improves reliability, and increases performance and control. In recent years, controlling the reactive power and reducing losses in power systems through the optimal placement of FACTS devices It is done using different smart algorithms. Genetic Algorithm (GA[2]) is one of the search techniques in computer science to find an approximate solution for optimization and search problems. 1-2 Flexible alternating current transmission systems FACTS (power system) extended and continuous. .  It can be said that the power system is the most complex tool of human construction today. One of the main components of the systems is transmission lines.  The task of these electric transmission lines is from power plants to consumption centers. Obviously, transmission lines must have high reliability and efficiency and low losses. On the other hand, the consumption of electrical energy is increasing day by day and the need for power transmission is felt. Therefore, either new transmission lines should be built or the existing lines should be equipped to enable more use at the lowest cost. Besides, in some cases, the construction of new transmission lines is difficult and sometimes impossible due to natural problems and environmental conditions and huge costs, and we have to make maximum use of the existing lines [5].FACTS equipment was first presented by [3] EPRI in 1990 in America [6]. These equipments are devices that, in addition to increasing the load of the system, bring the power under control to a great extent, and with their high speed, they are able to improve the stability of the system [7]. Kurd] 9,10[:

Reactive static power compensator [4] SVC

Series capacitor controller with thyristor TCSC[5]

Integrated power controller UPFC[6]

Static compensator [7]

Among the above, the first two cases have been discussed in this thesis. , SVC TCSC are very similar in structure. SVC is connected in parallel to the line by a step-down transformer [11], but TCSC are placed in series in the line. Since the price of this equipment is almost high, the use of these devices is still not very common and it is expected that the prices will decrease and the field of using this equipment in the transmission system will increase. it is them So that they are optimal both economically and in terms of influence. The methods used depend on the desired goal. 1-3 Genetic Algorithm (GA) Genetic algorithms are algorithms that work on the basis of natural evolution and superior survival, and evolutionary calculations are abstractly inspired by the basic concepts of natural evolution in the search to find different optimal solutions.

Genetic Algorithm, one of Darwin's natural selection principles, is used to find the optimal formula to predict or match the pattern. Genetic algorithms are often a good choice for prediction techniques based on chance. Briefly, genetic algorithm (GA) is a programming technique that uses genetic evolution as a problem-solving model. In the 1970s, a scientist from the University of Michigan named John Holland in his book (Adaptation in Natural and Artificial Systems) proposed the idea of ??using genetic algorithms in engineering optimization. The basic idea of ??this algorithm is the transmission of hereditary characteristics by genes. In this algorithm (GA), the problem to be solved is the input, and the solutions are coded according to a pattern (binary, real), which is called the fitness function.  The fitting function evaluates the candidate (selection), most of which are randomly selected, and consists of the following parts: fitting function, display, selection and change (crossover, mutation). The power loss index has attracted the attention of power producers. Although the reactive power production itself has an impact on the total cost, the reactive power planning includes two problems: the location, size, and location of the reactive power sources in the system. The parameters that need to be set in the reactive power distribution problem are: Tap transformers with the ability to change the tap are under the load and size of FACTS devices. In solving the problem of reactive power control, it is assumed that the load is spread and the amount of power produced by each generator is known. If we consider the problem of distribution of economic power, as the power system becomes bigger, the solution space of the problem will quickly become large and complicated. The low efficiency of traditional power systems in providing electrical energy has led to the use of statistical search methods for general optimization problems in recent years. The goal is to optimally control reactive power and minimize the power loss index in the transmission network.