Articles
<![CDATA[Optimal generation for wind-thermal power plant systems with multiple fuel sources ]]>
<![CDATA[Phan Nguyen Vinh]]>;
<![CDATA[Bach Hoang Dinh]]>;
<![CDATA[Van-Duc Phan]]>;
<![CDATA[Hung Duc Nguyen]]>;
<![CDATA[Thang Trung Nguyen]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijece.v11i2.pp1022-1028
<![CDATA[In this paper, the combined wind and thermal power plant systems are operated optimally to reduce the total fossil fuel cost (TFFC) of all thermal power plants and supply enough power energy to loads. The objective of reducing TFFC is implemented by using antlion algorithm (ALA), particle swarm optimization (PSO) and Cuckoo search algorithm (CSA). The best method is then determined based on the obtained TFFC from the three methods as dealing with two study cases. Two systems with eleven units including one wind power plant (WPP) and ten thermal power plants are optimally operated. The two systems have the same characteristic of MFSs but the valve loading effects (VLEs) on thermal power plants are only considered in the second system. The comparisons of TFFC from the two systems indicate that CSA is more powerful than ALA and PSO. Furthermore, CSA is also superior to the two methods in terms of faster search process. Consequently, CSA is a powerful method for the problem of optimal generation for wind-thermal power plant systems with consideration of MFSs from thermal power plants.]]>
<![CDATA[Optimal power generation for wind-hydro-thermal system using meta-heuristic algorithms ]]>
<![CDATA[Thuan Thanh Nguyen]]>;
<![CDATA[Van-Duc Phan]]>;
<![CDATA[Bach Hoang Dinh]]>;
<![CDATA[Tan Minh Phan]]>;
<![CDATA[Thang Trung Nguyen]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 5: October 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijece.v10i5.pp5123-5130
<![CDATA[In this paper, Cuckoo search algorithm (CSA) is suggested for determining optimal operation parameters of the combined wind turbine and hydrothermal system (CWHTS) in order to minimize total fuel cost of all operating thermal power plants while all constraints of plants and system are exactly satisfied. In addition to CSA, Particle swarm optimization (PSO), PSO with constriction factor and inertia weight factor (FCIWPSO) and Social Ski-Driver (SSD) are also implemented for comparisons. The CWHTS is optimally scheduled over twenty-four one-hour interval and total cost of producing power energy is employed for comparison. Via numerical results and graphical results, it indicates CSA can reach much better results than other ones in terms of lower total cost, higher success rate and faster search process. Consequently, the conclusion is confirmed that CSA is a very efficient method for the problem of determining optimal operation parameters of CWHTS.]]>
<![CDATA[Antlion optimization algorithm for optimal non-smooth economic load dispatch ]]>
<![CDATA[Thanh Pham Van]]>;
<![CDATA[Vaclav Snasel]]>;
<![CDATA[Thang Trung Nguyen]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 2: April 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijece.v10i2.pp1187-1199
<![CDATA[This paper presents applications of Antlion optimization algorithm (ALO) for handling optimal economic load dispatch (OELD) problems. Electricity generation cost minimization by controlling power output of all available generating units is a major goal of the problem. ALO is a metaheuristic algorithm based on the hunting process of Antlions. The effect of ALO is investigated by solving a 10-unit system. Each studied case has different objective function and complex level of restraints. Three test cases are employed and arranged according to the complex level in which the first one only considers multi fuel sources while the second case is more complicated by taking valve point loading effects into account. And, the third case is the highest challenge to ALO since the valve effects together with ramp rate limits, prohibited operating zones and spinning reserve constraints are taken into consideration. The comparisons of the result obtained by ALO and other ones indicate the ALO algorithm is more potential than most methods on the solution, the stabilization, and the convergence velocity. Therefore, the ALO method is an effective and promising tool for systems with multi fuel sources and considering complicated constraints.]]>
<![CDATA[Minimize electricity generation cost for large scale wind- thermal systems considering prohibited operating zone and power reserve constraints ]]>
<![CDATA[Phan Nguyen Vinh]]>;
<![CDATA[Bach Hoang Dinh]]>;
<![CDATA[Van-Duc Phan]]>;
<![CDATA[Hung Duc Nguyen]]>;
<![CDATA[Thang Trung Nguyen]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijece.v11i3.pp1905-1911
<![CDATA[Wind power plants (WPs) play a very important role in the power systems because thermal power plants (TPs) suffers from shortcomings of expensive cost and limited fossil fuels. As compared to other renewable energies, WPs are more effective because it can produce electricity all a day from the morning to the evening. Consequently, this paper integrates the optimal power generation of TPs and WPs to absolutely exploit the energy from WPs and reduce the total electricity generation cost of TPs. The target can be reached by employing a proposed method, called one evaluation-based cuckoo search algorithm (OEB-CSA), which is developed from cuckoo search algorithm (CSA). In addition, conventional particle swarm optimization (PSO) is also implemented for comparison. Two test systems with thirty TPs considering prohibited working zone and power reserve constraints are employed. The first system has one wind power plant (WP) while the second one has two WPs. The result comparisons indicate that OEB-CSA can be the best method for the combined systems with WPs and TPs.]]>
<![CDATA[Stochastic fractal search based method for economic load dispatch ]]>
<![CDATA[Ly Huu Pham]]>;
<![CDATA[Thang Trung Nguyen]]>;
<![CDATA[Lam Duc Pham]]>;
<![CDATA[Nam Hoang Nguyen]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 5: October 2019 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v17i5.12539
<![CDATA[This paper presents a nature-inspired meta-heuristic, called a stochastic fractal search based method (SFS) for coping with complex economic load dispatch (ELD) problem. Two SFS methods are introduced in the paper by employing two different random walk generators for diffusion process in which SFS with Gaussian random walk is called SFS-Gauss and SFS with Levy Flight random walk is called SFS-Levy. The performance of the two applied methods is investigated comparing results obtained from three test system. These systems with 6, 10, and 20 units with different objective function forms and different constraints are inspected. Numerical result comparison can confirm that the applied approach has better solution quality and fast convergence time when compared with some recently published standard, modified, and hybrid methods. This elucidates that the two SFS methods are very favorable for solving the ELD problem.]]>
<![CDATA[Modified moth swarm algorithm for optimal economic load dispatch problem ]]>
<![CDATA[Phu Trieu Ha]]>;
<![CDATA[Hanh Minh Hoang]]>;
<![CDATA[Thuan Thanh Nguyen]]>;
<![CDATA[Thang Trung Nguyen]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v18i4.15032
<![CDATA[In this study, optimal economic load dispatch problem (OELD) is resolved by a novel improved algorithm. The proposed modified moth swarm algorithm (MMSA), is developed by proposing two modifications on the classical moth swarm algorithm (MSA). The first modification applies an effective formula to replace an ineffective formula of the mutation technique. The second modification is to cancel the crossover technique. For proving the efficient improvements of the proposed method, different systems with discontinuous objective functions as well as complicated constraints are used. Experiment results on the investigated cases show that the proposed method can get less cost and achieve stable search ability than MSA. As compared to other previous methods, MMSA can archive equal or better results. From this view, it can give a conclusion that MMSA method can be valued as a useful method for OELD problem.]]>
<![CDATA[Finding optimal reactive power dispatch solutions by using a novel improved stochastic fractal search optimization algorithm ]]>
<![CDATA[Hai Van Tran]]>;
<![CDATA[Thanh Van Pham]]>;
<![CDATA[Ly Huu Pham]]>;
<![CDATA[Nghia Trong Le]]>;
<![CDATA[Thang Trung Nguyen]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 5: October 2019 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v17i5.10767
<![CDATA[In this paper, a novel improved Stochastic Fractal Search optimization algorithm (ISFSOA) is proposed for finding effective solutions of a complex optimal reactive power dispatch (ORPD) problem with consideration of all constraints in transmission power network. Three different objectives consisting of total power loss (TPL), total voltage deviation (TVD) and voltage stabilization enhancement index are independently optimized by running the proposed ISFSOA and standard Stochastic Fractal Search optimization algorithm (SFSOA). The potential search of the proposed ISFSOA can be highly improved since diffusion process of SFSOA is modified. Compared to SFSOA, the proposed method can explore large search zones and exploit local search zones effectively based on the comparison of solution quality. One standard IEEE 30-bus system with three study cases is employed for testing the proposed method and compared to other so far applied methods. For each study case, the proposed method together with SFSOA are run fifty run and three main results consisting of the best, mean and standard deviation fitness function are compared. The indication is that the proposed method can find more promising solutions for the three cases and its search ability is always more stable than those of SFSOA. The comparison with other methods also give the same evaluation that the proposed method can be superior to almost all compared methods. As a result, it can conclude that the proposed modification is really appropriate for SFSOA in dealing with ORPD problem and the method can be used for other engineering optimization problems.]]>
<![CDATA[Optimization of location and size of distributed generations for maximizing their capacity and minimizing power loss of distribution system based on cuckoo search algorithm ]]>
<![CDATA[Thuan Thanh Nguyen]]>;
<![CDATA[Trieu Ton Ngoc]]>;
<![CDATA[Thang Trung Nguyen]]>;
<![CDATA[Thanh-Phuc Nguyen]]>;
<![CDATA[Ngoc Au Nguyen]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v10i4.2278
<![CDATA[Maximizing capacity of distributed generations (DGs) embed into distribution network is a solution to attract investment for DGs installation on the distribution system. This paper introduces a approach of optimizing location and capacity of DGs for maximizing DGs capacity and minimizing the system’s power loss based on cuckoo search algorithm (CSA). The proposed problem and method are simulated on two test systems including the 33-node and 69-node networks. The numerical results have demonstrated that the proposed method not only reduces power losses but also maximizes the power of DGs embed into the distribution network. The results also introduce that the proposed CSA method is better performance that some previous methods in terms of power loss and DGs capacity. The results obtained in many independent runs for two test systems indicate that CSA in one of the reliable methods for the DGs placement problems.]]>
<![CDATA[Optimal solutions for fixed head short-term hydrothermal system scheduling problem ]]>
<![CDATA[Thanh Long Duong]]>;
<![CDATA[Van-Duc Phan]]>;
<![CDATA[Thuan Thanh Nguyen]]>;
<![CDATA[Thang Trung Nguyen]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 8, No 4: December 2020 ]]>
Publisher : <![CDATA[IAES Indonesian Section ]]>
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DOI: 10.52549/ijeei.v8i4.2269
<![CDATA[In this paper, optimal short-term hydrothermal operation (STHTO) problem is determined by a proposed high-performance particle swarm optimization (HPPSO). Control variables of the problem are regarded as an optimal solution including reservoir volumes of hydropower plants (HdPs) and power generation of thermal power plants (ThPs) with respect to scheduled time periods. This problem focuses on reduction of electric power generation cost (EPGC) of ThPs and exact satisfactory of all constraints of HdPs, ThPs and power system. The proposed method is compared to earlier methods and other implemented methods such as particle swarm optimization (PSO), constriction factor (CF) and inertia weight factor (IWF)-based PSO (FCIW-PSO), two time-varying acceleration coefficient (TTVACs)-based PSO (TVAC-PSO), salp swarm algorithm (SSA), and Harris hawk algorithm (HHA). By comparing EPGC from 100 trial runs, speed of search and simulation time, the suggested HPPSO method sees it is more robust than other ones. Thus, HPPSO is recommended for applying to the considered and other problems in power systems.]]>
<![CDATA[Application of a new constraint handling method for economic dispatch considering electric market ]]>
<![CDATA[Thanh Long Duong]]>;
<![CDATA[Ly Huu Pham]]>;
<![CDATA[Thuan Thanh Nguyen]]>;
<![CDATA[Thang Trung Nguyen]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v9i4.2351
<![CDATA[In this paper, optimal load dispatch problem under competitive electric market (OLDCEM) is solved by the combination of cuckoo search algorithm (CSA) and a new constraint handling approach, called modified cuckoo search algorithm (MCSA). In addition, we also employ the constraint handling method for salp swarm algorithm (SSA) and particle swarm optimization algorithm (PSO) to form modified SSA (MSSA) and modified PSO (MPSO). The three methods have been tested on 3-unit system and 10-unit system under the consideration of payment model for power reserve allocated, and constraints of system and generators. Result comparisons among MCSA and CSA indicate that the proposed constraint handling method is very useful for metaheuristic algorithms when solving OLDCEM problem. As compared to MSSA, MPSO as well as other previous methods, MCSA is more effective by finding higher total benefit for the two systems with faster manner and lower oscillations. Consequently, MCSA method is a very effective technique for OLDCEM problem in power systems.]]>
