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An optimized device-to-device (D2D) blockchain network for the insurance industry

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2023年第6期36卷 e5446-e5446页

作者： Bilgehan, Bulent Sabuncu, Ozlem Near East Univ Dept Elect & Elect Engn Mersin 10 Lefkosa Trnc Turkiye

Blockchain, a popular technology, remains the decentralized data management framework approved for use by many industries. The application to the insurance industry needs to offer mobility using the wireless network. The wireless network has many limitations to overcome. This paper focuses on such problems and introduces three levels of a solution to the problem. The first level is resolved using the edge computers as storage at the agencies and the partners. The second level of economic operation is solved by introducing a D2D network solution. The third level of high transactions over the network is considered using a two-stage optimization method. The introduced optimization algorithms are simulated, and results are compared with a classical step-by-step calculation method that is not feasible under real-time application. The optimization methods successfully determine the maximum channel rate with the interferences influencing the operation of such a system.

关键词： blockchain D2D fifth-generation mobile edge computing optimization algorithm the insurance industry

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Wind power forecasting system with data enhancement and algorithm improvement

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2024年 196卷

作者： Zhang, Yagang Kong, Xue Wang, Jingchao Wang, Hui Cheng, Xiaodan North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China North China Elect Power Univ Hebei Key Lab Phys & Energy Technol Box 205 Baoding 071003 Hebei Peoples R China Univ South Carolina Interdisciplinary Math Inst Columbia SC 29208 USA

Wind power generation has strong volatility. Accurate wind speed forecasting can not only avoid the waste of power resources, but also facilitate the development of clean energy and promote the energy transition worldwide. However, previous research has predominantly focused on the accuracy of wind power prediction, while ignoring the reliability of wind speed prediction system. In this research, a hybrid forecasting system with both accuracy and reliability of wind power forecasting is proposed. Firstly, a hybrid adaptive decomposition denoising algorithm is proposed to solve the unreasonable decomposition and residual noise. To improve the search performance, the seagull algorithm is optimized by chaotic system and Cauchy operator, and then the parameters of long short-term memory model are adjusted. Finally, based on data enhancement theory, an interval prediction model combined with kernel density estimation is proposed. The model is verified by the historical data of Sotavento wind farm in Spain and Eman wind farm in China. The average absolute percentage error values of wind speed point prediction are 2.87% and 8.01%, respectively. At the same confidence level, the interval prediction model proposed has narrower widths compared to the comparative model, with higher average interval scores. The results indicate that the point prediction model proposed in this research exhibits higher accuracy, while the interval prediction model demonstrates greater stability and reliability. These findings provide technical support for wind power forecasting.

关键词： Data decomposition and denoising Chaotic system optimization algorithm Hybrid forecasting system Data enhancement theory Wind interval prediction

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Risk-averse energy management of hydro/thermal/pumped storage complementarily operating with wind/solar: Balancing risk, cost and carbon emission

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2023年 60卷

作者： Li, Xudong Yang, Weijia Zhao, Zhigao Wang, Ran Yin, Xiuxing Liu, Pan Wuhan Univ State Key Lab Water Resources Engn & Management Wuhan 430072 Peoples R China

Large-scale grid integration of variable renewable energy is crucial for achieving decarbonized development. However, this integration requires frequent regulation of flexible power sources for complementary operation, which can lead to wear-and-tear and fatigue damage to key components. This poses potential risks to flexible power sources. Existing studies have primarily focused on limiting unit startups, while have neglected the risk of frequent power regulation. Thus, this work proposes a risk-averse short-term scheduling method for a Wind Solar-Cascade hydro-Thermal-Pumped storage hybrid energy system to balance frequent regulation risk, cost, and carbon emission: (1) a risk-averse short-term scheduling model is proposed, considering multilayer constraints;(2) a multi-objective hybrid African vulture optimization algorithm is proposed to effectively solve the scheduling problem including continuous and discrete variables. A case study in the Songhua River basin, China shows that: (1) compared with traditional models, the proposed model reduces the risk by 31.4% and enhances the comprehensive performance in balancing the three objectives by 22.4%;(2) the proposed algorithm performs robustness and search capability advantages, with improvements of 33.01% and 21.44% respectively, in solving the problem of challenging constraints and mixed decision variables. Overall, this work contributes to enhancing the management of large hybrid energy systems.

关键词： Hybrid energy system Hydropower Risk optimization algorithm Decarbonization

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Virtual Inertia Control in Autonomous Microgrids via a Cascaded Controller for Battery Energy Storage Optimized by Firefly algorithm and a Comparison Study with GA, PSO, ABC, and GWO

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ENERGIES 2023年第18期16卷 6611-6611页

作者： Amiri, Farhad Eskandari, Mohsen Moradi, Mohammad Hassan Bu Ali Sina Univ Fac Engn Elect Engn Dept Hamadan *** Iran Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

Modern (micro) grids host inverter-based generation units for utilizing renewable and sustainable energy resources. Due to the lack of physical inertia and, thus, the low inertia level of inverter-interfaced energy resources, the frequency dynamic is adversely affected, which critically impacts the stability of autonomous microgrids. The idea of virtual inertia control (VIC), assisted by battery energy storage systems (BESSs), has been presented to improve the frequency dynamic in islanded microgrids. This study presents the PD-FOPID cascaded controller for the BESS, a unique method for enhancing the performance of VIC in islanded microgrids. Using the firefly algorithm (FA), the settings of this controller are optimally tuned. This approach is robust to disruptions due to uncertainties in islanded microgrids. In several scenarios, the performance of the suggested approach is compared with those of other control techniques, such as VIC based on an MPC controller, VIC based on a robust H-infinite controller, adaptive VIC, and VIC based on an optimized PI controller. The simulation results in MATLAB show that the suggested methodology in the area of VIC is better than previous methods.

关键词： autonomous microgrids battery energy storage systems (BESSs) cascaded controller inverter-interfaced energy resources optimization algorithm renewable energy resources virtual inertia

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A Review on the Numerical Inversion Methods of Relative Permeability Curves

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Procedia Engineering 2012年 29卷 375-380页

作者： Jian Hou Dai-gang Wang Fu-quan Luo Yan-hui Zhang College of Petroleum Engineering China University of Petroleum Qingdao 266555 China

In general, relative permeability data can be obtained from laboratory coreflooding experiments. Such experimental data can be interpreted analytically or numerically. Compared to analytical methods, when the numerical inversion methods are applied to interpret the coreflooding experimental data, the reservoir performance obtained prior to and after breakthrough can be utilized comprehensively, the capillary effects and the heterogeneity of core samples can also be taken into account, so the estimated result is not only accurate but also complete. Moreover, the numerical inversion methods can be applied to large-scale reservoirs. This article introduces systematically the methodology of numerical inversion methods, and then reviews the present research status. Finally, several proposals of implicitly estimating relative permeability data are put forward from aspects of optimization algorithms’ properties, estimation of endpoint saturations and treatment scale by automatic history matching.

关键词： relative permeability curve numerical inversion method optimization algorithm research status

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Enterprise Profit Maximization in the Range of Products and Production Rate Selection Problems

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IFAC Proceedings Volumes 1983年第12期16卷 487-493页

作者： J. Gutenbaum A. Jakubowski System Research Institute Polish Academy of Sciences. Newelska 6 01-477 Warsaw Poland

In conditions of monopolistic position of enterprises, they exhibit unrestrained tendency to increase the prices of their products, without taking care for full utilization of their production capacities. Such a situation may occur in the planned and as well as the free-market national economy. The decision center is able to counteract this tendency by introducing a proper system of income taxes, which results in the optimal prices providing the maximal profit. Simultaneously, due to the high level of the prime costs of small production series, there exists some threshold value in the relation between profit and production rate. If the production rate is less than this value, it is unprofitable. For reasons mentioned, the problem of enterprise profit optimization in the case of production capacities constrained, is a non-trivial mathematical programming problem. It is a mixed discrete-continuous optimization problem: the decision variables connected with selecting ranges of products are of discrete (zero-one) type: the decision variables associated with the choice of the optimal production rate are continuous. The paper presents an optimization algorithm which can be used to solve this mixed discrete-continuous decision problem. The worked out method can be also applied to other socio-economic decision problems of similar type.

关键词： Enterprise profit maximization production capacities constraints discrete-continuous decision problem optimization algorithm dynamic programming method

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Method of assessment of mechanical characteristics of quasi-isotropic composite laminates using experimental data from fiber- optic strain sensors

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Procedia Structural Integrity 2017年 5卷 577-583页

作者： Mikhail Tashkinov Valeriy Matveenko Perm National Research Polytechnic University Komsomolsky Ave. 29 Perm 614990 Russia

The problem of analysis and prognosis of mechanical behavior of advanced composite materials and structures during their design, manufacturing and exploitation is urgent and attracts attention of many researchers. One of the most promising areas in the field of monitoring of the state of composite structures in the process of their exploitation is connected with creation of smart materials and smart systems based on the sensor elements. The real-time data about the structure state under the subsequent analysis can be used both for monitoring the mechanical state of the structures and for refining the mathematical models for the fracture processes prediction. The purpose of this work is to develop a combined computational and experimental methodology for estimating the mechanical characteristics of structures made of polymer composite materials (PCM). The computational component of the technique provides numerical simulation of mechanical behavior during quasistatic deformation of structures made of PCM. The experimental component is based on the measurement of deformations by fiber-optical strain sensors with Bragg gratings (FBG sensors) embedded in PCM. To refine the model parameters in accordance with the information received from the FBGs an algorithm is proposed, according to which the inverse problems are solved in order to ensure that the numerical and experimental results coincide with the specified accuracy. The implementation of the algorithm is demonstrated on the numerical example.

关键词： mechanical characteristics monitoring systems fiber-optic strain sensors optimization algorithm finite element method

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Benchmarking Philosophy based Search Framework

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Procedia Computer Science 2018年 131卷 647-655页

作者： As Xie Jz Mu Zhejiang University of Technology NO.18 Chaowang Road Hangzhou 310014 China

This paper put forward a general framework for intelligent optimization/search based on benchmarking philosophy, and whose "intelligence" is mainly dependent on the organizing tactics rather than the probability rules of its operators. According to the guiding principles and the specific methods of benchmarking, it is easy to design efficient optimization/search algorithms for various complex problems in science, engineering, and management.

关键词： Intelligent optimization optimization algorithm Benchmarking Philosophy

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A Novel Self-Adaptation Hybrid Artificial Fish-Swarm algorithm

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IFAC Proceedings Volumes 2013年第5期46卷 583-588页

作者： X.T. Hu H.Q. Zhang Z.C. Li Y.A. Huang Z.P. Yin No.38 Research Institute of CETC Hefei 230088 China (Tel: 86-0551-5391771) State Key Laboratory of Digital Manufacturing Equipment and Technology Wuhan 430074 China

In this paper, the social behaviors of fish swarm were classified in four ways: foraging behavior, stray behavior, reproductive behavior, and escaping behavior. Inspired by this, a novel artificial fish swarm algorithm (NAFSA) was proposed, which integrated the merits of the self-adaptation strategy, mutation strategy and hybrid strategy into the social behaviors of fish swarm. In the case of mutation strategy, the cloud theory was introduced into the escaping behavior, and the basic cloud generator was used as the mutation operator because of the properties of randomness and stable tendency of a normal cloud model. For the hybrid strategy, the selection, crossover and mutation operator in evolutionary algorithm were applied to define the reproductive ability of an artificial fish. Furthermore, the parameters of Step and Visual were developed in forms of hyperbolic tangent function to adjust the optimize performance dynamically during iterations process. Finally, ten standard test functions are used as the benchmark to validate the effectiveness of the NAFSA. Experimental results confirmed the superiority of NAFSA in terms of both solution quality and convergence speed.

关键词： optimization algorithm artificial fish-swarm algorithm cloud model evolutionary algorithm

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Robustness Test of Genetic algorithm on Generating Rules for Currency Trading

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Procedia Computer Science 2012年 13卷 86-98页

作者： Deng Shangkun Sun Yizhou Sakurai Akito School of Science for Open and Environmental Systems Graduate School of Science and TechnologyKeio University 3-14-1 Hiyoshi Kohoku-ku Yokohama-shi 223-8522 Japan

In trading in currency markets, reducing te mean of absolute or squared errors of predicted values is not valuable unless it results in profits. A trading rule is a set of conditions that describe when to buy or sell a currency or to close a position, which can be used for automated trading. To optimize the rule to obtain a profit in the future, a probabilistic method such as a genetic algorithm (GA) or genetic programming (GP) is utilized, since the profit is a discrete and multimodal function with many parameters. Although the rules optimized by GA/GP reportedly obtain a profit in out-of-sample testing periods, it is hard to believe that they yield a profit in distant out-of-sample periods. In this paper, we first consider a framework where we optimize the parameters of the trading rule in an in-sample training period, and then execute trades according to the rule in its succeeding out-of-sample period. We experimentally show that the framework very often results in a profit. We then consider a framework in which we conduct optimization as above and then execute trades in distant out-of-sample periods. We empirically show that the results depend on the similarity of the trends in the training and testing periods.

关键词： optimization algorithm Foreign exchange Genetic algorithm Robustness test Technical analysis Financial prediction

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