Operation risk assessment of active distribution networks considering probabilistic uncertainties of distributed generators‐loads and power management of VRB ESSs

Lei, Jiazhi; Gong, Qingjie; Gao, David Wenzhong; Wang, Baohua; Guan, Guan

doi:10.1049/iet-rpg.2019.1362

Cited by 9 publications

(7 citation statements)

References 28 publications

(45 reference statements)

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“…The authors in [10] have developed a risk assessment model operational for active DN using point estimation method (PEM) taking into consideration the probabilistic uncertainties. Results show that PEM is advantageous as compared to Monte-Carlo simulation and scenario-based modelling.…”

Section: Intelligent Methodsmentioning

confidence: 99%

“…Modified risk assessment methods for distribution networks with DGs and BES systems are required to be established. In [10], the author proposed a model for risk assessment, build on the point estimation method (PEM) that takes into account the probabilistic uncertainties of the vandinum redox flow battery (VRB)–energy storage systems (ESSs). Results show that the distributed DGs and ESSs and their appropriate power management help in reducing the operational risk of distribution system (DS) with renewable energy resources.…”

Section: Introductionmentioning

confidence: 99%

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A comprehensive review of reliability assessment methodologies for grid‐connected photovoltaic systems

Singh

Saket

Khan

2023

IET Renewable Power Gen

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This paper provides a comprehensive review of the research work related to Reliability Assessment Methodologies for grid‐connected photovoltaic (PV) systems performed in recent literature. Solar power is emerging as the fast growing source of energy in the world as a result of rising environmental concerns regarding the hazards of climatic change linked with the production of electricity using fossil fuels. Although PV systems can support small businesses and households on their own, many people prefer a grid‐connected PV system (PVS) because of the net profit it provides. Grid‐integrated PV system, however, comes with many reliability issues. Evaluating the reliability of grid‐integrated photovoltaic system is thus an important area of research. The article presents a critical survey of the state‐of‐art technologies for assessing the reliability of a PV system. Issues related to the reliability of the grid‐integrated PVS are spotted along with the solution techniques. Reliability indices for analyzing the PVS performance are also discussed.

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Section: Intelligent Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A comprehensive review of reliability assessment methodologies for grid‐connected photovoltaic systems

Singh

Saket

Khan

2023

IET Renewable Power Gen

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“…where NPV Total is the total net present value of a WT, NPV Comp refers to net present value of WT system components as given in (49), NPV LoadLoss is the net present value of loss of load. The NPV of components includes capital, maintenance, and repair values and are denoted by V Cap , V Main , and V Rep , respectively.…”

Section: Figure 11 Mcs Algorithm Based On Probability Modelsmentioning

confidence: 99%

“…The detailed qualitative description of point estimate methods can be obtained in [48]. The probabilistic uncertainties of the distributed generator, load, and ESS were considered in [49] and an operational risk assessment model based on PEM was proposed. An improved version of PEM namely 3PEM in [50] was proposed to evaluate the probability moments of probabilistic power flow.…”

Section: Wake Effect Model Wind Rose Diagram and Probabilistic Approachmentioning

confidence: 99%

Layout optimisation algorithms and reliability assessment of wind farm for microgrid integration: A comprehensive review

Kumar

Saket

Dheer

et al. 2021

IET Renewable Power Gen

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The paper represents a comprehensive review of the wind farm layout and reliability assessment of the wind farm integrated electrical power system. The authors have done a review on the proliferation of renewable energy which raises the uncertainties in the electrical power system. The uncertainties including wind speed and wake effect are important to deal with when an isolated microgrid is considered. The scenario becomes vigilant when the wind farms are integrated with the main grid. Due to uncertainties, the study of reliability evaluation of a wind integrated power system would become significant to analyse the electrical power system behaviour effectively. So, the paper discusses the layout optimisation methods of wind turbines considering the uncertainty parameters, mainly the wake effect. In this regard, the different wake models and optimisation methods based on a single-objective and multi-objective functions are reviewed in detail with the proper comparisons. The paper serves as a better illustration of the competency of these optimisation methods on the optimal wind turbine location on a wind farm. Furthermore, the paper extends the view on the reliability and cost assessment, and reliability improvement techniques of the wind integrated power system. This article provides comprehensive information, yields an attractive and subsequent tool for research requirements for the researchers to design the wind farm layout, and assessed the reliability of a wind integrated power system.

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“…Traditionally, stochastic methods are used to assess risk in power systems [18]. Such methods have been used recently to determine the risk of disturbances within grids with connected PV sources [19][20][21][22][23][24]. Stochastic methods are suitable due to the stochastic variability of PV generation, which is caused by the unpredictability of weather conditions leading to variable solar irradiance.…”

mentioning

confidence: 99%

Evaluating the Risk of Exceeding the Normal Operating Conditions of a Low-Voltage Distribution Network due to Photovoltaic Generation

2022

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Connecting photovoltaic micro-installations to a low-voltage network changes the operating conditions of the network. As a result, in certain situations, the permissible operating limits may be periodically exceeded. The risk of exceeding the normal operating conditions of the network depends on multiple factors, including the installed capacity of the photovoltaic sources. In this article, we use a time-series method to determine the annual risks of exceeding the bus voltage limits, the rated current of the lines and transformer, and the acceptable limit of the negative sequence component of bus voltage, as well as the risk of a reverse flow occurring, and the risk of energy losses increasing. We calculate these risks for different levels of penetration of the photovoltaic sources, different divisions of the rated power of the photovoltaic sources between individual phases, and different consumer load profiles. We perform calculations on a CIGRE test network using OpenDSS and statistical meteorological data for the Katowice (Poland) weather station. The results obtained indicate that connecting photovoltaic micro-installations to a low-voltage network has the greatest impact on the risk of reverse flow occurring and the risk of energy losses increasing. In addition, the risk of overvoltage and branch overload increases substantially. The method we present allows one to determine the value of the hosting capacity of a given low-voltage network, ensuring that the assumed risk of exceeding the normal operating conditions of the network is retained.

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Operation risk assessment of active distribution networks considering probabilistic uncertainties of distributed generators‐loads and power management of VRB ESSs

Cited by 9 publications

References 28 publications

A comprehensive review of reliability assessment methodologies for grid‐connected photovoltaic systems

A comprehensive review of reliability assessment methodologies for grid‐connected photovoltaic systems

Layout optimisation algorithms and reliability assessment of wind farm for microgrid integration: A comprehensive review

Evaluating the Risk of Exceeding the Normal Operating Conditions of a Low-Voltage Distribution Network due to Photovoltaic Generation

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