• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1329-1339
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• 2017

This paper presents the power scheduling of a multi-microgrid (MMG) system using an optimization technique called particle swarm optimization (PSO). The PSO technique has been shown to be most effective at solving the various problems of the economic dispatch (ED) in a power system. In addition, a new MMG system configuration is proposed in this paper, through which the optimal power flow is achieved. Both optimization and power trading methods within an MMG are studied. The results of implementing PSO in an MMG system for optimal power flow and cost minimization are obtained and compared with another attractive and efficient optimization technique called the genetic algorithm (GA). The comparison between these two effective methods provides very competitive results, and their operating costs also appear to be comparable. Finally, in this study, power scheduling and a power trading method are obtained using the MATLAB program.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.32 no.4
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• pp.145-154
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• 1983

This paper presents a new method for optimal real and reactive power dispatch for the economic operation of a power system. Unlike the usual approach of minimizing the transmission loss, this method minimizes the total production cost not only for the real power optimization problem, but also for the reactive power optimization. The control variables are real power generation of units for real power optimization, and reactive power optimization. The constraints are the operating limits on these control variables and the limits on the bus voltages. Methematical models are developed to represent the sensitivity relationships between dependent and control variables for both real and reactive power optimization modules, and thus eliminate the use of B-coefficients. In order to handle many functional inequality constraints, a modified version of the gradient projection method is developed for optimization procedure, and has shown a remarkable advantage in computation efficiency.

• Journal of Electrical Engineering and Technology
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• v.4 no.1
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• pp.19-27
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• 2009

This paper presents a newly developed optimization algorithm, the Bee Optimization Algorithm (BeeOA), to solve the economic power dispatch (EPD) problem. The authors have developed a derivative free and global optimization technique based on the working of the honey bee. The economic power dispatch problem is a nonlinear constrained optimization problem. Classical optimization techniques fail to provide a global solution and evolutionary algorithms provide only a good enough solution. The proposed approach has been examined and tested on two test systems with different objectives. A simple power dispatch problem is tested first on 6 generators and then the algorithm is demonstrated on 13 thermal unit systems whose incremental fuel cost function takes into account the value point loading effect. The results are promising and show the effectiveness and robustness of the proposed approach over recently reported methods.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1821-1830
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• 2018

With the wide application of intelligent household appliances, the optimization of electricity behavior has become an important component of home-based intelligent electricity. In this study, a multi-objective optimization model in an intelligent electricity environment is proposed based on economy and comfort. Firstly, the domestic consumer's load characteristics are analyzed, and the operating constraints of interruptible and transferable electrical appliances are defined. Then, constraints such as household electrical load, electricity habits, the correlation minimization electricity expenditure model of household appliances, and the comfort model of electricity use are integrated into multi-objective optimization. Finally, a continuous search multi-objective particle swarm algorithm is proposed to solve the optimization problem. The analysis of the corresponding example shows that the multi-objective optimization model can effectively reduce electricity costs and improve electricity use comfort.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.3
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• pp.181-188
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• 2019

In this study, the Heat & Mass balance design optimization tool has developed by linking the design input/output variables with the Heat & Mass balance calculation solver and optimization algorithm and also automating the iterative calculation process. As a result of testing this optimization tool for 10 kinds of power plant, it was expected to improve the NPV and IRR compared with general design methods.

• Journal of applied mathematics & informatics
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• v.26 no.1_2
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• pp.337-347
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• 2008

Based on social welfare maximum theory, the optimal scale of power plants entering generation power market being is researched. A static non-cooperative game model for short-term optimization of power plants with different cost is presented. And the equilibrium solutions and the total social welfare are obtained. According to principle of maximum social welfare selection, the optimization model is solved, optimal number of power plants entering the market is determined. The optimization results can not only increase the customer surplus and improve power production efficiency, but also sustain normal profits of power plants and scale economy of power production, and the waste of resource can also be avoided. At last, case results show that the proposed model is efficient.

• Journal of Power Electronics
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• v.3 no.2
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• pp.69-80
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• 2003

Computer aided engineering (CAE) is a systematic approach to develop a better product/application with maximum possible options and minimum transition time. This paper presents a comprehensive feasibility analysis of various CAE techniques for evaluation and optimization of power electronic converters and systems. Different CAE methods for analysis, design, and performance improvement are classified. In addition, their advantages compared to the conventional workbench experimental methods are explained in detail and through examples.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.1951-1972
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• 2018

Wi-Fi Access Point (AP) optimization approaches are used in indoor positioning systems for signal coverage enhancement, as well as positioning precision improvement. Although the huge power consumption of the AP optimization forms a serious problem due to the signal coverage requirement for large-scale indoor environment, the conventional approaches treat the problem of power consumption independent from the design of indoor positioning systems. This paper proposes a new Fast Water-filling algorithm Group Power Constraint (FWA-GPC) based Wi-Fi AP optimization approach for indoor positioning in which the power consumed by the AP optimization is significantly considered. This paper has three contributions. First, it is not restricted to conventional concept of one AP for one candidate AP location, but considered spare APs once the active APs break off. Second, it utilizes the concept of water-filling model from adaptive channel power allocation to calculate the number of APs for each candidate AP location by maximizing the location fingerprint discrimination. Third, it uses a fast version, namely Fast Water-filling algorithm, to search for the optimal solution efficiently. The experimental results conducted in two typical indoor Wi-Fi environments prove that the proposed FWA-GPC performs better than the conventional AP optimization approaches.

• Plant Journal
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• v.9 no.3
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• pp.43-47
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• 2013

Even if an expert who has majored energy engineering, it is a difficult concept to understand power output optimization and power efficiency optimization. In this study a diagram applying thermodynamic state value as specific exergy and exergy ratio was developed. Although general peoples who did not major energy engineering can be easily understand the concept of power output optimization and power efficiency through the developed diagram. A represented property that can identify the performance of power plant is the main steam temperature and pressure. At the developed diagram the maximum power output line and maximum power efficiency line are shown according to the temperature and pressure of main steam. Therefore we can identify how much a power plant approach to maximum power output and maximum power efficiency.

• Journal of Power Electronics
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• v.12 no.2
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• pp.368-376
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• 2012

This paper deals with an optimization approach of 3D space placement of power components under volume and thermal constraints. It consists in optimizing semiconductors positions on a heat sink by respecting the components junction temperatures and minimizing the heat sink size. The aim is to remove risks on the 3D converter components placement and ensure their effective integration before carrying out the first physical prototype. This approach is based on coupling an optimization environment with a thermal finite element simulation tool. A pre-sizing step using analytical models is performed to set the optimization computations coupled to numerical simulation.