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Öğe Adaptive protection method with fault current limiting mechanism for directional over current relays coordination problem(Gazi Univ, Fac Engineering Architecture, 2022) Yeroglu, Celaleddin; Akdag, OzanCoordination of Directional Over Current Relays (DOCR) is an important issue in the protection of power systems. In this paper, DOCR coordination, which has the problem of nonlinear and high constraints, is discussed The object function, reported in the literature, has been re-written by adding relay setting constraints multiplied by a weight factor to ensure optimum coordination of DOCRs. At first, the Evaporation Rate Controlled Water Cycle Algorithm (ER-WCA) and Harris Hawk Optimization (HHO) algorithms were used to compute the optimum coordination of the DOCR problem. The proposed algorithms are applied to the 9-bus test system. In addition, the results obtained using ER-WCA and HHO were compared with those obtained using other algorithms in the literature. Then, a new adaptive protection method, which adapts to changing power system conditions in power grids with Distributed Generation (DG) system, is proposed. In this new adaptive protection method, the HHO algorithm has been used to ensure optimum coordination of DOCRs. Also, in this new adaptive protection method, a structure with Fault Current Limiting (FCL) mechanism, which determines the critical fault point based on the both of the current and voltage index, is proposed in power systems. Then, by applying this new adaptive protection method to 10 bus Turkey distribution system, the effectiveness of the study was discussed.Öğe Combination of electromagnetic field and harris hawks optimization algorithms with optimization to optimization structure and its application for optimum power flow(Taylor & Francis Ltd, 2023) Akpamukcu, Mehmet; Ates, Abdullah; Akdag, OzanElectromagnetic Field Optimization (EFO) and Harris Hawk Optimization (HHO) algorithms are combined with the optimization to optimization (OtoO) approach, and the EFO-HHO algorithm pair is presented in this study. EFO method was used as the essential algorithm and HHO method was used as the auxiliary algorithm according to the OtoO structure. The constant parameters (R_rate, Ps_rate, P_field, N_field) of the EFO algorithm that affect the optimization performance are optimized with the HHO optimization algorithm for the related optimization problem. The proposed method was tested on 10 different benchmark functions according to different dimensional (30, 50100). The EFO-HHO algorithm pair can produce better results than the existing literature, especially in cases of increased dimension with the proposed approach. In addition to these, the OPF problem was tested on the IEEE 30 test bus system for the engineering application of the proposed method. The results are compared with the existing literature results. As it can be seen from the results, it has been shown on the real engineering problem that the optimization performance can be increased with the OtoO approach without changing the basic philosophy of the EFO algorithm.Öğe Effect of outside temperature on energy consumption of electric vehicles: Real-time big data and artificial intelligence-aided seahorse optimization approach(Gazi Univ, Fac Engineering Architecture, 2025) Ekici, Yunus Emre; Karadag, Teoman; Akdag, Ozan; Aydin, Ahmet ArifIn calculating the energy consumption of electric vehicles (EVs); it is very important to optimize the consumption efficiency and driving range by considering the outdoor temperature. Studies have shown that very low and very high temperatures reduce engine efficiency and significantly increase energy consumption, while affecting regenerative energy recovery. Therefore, in the presented study, the effects of outdoor temperature on range and energy consumption were investigated using real-time big data obtained from Electric Buses (EO). The field application of the study was carried out with 22 24.7-meter EOs. The EO route was divided into 4 different regions and the energy consumption for each region and the analysis of the outdoor temperature corresponding to this consumption were obtained using regression techniques. First, the energy consumption model was created and the driving cycle was calculated for each region. Then, the driving cycle for the entire route was created and the energy consumption on the route was expressed as a mathematical model. Trilayered Neural Network (TNN) gave the best result in the calculations of the entire route. Finally, the mathematical model obtained as a result of TNN was reconsidered using the SeaHorse optimization method. Considering the analysis for the entire route (R), it was calculated that the most efficient consumption is 3.02 kWh/km and this consumption value can be achieved with a temperature of 21.5oC. This study has become a reference study for other electric vehicle manufacturers in determining the range of their vehicles in different climate conditions.Öğe Energy consumption model with real-time data for driving range extension of electric buses(Elsevier, 2025) Ekici, Yunus Emre; Aydin, Ahmet Arif; Karadag, Teoman; Akdag, Ozan; Ates, AbdullahPreventing range anxiety in electric vehicles (EVs) requires efficient energy use and an accurate estimation of the battery capacity needed for the desired range. A longer range leads to reduced consumption and extends operational activities. Thus, extended driving range can be achieved, promoting a more environmentally sustainable transportation model. This contributes significantly to reducing greenhouse gas emissions and mitigating the environmental impact of transportation. In this study, 250,000 rows of real-world data were collected from electric Trolleybus vehicles for a realistic energy consumption estimation of EVs. First, a mathematical model was obtained from these data using Gaussian Process Regression (GPR) method. To reduce the error rate of this model and increase the accuracy of consumption estimation, it was necessary to re-analyze it with an optimization technique. The accuracy of the consumption prediction model is extremely important for increasing the range of EVs and enabling uninterrupted travels. To solve range anxiety problem, the mathematical model obtained by GPR method is re-optimized by SeaHorse optimization and a new energy consumption prediction model, SHO-EBECM (Seahorse Optimized-Electric Bus Energy Consumption Model), is obtained. The trained SHO-EBECM was applied to 20 real routes of public transportation with internal combustion engine buses in a metropolitan city and the RMSE (Root Mean Square Error) value has been calculated to be between 0.1470 and 0.2920. Based on the achieved error rate, it can be inferred that SHO-EBECM offers a solution with a reduced error rate in comparison to four other optimization techniques. Furthermore, considering global warming, carbon emissions and ecological balance, it is concluded that approximately 12,060 tons/year of CO2, 372.75 tons/ year of NO and NO2 gases can be prevented from being emitted to nature by converting internal combustion engine buses on 20 different routes to electric buses (E-Bus) with the help of SHO-EBECM.Öğe Enhancing electric vehicle range through real-time failure prediction and optimization: Introduction to DHBA-FPM model with an artificial intelligence approach(Elsevier, 2025) Ekici, Yunus Emre; Karadag, Teoman; Akdag, Ozan; Aydin, Ahmet Arif; Tekin, Hueseyin OzanElectrical and mechanical failures in electric vehicles (EVs) during passenger operation cause significant operational losses and elevated energy consumption, amplifying range anxiety. To address this issue, we utilized 250,000 rows of real-time data from electric trolleybuses operating in T & uuml;rkiye to develop a robust artificial intelligence (AI)-based optimization model for failure mitigation. Initially, Tri layered Neural Network (TNN) was employed to create a predictive function for electrical and mechanical failures, followed by comparative analyses across six optimization algorithms widely adopted in failure prediction studies. Among these, the Developed Honey Badger Algorithm with AI Approach (DHBA) emerged as the most effective, achieving a predictive accuracy improvement of 15 % over the standard Honey Badger Algorithm (HBA). The DHBA incorporates a Dynamic Fitness-Distance Balance (DFDB) mechanism and a novel spiral motion feature to enhance search precision, leading to the DHBA-FPM (Developed-Honey Badger Algorithm - Failure Prediction Model). The final DHBA-FPM model was applied to the 10 highest-density bus routes in T & uuml;rkiye to predict and optimize failures. Results indicate that applying the DHBA-FPM model across these routes yielded a 3.96 % average range increase in EVs, extending the total range by approximately 79,200 km annually. It can be concluded that the model could prevent the release of 238.7 tons/year of CO2, NO, and NO2 emissions through its potential to improve both the operational efficiency and sustainability of EVs in public transit networks.Öğe An evaluation of an offshore energy installation for the Black Sea region of Turkey and the effects on a regional decrease in greenhouse gas emissions(Wiley Periodicals, Inc, 2020) Akdag, Ozan; Yeroglu, CelaleddinOffshore wind power plants (OWPPs), which are not yet installed in Turkey, are becoming a popular method of energy production around the world because of their environmentally friendly nature. Thus, the installation of OWPPs should be an important agenda item in terms of Turkey's renewable energy policies as a contribution to the global greenhouse gas emission targets. This study proposes a model for installing OWPPs in a suitable part of the Black Sea region of Turkey and simulates the results. The suitable locations for OWPP installation were evaluated according to the technical and geographical conditions of this region and site criteria. Meteorological data from these locations were analyzed in the Wind Atlas Analysis and Application Program (WAsP) to determine the best location. In this study, an OWPP model with a 204.6 MW installed power capacity was proposed for the identified location, which has a wind speed of 8.77 m s(-1) and a wind power density of 348 W m(-2). Then, an OWPP model and regional energy transmission system was modeled using DigSilent software. The load flow analysis was performed using this model, and the results show that 7616 tons of toxic gas and 1.527 million tons of greenhouse gas could be reduced in the region in a year if the proposed OWPP were put into operation. Thus, with the installation of OWPP, this study shows that annual CO2 emissions can be reduced by 7.63% for the entire Black Sea region. (c) 2020 Society of Chemical Industry and John Wiley & Sons, Ltd.Öğe Impact of Outside Temperature on Driving Range and Energy Consumption Using Real-Time Big Data for Electric Buses(Institute of Electrical and Electronics Engineers Inc., 2024) Ekici, Yunus Emre; Karadag, Teoman; Aydin, Ahmet Arif; Akdag, OzanCalculating the energy consumption of electric vehicles (EVs) is crucial to optimize efficiency and driving range, taking into account the outdoor temperature. Research shows that low temperatures significantly increase motor and battery energy consumption while inhibiting regenerative energy recovery, with optimum efficiency achieved at around 20-30 degrees Celsius. Furthermore, the use of heating and cooling systems in different seasons also affects the overall efficiency by affecting battery energy consumption. Therefore, outdoor temperature and driving conditions must be taken into account to accurately assess and optimize the energy consumption of EVs. In this study, the effects of outdoor temperature on range and energy consumption are analyzed using real-time big data from Electric Buses (EB). The field application of the study is based on the EB route currently in operation in Malatya. The EB route is divided into 4 different regions and the energy consumption and the corresponding outdoor temperature for each region are analyzed using regression analysis techniques. As a result of the calculations, it was calculated that the most efficient consumption for the entire EB route is 3,02 kWh / km and this consumption value can be achieved with a temperature of 21,5° C. © 2024 IEEE.Öğe Load Flow Optimization of 154 kV Malatya Transmission Line Using Differential Evolution Algorithm(Ieee, 2017) Akdag, Ozan; Karadogan, Ismail; Okumus, Fatih; Yeroglu, Celaleddin; Karci, AliAmplitude, phase angle, active and reactive powers flowing in each busbar of a power system can be seen by performing a load flow analysis. From these data, it is possible to determine the voltage drop, the distribution of the forces, the loading of the equipment and the losses of the related power system. Then, Active power losses can be reduced by making improvements at the points where losses are present in the power system. Power losses can be reduced by reactive power compensation considerably. In this study, Differential Evolution (DE) algorithm is used to determine the values of the capacitor groups to be added to the corresponding busbar to reduce the losses of 154 kV transmission system. The optimum load flow is ensured by optimizing a transmission system with the developed algorithm.Öğe Modification of Harris hawks optimization algorithm with random distribution functions for optimum power flow problem(Springer London Ltd, 2021) Akdag, Ozan; Ates, Abdullah; Yeroglu, CelaleddinHarris hawks optimization (HHO) algorithm, which is inspired from Harris hawks hunting strategy, uses uniform random numbers in the optimization process. This paper proposes modifying HHO with seven types of random distribution function definitions that are chi-square distribution, normal distribution, exponential distribution, Rayleigh distribution, Student's distribution,Fdistribution, and lognormal distribution to show effects on stochastic search-based optimization algorithm performance. The modified HHO algorithm is tested via some benchmark test functions. Results are compared with each other and with classical HHO solutions. Then, the HHO and its modified versions are applied to optimum power flow (OPF), which is an important problem for power system engineering for decades. The algorithms are applied to IEEE 30-bus test system to minimize total fuel cost of the power system, active/reactive power losses, and emission, by comparing with recent OPF researches. Considering the applicability of the proposed approach and the results achieved, one can confirm that it might be a different alternative method for solving OPF problems. One of the important results of the paper in the IEEE 30-bus test system is that the cost of fuel is calculated as 798.9105 $/h with classical HHO, while it is calculated as 798.66 $/h with the HHO modified with SD function.Öğe Modified Archimedes optimization algorithm for global optimization problems: a comparative study(Springer London Ltd, 2024) Nurmuhammed, Mustafa; Akdag, Ozan; Karadag, TeomanArchimedes Optimization Algorithm (AOA) is a recent optimization algorithm inspired by Archimedes' Principle. In this study, a Modified Archimedes Optimization Algorithm (MDAOA) is proposed. The goal of the modification is to avoid early convergence and improve balance between exploration and exploitation. Modification is implemented by a two phase mechanism: optimizing the candidate positions of objects using the dimension learning-based (DL) strategy and recalculating predetermined five parameters used in the original AOA. DL strategy along with problem specific parameters lead to improvements in the balance between exploration and exploitation. The performance of the proposed MDAOA algorithm is tested on 13 standard benchmark functions, 29 CEC 2017 benchmark functions, optimal placement of electric vehicle charging stations (EVCSs) on the IEEE-33 distribution system, and five real-life engineering problems. In addition, results of the proposed modified algorithm are compared with modern and competitive algorithms such as Honey Badger Algorithm, Sine Cosine Algorithm, Butterfly Optimization Algorithm, Particle Swarm Optimization Butterfly Optimization Algorithm, Golden Jackal Optimization, Whale Optimization Algorithm, Ant Lion Optimizer, Salp Swarm Algorithm, and Atomic Orbital Search. Experimental results suggest that MDAOA outperforms other algorithms in the majority of the cases with consistently low standard deviation values. MDAOA returned best results in all of 13 standard benchmarks, 26 of 29 CEC 2017 benchmarks (89.65%), optimal placement of EVCSs problem and all of five real-life engineering problems. Overall success rate is 45 out of 48 problems (93.75%). Results are statistically analyzed by Friedman test with Wilcoxon rank-sum as post hoc test for pairwise comparisons.Öğe Multi-factor optimization for electric bus charging stations: Integrating electrical, social, and environmental perspectives(Elsevier, 2025) Nurmuhammed, Mustafa; Akdag, Ozan; Karadag, Teoman; Kafafy, MagedIn this study, a method that provides the optimal placement of Charging Stations (CSs) for Electric Buses (EBs) on the distribution network considering electrical, social, and environmental aspects is proposed. The parameters used in the study are power loss, voltage deviation, voltage stability index, population density, traffic density, proximity to points of interest and location cost. The energy consumption of the EBs is calculated by linear modeling method using ten parameters. To test the applicability and effectiveness of the proposed method, IEEE33 and IEEE-69 bus systems are modified in accordance with EBs, and the distances between the stops are set as routes. Afterwards, CS placement is performed for different scenarios and power effects are shown. The power loss is decreased by 17.34 % in the 33-bus and 8.75 % in the 69-bus test systems. Accordingly, 106 and 25 kgs of CO2 per hour can be prevented, respectively. In summary, this study provides a method that comprehensively evaluates both electrical and social-environmental factors for the optimal placement of CSs for EBs. Additionally, the practical applications of the proposed method offer valuable data for reducing power loss and CO2 emissons, thus providing important insights for sustainable urban transportation solutions. The findings of the study can form the basis for future research on optimizing EV charging infrastructure (based on both social/environmental and electrical data) and guide policymakers and urban planners in developing more efficient and sustainable transportation networks.Öğe A Novel Energy Consumption Prediction Model of Electric Buses Using Real-Time Big Data From Route, Environment, and Vehicle Parameters(Ieee-Inst Electrical Electronics Engineers Inc, 2023) Ekici, Yunus Emre; Akdag, Ozan; Aydin, Ahmet Arif; Karadag, TeomanElectric vehicles (EVs) have positive impacts on reducing oil dependence and exhaust emissions. However, the range problem of EVs is a factor that raises concerns for individual users and bus operators. For this reason, studies on increasing the range of the electric buses in public transportation is extremely important to ensure optimum operation. In this study, a novel energy consumption model, MTECM (Malatya Trolleybus Energy Consumption Model), is developed using the multi-parameter linear regression method. The real-time big data was collected on the field of Trolleybus vehicles, which have been operated for 8 years in Malatya / Turkiye. Firstly, by calculating the correlation of the parameters affecting this model, the parameters that are suitable for the purpose of our study are determined and regression analysis is performed on the original Trolleybus dataset. A total of 75.497.472 data are analyzed for this model. The RMSE (Root Mean Square Error) of MTECM is calculated as 0.29996. The trained model is applied to the 10 busiest routes in Malatya in terms of passenger density. The RMSE value on these routes is calculated between 0.30299 and 0.31421. Based on the results, with lower error rates, the proposed novel model is more efficient than other studies in the literature. In addition, energy consumption can be calculated for any route planned to establish an electric bus operation with MTECM. Therefore, according to the consumption obtained, the correct determination and selection of parameters that significantly affect the investment cost such as route, vehicle length, engine power, and battery capacity can be made.Öğe A novel modified Archimedes optimization algorithm for optimal placement of electric vehicle charging stations in distribution networks(Elsevier, 2023) Nurmuhammed, Mustafa; Akdag, Ozan; Karadag, TeomanThe number of electric vehicle charging stations (EVCSs) is increasing alongside electric vehicles (EVs). The unplanned rollout of EVCSs can lead to problems such as deterioration of the voltage profile, increased active power losses, and maximum load demand in distribution networks (DNs). Therefore, EVCSs should be placed in optimum locations on the DNs. The complexity of the problem requires efficiency and performance. In this paper, a novel modified Archimedes optimization algorithm (MAOA) is proposed for optimal placement of EVCSs in DNs. Five parameters of the AOA were modified using the Honey Badger Algorithm (HBA). This placement is based on a combination of indexes: power loss, voltage deviation, and voltage stability index (VSI). Next, Newton-Raphson based load flow analysis is used to compute the minimum cost. MAOA, AOA and eight other optimization algorithms are compared. The results provide the most appropriate buses in the DN. The efficiency of the proposed algorithm was tested on IEEE 33, 69, and 10-bus Hatay power network systems. In all test systems, the proposed MAOA provided the best results with consistently low standard deviation values. The results suggest that the proposed method is more effective in the integration of EVCSs into the DN. (c) 2017 Elsevier Inc. All rights reserved.Öğe A Novel Newton Raphson-Based Method for Integrating Electric Vehicle Charging Stations to Distribution Network(Aves, 2023) Nurmuhammed, Mustafa; Akdag, Ozan; Karadag, TeomanElectric vehicle sales are rising due to numerous factors such as government policies, falling prices, advanced, and eco-friendly technology. Built-in battery packs are charged using the energy from the distribution network. When a large number of electric vehicles are simultaneously charged with high power, the distribution network can be adversely affected. Differences in electricity supply and demand on the distribution network, voltage imbalance, and power losses are among the examples of effects. In this study, a mathematical model has been proposed in order to properly handle the effects of charging stations on the distribution network. According to the model, the optimum placement of electric vehicle charging stations is achieved within the constraints of the distribution network. In order to prove the robustness and validity of this model, the proposed model is applied to IEEE 33-bus radial test system. Results are discussed in cases.Öğe Optimal directional overcurrent relay coordination using MRFO algorithm: A case study of adaptive protection of the distribution network of the Hatay province of Turkey(Elsevier Science Sa, 2021) Akdag, Ozan; Yeroglu, CelaleddinCoordination of Directional Over Current Relays (DOCR) is an important issue in the protection of power sys-tems. In this paper, the object function of DOCR has been modified for better coordination. Manta Ray Foraging Optimization (MRFO) algorithm is adapted to the DOCR with the modified object function to maintain optimum relay coordination by preserving the relay coordination margin between the relay pairs. In order to show the success of the proposed technique, the algorithm has been applied to the 9-bus and 15-bus test system. The obtained results have been compared to other algorithms in the literature. Further, an adaptive protection structure, which provides optimum coordination of DOCR with MRFO algorithm according to changing power system conditions, has been proposed for a network that include Distributed Generation (DG) power plant. The proposed adaptive protection structure has been applied to the virtual model of a cross-section of 10 bus dis-tribution networks that includes a Wind Powered DG (WP-DG) in the Hatay province of Turkey.Öğe Optimization of Proportional-Integral-Derivative Parameters for Speed Control of Squirrel-Cage Motors with Seahorse Optimization(Aves, 2024) Ekici, Yunus Emre; Akdag, Ozan; Aydin, Ahmet Arif; Karadag, TeomanThe two different motion behaviors of seahorses in nature served as inspiration for the seahorse optimization (SHO) method, which is a new metaheuristic swarm intelligence-based approach to solving fundamental engineering problems. In this study, the propo rtion al-in tegra l-der ivati ve (PID) parameters for the simplified speed control of the manipulator joint using squirrel-cage induction motors were calculated with the SHO algorithm. As a result of these calculations, Kp, Ki, and Kd values were obtained as 0.0430, 0.00474, and 0.03254, respectively. Then, the time for the squirrel-cage motor to reach 50 rpm (revolutions per minute) and 90 rpm was calculated with the help of SHO. In PID + SHO operation, the squirrel-cage electric motor reached 50 rpm in 3 seconds and 90 rpm in 8 seconds. In this study, in which the SHO optimization method was used, it was calculated that the acceleration of the squirrel-cage motor and reaching the desired value gave 50% better results compared to the particle swarm optimization algorithm.Öğe Overcurrent Relay Coordination of 154/34,5 kV Hasancelebi Substation by League Championship Algorithm(Ieee, 2017) Seyyarer, Abubekir; Akdag, Ozan; Hark, Cengiz; Karci, Ali; Yeroglu, CelaleddinIn this study, non-directional overcurrent relay coordination was done in 154/34.5 kV Malatya Teias Hasancelebi transformer centre using League Championship Algorithm (LCA). Standard inverse time characteristic based on IEC 255-3 is used for the relay is coordinated. The results obtained by the LCA have been used in virtual model, obtained by DigSilent software for overcurrent relays at the Hasancelebi transformer centre. Then, the overcurrent relay coordination was performed by examining the response of the overcurrent relays to the 3-phase fault currents generated in the model.Öğe Redefining urban mobility: Real-world regenerative braking optimization via bio-inspired AI for electric buses energy efficiency(Pergamon-Elsevier Science Ltd, 2025) Ekici, Yunus Emre; Karadag, Teoman; Akdag, OzanIn the era of global decarbonization and sustainable urban mobility, optimizing energy recovery in electric public transport systems has become a strategic imperative. This study presents a comprehensive and data-driven investigation into the optimization of regenerative braking (RB) performance in hybrid electric trolleybuses operating in T & uuml;rkiye. Unlike conventional research relying on controlled driving cycles or laboratory simulations, this work employs real-world operational data totaling over 79 million records collected over five years to construct a high-fidelity predictive model. Using a novel meta-heuristic algorithm inspired by the physiological mechanism of water uptake and transport in plants (WUTP), eight critical parameters influencing RB power including vehicle speed, gradient, acceleration, passenger mass, ambient temperature, and auxiliary system loads were integrated into a robust mathematical framework. The resulting model, WUTP-EBREM, accurately predicts regenerative braking power under varying operational conditions, achieving a minimal error rate (RMSE: 0.12 %). This enables a fine-grained understanding of how instantaneous operating dynamics affect energy recovery in electric buses. Subsequently, the model was applied to 50 of the city's busiest bus routes, each with distinct topographical and operational characteristics. Route-based analyses revealed substantial variability in RB potential, highlighting that optimal energy recovery depends not only on vehicle design but also on contextual factors such as slope patterns and thermal loads. The findings offer direct implications for fleet energy planning, battery sizing, and route optimization, providing actionable insights for transit operators and electric vehicle manufacturers alike. Beyond its empirical contributions, this research introduces a scalable modeling architecture adaptable to various geographic regions and climate conditions, bridging the gap between theoretical energy models and field-level implementation. By capturing the stochastic, non-linear nature of urban electric bus operation, the study sets a precedent for integrating artificial intelligence into real-time transport energy optimization. The WUTP-EBREM model stands as a unique decision-support tool for smart transportation planning, offering both academic value and immediate practical utility. This work is poised to inform future studies in energy-aware vehicle control systems, sustainable transit infrastructure, and intelligent fleet management strategies in electrified urban environments.Öğe Tailoring Energy Efficiency for Urban Electric Buses: The GTECM Model for Enhanced Range and Sustainable Operation Using Real-Time Big Data(Ieee-Inst Electrical Electronics Engineers Inc, 2025) Ekici, Yunus Emre; Karadag, Teoman; Akdag, Ozan; Aydin, Ahmet Arif; Tekin, Huseyn OzanThe increasing depletion of fossil fuels and growing environmental concerns are increasing the need for energy efficient and sustainable solutions, particularly in transport. At this point, especially in public transport, electric vehicles (EVs) offer a promising alternative; however, issues such as range anxiety and energy efficiency require comprehensive solutions. This study introduces the Gauss-based Trolleybus Energy Consumption Model (GTECM) for electric buses, harnessing real-time big data to mitigate range anxiety and enhance energy efficiency. This model employs Gaussian Process Regression to a large-scale dataset including 100,000 entries collected over six months in T & uuml;rkiye. With an overall Root Mean Square Error (RMSE) of 0.013905, GTECM substantially outperforms linear approaches across T & uuml;rkiye's primary routes, exhibiting route-specific RMSE values between 0.28117 and 0.30540. Empirical findings suggest potential energy savings of up to 50%, alongside a 10% extension in driving range, thereby mitigating an estimated 4,220 tons of CO2 and 129.88 tons of NO2 emissions annually. Moreover, the projected amortization period for diesel-to-electric bus conversion stands at 6.83 years, underscoring GTECM's pragmatic utility for sustainable urban transit optimization. The findings of the study can form the basis for future research and guide policy makers and urban planners in the development of more efficient and sustainable transport networks.Öğe V2G Uygulamalarında Elektrikli Araçların Dağıtım Şebekesine Optimum Entegrasyonunun Analizi İçin Newton Raphson Temelli Yeni Bir Model(2024) Akdag, Ozan; Karadag, Teoman; Nurmuhammed, Mustafa; Kuzu, BünyaminSon yıllarda, Elektrikli Araç (EA) üretimi ve kullanımı hızla artmaktadır. EA'lar, yapısı gereği enerji depolama kapasitesine sahip olduklarından, elektrik şebekesi üzerinde yedek güç kaynağı ve yardımcı hizmetler sağlama gibi işlevleri yerine getirebilecekleri fikirleri ortaya çıkmıştır. Bu fikirlerden biri, araçtan şebekeye enerji transferi olan Vehicle-to-Grid (V2G) konseptidir. Bu çalışmada, EA'lara ait şarj istasyonlarının şebekeye optimum şekilde entegrasyonu ve EA'ların V2G konsepti ile şebekeye elektrik enerjisi aktarımının incelenmesi için Newton Raphson temelli yeni bir model önerilmektedir. Önerilen çalışmanın etkinliğinin incelenmesi için öncelikle IEEE 33 bara sistemi DigSilent yazılımı ile modellemiştir. Sonrasında bu çalışmada sunulan yeni model ile güç sisteminde EA şarj istasyonlarının optimum entegrasyonu sağlatılmıştır. Çalışma kapsamında, EA'ların şarj/deşarj durumları simüle edilerek V2G konsepti analiz edilmiştir. Böylece, bu çalışma ile EA'ların hem mevcut şebeke üzerindeki etkileri hem de çevresel etkileri detaylı olarak incelenmiştir. Analiz sonucunda, 156 EA radyal dağıtım şebekesine (optimum) entegre edilmiş ve 10 saatte toplam 4464 kWh elektrik enerjisi şebekeye aktarılarak yaklaşık 1519 kg CO2 salınımı azaltılmıştır. Bu çalışma, V2G konsepti ile elektrik şebekesine pik talep saatlerinde destek olmak, yük dengelenmesi, araçlarda depolanan yenilenebilir enerjinin kullanılabilmesi, sistemdeki darboğazların ve karbon salınımının azaltılması için EA'ların şebeke entegrasyonunu teşvik etmektedir.
