• Energy Research

Locating electric vehicle charge stations has always been an important problem for electric distributers. Many basic and complex solutions have been provided by algorithms and methods to solve this problem in real and assumed grids. However, the data, which has been used in those algorithms, are not consistent with the diversity of locations, thus, do not meet the expected results. Grid locations are the most important aspects of this issue in the eyes of designers, investors, and the general public. Locating charge stations must be determined by plans which have influenced majority in the society. In some countries, power quality has been increased by storages, which are used in vehicle-to-grid (V2G) and similar operations. In this paper, all of the variables for locating charging stations are explained according to Ankara metropolitan. During the implemented analysis and literature reviews, an algorithm, based on location and grid priorities and infrastructures, are 154 kV and 33 kV, have been designed. Genetic algorithms have been used to demonstrate this method even though other algorithms can also be adopted to meet the priority.

The share of renewable energy sources (RES) in power systems has been increasing in recent years. Future power systems will have lower inertia and difficult controllability, especially due to intermittent and variable renewable energy that is not dispatchable easily due to its fluctuating nature. Thus, it is necessary to increase the grid’s flexibility to ensure system stability. For this need, new technologies such as battery energy storage systems (BESS) are widely discussed. It is thought to be very useful to create a fast and accurate response in frequency control services with BESSs, especially in low inertia grid conditions. The sizing, charge-discharge control, and lifetime of a BESS providing frequency control service depend heavily on the changes that may occur in the power systems. So, it is a very complex issue to decide on during the investment phase. In this study, the optimum sizing, lifetime, and techno-economic evaluations of BESS providing primary frequency control (PFC) service have been made by grid's frequency data-driven. For this purpose, firstly; the BESS design providing PFC is created for Turkey’s electricity system. Secondly, with the developed algorithm, the number of charge-discharge cycles of the BESS is calculated and the lifetime and capacity fading of the BESS are determined according to the frequency deviation. Finally, economic evaluations have been made for BESS considering the investment- operating costs and PFC market prices.

Wind energy acquires reputation since the previous few decades. The importance of wind energy topic leads new investigation ranges due to the operation of turbines. The operation of the turbine mainly occurs in the generator. In this study, a MATLAB/GUI (Graphical User Interface) has been developed to compare the differences between the generator types used in wind turbines. The effect of each generator on the grid has been investigated and compared. Commonly used wind turbine generators Permanent magnet synchronous generators (PMSG), double-fed asynchronous generators (DFIG), squirrel cage induction generators (SCIG) and wounded rotor induction generators (WRIG) are considered. The wind turbine models are modelled using MATLAB/ Simulink interface using modelling parameters under the condition of having the same 1.5 MW total installed power. Therefore, the selection of the most suitable generator and the effects of the generator on the grid (FFT analysis results) will be investigated. The user has the option to determine the wind speed, pitch angle and generator parameter values of the simulation.

The study presents a combined protection approach for induction motors (IMs). To achieve this, the current, voltage, speed and temperature values of the IM were measured with sensors and processed automatically with the developed software in C. The processes were then inserted into a microcontroller. The experimental results have shown that the IM was protected against the possible problems encountered in online operation. The approach presented in this work provides flexibility, accuracy and reliability for smooth protection with the help of the combined protection approach. Moreover, the protection system can easily be applied to larger motors after small modifications in the software developed. It is also expected that the proposed system would provide faster and low cost protection.

This paper presents, a fault simulation on a microgrid consisting of a wind turbine, a solar panel and fuel cell. The power produced by different sources is combined on the same DC bus and converted to AC form using a three phase inverter in order to transfer it to 3-phase AC load. The system takes some precautions against some undesired situations. In case of a fault, the failed section of microgrid has been seperated from the grid rapidly using breakers located at the output side of each energy sources and inverters.

One of the most significant issues arising from the intermittency of renewable sources is the discrepancy between the hours of peak generation and hours of peak load. This issue complicates the process of supply and demand balancing because the electrical energy generated by photovoltaic arrays cannot be directly stored. Energy storage systems provide a solution to this issue by storing the excess energy generated during peak generation time which will be delivered to the grid during peak load, increasing system energy efficiency. In this work, the possibility of utilizing a Megawatt scale energy storage system, such as a battery bank, to improve the efficiency of a proposed 10-MW Biglow Canyon solar array through solar peak shaving is discussed in detail. With an overview of the benefits of peak shaving and a comparative study of energy storage technologies, an assessment of the most suitable commercially available storage systems is broadly discussed in the paper. Three control algorithms based on battery storage, state of charge, and constant output are proposed to balance the difference between generation and usage using appropriate energy storage system. Simulations are performed with System Advisor Model software to gain an insight of the possible losses and to forecast the monthly energy production costs.

This paper presents a novel technique based on artificial neural networks (ANNs) to correct the line power factor with variable loads. A synchronous motor controlled by the neural compensator was used to handle the reactive power of the system. The ANN compensator was trained with the extended delta-bar-delta learning algorithm. The parameters of the ANN were then inserted into a PIC 16F877 controller to get a better and faster compensation. The results have shown that the proposed novel technique developed in this work overcomes the problems occurring in conventional compensators including over or under compensation, time delay and step changes of reactive power and provides accurate, low cost and fast compensation compared to the technique with capacitor groups.

Over the last twin decades, significant advancements have occurred in global electricity grids due to the widespread adoption of renewable energy resources (RES). While these sources play an essential role in total generation cost reduction, transmission power loss minimization, and reduction of environmental hazards related to traditional power plants. Still, however, the optimal planning and operation of the power system in the presence RES is considered a primary challenge due to the their stochastic natural and intermittency. One of the most complex and motivating issues in a power system is optimal power flow (OPF), a constrained optimization problem characterized by non-linearity and non-convexity. From these specifications, researchers competed in the past decades to find optimal solutions to stochastic OPF problems while keeping system stability. To tackle this challenge, an effective optimization algorithm which mimics on the foraging behavior of dwarf mongooses’ in the nature is introduced. The objective function considers reserve cost for overestimation and penalty cost for underestimation of intermittent renewable sources. To show the robustness and efficacy of the recommended optimizer, case studies on the customized IEEE 30-bus system and a realistic power system DZA 26-bus (isolated grid) are undertaken. Numerical findings show that the proposed DMOA beats all previous published-results and performs better over a variety of objective functions while finding high-quality optimally viable solutions. The obtained results demonstrate that the DMOA realized exceptional performance for both the test networks, with total generation cost minimized values of 780.982 ${\$}$ /h and 8283.942 US ${\$}$ /h, respectively. These results highlight the precision and robustness of DMOA in effectively addressing various instances of the OPF problem Furthermore, the one-way analysis of variance (ANOVA) test, a statistical approach, was employed to evaluate the superiority of the proposed algorithm and to highlight a certain level of confidence to our study.