Reliability Evaluation of a Distribution Network with Microgrid Based on a Combined Power Generation System

Abstract

Distributed generation (DG), battery storage (BS) and electric vehicles (EVs) in a microgrid constitute the combined power generation system (CPGS). A CPGS can be applied to achieve a reliable evaluation of a distribution network with microgrids. To model charging load and discharging capacity, respectively, the EVs in a CPGS can be divided into regular EVs and ruleless EVs, according to their driving behavior. Based on statistical data of gasoline-fueled vehicles and the probability distribution of charging start instant and charging time, a statistical model can be built to describe the charging load and discharging capacity of ruleless EVs. The charge and discharge curves of regular EVs can also be drawn on the basis of a daily dispatch table. The CPGS takes the charge and discharge curves of EVs, daily load and DG power generation into consideration to calculate its power supply time during islanding. Combined with fault duration, the power supply time during islanding will be used to analyze and determine the interruption times and interruption duration of loads in islands. Then the Sequential Monte Carlo method is applied to complete the reliability evaluation of the distribution system. The RBTS Bus 4 test system is utilized to illustrate the proposed technique. The effects on the system reliability of BS capacity and V2G technology, driving behavior, recharging mode and penetration of EVs are all investigated. OPEN ACCESSEnergies 2015, 8 1217

Comprehensive Benefit Evaluation of the Power Distribution Network Planning Project Based on Improved IAHP and Multi-Level Extension Assessment Method

“…The feasibility of this proposed approach has been verified by using the analysis of an example and by proposing some measures to improve the comprehensive benefit. The experimental results indicate that the comprehensive benefit grade of the power distribution network planning project is "better" since the correlation degree is [0.024, 0.055] at j = 4 and the benefit grade variable eigenvalue is j * ∈ [3.33, 3.418] ∈ [3,4]. In brief, this paper offers a new method to solve similar problems of power distribution network construction projects.…”

“…Use j * ∈ [0, 1], [1,2], [2,3] and [3,4] to represent the comprehensive benefit level "poor", "fair", "good", and "better", respectively. In this paper, the benefit grade variable eigenvalue j * can be computed by using Equations (24) and (25), which is j * ∈ [3.33, 3.418] ∈ [3,4]. Therefore, the evaluation result indicates that the comprehensive benefit grade of the power distribution network planning project is "better", and there is a development trend towards "better".…”

“…How can we analyze the comprehensive benefit of urban distribution network planning quantitatively? The traditional assessment of an urban distribution network planning project has been mainly the individual evaluations, including reliability [1][2][3][4][5][6], safety [7], power quality [8], risk [9], and efficiency [10]. These studies tend to evaluate the technical level of the power distribution and subjectivity in terms of determining the weight, which previously affected the evaluation of the benefit of the power distribution network planning project, are all resolved.…”

A Novel, Stable, and Economic Power Sharing Scheme for an Autonomous Microgrid in the Energy Internet

“…Due to the environmental and geopolitical costs of fossil fuels, a federal program was created to stimulate essential innovation in energy technologies, such as wind and solar power [1][2][3][4]. In China, the cumulative installed capacity of wind power will reach 100 GW by 2015 and will easily meet the target of 200 GW by 2020 [5,6].…”

Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles

“…Due to the inconsistent and varied characteristics of lithium-ion battery cells, Gong et al [19] and Liu et al [20] proposed the data-driven biascorrection-based modeling method and model-based sensor fault diagnosis method, which can significantly reduce the computation work and remain good model accuracy. Bai et al [21] applied a combined power generation system (CPGS) to achieve a reliable evaluation of a distribution network with microgrids combined with fault duration. In addition, many model-based diagnostic algorithms such as extended kalman were presented that diagnoses thermal faults in Lithium-ion batteries [22][23][24][25].…”