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    COMPARATIVE EVALUATION OF STAND-ALONE HYBRID POWER SYSTEM WITH DIFFERENT ENERGY STORAGES
    (Parlar Scientific Publications (P S P), 2021) Ozturk, Zafer; Tosun, Salih; Ozturk, Ali; Akar, Onur
    Nowadays, increasingly technological improvements and population augmentation lead to searching for new energy resources. The limited energy resources against growing energy demand lead increase in the trend towards Renewable Energy Resources (RES) such as solar and wind. The lack of continuity in energy generation of RES due to weather conditions causes fluctuations in the power network. The energy surplus from RES can be charged, and then the stored energy can be discharged if needed to prevent the fluctuation. The utilization of storage increases energy costs. However, it is possible to reduce this cost thanks to proper energy management. Thus, it is of great importance to determine the most appropriate storage management according to generation and loading profile. In this study, management of a hybrid power system (HPS) is proposed for the facility location in Balikesir, Turkey. The proposed HPS includes modeling of solar panels (PV), wind turbine (WT), diesel generators (DC), and six different battery types. The technical and economic performances of HPS configurations containing six different battery types are optimized. Besides, the sensitivity of the results is analyzed. A new approach for optimal battery selection is presented to increase the efficiency of HPS.
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    A novel honey badger algorithm based load frequency controller design of a two-area system with renewable energy sources
    (Elsevier, 2023) Ozumcan, Sercan; Ozturk, Ali; Varan, Metin; Andic, Cenk
    When it comes to the process of ensuring the stability, quality, and reliability of a power system, one of the most crucial components is known as the load frequency controller (LFC). It does this by ensuring that there is a balance between the amount of power that is produced and the amount that is consumed. This paper proposes a novel evolutionary approach, referred to as the Honey Badger Algorithm (HBA), PI/PID controllers should be configured in the best possible way in order to address the LFC problem in the electrical power system. The research takes into account a power system that is integrated between two areas and uses renewable energy sources, such as a wind system and a solar system. The utilization of renewable energy sources has the potential to yield favorable outcomes in frequency control through the provision of prompt and adaptable responses to fluctuations in system frequency, helping to maintain grid stability. The proposed HBA method is utilized to refine the controller parameter values, using a fitness function anchored on the integral of absolute error (IAE) and the integral time multiplied by absolute error (ITAE). The performance of the proposed HBA-based controller has evaluated under 5% step load perturbation (SLP) in area-1. The HBA-based controllers demonstrate greater performance in terms of settling time, overshoot, and fitness value when compared to other well-known optimization algorithms such as Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Grey Wolf Optimization (GWO). According to the obtained results, the IAE-based PID controller has the best performance. The HBA-based PID controller is evaluated according to the following performance criteria; the objective function value is 0.4201, the settling time values and overshoot values for the area-1, area-2 and tieline are 15.6, 33.7 and 27.9 s and -6.6, -0.7 and -0.0071 Hz, respectively. According to the findings, the HBA is both a dependable and effective tool for finding solutions to LFC research problems in multi-source power systems.
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    Optimal PMU Placement for T?rkiye 400 kV Interconnected Power System Observability with Dragonfly Algorithm
    (Univ Osijek, Tech Fac, 2023) Bozal, Beytullah; Ozturk, Ali; Tosun, Salih; Hos, Bulent
    The Phasor Measurement Unit (PMU) is a modern measuring device built on the system for monitoring, controlling, and protecting power systems. Since the costs of PMU devices are very high, they must be placed in the system in optimum numbers and in a way that monitors the whole system. This study determined the locations and numbers of the optimal number of PMU devices that can monitor the whole system. Integer Linear Programming (ILP) and Binary Particle Swarm Optimization (BPSO) methods are proposed to solve the optimum PMU placement (OPP) problem. Then, the solution to the problem is carried out using Dragonfly Algorithm (DA), which is proposed as a new heuristic method. Solution methods were applied to the IEEE 14-Bus Test System and T0rkiye 400 kV Interconnected Power System, and the results were compared. In addition, the results of the proposed methods were compared with the results of different studies in the literature. Thanks to the ILP, BPSO, and DA methods proposed in this study, it has been determined that power systems can be observed with fewer PMU devices. The DA method offers a great cost advantage as it is the method that provides a solution with 5 fewer PMU devices for the 400 kV Interconnected Power System in T0rkiye.
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    A robust crow search algorithm based power system state estimation
    (Elsevier, 2023) Andic, Cenk; Ozturk, Ali; Turkay, Belgin
    The State Estimation (SE) computational procedure plays a crucial role in modern electric power system security control by monitoring and analyzing operational conditions and predicting any emergency. In order to estimate state variables, Power System State Estimation (PSSE) takes into account the magnitudes and phases of voltage on each bus. To address the state estimation challenges in power systems, in this paper, we propose a novel application of the Crow Search Algorithm (CSA) specifically tailored for the state estimation problem. We have assessed the introduced algorithm using the frameworks of both the IEEE 14-bus and IEEE 30-bus test systems. The first formulation is the Weighted Least Square (WLS) method, and the second is the Weighted Least Absolute Value (WLAV) method, both of which are objective function formulations. By comparing the results, it is clear that CSA-based SE is superior to the other metaheuristic algorithms considered, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Swarm Optimization (ABSO). As a point of comparison, we use the Newton-Raphson method for calculating load flow. It has been shown that the proposed CSA-based SE technique has better accuracy than the other two algorithms in all different test systems. With this study, the power system is operated more accurately and reliably by the operators operating the system. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Peer-review under responsibility of the scientific committee of the 2022 7th International Conference on Renewable Energy and Conservation, ICREC, 2022.