Browsing by Author "Ozdemir, Aydogan"

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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.
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).
Branch outage solution using particle swarm optimization
(2008) Ceylan, Oğuzhan; Ozdemir, Aydogan; Dağ, Hasan
For post outage MW line flows and voltage magnitude calculations most of the methods use linear methods because of their simplicity. Especially for reactive power flow calculations one can face high errors. In this paper we use a minimization method that minimizes the errors resulting from the linear system model implementation. We solve the optimization problem using particle swarm optimization. We give some outage examples using IEEE 14 bus IEEE 30 bus and IEEE 57 bus data and compare the results with full ac load flow calculation. © 2008 Australasian Universities Power Engineering Conference (AUPEC'08).
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.
Double Branch Outage Modeling and Its Solution Using Differential Evolution Method
(2011) Ceylan, Oğuzhan; Ozdemir, Aydogan; Dağ, Hasan
Power system operators need to check the system security by contingency analysis which requires power flow solutions repeatedly. AC power flow is computationally slow even for a moderately sized system. Thus fast and accurate outage models and approximated solutions have been developed. This paper adopts a single branch outage model to a double branch outage one. The final constrained optimization problem resulted from modeling is then solved by using differential evolution method. Simulation results for IEEE 30 and 118 bus test systems are presented and compared to those of full AC load flow in terms of solution accuracy. © 2011 IEEE.
Gravitational Search Algorithm for Post-Outage Bus Voltage Magnitude Calculations
(2010) Ceylan, Oğuzhan; Ozdemir, Aydogan; Dağ, Hasan
Branch outage problem is one of the key problems in power system security analysis. This paper solves branch outage problem using a bounded approach. Local constrained optimization problem in the bounded approach is solved by the gravitational search algorithm. Test results of IEEE 14 30 and 118 bus systems are compared to those of ac load flow method in terms of both accuracy and speed.
Harmony Search Method Based Parallel Contingency Analysis
(2010) Ceylan, Oğuzhan; Dağ, Hasan; Ozdemir, Aydogan
Power system security management is one of the key problems in power system analysis. Power system management center operators need to simulate contingencies as fast as possible and take the remedial actions in time. These contingency analyses comprise of the outages of the branches transformers and other power system components. This paper solves branch outage problem using a bounded approach. Harmony search method is used to solve the local constrained optimization problem of the bounded approach. Several outage simulations using IEEE 14 30 and 118 bus systems are performed and compared against AC power flow in terms of accuracy. In order to speed up the contingency simulation the problem is also solved in a parallel environment. Speed up results of the IEEE 118 and 300 bus test systems are given. ©2010 IEEE.
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.
Post Outage Bus Voltage Calculations for Double Branch Outages
(IEEE, 2012) Ceylan, Oğuzhan; Ozdemir, Aydogan; Dağ, Hasan
Secure operation of electrical power systems is vital hence fast and accurate post-outage state calculations are important for contingency analysis. Contingency analysis includes simulations of both the single and double branch outages. This paper presents constrained optimization problem of a recently developed double branch outage model. Harmony search algorithm is used as an optimization tool. IEEE 30 Bus Test system simulation results are given and compared with those of the AC load flow in terms of computational accuracy. Speed test results of IEEE 14 30 57 118 and 300 Bus Test Systems are illustrated and compared with those of the AC load flow calculations. © 2012 IEEE.
Post-outage state estimations for outage management
(IFAC Secretariat, 2011) Ceylan, Oğuzhan; Ozdemir, Aydogan; Dağ, Hasan
Real time outage information is required to the utility operators for outage management process. In addition to some basic information regarding the outage post-outage system status will help to improve the response to outages and management of system reliability. This paper presents particle swarm optimization based reactive power estimations for branch outages. Post outage voltage magnitudes and reactive power flows results for IEEE 14 and IEEE 30 bus systems are given. Simulation results show that post outage voltage magnitudes and reactive power flows can be computed with a reasonable accuracy. © 2011 IFAC.
Probabilistic Approach To Assess and Minimize the Voltage Violation Risk in Active Distribution Networks
(Ieee, 2024) Kenari, Meghdad Tourandaz; Ozdemir, Aydogan; Heidari, Alireza
The increasing trend in using renewable energy resources in distribution systems has encouraged system operators to find the best methods to decrease the growing uncertainty's impact on system operation. A probabilistic approach based on the combination of Monte Carlo simulation and Particle Swarm Algorithm is proposed in this paper to reduce the risk of voltage magnitude violations. Also, a novel criterion is used to assess the risk of voltage magnitude violations in distribution system operation. This index is based on providing voltage samples using a probabilistic approach. Therefore, enhancing the confidence level of voltage risk is considered an objective function in finding the optimum location of energy storage systems. The proposed approach is applied to the IEEE 33-bus test system, and the results show that two ESS units installed at appropriate locations can solve all the voltage magnitude violation problems.
Simultaneous Impacts of Correlated Photovoltaic Systems and Fast Electric Vehicle Charging Stations on the Operation of Active Distribution Grids
(Elsevier, 2024) Kenari, Meghdad Tourandaz; Ozdemir, Aydogan
This paper presents two novel probabilistic models developed to account for the uncertainties of aggregated fast electric vehicle charging stations (FEVCSs) demand and correlated photovoltaic (PV) injections in active distribution network (ADN) analysis. Both models are more precise than the available ones. A probabilistic model based on the Beta distribution is used for solar radiance, while the shared random variables technique is proposed considering correlated solar radiation random variables. Furthermore, a probabilistic negative exponential load model is extended for modeling the FEVCSs based on the Weibull probability density function. Moreover, the proposed probabilistic load flow (PLF) model is solved using the combined cumulants and saddle-point approximation method. Numerical tests are provided and discussed by applying the IEEE 69-bus distribution system for different PV correlation coefficients and FEVCS load models. The results demonstrate how the uncertainty of PLF outputs is increased by integrating FEVCSs and correlated PV resources into the distribution network. In addition, simulation results validate that the cumulants-based methodology provides satisfactory accuracy with a low computational cost.
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.