Browsing by Author "Ceylan, Oguzhan"

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Data-Driven Local Control Design for Dead Band Control of Load Tap Changers
(IEEE, 2024) Savasci, Alper; Ceylan, Oguzhan; Paudyal, Sumit
This study presents an off-line optimization-guided machine learning approach for coordinating the local control rules of on-load tap changers (OLTCs) and step-voltage regulators (SVRs). Based on a bang-bang control rule, these legacy devices autonomously regulate the feeder voltage around the nominal level by varying the tap position in the lower or raise direction. The characterizing parameter of the local control rule is the dead band, which affects the number of tap switching in operation and is directly related to the economical use life of the equipment. The bandwidth is typically set within a standard voltage range and is generally kept constant in daily operation. However, adjusting the bandwidth dynamically can prevent excessive tap switching while maintaining satisfactory voltage regulation for varying loading and distributed generation conditions. Our approach aims to set the bandwidth parameter systematically and efficiently through a machine learning-based scheme, which is trained with a dataset formed by solving the distribution network optimal power flow (DOPF) problem. The performance of learning the bandwidth parameter is demonstrated on the modified 33-node feeder, which is promising for integrated voltage control schemes.
Citation - WoS: 1
Citation - Scopus: 3
Feature Selection Using Self Organizing Map Oriented Evolutionary Approach
(Ieee, 2021) Ceylan, Oguzhan; Taskin, Gulsen
Hyperspectral images are the multidimensional matrices consisting of hundreds of spectral feature vectors. Thanks to these large number of features, the objects on the Earth having similar spectral characteristics can easily be distinguished from each other. However, the high correlation and the noise between these features cause a significant decrease in the classification performances, especially in the supervised classification tasks. In order to overcome these problems, which is known in the literature as Hughes's effects or curse of dimensionality, dimensionality reduction techniques have frequently been used. Feature selection and feature extraction methods are the ones used for this purpose. The feature selection methods aim to remove the features, including high correlation and noise, out of the original feature set. In other words, a subset of relevant features that have the ability to distinguish the objects is determined. The feature extraction methods project the high dimensional space into a lower-dimensional feature space based on some optimization criterion, and hence they distort the original characteristic of the dataset. Therefore, the feature selection methods are more preferred than the feature extraction methods since they preserve the originality of the dataset. Based on this motivation, an evolutionary based optimization algorithm utilizing self organizing map was accordingly modified to provide a new feature selection method for the classification of hyperspectral images. The proposed method was compared to well-known feature selection methods in the classification of two hyperspectral datasets: Botswana and Indian Pines. According to the preliminary results, the proposed method achieves higher performance over other feature selection methods with a very less number of features.
Citation - WoS: 4
Citation - Scopus: 5
Cuckoo Search Algorithm for Optimal Siting and Sizing of Multiple Distributed Generators in Distribution Grids
(IEEE, 2019) Ahmadi, Bahman; Ceylan, Oguzhan; Ozdemir, Aydogan
Distribution networks (DNs) are facing numerous challenges such as variability of demands, environmental issues, high power losses, and fluctuating v oltage p rofiles. Distributed energy resources (DERs) are becoming more important due to their economic and environmental impacts. This paper presents optimal siting and sizing of the Photovoltaics (PVs) and Wind Turbines (WTs) to improve the voltage magnitude profiles. This planning problem is formulated using DER generation and load profiles of the three representative days, one for each season. The resulting constrained optimization problem is solved using Cuckoo Search Algorithm (CSA). The proposed solution approach is applied to the 33 bus and 69 bus radial distribution networks. Several simulations are performed for the performance analysis of the methods and the results are compared to several available ones.
Voltage Profile Improvement in Unbalanced Distribution Networks for Probabilistic Generation and Consumption
(Ieee, 2024) Bamatraf, Mohammed; Ceylan, Oguzhan; Pisica, Ioana; Ozdemir, Aydogan
Due to their technical, economical, and environmental advantages, active distribution networks implement renewable energy resources (RERs) such as photovoltaic (PV) units in distribution networks DNs. However, some drawbacks may arise due to the intermittent nature of RERs, such as voltage fluctuations and increased system losses. This paper presents an optimization problem that is solved by sequential linear programming (SLP) to improve the voltage profile of the unbalanced distribution network. A probabilistic approach was applied to both the load profile and the active power generation of the PV units. SLP is applied to the modified IEEE 34 Bus Test system. The method optimizes the voltage deviations by changing the taps of the voltage regulators and the reactive power injected by the inverters of the PV systems and, in some cases, by switching a shunt capacitor. MATLAB simulations are done at different times of the day with different loads and PV outputs to compare base case and optimal case voltage profiles. The results show better voltage profiles after applying the presented approach.
Citation - WoS: 1
Citation - Scopus: 2
Voltage Control of Unbalanced Distribution Systems With Penetration of Renewable Sources: a Gradient-Based Optimization Approach
(IEEE, 2022) Ebadi, Ramin; Senyuz, Hande; Aboshady, Fathy; Ceylan, Oguzhan; Pisica, Ioana; Ozdemir, Aydogan
The penetration of distributed energy resources (DERs), including renewable energy sources (RES), into electric power systems has led to several challenges for the system operators. Despite various economic and environmental benefits offered by RES, the issue of voltage rise due to active power injection from RES is still an open problem. On the other hand, voltage decrease due to high load in distribution systems is another challenge faced by operators. In this study, we investigated the problem of over-voltage and under-voltage in the operation of unbalanced 3-phase distribution systems with penetration of RES. Moreover, We utilize derivative-based Exterior Penalty Function (EPF) optimization to solve the voltage deviation problem. The results of the tests conducted on a modified IEEE 13 Bus Test System have confirmed that the use of the tap changer voltage regulators and reactive power from PVs connected close to inverters can effectively contribute to the voltage control problem.
Citation - WoS: 3
Citation - Scopus: 4
Advanced Restoration Management Strategies in Smart Grids: the Role of Distributed Energy Resources and Load Priorities
(Pergamon-elsevier Science Ltd, 2025) Ahmadi, Bahman; Ceylan, Oguzhan; Ozdemir, Aydogan
Fast restoration following long outages is a challenge in the smart city management process. It is necessary to accurately characterize the real operating conditions of the system for optimal restoration. This study focuses on two key factors of a practical distribution system restoration. The first factor is cold load pickup (CLPU), which commonly occurs after an outage and is caused by thermostatically controlled loads. A time-dependent CLPU is modeled to accurately describe the restored load behaviors. The second factor is the effect of the distributed generators (DG), energy storage systems (ESSs), and load priority factors on the system's restoration process. To address this challenge, a robust optimization model is proposed that fully considers the effect of DG, and ESS units and uncertainty of CLPU. The proposed models are tested on the IEEE 33-node and 69-node test systems using the Advanced Grey Wolf Algorithm (AGWO). The simulation scenarios are designed to uncover optimal scheduling strategies for the restoration process corresponding to each Pareto solution of a previous study. The results are discussed for several distinct initial conditions. Moreover, a comparative evaluation is done, contrasting the outcomes achieved through the AGWO algorithm with those stemming from alternative heuristic methods.
An Ant-Lion Optimization Based Approach to Solve Phase Balancing Problem in Distribution Networks
(IEEE, 2024) Yesilyurt, Gunnur; Ceylan, Oguzhan
Phase unbalance is a significant issue for power distribution networks. It can lead to increased energy losses and voltage instability, undermining the electrical grid's reliability and efficiency. We propose an approach to minimize voltage unbalance through reactive power management from PV installations and the optimization of charging/discharging of energy storage devices utilizing a control algorithm based on Ant-Lion Optimizer. We tested the approach on the IEEE 123-Bus Test System, incorporating PV generations by daily simulations. From the results, the combined operation of reactive power support from PVs and Storage Units with the help of the ALO algorithm offers a promising solution to the phase unbalance problem.
Citation - Scopus: 1
Power Output Prediction of Wave Farms Using Fully Connected Networks
(IEEE, 2021) Burramukku, Bhavana; Ceylan, Oguzhan; Neshat, Mehdi
One of the most important factors in the amount of power generated by a wave farm is the Wave Energy Converters (WECs) arrangement along with the usual wave conditions. Therefore, forming an appropriate arrangement of WECs in an array is a significant parameter in maximizing power absorption. This paper focuses on developing a fully connected neural model in order to predict the total power output of a wave farm based on the placement of the converters, derived from the four real wave scenarios on the southern coast of Australia. The applied converter model is a fully submerged three-tether converter called CETO. Data collected from the test sites is used to design a neural model for predicting the wave farm's power output produced. A precise analysis of the WEC placement is investigated to reveal the amount of power generated by the wave farms on the test site. We finally proposed a suitable configuration of a fully connected neural model to forecast the power output with high accuracy.
Citation - WoS: 3
Citation - Scopus: 6
Heuristic Optimization Approaches for Capacitor Sizing and Placement: a Case Study in Kazakhstan
(Mdpi, 2022) Baimakhanov, Olzhas; Senyuz, Hande; Saukhimov, Almaz; Ceylan, Oguzhan
Two methods for estimating the near-optimal positions and sizes of capacitors in radial distribution networks are presented. The first model assumes fixed-size capacitors, while the second model assumes controllable variable-size capacitors by changing the tap positions. In the second model, we limit the number of changes in capacitor size. In both approaches, the models consider many load scenarios and aim to obtain better voltage profiles by minimizing voltage deviations and active power losses. We use two recently developed heuristic algorithms called Salp Swarm Optimization algorithm (SSA) and Dragonfly algorithm (DA) to solve the proposed optimization models. We performed numerical simulations using data by modifying an actual distribution network in Almaty, Kazakhstan. To mimic various load scenarios, we start with the baseline load values and produce random variations. For the first model, the optimization algorithms identify the near-optimal positioning and sizes of fixed-size capacitors. Since the second model assumes variable-size capacitors, the algorithms also decide the tap positions for this case. Comparative analysis of the heuristic algorithms shows that the DA and SSA algorithms give similar results in solving the two optimization models: the former gives a slightly better voltage profile and lower active power losses.
Citation - WoS: 36
Citation - Scopus: 50
A Multi-Objective Optimization Evaluation Framework for Integration of Distributed Energy Resources
(Elsevier, 2021) Ahmadi, Bahman; Ceylan, Oguzhan; Ozdemir, Aydogan
Renewable distributed generation and energy storage systems (ESSs) have been a gamechanger for a reliable and sustainable energy supply. However, this new type of generation should be optimally planned and operated to maximize the expected benefits. In this regard, this paper presents a new formulation for optimal allocation and sizing of distributed energy resources and operation of ESSs to improve the voltage profiles and minimize the annual costs. The multi-objective multiverse optimization method (MOMVO) is used as a solution tool. Moreover, the resulting Pareto optimal solution set is minimized under economic concerns and cost sensitivity to provide a decision-support for the utilities. The proposed formulation and solution algorithm are tested for the revised 33-bus and 69-bus test systems where the load and renewable generation characteristics are taken from real Turkish data. When compared with the base case operating conditions, the proposed formulation eliminated all the voltage magnitude violations, and provided almost 50% loss reductions and 20% energy transfers to off-peak hours. Moreover, Pareto fronts of the proposed method are found to better than the ones provided by non dominated sorting genetic algorithm and multi-objective particle swarm optimization, according to two multi-objective optimization metrics.
Citation - WoS: 1
Citation - Scopus: 2
Analysis of Local and Centralized Control of Pv Inverters for Voltage Support in Distribution Feeders
(IEEE, 2021) Ceylan, Oguzhan; Paudyal, Sumit; Pisica, Ioana
Higher photovoltaic penetration on distribution system brings operational challenges including overvoltage issues. With smart inverters, efficient voltage control can be achieved through adjusting active/reactive powers of inverters. However, reactive power may not be as effective as active power in regulating voltage due to high R/X ratio of distribution networks. Thus, active power curtailment (APC) techniques in coordination with reactive power control are required in distribution networks. In this study, we aim to evaluate the performances of a sensitivity based method and an optimal power flow (OPF) based centralized method of reactive power control (in coordination with APC) from inverters in managing voltage profile on distribution networks. We performed simulations on a 730-node MV/LV system upto 100% PV penetration. Based on the case studies using different penetration levels of PVs, we observed that: a) sensitivity based method is not always able to solve overvoltage issues and energy curtailments are high, and b) OPF-based method can ensure that voltage remains within the operational bound with significantly less energy curtailment.
Citation - WoS: 24
Citation - Scopus: 37
An advanced Grey Wolf Optimization Algorithm and its application to planning problem in smart grids
(Springer, 2022) Ahmadi, Bahman; Younesi, Soheil; Ceylan, Oguzhan; Ozdemir, Aydogan
Due to the complex mathematical structures of the models in engineering, heuristic methods which do not require derivative are developed. This paper improves recently developed Grey Wolf Optimization Algorithm by extending it with three new features: namely presenting a new formulation for evaluating the positions of search agents, applying mirroring distance to the variables violating the limits, and proposing a dynamic decision approach for each agent either in exploration or exploitation phases. The performance of Advanced Grey Wolf Optimization (AGWO) method is tested using several optimization test functions and compared to several heuristic algorithms. Moreover, a planning problem in smart grids is solved by considering different objective functions using 33 and 141 bus distribution test systems. From the numerical simulation results, we observe that, AGWO is able to find the best results compared to other methods from 10 and 9 out of 13 test functions for 30 and 60 variables, respectively. Similar to this, it finds best function values for 5 out of 10 fixed number of variable test functions. Also, the result of the CEC-C06 2019 benchmark functions shows that AGWO outperforms 8 for optimization problems from 10. In power distribution system planning problem, better objective function values were determined by using AGWO, resulting a better voltage profile, less losses, and less emission costs compared to solutions obtained by Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms.
Citation - WoS: 2
Citation - Scopus: 2
A Topology Detector Based Power Flow Approach for Radial and Weakly Meshed Distribution Networks
(Ieee, 2024) Yetkin, E. Fatih; Ceylan, Oguzhan; Pisica, Ioana; Ozdemir, Aydogan
Power distribution networks may need to be switched from one radial configuration to another radial structure, providing better technical and economic benefits. Or, they may also need to switch from a radial configuration to a meshed one and vice-versa due to operational purposes. Thus the detection of the structure of the grid is important as this detection will improve the operational efficiency, provide technical benefits, and optimize economic performance. Accurate detection of the grid structure is needed for effective load flow analysis, which becomes increasingly computationally expensive as the network size increases. To perform a proper load flow analysis, one has to build the distribution load flow (DLF) matrix from scratch cost of which is unavoidable with the growing size of the network. This will considerably increase the computation time when the system size increases, compromising applicability in online implementations. In this study we introduce a novel graph-based model designed to rapidly detect transitions between radial and weakly meshed systems. By leveraging the characteristic properties of Sparse Matrix-Vector product (SpMV) operations, we accelerate power flow calculations without necessitating the complete reconstruction of the DLF matrix. With this approach we aim to reduce the computational costs and to improve the feasibility of near-online implementations.
Citation - WoS: 1
Citation - Scopus: 2
A Real-World Case Study Towards Net Zero: Ev Charger and Heat Pump Integration in End-User Residential Distribution Networks
(Mdpi, 2025) Tun, Thet Paing; Ceylan, Oguzhan; Pisica, Ioana
The electrification of energy systems is essential for carbon reduction and sustainable energy goals. However, current network asset ratings and the poor thermal efficiency of older buildings pose significant challenges. This study evaluates the impact of heat pump and electric vehicle (EV) penetration on a UK residential distribution network, considering the highest coincident electricity demand and worst weather conditions recorded over the past decade. The power flow calculation, based on Python, is performed using the pandapower library, leveraging the actual distribution network structure of the Hillingdon area by incorporating recent smart meter data from a distribution system operator alongside historical weather data from the past decade. Based on the outcome of power flow calculation, the transformer loadings and voltage levels were assessed for existing and projected heat pump and EV adoption rates, in line with national policy targets. Findings highlight that varied consumer density and diverse usage patterns significantly influence upgrade requirements.
Citation - WoS: 4
Citation - Scopus: 12
Allocation of Distributed Generators Using Parallel Grey Wolf Optimization
(IEEE, 2021) Younesi, Soheil; Ahmadi, Bahman; Ceylan, Oguzhan; Ozdemir, Aydogan
This paper solves the allocation problem of distributed generators (DGs) in smart grids utilizing a grey wolf optimization (GWO) algorithm. By parallelizing GWO, it presents the impact of using various number of processors on speedup, efficiency. To decrease the computation time required to perform the simulations, different migration rates are applied for different number of processors. Moreover, the accuracy obtained using different number of processors is analyzed. The simulations are performed for a 33-bus distribution test system using MATLAB's parallel computing toolbox. From the simulation results it is observed that parallel GWO can be used as a tool for distribution system optimization.
Citation - WoS: 44
Citation - Scopus: 59
Distributed Energy Resource Allocation Using Multi-Objective Grasshopper Optimization Algorithm
(Elsevier Science Sa, 2021) Ahmadi, Bahman; Ceylan, Oguzhan; Ozdemir, Aydogan
The penetration of small-scale generators (DGs) and battery energy storage systems (BESSs) into the distribution grid is growing rapidly and reaching a high percentage of installed generation capacity. These units can play a significant role in achieving various objectives if installed at suitable locations with appropriate sizes. In this paper, we present a new multi-objective optimization model to improve voltage profiles, minimize DG and BESS costs, and maximize energy transfer between off-peak and peak hours. We allocate and size DG and BESS units to achieve the first two objectives, while optimizing the operation strategy of BESS units for the last objective. The Multi-Objective Grasshopper Optimization Algorithm (MOGOA) is used to solve the formulated constrained optimization problem. The proposed formulation and solution algorithm are tested on 33-bus and 69-bus radial distribution networks. The advantages of the Pareto solutions are discussed from various aspects, and the Pareto solutions are subjected to cost analysis to identify the best solutions in the context of the worst voltage profiles at peak load times. Finally, the performance of the MOGOA algorithm is compared with the other heuristic optimization algorithms using two Pareto optimality indices.
Citation - WoS: 7
Citation - Scopus: 11
A Heuristic Methods-Based Power Distribution System Optimization Toolbox
(Mdpi, 2022) Ozlue, Ismail Alperen; Baimakhanov, Olzhas; Saukhimov, Almaz; Ceylan, Oguzhan
This paper proposes a toolbox for simulating the effective integration of renewable energy sources into distribution systems. The toolbox uses four heuristic methods: the particle swarm optimization (PSO) method, and three recently developed methods, namely Gray Wolf Optimization (GWO), Ant Lion Optimization (ALO), and Whale Optimization Algorithm (WOA), for the efficient operation of power distribution systems. The toolbox consists of two main functionalities. The first one allows the user to select the test system to be solved (33-, 69-, or 141-bus test systems), the locations of the distributed generators (DGs), and the voltage regulators. In addition, the user selects the daily active power output profiles of the DGs, and the tool solves the voltage deviation problem for the specified time of day. The second functionality involves the simulation of energy storage systems and provides the optimal daily power output of the resources. With this program, a graphical user interface (GUI) allows users to select the test system, the optimization method to be used, the number of DGs and locations, the locations and number of battery energy storage systems (BESSs), and the tap changer locations. With the simple user interface, the user can manage the distribution system simulation and see the results by making appropriate changes to the test systems.
Citation - Scopus: 1
Optimization of Multilevel Inverters Using Novelty-driven Multi-verse Optimization Algorithm
(IEEE, 2021) Ceylan, Oguzhan; Neshat, Mehdi; Mirjalili, Seyedali
All over the world, renewable energy technologies which need power electronics based inverters in their designs are becoming more and more popular, thus detailed analysis to test the operational efficiency is required. This paper utilizes a new adaptive Multi-verse Optimization (MVO) Algorithm combined with novelty search method to solve harmonic elimination problem in multilevel inverters. We compare the obtained numerical simulations to those obtained by using the grey wolf optimization and standard MVO algorithm. The numerical simulations are performed on 7, 11, and 15 level inverters with different modulation indexes. From the simulation results, we observe that adaptive novelty search Multi-verse Optimization (MVO) based approach was able to obtain less total harmonic distortion for different modulation indexes.
A Reinforcement Learning Based Approach to Solve Voltage Issues in Distribution Networks
(IEEE, 2025) Cakir, Muhammed Turhan; Nayir, Hasan; Demir, Alper; Kaya, Huseyin; Ceylan, Oguzhan
This paper proposes a Proximal Policy Optimization (PPO)-based reinforcement learning approach to solve over-voltage problem in power distribution networks. The approach aims to minimize the voltage deviations and to keep voltage magnitudes in the allowed ranges. The numerical simulations are performed on a modified unbalanced 123 node network. The modified test system includes a total number of 34 single phase Photovoltaics (200 kVA) connected to three phases. We modified the base case load profile based on real-world daily variations obtained from EPIAS. The PV generation profile was modeled according to a typical sunny day. Using OpenDSS and Python, we implemented PPO-based RL to optimize the setpoints of smart inverters and voltage regulators. The model was trained with load and solar profiles at 09:00, 12:00, and 16:00 to derive optimal voltage regulation strategies for these time points. From the simulation results, we observed that the proposed PPO-based RL approach significantly reduces voltage deviations across all phases, which may help efficient operation of the distribution networks.
Citation - WoS: 2
Citation - Scopus: 1
Multi-Objective Distributed Energy Resource Integration in Radial Distribution Networks
(IEEE, 2021) Ahmadi, Bahman; Younesi, Soheil; Ceylan, Oguzhan; Ozdemir, Aydogan
Despite numerous studies on the optimal design and planning of distribution networks (DNs), little attention has been paid to improving the reliability of the distribution systems through optimal operation and planning of distributed generations (DGs) and energy storage systems (ESSs). This paper aims to integrate multi-type DG units and ESSs into the radial DNs to improve network reliability, decrease the losses, and maintain the voltage profiles. System Average Interruption Frequency Index (SAIFI) and Average Energy Not Supplied (AENS) are used as representative reliability indices. Objective functions are formulated and solved by using the slime mould algorithm (SMA). The proposed model's performance is tested on a balanced 33-bus system using the MATLAB environment. Then, the best solution is selected and compared with the base case values. Finally, SMA based solution is compared to those of genetic algorithm and particle swarm algorithm to validate the SMA's performance for finding the near-global solution.