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Готові списки джерел за темами / Optimization of power management / Статті в журналах

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Статті в журналах з теми "Optimization of power management"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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1

Benini, L., A. Bogliolo, G. A. Paleologo, and G. De Micheli. "Policy optimization for dynamic power management." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 18, no. 6 (June 1999): 813–33. http://dx.doi.org/10.1109/43.766730.

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2

Riaz, Muhammad, Sadiq Ahmad, Irshad Hussain, Muhammad Naeem, and Lucian Mihet-Popa. "Probabilistic Optimization Techniques in Smart Power System." Energies 15, no. 3 (January 24, 2022): 825. http://dx.doi.org/10.3390/en15030825.

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Анотація:

Uncertainties are the most significant challenges in the smart power system, necessitating the use of precise techniques to deal with them properly. Such problems could be effectively solved using a probabilistic optimization strategy. It is further divided into stochastic, robust, distributionally robust, and chance-constrained optimizations. The topics of probabilistic optimization in smart power systems are covered in this review paper. In order to account for uncertainty in optimization processes, stochastic optimization is essential. Robust optimization is the most advanced approach to optimize a system under uncertainty, in which a deterministic, set-based uncertainty model is used instead of a stochastic one. The computational complexity of stochastic programming and the conservativeness of robust optimization are both reduced by distributionally robust optimization.Chance constrained algorithms help in solving the constraints optimization problems, where finite probability get violated. This review paper discusses microgrid and home energy management, demand-side management, unit commitment, microgrid integration, and economic dispatch as examples of applications of these techniques in smart power systems. Probabilistic mathematical models of different scenarios, for which deterministic approaches have been used in the literature, are also presented. Future research directions in a variety of smart power system domains are also presented.

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3

Devika, B., and P. N. Sudha. "Power optimization in MANET using topology management." Engineering Science and Technology, an International Journal 23, no. 3 (June 2020): 565–75. http://dx.doi.org/10.1016/j.jestch.2019.07.008.

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4

Al-Falahi, Monaaf D. A., Kutaiba S. Nimma, Shantha D. G. Jayasinghe, Hossein Enshaei, and Josep M. Guerrero. "Power management optimization of hybrid power systems in electric ferries." Energy Conversion and Management 172 (September 2018): 50–66. http://dx.doi.org/10.1016/j.enconman.2018.07.012.

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5

Aman, Nurlely. "A Sustainable Forest Management Optimization Model to Preserve Water Allocation for Hydroelectric Power Plant." Indian Journal of Applied Research 3, no. 9 (October 1, 2011): 15–18. http://dx.doi.org/10.15373/2249555x/sept2013/181.

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6

Wang, Weida, Changle Xiang, Hui Liu, and Shipeng Jia. "A model-predictive-control-based power management strategy for a power-split electromechanical transmission." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 230, no. 14 (August 5, 2016): 1987–2001. http://dx.doi.org/10.1177/0954407016630911.

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Анотація:

A power management strategy is a key necessity for power-split electromechanical transmission systems. A model predictive control strategy which is based on finite-horizon optimization and can combine the advantages of instantaneous optimization and global optimization is a good solution for online optimization of the power management. Therefore, a model-predictive-control-based power management strategy is proposed for a two-mode electromechanical transmission. A model predictive control strategy consists of two parts: a predictive model and a receding-horizon optimization algorithm. A predictive model is used for predicting future information on the electromechanical transmission states, and real-time receding-horizon optimization with a finite horizon is adopted for optimal decision making. First, the predictive model, including the battery state and the transmission output torque, which provides a priori knowledge for optimal calculation, is proposed. Then, to ensure optimal operating areas of the engine and the motors, a novel overall efficiency calculation method for the whole powertrain including the engine, the motors, the power-split coupled machine and the battery is proposed and regarded as the optimization objective. The overall efficiency not only is focused on the engine fuel economy but also determines the power loss of the motors, the battery and the planetary gears together, which enhances the fuel economy and the transmission efficiency significantly. Based on the predictive model and receding-horizon optimization, the MPC strategy is established and tested by hardware-in-the-loop simulations under Urban Dynamometer Driving Schedule and New European Driving Cycle conditions. The test results showed that the power management strategy can enhance the fuel economy and proved to be a potential real-time optimization method for power distribution in the electromechanical transmission system; this strategy can provide theoretical support for actual application of electromechanical transmission systems.

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7

Dolgov, Arseny, Regan Zane, and Zoya Popovic. "Power Management System for Online Low Power RF Energy Harvesting Optimization." IEEE Transactions on Circuits and Systems I: Regular Papers 57, no. 7 (July 2010): 1802–11. http://dx.doi.org/10.1109/tcsi.2009.2034891.

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8

Al-Falahi, Monaaf, Tomasz Tarasiuk, Shantha Jayasinghe, Zheming Jin, Hossein Enshaei, and Josep Guerrero. "AC Ship Microgrids: Control and Power Management Optimization." Energies 11, no. 6 (June 5, 2018): 1458. http://dx.doi.org/10.3390/en11061458.

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9

Niu, Dong Xiao, and Qian Zhang. "A Robust Revenue Optimization Approach for Power Plant Revenue Management." Applied Mechanics and Materials 373-375 (August 2013): 1784–87. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1784.

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Анотація:

This paper examines optimization of revenue of power plants facing stochastic demand with varied prices. A network optimization model is proposed for power plant revenue management under an uncertain environment. The network optimization has a stochastic programming formulation designed to capture the randomness of the unknown demand. A novel approach of robust optimization and PSO are applied to solve the problem on a scenario-basis. Decision-makers risk aversion is considered in the objective function. Mean absolute value is used to measure risk of deviation of revenue from its expected value.

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10

Iwamura, Kazuaki, Yosuke Nakanishi, Udom Lewlomphaisarl, Noel Estoperez, and Abraham Lomi. "Facility Planning Optimization Platform, GGOD, for Expandable Cluster-type Micro-grid Installations and Operations." Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences 58, S (October 12, 2021): 101–7. http://dx.doi.org/10.53560/ppasa(58-sp1)742.

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Анотація:

This paper describes the architecture and the utilization for a facility planning optimization platform called GGOD, “Grid of Grids Optimal Designer” and applies it to expandable cluster-type micro-grid installations and operations. The expandable cluster-type micro-grid is defined as a group of micro-grids that are connected by bi-directional power transfer networks. Furthermore, power sources are also networked. Especially, by networking among power sources, powers necessary for social activities in-demand areas are secured. The proposed architecture is based on service-oriented architecture, meaning that optimization functions are executed as services. For flexibility, these services are executed by requests based on extensible mark-up language texts. The available optimizations are written in meta-data, which are accessible to end-users from the meta-data database system called clearinghouse. The meta-data are of two types, one for single optimization and the other for combined optimization. The processes in GGOD are conducted by the management function which interprets descriptions in meta-data. In meta-data, the names of optimization functions and activation orders are written. The basic executions follow sequential, branch, or loop flow processes, which execute combined optimizations, compare more than two kinds of optimization processes, and perform iterative simulations, respectively. As an application of the proposed architecture, the power generation sites and transmission networks are optimized in a geospatial integrated-resource planning scenario. In this application, a structure and a method for the combination of component functions in GGOD are exemplified. Moreover, GGOD suggests promotions of a lot of applications by effective combinations of basic optimization functions.

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11

KumarDeb, Sumanta, and Sauvik Bal. "Power Consumption Optimization of Location Management in PCS Network." International Journal of Computer Applications 117, no. 12 (May 20, 2015): 5–8. http://dx.doi.org/10.5120/20604-3209.

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12

Akbulut, Ahmet, and Aykut Kalaycıoğlu. "Power management optimization for femtocells in heterogeneous LTE networks." Pamukkale University Journal of Engineering Sciences 25, no. 5 (2019): 532–38. http://dx.doi.org/10.5505/pajes.2018.93753.

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13

et al., Oliynyk. "Genetic algorithm based optimization method for reactive power management." Annals of Electrical and Electronic Engineering 2, no. 7 (July 2019): 16–21. http://dx.doi.org/10.21833/aeee.2019.07.004.

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14

Akbulut, Ahmet, and Aykut Kalaycıoğlu. "Power management optimization for femtocells in heterogeneous LTE networks." Pamukkale University Journal of Engineering Sciences 25, no. 5 (2019): 532–38. http://dx.doi.org/10.5505/pajes.2019.93753.

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15

Sangeetha, P., and S. Suja. "Efficient Optimization Algorithm for Dwindling Payment and Power Management." Circuits and Systems 07, no. 09 (2016): 2142–53. http://dx.doi.org/10.4236/cs.2016.79186.

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16

Suruli, Karthik, and Vennila Ila. "Social Spider Optimization Algorithm-Based Optimized Power Management Schemes." Electric Power Components and Systems 48, no. 11 (July 2, 2020): 1111–24. http://dx.doi.org/10.1080/15325008.2020.1834643.

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17

Sechilariu, Manuela. "Intelligent Energy Management of Electrical Power Systems." Applied Sciences 10, no. 8 (April 24, 2020): 2951. http://dx.doi.org/10.3390/app10082951.

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Анотація:

Smart grid implementation is facilitated by multi-source energy systems development, i.e., microgrids, which are considered the key smart grid building blocks. Whether they are alternative current (AC) or direct current (DC), high voltage or low voltage, high power or small power, integrated into the distribution system or the transmission network, multi-source systems always require an intelligent energy management that is integrated into the power system. A comprehensive intelligent energy system aims at providing overall energy efficiency with regard to the following: increased power generation flexibility, increased renewable generation systems, improved energy consumption, reduced CO2 emission, improved stability, and minimized energy cost. This Special Issue presents recent key theoretical and practical developments that concern the models, technologies, and flexible solutions to facilitate the following optimal energy and power flow strategies: the techno-economic model for optimal sources dispatching (mono and multi-objective energy optimization), real-time optimal scheduling, and real time optimization with model predictive control.

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18

BETTER, MARCO, FRED GLOVER, GARY KOCHENBERGER, and HAIBO WANG. "SIMULATION OPTIMIZATION: APPLICATIONS IN RISK MANAGEMENT." International Journal of Information Technology & Decision Making 07, no. 04 (December 2008): 571–87. http://dx.doi.org/10.1142/s0219622008003137.

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Simulation optimization is providing solutions to important practical problems previously beyond reach. This paper explores how new approaches are significantly expanding the power of simulation optimization for managing risk. Recent advances in simulation optimization technology are leading to new opportunities to solve problems more effectively. Specifically, in applications involving risk and uncertainty, simulation optimization surpasses the capabilities of other optimization methods not only in the quality of solutions but also in their interpretability and practicality. In this paper, we demonstrate the advantages of using a simulation optimization approach to tackle risky decisions, by showcasing the methodology on two popular applications from the areas of finance and business process design.

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19

Yu, Zhou, Hu Weifeng, Wang Dezhi, Xu Zheng, and Yu Tao. "A Multi-objective Optimization Algorithm for Multiple Home Users Intelligent Power Management and Control Based on Pareto and Nash Equilibrium Game." E3S Web of Conferences 95 (2019): 03001. http://dx.doi.org/10.1051/e3sconf/20199503001.

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Анотація:

A multi-objective optimization model for multiple home users intelligent power management and control is proposed. A photovoltaic power model, an electric vehicle battery model and a load model are developed first, and then a strategy of home intelligent power management is presented based on battery operation and PV spontaneous self-use. Secondly, a multi-objective optimization model of multiple home users intelligent power management, including the user comfort, economy and optimization of load curve, is provided under the constraints. Then using a multi-objective optimization algorithm and Nash equilibrium game theory to solve the multi-objective problem. Finally, the 100-home power management and control simulation case show that the presented algorithm can improve the comfort and the economy of users effectively, but also help the power grid to peak load shifting.

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20

Kumar, Manoj. "Scenario and Robust Optimization in Risk Management." International Journal of Risk and Contingency Management 5, no. 4 (October 2016): 27–41. http://dx.doi.org/10.4018/ijrcm.2016100103.

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Анотація:

Simulation Optimization is providing solutions to important practical problems previously beyond reach. This paper explores how new approaches are significantly expanding the power of Simulation Optimization for managing risk. Recent advances in Simulation Optimization technology are leading to new opportunities to solve problems more effectively. Specifically, in applications involving risk and uncertainty, Simulation Optimization surpasses the capabilities of other optimization methods, not only in the quality of solutions, but also in their interpretability and practicality. In this paper, the author demonstrates the advantages of using a Simulation Optimization approach to tackle risky decisions, by showcasing the methodology on two popular applications from the areas of finance and business process design.

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21

Park, Gi-Hun, Su-Han Pyo, Jang-Hyun Park, and Tae-Sik Park. "A Study on Portfolio Optimization for Power Transformer Asset Management." Journal of the Korean Institute of Illuminating and Electrical Installation Engineers 34, no. 5 (May 31, 2020): 51–58. http://dx.doi.org/10.5207/jieie.2020.34.5.051.

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22

Sun, Huo-Ching, and Yann-Chang Huang. "Optimization of Power Scheduling for Energy Management in Smart Homes." Procedia Engineering 38 (2012): 1822–27. http://dx.doi.org/10.1016/j.proeng.2012.06.225.

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23

Cai, Wen, and Ryan L. Harne. "Electrical power management and optimization with nonlinear energy harvesting structures." Journal of Intelligent Material Systems and Structures 30, no. 2 (November 2, 2018): 213–27. http://dx.doi.org/10.1177/1045389x18808390.

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In recent years, great advances in understanding the opportunities for nonlinear vibration energy harvesting systems have been achieved giving attention to either the structural or electrical subsystems. Yet, a notable disconnect appears in the knowledge on optimal means to integrate nonlinear energy harvesting structures with effective nonlinear rectifying and power management circuits for practical applications. Motivated to fill this knowledge gap, this research employs impedance principles to investigate power optimization strategies for a nonlinear vibration energy harvester interfaced with a bridge rectifier and a buck-boost converter. The frequency and amplitude dependence of the internal impedance of the harvester structure challenges the conventional impedance matching concepts. Instead, a system-level optimization strategy is established and validated through simulations and experiments. Through careful studies, the means to optimize the electrical power with partial information of the electrical load is revealed and verified in comparison to the full analysis. These results suggest that future study and implementation of optimal nonlinear energy harvesting systems may find effective guidance through power flow concepts built on linear theories despite the presence of nonlinearities in structures and circuits.

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24

Xu, J., P. B. Luh, F. B. White, E. Ni, and K. Kasiviswanathan. "Power Portfolio Optimization in Deregulated Electricity Markets With Risk Management." IEEE Transactions on Power Systems 21, no. 4 (November 2006): 1653–62. http://dx.doi.org/10.1109/tpwrs.2006.879272.

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25

Zhang, Qian, Liyu Xia, and Wan He. "The optimization research of investment management in power grid enterprise." IOP Conference Series: Earth and Environmental Science 332 (November 5, 2019): 042016. http://dx.doi.org/10.1088/1755-1315/332/4/042016.

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26

SheelaSobanaRani, K., and N. Devarajan N.Devarajan. "Fuzzy based Optimization for Power Management in Wireless Sensor Networks." International Journal of Computer Applications 48, no. 4 (June 30, 2012): 10–16. http://dx.doi.org/10.5120/7335-9980.

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27

Vijayakumar, K. "Multiobjective Optimization Methods for Congestion Management in Deregulated Power Systems." Journal of Electrical and Computer Engineering 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/962402.

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Анотація:

Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1) transmission line over load and (2) congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP) and nondominated sorting genetic algorithm II methods are used to solve this problem. FEP uses the combined advantages of fuzzy and evolutionary programming (EP) techniques and gives better unique solution satisfying both objectives, whereas nondominated sorting genetic algorithm (NSGA) II gives a set of Pareto-optimal solutions. The methods propose an efficient and reliable algorithm for line overload alleviation due to critical line outages in a deregulated power markets. The quality and usefulness of the algorithm is tested on IEEE 30 bus system.

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28

Luiz, Saulo O. D., Angelo Perkusich, Bruna M. J. Cruz, Breno H. M. Neves, and Gabriela M. da S. Araujo. "Optimization of timeout-based power management policies for network interfaces." IEEE Transactions on Consumer Electronics 59, no. 1 (February 2013): 101–6. http://dx.doi.org/10.1109/tce.2013.6490247.

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29

Faria, Pedro, Zita Vale, Joao Soares, and Judite Ferreira. "Demand Response Management in Power Systems Using Particle Swarm Optimization." IEEE Intelligent Systems 28, no. 4 (July 2013): 43–51. http://dx.doi.org/10.1109/mis.2011.35.

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30

Fadaei, Amir Hosein, Nader Maleki Moghaddam, Ehsan Zahedinejad, Mohammad Mehdi Fadaei, and Shabnam Kia. "Fuel management optimization based on power profile by Cellular Automata." Annals of Nuclear Energy 37, no. 12 (December 2010): 1712–22. http://dx.doi.org/10.1016/j.anucene.2010.07.009.

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31

Lorca, Álvaro, and José Prina. "Power portfolio optimization considering locational electricity prices and risk management." Electric Power Systems Research 109 (April 2014): 80–89. http://dx.doi.org/10.1016/j.epsr.2013.12.004.

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32

Maggi, Lorenzo, Alvaro Valcarce, and Jakob Hoydis. "Bayesian Optimization for Radio Resource Management: Open Loop Power Control." IEEE Journal on Selected Areas in Communications 39, no. 7 (July 2021): 1858–71. http://dx.doi.org/10.1109/jsac.2021.3078490.

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33

Liu, Fa Gui, Xiao Yong Xing, and Ze Xiang Wu. "Dynamic Power Management for Embedded Wireless Devices." Advanced Materials Research 268-270 (July 2011): 540–45. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.540.

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The energy consumption of WLAN card makes up a significant proportion in the embedded system, which makes the effective power management on WLAN card play an important role on energy-saving and WLAN card longevity. This paper introduces a method of dynamic power management for WLAN card: manipulating the WLAN card driver to achieve the transition state of the WLAN card, acquiring WLAN card load which ultimately achieves the dynamic power management optimization algorithm. The experiment is presented to prove the feasibility of the implementation for the purpose of energy consumption saving on WLAN card.

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34

Chen, Ji Lin, Nan Zhang, Chang Feng Qin, Na Na Liu, Wei Jiang Qiu, and Jian Yong Hu. "The Optimization and Implementation of Power System Parallel Computing Management Platform." Applied Mechanics and Materials 687-691 (November 2014): 3365–70. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3365.

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With the rapid development of computer hardware, parallel computing cluster is becoming more and more powerful. It becomes focus of attention that how to make full use of the parallel abilities in computing cluster for grid service faster and more agility in the analysis of power system simulation. This paper introduces the optimization and implementation of power system parallel computing management platform based on online and offline power system research. It improves the computing resource utilization, enhances the general platform, standardizes management of computing interface at the same time and realizes the application process can be configured for the customs. At present, the optimization and implementation of power system parallel computing management platform has been successfully applied to online warning research and auxiliary decision in the power system, and it is also applied to offline study prediction, collaborative system for power grid operation mode calculation and so on.

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35

D., Danalakshmi, Gopi R., A. Hariharasudan, Iwona Otola, and Yuriy Bilan. "Reactive Power Optimization and Price Management in Microgrid Enabled with Blockchain." Energies 13, no. 23 (November 24, 2020): 6179. http://dx.doi.org/10.3390/en13236179.

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Анотація:

The energy market is gradually changing from centralized trading to peer-to-peer trading due to the tremendous increase in a microgrid with green energy resources. When more generating units are included in the microgrid, the possibilities of more reactive power flows exist in the system that leads to high transmission loss which has to be optimized. The reactive power is one of the essential ancillary services in the microgrid towards preserving the voltage in the transmission and distribution line. The major contribution of the paper is towards managing the ancillary service in the distributed energy network economically and technically. This study aims to estimate and optimize the power loss, reactive power, and price management as well. Towards optimization, the self-balanced differential evolution algorithm (SBDE) is used in this study. A distribution system operator is involved in coordinating the sellers and buyers. The proposed layered microgrid architecture uses the blockchain technology for reactive power price management by providing transparency and security among peers. The process of converging various transactions into a block and adding in the distributed blockchain is illustrated. Multiple transactions are performed by using the proposed methodology, giving efficient energy transaction. The results show that the power loss is minimized using SBDE algorithm for different cases. Additionally, the study has demonstrated the price allocation of the optimal reactive power obtained from providers. The blockchain technology embedded in reactive power pricing will play a significant role in the evolution of traditional power distribution systems to active distribution networks.

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36

Anh, Ho Pham Huy, and Cao Van Kien. "Optimal energy management of microgrid using advanced multi-objective particle swarm optimization." Engineering Computations 37, no. 6 (February 7, 2020): 2085–110. http://dx.doi.org/10.1108/ec-05-2019-0194.

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Анотація:

Purpose The purpose of this paper is to propose an optimal energy management (OEM) method using intelligent optimization techniques applied to implement an optimally hybrid heat and power isolated microgrid. The microgrid investigated combines renewable and conventional power generation. Design/methodology/approach Five bio-inspired optimization methods include an advanced proposed multi-objective particle swarm optimization (MOPSO) approach which is comparatively applied for OEM of the implemented microgrid with other bio-inspired optimization approaches via their comparative simulation results. Findings Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization methods. Moreover, the proposed MOPSO is successfully applied to perform 24-h OEM microgrid. The simulation results also display the merits of the real time optimization along with the arbitrary of users’ selection as to satisfy their power requirement. Originality/value This paper focuses on the OEM of a designed microgrid using a newly proposed modified MOPSO algorithm. Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization approaches.

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37

Lagouir, Marouane, Abdelmajid Badri, and Yassine Sayouti. "Multi-Objective Optimization Dispatch Based Energy Management of A Microgrid Running Under Grid Connected and Standalone Operation Mode." International Journal of Renewable Energy Development 10, no. 2 (January 10, 2021): 333–43. http://dx.doi.org/10.14710/ijred.2021.34656.

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Анотація:

This paper presents a novel optimization approach for a day-ahead power management and control of a DC microgrid (MG). The multi-objective optimization dispatch (MOOD) problem involves minimizing the overall operating cost, pollutant emission levels of (NOx, SO2 and CO2) and the power loss cost of the conversion devices. The weighted sum method is selected to convert the multi-objective optimization problem into a single optimization problem. Then, analytic hierarchy process (AHP) method is applied to determine the weight coefficients, according to the preference of each objective function. The system’s performance is evaluated under both grid connected and standalone operation mode, considering power balancing, high level penetration of renewable energy, optimal scheduling of charging/discharging of battery storage system, control of load curtailment and the system technical constraints. Ant lion optimizer (ALO) method is considered for handling MOOD, and the performance of the proposed algorithm is compared with other known heuristic optimization techniques. The simulation results prove the effectiveness and the capability of the developed approach to deal better with the coordinated control and optimization dispatch problem.They also revealed that economically running the MG system under grid connected mode can reduce the overall cost by around 4.70% compared to when it is in standalone operation mode.

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38

Jia, Xiu Fang, Shen Mao Huang, Kai She, and Hui Chun Hua. "The Research on Harmonic Pollution Management." Advanced Materials Research 726-731 (August 2013): 1999–2003. http://dx.doi.org/10.4028/www.scientific.net/amr.726-731.1999.

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Анотація:

In view of the present situation of harmonic management in our country at present, put forward the new ideas of current harmonic flexible management. First on the behavior of harmonic pollution analyzed by harmonic pollution, behavior is a kind of external diseconomy activities, and to this kind of behavior is discussed, the method of internal external diseconomy activities of power harmonic pollution emission behavior of collection of sewage charges corresponding. Secondly, the harmonic pollution emissions trading, and the transaction is the social cost analysis, obtained by using the harmonic pollution emissions trading can make the ratio of sewage costs simply implement uniform standards to be low, the social effect can be more optimization. Implementation of the harmonic pollution discharge fee and harmonic pollution emissions trading can make power users to achieve profit optimization and take the initiative to improve power quality.

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39

Martínez, José Alexander, and Fredy Hernán Martínez Sarmiento. "Optimization of maintenance management of trees in powerdistribution systems." Revista Tecnura 17, no. 35 (March 19, 2013): 12. http://dx.doi.org/10.14483/udistrital.jour.tecnura.2013.1.a01.

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We propose a data-analysis-based methodology for maintaining trees that affect power distribution networks. From the information captured in the field, we use fuzzy-logic-based techniques and models together with genetic algorithms so as to focus maintenance activities on the optimal time and place. The strategy is aimed at two key aspects: first, the history of failure events caused by trees on medium-voltage power supply systems; this information is used to calculate quality indicators such as energy-not-supplied (ENS) and number-of-users (NU) affected due to falling trees only. In this case we use a fuzzy-logic-based system to weight and find the critical circuits on which forest maintenance should be performed. Second, we construct an objective function based on tree-to-network distances and growth patterns of trees (classified per species) in order to simulate forest maintenance; this is achieved using a genetic algorithm to determine optimum pruning cycles for a given power supply system. We use real historical data of medium voltage circuits for system setting as well as for performance evaluation.

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40

Hu, Qi Zhou, Xiu Ge Zhang, and Xun Cheng Huang. "Optimization Models Based on Line Loss Post Management in Power System." Advanced Materials Research 774-776 (September 2013): 1891–96. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.1891.

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This paper researched the post management on line loss management in power system. As while, This article studied the characters of the line loss management in power system depend on the practical experiences. it would pose a new problem to address the regulatory lacunae revealed by post management department. Meanwhile, The article established a optimal model to restrict the working time of the manager which is so-called post rotation cycle. And then, the paper gave out the further discussion about the model, and the model proved to be reliable and practical. It is of great value to probe into how to efficiently utilize management resources to improve corporation management ability and level and achieve the advanced goals.

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41

Xie, Jiaping, Weisi Zhang, Lihong Wei, Yu Xia, and Shengyi Zhang. "Price optimization of hybrid power supply chain dominated by power grid." Industrial Management & Data Systems 119, no. 2 (March 11, 2019): 412–50. http://dx.doi.org/10.1108/imds-01-2018-0041.

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Purpose The purpose of this paper is to examine the impact of renewable energy on the power supply chain and to study whether the renewable generator or the power grid that purchases power from the power spot market is better when the actual generation of renewable energy is insufficient. The authors want to compare and analyze the different power supply chain operation modes and discuss the optimal mode selection for renewable energy generator and power grid in different situations. Design/methodology/approach This paper studies the grid-led price competition game in the power supply chain, in which the power grid as a leader decides the price of transmission and distribution, and generators determine the power grid price. The renewable energy power generator and the traditional energy power generator conduct a price competition game; on the other hand, the power grid and power generators conduct Stackelberg games. The authors analyze the power supply of single power generator and two power generators, respectively, and research on the situation that the renewable energy cannot be fully recharged when the actual power generation is insufficient. Findings The study finds that both renewable and traditional power grid prices decline as price sensitivity coefficient of demand and installed capacity of renewable energy generators increase. Power grid premium decreases as the price sensitivity coefficient of demand increases, but rises as the installed capacity of renewable energy generator increases. When there is a shortage of power, if the installed capacity of renewable energy is relatively small and price sensitivity coefficient of demand is relatively large, the grid purchases the power from power spot market and shares cost with renewable energy generators, leading to higher expected profits of the renewable energy generators. On the contrary, the renewable energy generators prefer to make up power shortage themselves. For the power grid, purchasing the power by the renewable energy generators when there is a power shortage can bring more utility to the power grid when the installed capacity of renewable energy is lower and the demand price sensitivity coefficient is higher. When the installed capacity of renewable energy is high and the price sensitivity coefficient of demand is moderate, or the installed capacity of renewable energy is moderate and the demand price sensitivity coefficient is high, a generator that simultaneously possesses two kinds of energy source will bring more utility to the power grid. If the installed capacity of renewable energy and the demand price sensitivity coefficient both are small or the installed capacity of renewable energy and the price sensitivity coefficient of demand both are large, the power grid prefers to purchase the power by itself when there is a power shortage. Practical implications The goal of our paper analysis is to explore the implications of the theoretical model and address the series of research questions regarding the impact of the renewable energy on the power supply chain. The results of this study have key implications for reality. This paper sheds light on the power supply chain operation mode selection, which can potentially be used for the renewable energy generators to choose their operating mode and can also help traditional energy generators and power grid enterprises maximize their utility. This paper also has some references for the government to formulate the corresponding renewable energy development policy. Originality/value This paper studies the power operation mode under the uncertainty of supply and demand, and compares the advantages and disadvantages of renewable energy generator that makes up the shortage or the power grid purchases the power from power spot market then shares cost with the renewable energy generator. This paper analyzes the power grid-led coordination problem in a power supply chain, compares and analyzes the price competition game model of single power generator and dual power generators, and compares the different risk preferences of power grid.

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42

Haoxiang, Wang. "MULTI-OBJECTIVE OPTIMIZATION ALGORITHM FOR POWER MANAGEMENT IN COGNITIVE RADIO NETWORKS." Journal of Ubiquitous Computing and Communication Technologies 2019, no. 02 (December 25, 2019): 97–109. http://dx.doi.org/10.36548/jucct.2019.2.004.

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The cognitive radio networks is an adaptive and intelligent radio network that is capable of automatically identifying the available channels in the spectrum that is wireless. Cognitive radios modify the parameters supporting the conveyance according to the needs of communication to enhance the operating radio behavior and avail a concurrent communication within the allotted spectrum band at one location. To improvise the parameter configuration the intelligent optimization techniques are been followed nowadays. The paper puts forth a multi-objective optimization algorithm (MO-OPA) for the power management in the cognitive radio networks. The proposed method utilizes the hybridized evolutionary algorithm to reduce the power consumption by minimizing the delay in the communication, intervention and the error rate of the packets. The validation of the proposed method is done to using the network simulator-2 to evince the capabilities of the proposed MO-OPA.

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43

Yang, Zhaoguang, Mingyuan Ren, Juan Shao, and Jiongxuan He. "Research on Power Optimization Operation Based on Computer Analysis of Energy Saving Dispatching and Demand Side Management." Journal of Physics: Conference Series 2023, no. 1 (September 1, 2021): 012032. http://dx.doi.org/10.1088/1742-6596/2023/1/012032.

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Abstract At present, the energy-saving application of power system has become an important means to ensure the sustainable development of social economy. However, there are still many deficiencies and problems in energy-saving dispatching and demand side management of power system, which urgently needs innovation and improvement. Based on this, this paper first analyzes the development status of energy-saving power operation mechanism, then studies the power operation optimization based on computer-based energy-saving dispatching, and finally gives the power operation optimization strategy based on power demand side management.

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44

Wang, Jidong, Boyu Chen, Peng Li, and Yanbo Che. "Distributionally robust optimization of home energy management system based on receding horizon optimization." Frontiers in Energy 14, no. 2 (March 30, 2020): 254–66. http://dx.doi.org/10.1007/s11708-020-0665-4.

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45

Ren, Yuan, Zhi Dan Zhong, and Tian Yu Luo. "A Combined Particle Swarm Optimization-Wavelet Transform Based Strategy for Power Management of PEM Fuel Cell Powered Hybrid System." Advanced Materials Research 971-973 (June 2014): 954–57. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.954.

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This paper proposed a combined particle swarm optimization-wavelet-transform based power management for PEM fuel cell powered hybrid electric vehicles which including a PEM fuel cell, a lithium-ion battery and an ultra-capacitor. This combined particle swarm optimization-wavelet-transform based frequency decoupling power management strategy is capable of identifying the high frequency of the ultra-capacitor, the PEM fuel cell low frequency and the lithium-ion battery medium frequency power demand of the hybrid systems. The hybrid system efficiency and life cycle can be extended. We made simulation by using the SimpowerSystem of MATLAB, the results shows the combined particle swarm optimization-wavelet transform based strategy for power management is validated.

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46

Sitharthan, R., CK Sundarabalan, KR Devabalaji, T. Yuvaraj, and A. Mohamed Imran. "Automated power management strategy for wind power generation system using pitch angle controller." Measurement and Control 52, no. 3-4 (March 2019): 169–82. http://dx.doi.org/10.1177/0020294019827330.

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In this literature, a new automated control strategy has been developed to manage the power supply from the wind power generation system to the load. The main objective of this research work is to develop a fuzzy logic–based pitch angle control and to develop a static transfer switch to make power balance between the wind power generation system and the loads. The power management control system is a progression of logic expressions, designed based on generating power and load power requirement. The outcome of this work targets at an improved power production, active and reactive power compensation and ensures system load constraints. To validate the proposed control strategy, a detailed simulation study is carried out on a 9-MW wind farm simulation simulated in MATLAB/Simulink environment.

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47

Graa, Amel, and Farid Benhamida. "A review on optimization methods applied to energy management system." Serbian Journal of Management 15, no. 2 (2020): 371–82. http://dx.doi.org/10.5937/sjm15-22519.

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Energy is fundamental in supporting people's daily subsists and the continual mission for human life improvement. The computer-aided instruments system used by electric utility grids or microgrids operators to control, monitor, and optimize the power system operation is generally named Energy Management System (EMS). The topic of optimization methods applied to decision making problems in such system is a difficult and complex combination of mathematical formulation, modeling and algorithmic solution. The best result in such process is applied to the problem to be optimized, which must be studied in great depth. Furthermore, difficult mathematical calculations and procedures can be elaborated; also, computer knowledge and software engineering competences must be provided. The subject of this paper is an overview of the existing important optimization methods used in electric power management system such as unit commitment, optimal power flow and economic dispatch.

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48

Aziz, Muhammad Adnan, Ijaz Mansoor Qureshi, Tanweer Ahmed Cheema, and Engr Akram Rashid. "COMMUNITY BASED HOME ENERGY MANAGEMENT SYSTEM." IIUM Engineering Journal 18, no. 1 (May 30, 2017): 43–55. http://dx.doi.org/10.31436/iiumej.v18i1.657.

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In a Smart Grid (SG) scenario, domestic consumers can gain cost reduction benefit by scheduling their Appliance Activation Time (AAT) towards the slots of low charge. Minimization in cost is essential in Home Energy Management Systems (HEMS) to induce consumers acceptance for power scheduling to accommodate for a Demand Response (DR) at peak hours. Despite the fact that many algorithms address the power scheduling for HEMS, community based optimization has not been the focus. This paper presents an algorithm that targets the minimization of energy costs of whole community while keeping a low Peak to Average Ratio (PAR) and smooth Power Usage Pattern (PUP). Objective of cost reduction is accomplished by finding most favorable AAT by Particle Swarm Optimization (PSO) in conjunction with Inclined Block Rate (IBR) approach and Circular Price Shift (CPS). Simulated numerical results demonstrate the effectiveness of CPS to assist the merger of PSO & IBR to enhance the reduction/stability of PAR and cost reduction.

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49

Wu, Haotian, Hang Li, and Xueping Gu. "Optimal Energy Management for Microgrids Considering Uncertainties in Renewable Energy Generation and Load Demand." Processes 8, no. 9 (September 2, 2020): 1086. http://dx.doi.org/10.3390/pr8091086.

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This paper proposes an efficient power management approach for the 24 h-ahead optimal maneuver of Mega–scale grid–connected microgrids containing a huge penetration of wind power, dispatchable distributed generation (diesel generator), energy storage system and local loads. The proposed energy management optimization objective aims to minimize the microgrid expenditure for fuel, operation and maintenance and main grid power import. It also aims to maximize the microgrid revenue by exporting energy to the upstream utility grid. The optimization model considers the uncertainties of the wind energy and power consumptions in the microgrids, and appropriate forecasting techniques are implemented to handle the uncertainties. The optimization model is formulated for a day-ahead optimization timeline with one-hour time steps, and it is solved using the ant colony optimization (ACO)-based metaheuristic approach. Actual data and parameters obtained from a practical microgrid platform in Atlanta, GA, USA are employed to formulate and validate the proposed energy management approach. Several simulations considering various operational scenarios are achieved to reveal the efficacy of the devised methodology. The obtained findings show the efficacy of the devised approach in various operational cases of the microgrids. To further confirm the efficacy of the devised approach, the achieved findings are compared to a pattern search (PS) optimization-based energy management approach and demonstrate outperformed performances with respect to solution optimality and computing time.

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50

Zhang, Li Guo, Zhou Liu, and Guo Jing He. "Analysis on Voltage and Reactive Power in Civil Lighting." Applied Mechanics and Materials 608-609 (October 2014): 937–43. http://dx.doi.org/10.4028/www.scientific.net/amm.608-609.937.

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Street lamp has become the essential functional unit in society for its widely use , Street lamp power energy saving has become a very worth studying problem high demand. Aiming at such a demand, this paper proposed a kind of optimizational energy saving streetlight that based on the voltage and reactive power, this article put forward such a research mainly aims at the demand of Jinan street lamp management. The research improved the grid power factor, reduced the network pressure drop and reduces the energy consumption of street lamp. It upload the master station through GPRS network real-timely, And accept command such as mode conversion, parameter setting, time checking and capacitor switching. It implements the voltage optimization function, remote monitoring and control functions on the basis of ensuring road illumination and uniformity, Which realized street light energy-saving and reduced the loss of lines and transformers at the same time. And it protect lighting lamps effectively. Examples show that the study further improved the management level of Jinan street lamp, it realized the double harvest of electricity environment optimization and electrical energy saving, Which made a great contribution to energy-saving society construction.

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