Ni Ketut Aryani

Optimal placement and sizing of Distributed Generation using Quantum Genetic Algorithm for reducing losses and improving voltage profile

In this paper Quantum Genetic Algorithm (QGA) is combined with The Newton Raphson power flow (NR power flow) to optimize the placement and sizing of Distributed Generations (DGs) in electrical power systems. QGA is used to find the optimal placement and generate real power of DG in accordance with mathematical calculations and NR Power Flow is used to calculate the loss on the network and determine the voltage at bus. The goal is to minimize the losses, while at the same time still maintain the acceptable voltage profiles. DGs may be placed at any load bus. Which load buses to have the DGs and of what size they are respectively are determined using this proposed method. Observations are based on standard IEEE 14 buses input and results are compared to the results of network without DG and network with DG by other methods.

Dynamic economic dispatch of hybrid microgrid with energy storage using quadratic programming

Electrical power demand increases rapidly due to the development of technology. In the contrary, the availability of non-renewable energy sources surely decreases. This problem will impact on the national energy security. To meet the need of a large electric power, it is required to develop a large area small scales of distributed generations. Distributed generations utilize renewable energy sources, such as PV, in order to minimize the use of non-renewable energy sources. To maximize the utilization of renewable resources, it is necessary to apply energy storage. This storage is needed to store excess energy generated by renewable energy based power plants. With the distributed generations and energy storage which are connected to the main grid through microgrid, it is important to optimize the operation of the power system in order to meet daily load. In this paper, the optimization problem is formulated as Dynamic Economic Dispatch which is applied on hybrid microgrid with energy storage. The problem is solved using Matlab-based quadratic programming

Dynamic OPF considering different charging and discharging cost of energy storage using mixed-integer quadratic programming

This paper presents a study about optimization in microgrid with energy storage. The development of technology makes it possible for energy storage utilization as solution for energy management problem due to renewable integration. The ability of energy storage to store and supply power can be used to solve intermittency problem of renewable energy. Furthermore, the possibility of energy storage integration to make profit is researched deeper by considering charging and discharging cost. The DC Optimal Power Flow calculation is done to optimize the system with energy storage using Mixed-Integer Quadratic Programming. The output of this research are generation power and energy storage power in each period with minimum generation cost and the effect of different charging and discharging cost towards it.

Analysis of physical and electrical characteristics of transformer oil insulation during accelerated thermal aging experiment and its lifetime estimation using arrhenius law and breakdown voltage test

In this paper, the physical and electrical characteristics of accelerated thermal aged transformer oil insulation were investigated. The accelerated thermal aging of commercial transformer oil was conducted by increasing its temperature with different time. They were 100°C for 168 hours, 115°C for 96 hours, and 125°C for 48 hours. The lifetime of the tested oil was then estimated based on Arrhenius law and breakdown voltage test. The result showed that the physical characteristic of the tested oil during accelerated thermal aging could be analyzed based on its color changes, in which the higher level of thermal aging resulted in darker oils color. The electrical characteristic was associated with significant decreasing of breakdown voltage. The lifetime estimation based on Arrhenius law for the tested oil with 100°C, 115°C, and 125°C were about 25663 hours, 4300 hours, and 1408 hours, respectively. The lifetime estimation based on breakdown voltage test for the tested oil with 100°C, 115°C, and 125°C were about 20064 hours, 1526 hours, and 1106 hours, respectively.

Manajemen Gangguan Jaringan Distribusi 20 KV Kota Surabaya Berbasis Geographic Information System (GIS) Menggunakan Metode Algoritma Genetika

Sistem Distribusi merupakan bagian yang tidak terpisahkan dari sistem tenaga listrik.Sistem distribusi merupakan sub sistem tenaga listrik yang yang paling dekat dengan pelanggan yang berfungsi untuk menyalurkan tenaga listrik. Gangguan dalam operasi sistem tenaga listrik merupakan kejadian yang dapat menyebabkan bekerjanya pengaman tenaga listrik. Adanya gangguan pada suatu sistem tenaga listrik atau penyediaan listrik ini tidak dikehendaki, tetapi merupakan kenyataan yang tidak dapat dihindarkan. Kondisi tersebut tidak bisa dibiarkan dalam waktu lama karena akan membuat kerusakan pada peralatan-peralatan listrik dan menimbulkan kerugian yang besar bagi penyedia listrik. Tindakan untuk mengurangi penurunan tegangan yang cukup besar pada sistem tenaga listrik harus segera diupayakan karena keadaaan tersebut jika dibiarkan secara terus-menerus maka akan menyebabkan terjadinya penurunan keandalan sistem tenaga listrik dan kualitas energi listrik yang disalurkan. Manajemen gangguan merupakan himpunan fungsi proses dari Identifikasi, Isolasi dan Restorasi gangguan. Pada Penelitian ini Isolasi dan restorasi dilakukan dengan metode algoritma genetika dan upaya load shedding pada jaringan yang mengalami drop tegangan. Untuk mendapatkan restorasi yang paling optimal dengan mendapatkan rugi-rugi daya pada jaringan distribusi diharap dengan algoritma genetika daan loadshedding dapat mengatasi gangguan pada jaringan distribusi.

Unit commitment with non-smooth generation cost function using binary particle swarm optimization

This paper deals with generation unit scheduling, well known as unit commitment (UC). Rather than using quadratic generation cost function, this paper utilizes non-smooth generation cost function (NSGCF). As NSGCF is difficult to handle using conventional technique such as quadratic programming, the metaheuristic technique, particle swarm optimization in this case, is used to solve the economic dispatch which is part of UC. In this UC, minimum up time, minimum down time, start up cost and shunt down cost are considered. In addition, spinning reserve is taken into account. Because UC is mix-integer optimization problem, Binary Particle Swarm Optimization (BPSO) is applied to complete UC. To show the effectiveness of the approach, system with 6 generators is utilized.