Browsing by Author "Ceylan, Oguzhan"

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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.
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.
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.
The Arithmetic Optimization Algorithm for Optimal Energy Resource Planning
(IEEE, 2021) Ahmadi, Bahman; Younesi, Soheil; Ceylan, Oguzhan; Ozdemir, Aydogan
This study presents a new formulation regarding optimal placement and sizing of multi-type distributed generations (DGs) and energy storage systems (ESSs) to enhance the reliability of a radial distribution system and to reduce the line losses employing Arithmetic Optimization Algorithm (AOA) method. The model determines the number of DGs and ESSs automatically, and is designed to minimize the losses and the reliability indices such as Customer Average Interruption Duration Index (CAIDI). The performance of the algorithm is tested on 69-bus radial distribution system. The objective functions corresponding to optimal type, location, and size of distributed energy resources are compared to the base-case values. Finally, a comparative performance analysis of the proposed algorithm is performed in terms of reliability indices and power losses with Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO).
Cascaded H-Bridge Multilevel Inverters Optimization Using Adaptive Grey Wolf Optimizer With Local Search
(Springer, 2021) Ceylan, Oguzhan; Neshat, Mehdi; Mirjalili, Seyedali
With the transformation of transmission and distribution grids into smart grids that are more dominated by renewable energy, power electronics-based inverters that can improve power quality are becoming more visible. In order to maximize the output voltage quality and reduce the total harmonic distortion (THD), efficient operation of inverters is required. Therefore, in this paper, the problem of harmonic elimination in multilevel inverters is solved by using an adaptive grey wolf optimizer with local search. We have performed a grid search-based landscape analysis of the seven-level inverter to understand the behaviour of the proposed algorithm. For verification, the numerical results of the proposed adaptive grey wolf optimizer are compared with those of the original grey wolf optimization algorithm, a modified version of the grey wolf optimization algorithm, the particle swarm optimization algorithm, multi-verse optimization algorithm, and salp swarm algorithm. In the simulations, we solved the optimization model for three different structures of multilevel inverters (7, 11, and 15 levels) by changing the modulation indexes. It is found that the adaptive grey wolf optimization provides lower total harmonic distortion for different modulation indexes.
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.
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.
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.
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.
Multi-Criteria Decision Making in Optimal Operation Problem of Unbalanced Distribution Networks Integrated With Photovoltaic Units
(Ieee-inst Electrical Electronics Engineers inc, 2024) Ebadi, Ramin; Aboshady, F. M.; Ceylan, Oguzhan; Pisica, Ioana; Ozdemir, Aydogan
The use of renewable energy sources is increasing day by day due to their economic and environmental benefits. However, improper penetration of renewable energy into power grids can lead to problems such as over-voltages and higher active power losses. Therefore, the voltage regulation problem in distribution networks is critical due to the increasing integration of renewable energy sources. On the other hand, an increase in renewable energy penetration leads to lower operational costs due to decreased energy purchases from the overhead grid. Therefore, it can be challenging for distribution system operators (DSOs) to decide the trade-off between more Photovoltaic (PV) integration for cost minimization or less penetration to minimize voltage deviation from a rated value. In this study, we formulated this trade-off as a novel multi-objective optimization framework, aiming to minimize operating costs and voltage deviations from a rated value in an unbalanced distribution grid. The proposed formulation is applied to the modified IEEE 34-bus unbalanced distribution network, where the epsilon-constraint method is utilized for solving the resulting multi-objective optimization problem along with the Exterior Penalty Functions (EPF) method. The simulation results show that the proposed approach provides the DSO with a better view of decision-making in the optimal operation of the distribution networks.
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.
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.
Optimal Dead Band Control of Occupant Thermostats for Grid-Interactive Homes
(Ieee, 2024) Savasci, Alper; Ceylan, Oguzhan; Paudyal, Sumit
Efficient and grid-aware management of home-scale heating, ventilation, and air conditioning (HVAC) systems is one of the key enablers of demand-side management (DSM) and associated grid services in the residential sector. HVACs regulate the indoor temperature around a set point through a thermostat operating within a closed-loop control scheme. Conventional thermostats typically have a built-in temperature dead band or differential where the thermostat is idle, and HVAC stays at the most recent state (On/Off). The temperature dead band is an important control parameter that can help save energy as well as preventing frequent On/Off switching cycles leading to excessive wear and tear on the equipment. However, strategic and dynamic adjustment of the dead band can be a challenging task for an occupant. This paper proposes a mixed-integer linear program (MILP)-based tuning scheme to optimally determine the dead band. The novelty in this formulation is the inclusion of thermostat hysteresis curve modeled by piecewise techniques for tuning the dead band accurately. The proposed formulation is solved as a receding horizon manner for normal as well as under a demand response (DR) event and has been found it can achieve up to 10% reduction in energy consumption without degrading the regulation performance significantly.
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.
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.
Recycling Newton-Krylov algorithm for efficient solution of large scale power systems
(Elsevier Sci Ltd, 2023) Yetkin, E. Fatih; Ceylan, Oguzhan
Power flow calculations are crucial for the study of power systems, as they can be used to calculate bus voltage magnitudes and phase angles, as well as active and reactive power flows on lines. In this paper, a new approach, the Recycling Newton-Krylov (ReNK) algorithm, is proposed to solve the linear systems of equations in Newton-Raphson iterations. The proposed method uses the Generalized Conjugate Residuals with inner orthogonalization and deflated restarting (GCRO-DR) method within the Newton-Raphson algorithm and reuses the Krylov subspace information generated in previous Newton runs. We evaluate the performance of the proposed method over the traditional direct solver (LU) and iterative solvers (Generalized Minimal Residual Method (GMRES), the Biconjugate Gradient Stabilized Method (Bi-CGSTAB) and Quasi-Minimal Residual Method (QMR)) as the inner linear solver of the Newton-Raphson method. We use different test systems with a number of busses ranging from 300 to 70000 and compare the number of iterations of the inner linear solver (for iterative solvers) and the CPU times (for both direct and iterative solvers). We also test the performance of the ReNK algorithm for contingency analysis and for different load conditions to simulate optimization problems and observe possible performance gains.
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.
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.
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.