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Three-phase power flow calculation considering probability and interval uncertainties for power distribution systems

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第15期13卷 3334-3345页

作者： Wang, Chun Ao, Xin Fu, Wenbin Nanchang Univ Dept Elect & Automat Engn Nanchang Jiangxi Peoples R China State Grid Jiangxi Ganxi Power Supply Co Xinyu Peoples R China

Factors such as the stochastic nature of weather condition and arbitrariness of loads make power flow have both probability and interval properties. In order to solve power flow considering probability and interval uncertainties for power distribution systems, an approximate method, which combines affine linear three-phase power flow and latin hypercube sampling (LHS) method, is proposed. The formulation of affine node voltage is derived based on affine inverse operation and conservative estimation method. Bound influences of interval variables (BIIVs) of affine node voltage are used to determine the upper bound and lower bound of the result of probabilistic power flow. Further decomposition of BIIVs, which contributes to reduce the conservation of results, is given. The effectiveness of the proposed method is verified using the modified IEEE 13-bus system and IEEE 123-bus system by comparing with LHS-Monte-Carlo simulation method.

关键词： load flow power distribution probability distribution networks Monte Carlo methods interval uncertainties power distribution systems approximate method affine linear three-phase power flow latin hypercube sampling method affine node voltage affine inverse operation conservative estimation method interval variables probabilistic power flow LHS-Monte-Carlo simulation method phase power flow calculation probability stochastic nature weather condition interval properties

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Uncertainties in the methods of Flood Discharge Measurement

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WATER RESOURCES MANAGEMENT 2013年第1期27卷 153-167页

作者： Chen, Yen-Chang Hsu, Yung-Chia Kuo, Kuang-Ting Natl Chiao Tung Univ Disaster Prevent & Water Environm Res Ctr Hsinchu Taiwan Natl Taipei Univ Technol Dept Civil Engn Taipei Taiwan

This study demonstrates an application of uncertainty analysis in evaluating methods of discharge measurement including: the velocity-area, rating curve and efficient methods based on the probabilistic velocity distribution equation. The measurement of river discharge plays a large part in the distribution of water resources. The conventional methods of discharge measurement are costly, time-consuming, and dangerous. Therefore the efficient method of discharge measurement which bases on the relationship between maximum and mean velocities being constant was employed to justify its alternative for the conventional methods: velocity-area and rating curve methods. Distribution test was applied to investigate the statistical properties of the uncertainties involved in the three methods of discharge measurement. latin hypercube sampling (LHS) method was employed accordingly to assess the discharge features of the three methods of discharge measurement. The main purpose of this study is to quantify the uncertainty involved in several discharge measurement methods and justify the availability and reliability of using the efficient method as an alternative of the conventional methods. Results show that the correlation analysis also validates that the efficient method is a more reliable method than the rating curve method to yield accurate discharge measurements. Moreover, it also yielded comparably accurate measurements as those by the velocity-area method.

关键词： Efficient method of discharge measurement Uncertainty analysis latin hypercube sampling method Correlation analysis

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Expectation-maximisation model for stochastic distribution network planning considering network loss and voltage deviation

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第2期13卷 248-257页

作者： Qi, Baoxia Chen, Jiajia Zhao, Yanlei Jiao, Pihua Shandong Univ Technol Sch Elect & Elect Engn Zibo 255000 Peoples R China

The penetration of wind power is increasing in distribution network for reducing reliance on fossil fuels and covering continuously increasing demand for energy. However, it is argued that the forecasting error of wind power cannot be avoided even using the best forecasting approach. Therefore, in this study, a mean-variance-skewness based expectation maximisation (MVSbEM) model has been proposed by maximisation of the mean and skewness while simultaneously minimisation of the variance to obtain the optimal trade-off relationship between the profit and risk of distribution network planning (DNP) considering uncertain wind power integrated. In the MVSbEM model, the indexes of network loss, voltage deviation, and investment cost are concurrently taken into account under several kinds of actual operation constraints. In addition, the authors have made a full investigation on the MVSbEM by considering different forecasting errors, power factors of wind power, the different forecasting wind speeds, the number of wind turbines as well as the lines and substations upgrading. Furthermore, in order to reduce the computational burden, the latin hypercube sampling method is used to sample uncertain wind speed. The feasibility and effectiveness of the MVSbEM model have been comprehensively evaluated on a modified IEEE 33-bus system.

关键词： particle swarm optimisation power distribution planning sampling methods wind turbines stochastic processes investment simulated annealing wind power plants continuously increasing demand forecasting error forecasting approach mean-variance-skewness based expectation maximisation model uncertain wind power MVSbEM model network loss voltage deviation power factors different forecasting wind wind turbines sample uncertain wind speed wind power penetration expectation-maximisation model stochastic distribution network planning substations upgrading latin hypercube sampling method simulated annealing particle swarm optimisation algorithm stochastic DNP

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Optimal Coordination of Plug-In Electric Vehicles in Power Grids With Cost-Benefit Analysis-Part I: Enabling Techniques

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IEEE TRANSACTIONS ON POWER SYSTEMS 2013年第4期28卷 3546-3555页

作者： Luo, Zhuowei Hu, Zechun Song, Yonghua Xu, Zhiwei Lu, Haiyan Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Broadway NSW 2007 Australia

Plug-in electric vehicles (PEVs) appear to offer a promising option for mitigating greenhouse emission. However, uncoordinated PEV charging can weaken the reliability of power systems. The proper accommodation of PEVs in a power grid imposes many challenges on system planning and operations. This work aims to investigate optimal PEV coordination strategies with cost-benefit analysis. In Part I, we first present a new method to calculate the charging load of PEVs with a modified latin hypercube sampling (LHS) method for handling the stochastic property of PEVs. We then propose a new two-stage optimization model to discover the optimal charging states of PEVs in a given day. Using this model, the peak load with charging load of PEVs is minimized in the first stage and the load fluctuation is minimized in the second-stage with peak load being fixed as the value obtained in the first stage. An algorithm based on linear mixed-integer programming is provided as a suitable solution method with fast computation. Finally, we present a new method to calculate the benefit and cost for a PEV charging and discharging coordination strategy from a social welfare approach. These methods are useful for developing PEV coordination strategies in power system planning and supporting PEV-related policy making.

关键词： Coordinated charging cost-benefit analysis latin hypercube sampling method plug-in electric vehicle/s (PEV/s) vehicle to grid (V2G)

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Two-stage stochastic programming approach for the medical drug inventory routing problem under uncertainty

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 128卷 358-370页

作者： Nikzad, Erfaneh Bashiri, Mahdi Oliveira, Fabricio Shahed Univ Dept Ind Engn Tehran Iran Aalto Univ Sch Sci Dept Math & Syst Anal Syst Anal Lab FI-00076 Aalto Finland

Medical drug shortages are an important issue in health care, since they can significantly affect patients' health. Thus, selecting the appropriate distribution and inventory policies plays an important role in decreasing drug shortages. In this context, inventory routing models can be used to determine optimal policies in the context of medical drug distribution. However, in real-world conditions, some parameters in these models are subject to uncertainty. This paper examines the effects of uncertainty in the demand by relying on a two-stage stochastic programming approach to incorporate it into the optimization model. A two-stage model is then proposed and two different approaches based on chance constraints are used to assess the validity of the proposed model. In the first model, a scenario-based two-stage stochastic programming model without probabilistic constraint is proposed, while in the other two models, proposed for validation of the first model, probabilistic constraints are considered. A mathematical-programming based algorithm (a matheuristic) is proposed for solving the models. Moreover, the latin hypercube sampling method is employed to generate scenarios for the scenario-based models. Numerical examples show the necessity of considering the stochastic nature of the problem and the accuracy of the proposed models and solution method.

关键词： Two-stage stochastic programming Stochastic inventory routing problem Medical drug distribution latin hypercube sampling method Chance constraints Matheuristic algorithm

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Reliability and cost optimization of electronic devices considering the component failure rate uncertainty

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2004年第3期84卷 271-284页

作者： Zafiropoulos, EP Dialynas, EN Natl Tech Univ Athens Sch Elect & Comp Engn Zografos 15773 Greece

The objective of this paper is to present an efficient computational methodology to obtain the optimal system structure of electronic devices by using either a single or a multiobjective optimization approach, while considering the constraints on reliability and cost. The component failure rate uncertainty is taken under consideration and it is modeled with two alternative probability distribution functions. The latin hypercube sampling method is used to simulate the probability distributions. An optimization approach was developed using the simulated annealing algorithm because of its flexibility to be applied in various system types with several constraints and its efficiency in computational time. This optimization approach can handle efficiently either the single or the multiobjective optimization modeling of the system design. The developed methodology was applied to a power electronic device and the results were compared with the results of the complete enumeration of the solution space. The stochastic nature of the best solutions for the single objective optimization modeling of the system design was sampled extensively and the robustness of the developed optimization approach was demonstrated. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： single objective and multiobjective optimization system reliability failure rate uncertainty Monte Carlo simulation latin hypercube sampling method simulated annealing algorithm

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Dynamic Reconstruction Strategy of Distribution Network Based on Uncertainty Modeling and Impact Analysis of Wind and Photovoltaic Power

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IEEE ACCESS 2024年 12卷 64069-64078页

作者： Luo, Fei Bu, Qiangsheng Ye, Zhigang Yuan, Yubo Gao, Lei Lv, Pengpeng China Natl Grid Jiangsu Elect Power Co Ltd Elect Power Res Inst Nanjing 210000 Jiangsu Peoples R China

The distribution network with high penetration of renewable energy such as wind and photovoltaic power has higher flexibility and power supply efficiency, but it also faces more faults and uncertainties. Traditional dynamic reconfiguration under fault conditions are still limited by problems such as low load recovery rate and strong decision conservatism. To overcome these challenges, this article proposes a dynamic reconstruction strategy for distribution network under fault conditions that takes into account multivariate uncertainty. Firstly, in response to the uncertainty of distributed power generation output and load demand in the distribution network, an interval prediction method is adopted to construct a uncertainty model for source and load side. Then, the latin hypercube sampling method is used to generate multiple operation scenarios, and computational efficiency is improved by reducing scenario samples using Cholesky sorting principle and synchronous backpropagation reduction method. Finally, a robust dynamic reconstruction model based on mixed-integer second-order cone programming (MISOCP) is constructed, and the feasibility and robustness of the proposed dynamic strategy are verified using the improved IEEE-33 node system. Through analysis, the proposed method effectively addresses the risk factors in the operation, thus improving the safety and reliability of the distribution network.

关键词： Distribution networks Uncertainty Heuristic algorithms Load modeling Optimization Voltage Solar power generation Predictive models hypercubes Distribution network dynamic reconstruction interval prediction method latin hypercube sampling method mixed-integer second-order cone programming (MISOCP)

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Home energy management in a residential smart micro grid under stochastic penetration of solar panels and electric vehicles

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SOLAR ENERGY 2020年 212卷 6-18页

作者： Alilou, Masoud Tousi, Behrouz Shayeghi, Hossein Urmia Univ Dept Elect Engn Orumiyeh Iran Univ Mohaghegh Ardabili Energy Management Res Ctr Ardebil Iran

Implementing demand side management programs in a residential area causes to increase the role of consumers in managing the total power network. Moreover, the owner of the smart home can reduce energy dependence on the power network and also his electricity bill by using optimal managing the operational schedule of home appliances and available generated power of renewable distributed generation and electric vehicle. In this paper, a new multi-objective scheduling method based on intelligent algorithms is utilized for energy managing in smart homes of a residential micro grid. Home appliances, rooftop photovoltaic panel and plug-in hybrid electric vehicle are schedulable devices of each smart home. Photovoltaic and electric vehicle uncertainties are also considered. The combination algorithm of the multi-objective dragonfly algorithm and analytical hierarchy process method is used for optimizing the techno-economic objective function and finding the best schedule of devices. Real-time pricing tariff is considered as the price-based demand response program. For evaluating the efficiency of the proposed method, it is applied to a smart micro grid with 20-smart home. The numerical result demonstrates the appropriate performance of the proposed home energy management method in reducing the electricity bill of smart homes and peak demand of the residential smart micro grid.

关键词： Electricity bill latin hypercube sampling method Smart grid Multi-objective dragonfly algorithm Rooftop photovoltaic panel Smart home energy management

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Optimal expansion planning of isolated microgrid with renewable energy resources and controllable loads

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IET RENEWABLE POWER GENERATION 2017年第7期11卷 931-940页

作者： Wang, Zhaojian Chen, Ying Mei, Shengwei Huang, Shaowei Xu, Yin Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Beijing Jiaotong Univ Sch Elect Engn Beijing 100044 Peoples R China

As the load demand in a microgrid increases, more distributed generators (DGs) should be installed to meet the demand, which makes the microgrid expansion planning very important. To obtain the optimal expansion strategy, a tri-level expansion planning framework is presented for an isolated microgrid in this study, which is composed of demand expansion, capacity optimisation and operation optimisation. The uncertainties of load forecasting are considered. latin hypercube sampling method is utilised to generate the load demand scenarios. Controllable load is also considered in the expansion, which can be switched off and on as required. Considering the complexity of the operation optimisation problem, particle swarm optimisation is used to obtain the planning results. Finally, numerical simulations for an isolated microgrid in Weizhou Island, Guangxi, China are utilised to validate the effectiveness of the proposed model as well as its solving algorithm.

关键词： distributed power generation power distribution planning renewable energy sources particle swarm optimisation numerical analysis load forecasting optimal expansion planning isolated microgrid renewable energy resources controllable loads distributed generators DG trilevel expansion planning framework demand expansion capacity optimisation load forecasting latin hypercube sampling method load demand scenarios operation optimisation problem particle swarm optimisation numerical simulations Weizhou Island

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Hard point parameter optimization of a wheel-hubdriven electric vehicle suspension based on a three-step screening method

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2021年第4期15卷 681-694页

作者： Shi Peicheng Xu Zengwei Wang Kefei Zhang Rongyun Xiao Ping Anhui Polytech Univ Anhui Engn Technol Res Ctr Automot New Tech Wuhu 241000 Peoples R China Chery Automobile Co Ltd Wuhu 241009 Peoples R China

In order to solve the adverse effect on wheel-hubdriven electric vehicle ride comfort caused by the introduction of hub motor, a three-step screening method is proposed to match and optimize the hard point parameters of vehicle suspension. First, a multibody dynamic model of the prototype vehicle suspension was established based on a multibody dynamic method, and the analytical formulas of the electromagnetic force of the motor were given. Based on the specific conditions of the vehicle and the motor, a dynamics analysis of the suspension was carried out to investigate the effect of the fluctuation of the electromagnetic force of the hub motor on the wheel alignment parameters. Second, the calculation model of the suspension dynamics response was established according to the experiment designed by the latin hypercube sampling method, and a sensitivity analysis of the suspension hard point coordinate was carried out to obtain the sensitive hard point parameters. Finally, the linear weighted synthetic optimization model of the front wheel alignment parameters of the wheel-hubdriven electric vehicle was elaborated using a multi-objective optimization method, and the multi-objective function was converted into a single objective evaluation function to carry out better suspension hard point parameter optimization. The results show that by optimizing the suspension hard point parameter, the wheel alignment parameters can be controlled within a reasonable range. This optimization ensures that the variation rate of the front wheel alignment parameters of the wheel-hubdriven electric vehicle meets the vehicle design requirements, thus eliminating the adverse impact on vehicle ride comfort caused by the introduction of the hub motor. This paper also illustrates that the three-step screening method is an efficient method of parameter matching, which can satisfactorily solve the problem of engineering applications. The three-step screening method can teach assistant engineer ho

关键词： Wheel-hubdriven electric vehicle Wheel alignment parameter Three-step screening method latin hypercube sampling method Parameter sensitivity analysis

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