Browsing by Author "Ahmadi, Bahman"

Now showing 1 - 9 of 9

Citation Count: 11
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 Count: 1
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 Count: 8
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).
Citation Count: 4
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.
Citation Count: 23
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 Count: 19
Grey Wolf Optimizer for Allocation and Sizing of Distributed Renewable Generation
(Institute of Electrical and Electronics Engineers Inc., 2019) Ahmadi, Bahman; Ceylan, Oğuzhan; Özdemir, Aydoğan
Increasing penetration of distributed energy resources (DERs) have brought operational and control philosophy changes in Smart Grids (SGs). Renewable energy based technologies are becoming more important due to their economic and environmental impacts. Distributed generations (DGs) in the form of small renewable energy resources such as solar photovoltaics (PVs) and Wind Turbines (WTs) are connected in radial distribution networks near to the loads. This paper presents optimal siting and sizing of distributed renewable energy resource to maintain voltage magnitude profiles. Bus voltage magnitude differences for each hour in a day of a distribution system are formulated as an objective function. Three consecutive days are taken into account representing the three seasons of a year. A new nature inspired algorithm Grey Wolf Optimizer (GWO) is used as a solution tool. The proposed formulation is applied to 33 bus and 69 bus radial distribution networks. MATLAB simulations are performed to validate the performance of the approach. Simulation results are discussed and compared with of the several available ones'.
Citation Count: 2
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.
Citation Count: 20
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 Count: 11
Optimal Allocation Of Multi-Type Distributed Generators For Minimization Of Power Losses In Distribution Systems
(Institute of Electrical and Electronics Engineers Inc., 2019) Ahmadi, Bahman; Ceylan, Oğuzhan; Özdemir, Aydoğan
Distributed generation (DG), including photovoltaics (PVs), wind turbines (WTs) are becoming vital for Active Distribution Networks (ADN). Therefore, optimal sizing and allocation of these units can improve voltage profiles and reduce active power losses. This study concentrates on optimal allocation and sizing of two kinds of DG units (PVs and WTs) with 1 MW maximum size limits, due to the regulations in Turkey. The Whale optimization algorithm (WOA) and Grey wolf optimization algorithm (GWO) are used as optimization tools to minimize active power losses of 33 and 69 Bus Test Systems. The performance analysis of the methods are performed through simulations and the numerical results are compared in terms of optimal OF values and convergence characteristics.