• Title, Summary, Keyword: power optimization

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Optimal Power Scheduling in Multi-Microgrid System Using Particle Swarm Optimization

  • Pisei, Sen;Choi, Jin-Young;Lee, Won-Poong;Won, Dong-Jun
    • 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.

A New Algorithm for Optimal Real and Reactive Power Dispatch (최적유효 및 무요전력배분을 위한 신 앨고리즘)

  • Park, Young-Moon;Lee, Kwang-Yon
    • 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.

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Dual-Algorithm Maximum Power Point Tracking Control Method for Photovoltaic Systems based on Grey Wolf Optimization and Golden-Section Optimization

  • Shi, Ji-Ying;Zhang, Deng-Yu;Ling, Le-Tao;Xue, Fei;Li, Ya-Jing;Qin, Zi-Jian;Yang, Ting
    • Journal of Power Electronics
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    • v.18 no.3
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    • pp.841-852
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    • 2018
  • This paper presents a dual-algorithm search method (GWO-GSO) combining grey wolf optimization (GWO) and golden-section optimization (GSO) to realize maximum power point tracking (MPPT) for photovoltaic (PV) systems. First, a modified grey wolf optimization (MGWO) is activated for the global search. In conventional GWO, wolf leaders possess the same impact on decision-making. In this paper, the decision weights of wolf leaders are automatically adjusted with hunting progression, which is conducive to accelerating hunting. At the later stage, the algorithm is switched to GSO for the local search, which play a critical role in avoiding unnecessary search and reducing the tracking time. Additionally, a novel restart judgment based on the quasi-slope of the power-voltage curve is introduced to enhance the reliability of MPPT systems. Simulation and experiment results demonstrate that the proposed algorithm can track the global maximum power point (MPP) swiftly and reliably with higher accuracy under various conditions.

Economic Power Dispatch with Valve Point Effects Using Bee Optimization Algorithm

  • Kumar, Rajesh;Sharma, Devendra;Kumar, Anupam
    • 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.

Multi-Objective Optimization Model of Electricity Behavior Considering the Combination of Household Appliance Correlation and Comfort

  • Qu, Zhaoyang;Qu, Nan;Liu, Yaowei;Yin, Xiangai;Qu, Chong;Wang, Wanxin;Han, Jing
    • 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.

Evaluation of Optimal Transfer Capability in the Haenam-Jeju HVDC System Based on Cost Optimization

  • Son Hyun-Il;Kim Jin-O;Lee Hyo-Sang;Shin Dong-Joon
    • KIEE International Transactions on Power Engineering
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    • v.5A no.3
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    • pp.303-308
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    • 2005
  • The restructure of the electrical power industry is accompanied by the extension of the electrical power exchange. One of the key pieces of information used to determine how much power can be transferred through the network is known as available transfer capability (ATC). The traditional ATC deterministic approach is based on the severest case and it involves a complex procedure. Therefore, a novel approach for A TC calculation is proposed using cost optimization in this paper. The Jeju Island interconnected HVDC system has inland KEPCO (Korean Electric Power Corporation) systems, and its demand is increasing at the rate of about $\10[%]$ annually. To supply this increasing demand, the capability of the HVDC system must be enlarged. This paper proposes the optimal transfer capability of the HVDC system between Haenam in the inland and Jeju in Cheju Island through cost optimization. The cost optimization is based on generating cost in Jeju Island, transfer cost through Jeju-Haenam HVDC system and outage cost with one depth (N-1 contingency).

Automation of Heat & Mass Balance Design Optimization Method for Power Plant (화력발전시스템 Heat and Mass Balance 최적설계 자동화기법)

  • Baek, SeHyun;Jang, jihoon;Kim, YoungJoo
    • 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.

GAME MODEL AND ITS SOLVING METHOD FOR OPTIMAL SCALE OF POWER PLANTS ENTERING GENERATION POWER MARKET

  • Tan, Zhongfu;Chen, Guangjuan;Li, Xiaojun
    • 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.

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Group Power Constraint Based Wi-Fi Access Point Optimization for Indoor Positioning

  • Pu, Qiaolin;Zhou, Mu;Zhang, Fawen;Tian, Zengshan
    • 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.

Power and Efficiency Optimization through Exergy Analysis of Power Plant (발전 플랜트의 엑서지 해석으로부터 발전량 및 발전효율 최적화)

  • Kim, Deok-Jin;Lee, Jae-Byoung;Kang, Su-Hwan
    • 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.

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