Minimization of voltage deviation, power losses and cost by using tap changers and distributed generators in electrical distribution systems

With the development of technology energy consumption and power demand are increased. The need for innovation of the electricity network has occurred. Power distribution system is becoming a more complex task in power system engineering. Because of the increased necessity, title of smart grid came to the fore. In this thesis, distribution systems which is a part of smart grid, have been emphasized. The changes in the distribution systems and the problems which may occur has been mentioned. Studies on this subject have been transferred. One of the most important task in this manner is voltage regulation. Especially in residential areas loads in power distribution system is directly dependent on daily consumption of electrical power. Besides that the integration of distributed generation effects the voltage profile. It may cause several problems such as high voltage fluctuations, power losses need to be solved. To overcome this problems tap changers of voltage regulators are preffered. However, due to the mechanical constraints, the number of operations of tap changers is limited. The active and reactive power outputs of the distributed generators are used to reduce the voltage fluctuations and decrease the active power losses. A multiobjective optimization problem and pareto front are explained. Optimization solution methods are mentioned. In this study genetic algorithm technique and NSGA (Non-dominated Sorting Genetic Algorithm) II toolbox are used. To get voltage profile and power loss values, load flow simulation is runned. Forward and backward iteration methods are used to do load flow analysis. Objective function code is formed to solve multiobjective optimization problem. Objective function code includes load flow analysis code. Objective function contains the power flow code for IEEE 33 bus test system. In this study daily simulation is performed. Voltage values and power losses for each bus are found. After that buses which will include tap changer or distributed generator are determined. The boundary values for tap changer and distributed generator are writed manually. The boundary value for distributed generator is determined by using the voltage profile of IEEE 33 bus test system. All simulations are performed in Matlab. Daily load profiles of the buses are created by using hourly load data and the test system is modified to include several voltage regulators and DGs. The multiobjective optimization model is solved with hourly intervals. The numerical simulations are performed by using the NSGA II toolbox. This study solves a daily multiobjective optimization model. The objective functions which are used in this study: 1) Regulation of the voltage profile, 2) Minimization of the active power losses, 3) Minimization of the cost. To realize these objectives first of all, voltage profiles and active power losses are obtained for 33 bus test system. To get voltage profile and power loss profile of buses load flow simulation is runned for 33 bus radial distribution system. To close the voltage value to 1 pu, tap changers and distributed generators are added to the system. Thus voltage is regulated, power loss and cost is minimized. Firstly, load flow simulation has been done for distribution systems to determine the buses which have maximum voltage drop. For each hour, voltage profile for each bus, total power loss, and power profile have been found. By using the voltage profile, maximum voltage drop for which hour in which bus, has been investigated. Three different cases were simulated: initially taps of voltage regulators were taken into consideration, then reactive power outputs of DGs were adjusted, and finally, both tap positions of the voltage regulators and active and reactive power outputs of DGs were optimized. In order to regulate voltage profile for buses, and decrease lost power firstly only tap changers, secondly only distributed generators and thirdly, both distributed generators and tap changers are added. In case 2, for the added distributed generators which has 200kW active power, suitable reactive power value has been found by using MATLAB-NSGA II. In a similar way, in case 1 only tap changers are added and suitable values has been found by using MATLAB-NSGA II. In case 3 both distributed generators and tap changers have been added and active power value which minimize cost of distributed generators , reactive power value for distributed generators, an also appropriate values for tap changers have been found by using MATLAB-NSGA II. In case 3 by using the found values for distributed generators and tap changers, load flow simulation is runned. It is seen that voltage profile is regulated and power losses is minimized. For all cases, values which are found after the optimization is used for load flow simulation. And it is seen that after the modificication of the system voltage profile is regulated, power loss is minimized and cost is minimized. When the simulation results are compared, results show that better voltage profiles with less power losses and costs might be obtained by adding the voltage regulators. In long term adding renewable energy source is economical and it is better for environment. Additionally, renewable source is limitless. Voltage regulator is mechanical device and in long term it may break down. Moreover, voltage regulator is expensive. Because of these reasons adding distributed generator to the electrical distribution systems is desired. Adding the distributed generator to the electrical power system, using distributed generator efficiently, optimizing the distributed generator active and reactive power output, the problems which may occure after the modification are explained in this study. Additionally, adding the tap changer to the electrical power system, using tap changer efficiently, optimizing the tap changer, the problems which may occure after the modification are explained. Key words : voltage regulation for distribution systems, minimizing the power loss for distribution systems, minimizing the cost for distribution systems, multiobjective optimization