Yazar "Dogan, Ahmet" seçeneğine göre listele

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    A Review on Machine Learning Models in Forecasting of Virtual Power Plant Uncertainties
    (Springer, 2023) Dogan, Ahmet; Cidem Dogan, Demet
    The penetration rates of renewable sources and energy storage systems in the energy market have risen considerably due to environmental and economic concerns. In addition, new types of loads such as electric vehicle charging are added to the grid recently. Inherent uncertainty of renewable generation and new type of loads make the power grid more complex and difficult to manage from economic and technical aspects. Virtual power plant (VPP) is a key concept of future smart grid integrating a variety of power sources, controllable loads, and storage devices. VPP environment aims to enhance the stability of the grid and maximize the revenue. Achieving these objectives mostly depends on the precise forecasting of three major uncertainties; renewable generation, load demand and electricity price. On the other side, machine learning (ML) models are quite efficient for complex uncertainties with large scale dataset compared to traditional approaches. In this paper, mostly employed ML models for forecasting VPP uncertainties are analyzed. Firstly, VPP components and operation of the system are explained. Then, preprocessing techniques, ML methods and performance evaluation criteria for forecasting approaches are presented. Contributions and limitations of recent works are critically discussed and separately tabulated. Finally, several future research opportunities are released at the conclusion of this paper.
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    Analyzing flexibility options for microgrid management from economical operational and environmental perspectives
    (Elsevier Sci Ltd, 2024) Dogan, Ahmet
    Power grids have undergone a massive transformation due to increasing number of renewable/non renewable distributed generators, storage technologies and electrical vehicles. This paper focuses on the flexible energy management of grid connected microgrid (MG) in order to analyze the effects of the flexibility options such as electric vehicle with demand response (EV -DR), battery storage system (BSS), hydrogen storage system (HSS) and dynamic line rating (DLR) in presence of renewable sources. In addition, microturbine (MT), fuel cell (FC), biomass (BIO), geothermal (GEO), and diesel engine (DIE) whose output is controllable are also considered as flexible sources. In this paper, optimum operation of grid-connected MG is modeled as a mixed-integer linear programming problem considering uncertainties of wind turbine (WT) and photovoltaic (PV) sources. A day-ahead forecasting of irradiance and wind speed are performed with Decision Tree Regression (DTR) and Long Short Term Memory (LSTM) algorithms for sensitive calculation of PV and WT output. The cost function merges operating costs including fuel, operation & maintains (O&M), depletion costs with emission cost of CO2, SO2, and NOx. Obtained numerical results show that total cost of the MG is reduced by 9.40% and emission cost is diminished by 5.59% with inclusion of all considered flexibility options. Further, load factor is improved from 0.7932 to 0.8236 with 100% flexibility condition for MG.
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    Load Frequency Control of Two Area and Multi Source Power System Using Grey Wolf Optimization Algorithm
    (IEEE, 2019) Dogan, Ahmet
    In this study, load frequency of two area interconnected power systems are controlled based on Proportional Integral Derivative (PID) controller structures and gain parameters of controllers are decided using Grey Wolf Optimization (GWO) algorithm. Dynamic response of the proposed structure is investigated considering integral of time multiplied absolute error (ITEA) as cost function in a two area and multi source power system. Capability and efficiency of GWO algorithm is illustrated in comparison to Particle Swarm Optimization (PSO) and Artificial Bee colony (ABC). It is observed that GWO provides minimum value of cost function and better dynamic response among the considered algorithms.
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    Öğe
    Optimum sitting and sizing of WTs, PVs, ESSs and EVCSs using hybrid soccer league competition-pattern search algorithm
    (Elsevier - Division Reed Elsevier India Pvt Ltd, 2021) Dogan, Ahmet
    With increasing environmental sensitivity and developing technology, Distributed Generations (DGs), Electric Vehicles Charging Stations (EVCSs), and Energy Storage Systems (ESSs) have been significant part of a distribution grid. However, inappropriate sitting and sizing of these components may negatively affect the performance of distribution grid. Besides, efficient operation of distribution grid can be achieved by only choosing the proper sizes of DGs, EVCSs, ESSs and placing them at the appropriate locations in the grid. In this paper, a weighted sum multi-objective function is formulated for reducing power loss, increasing voltage level and integration capacity of DGs, EVCSs, ESSs, simultaneously. Also, hybrid Soccer League Competition-Pattern Search (hSLC-PS) optimization algorithm is proposed to improve fine-tune performance of optimization. Simulations are carried out considering time-varying and stochastic nature of Photovoltaics (PVs) and Wind Turbines (WTs) generations and EVCSs load demand. The performance of the proposed method has been investigated on 33-bus and 85-bus test systems under different conditions and the statistical results are compared to SLC and Grey Wolf Optimization (GWO) algorithms to validate the effectiveness of the proposed algorithm. Statistical results clearly reveal the superiority of hSLC-PS in simultaneous sitting and sizing of DGs, EVCSs and ESSs. (C) 2020 Karabuk University. Publishing services by Elsevier B.V.