Distribution network operational risk assessment and early warning considering multi‐risk factors

Chen, Lijuan; Liu, Li; Yan, Pengpeng; Chen, Wenjin; Huang, Hongyang; Wu, Tiantian; Xu, Xiaohui

doi:10.1049/iet-gtd.2019.1198

Cited by 15 publications

(4 citation statements)

References 31 publications

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“…The data layer extracts the basic knowledge elements under the specific application background from structured, semi-structured, and unstructured multisource massive heterogeneous data related to the research field or theme by using automatic or semi-automatic technology, that is, the entity, concept, and attribute. It forms a huge database [8]. The pattern layer summarizes and condenses the data layer's huge and rich basic data.…”

Section: Feature Extraction Of Distribution Network Operation Indexmentioning

confidence: 99%

Reliability of distribution network based on entity extraction model of characteristic spectrum

Liu,

Yang,

Chai

et al. 2023

Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023)

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Section: Feature Extraction Of Distribution Network Operation Indexmentioning

confidence: 99%

Reliability of distribution network based on entity extraction model of characteristic spectrum

Liu,

Yang,

Chai

et al. 2023

Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023)

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“…Most existing operational risk assessment methods for distribution networks are based on reliability theory, which uses equipment failure rate and outage time as indicators (Su et al, 2014;Chen et al, 2020). While useful, these approaches have certain limitations and more comprehensive and refined methods are required to evaluate the conditions of the distribution network.…”

Section: Introductionmentioning

confidence: 99%

Assessment method of distribution network health level based on multivariate information

Zhu

Huang

2023

Front. Energy Res.

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In order to enhance self-monitoring and self-diagnosis capabilities in smart distribution networks, this paper proposes a method for assessing the health level of the network based on multivariate information. First, we construct an evaluation indicator system for the health of the smart distribution network by integrating the smart distribution network information system. Next, we utilize the improved back propagation (BP) neural network and multivariate indicator information to calculate the health indexes of both the grid layer and equipment. We then solve the health index of the equipment layer based on network topology and goal-oriented methodology. Furthermore, by utilizing the health information of both the equipment and grid layer, we apply fuzzy evaluation and Dempster-Shafer (D-S) evidence theory to obtain the health level of the distribution network. We provide a comprehensive evaluation of the overall health status of the smart distribution network. Finally, the proposed method is validated using data from a regional distribution network. The results demonstrate its effectiveness in improving the smart distribution networks’ overall health and stability by enabling more effective self-monitoring and self-diagnosis.

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“…CVaR is considered a risk evaluation method to avoid over-optimistic solutions in a two-layer adaptive stochastic model for an optimal multi-energy microgrid (wind, PV, thermal, Battery, and capacitor) under a voltage security constraint environment [20]. The CVaR model for time-varying economic risk with time-sequential security assessment in a probabilistic model containing uncertainty in EV distribution and renewable energy of distribution system is presented in ref [21]. Risk due to reserve shortage while the optimal allocation of reserve capacity for islanded microgrid operation under intermittency of renewable and fluctuation in load is modeled using CVaR in [22].…”

Section: Introductionmentioning

confidence: 99%

An Economic Risk Analysis in Wind and Pumped Hydro Energy Storage Integrated Power System Using Meta-Heuristic Algorithm

et al. 2021

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Due to the restructuring of the power system, customers always try to obtain low-cost power efficiently and reliably. As a result, there is a chance to violate the system security limit, or the system may run in risk conditions. In this paper, an economic risk analysis of a power system considering wind and pumped hydroelectric storage (WPHS) hybrid system is presented with the help of meta-heuristic algorithms. The value-at-risk (VaR) and conditional value-at-risk (CVaR) are used as the economic risk analysis tool with two different confidence levels (i.e., 95% and 99%). The VaR and CVaR with higher negative values represent the system in a higher-risk condition. The value of VaR and CVaR on the lower negative side or towards a positive value side indicates a less risky system. The main objective of this work is to minimize the system risk as well as minimize the system generation cost by optimal placement of wind farm and pumped hydro storage systems in the power system. Sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and moth flame optimization algorithms (MFO) are used to solve optimal power flow problems. The novelty of this paper is that the MFO algorithm is used for the first time in this type of power risk curtailment problem. The IEEE 30 bus system is considered to analyze the system risk with the different confidence levels. The MVA flow of all transmission lines is considered here to calculate the value of VaR and CVaR. The hourly VaR and CVaR values of the hybrid system considering the WPHS system are reported here and the numerical case studies of the hybrid WPHS system demonstrate the effectiveness of the proposed approach. To validate the presented approach, the results obtained by using the MFO algorithm are compared with the SQP and ABC algorithms’ results.

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Distribution network operational risk assessment and early warning considering multi‐risk factors

Cited by 15 publications

References 31 publications

Reliability of distribution network based on entity extraction model of characteristic spectrum

Reliability of distribution network based on entity extraction model of characteristic spectrum

Assessment method of distribution network health level based on multivariate information

An Economic Risk Analysis in Wind and Pumped Hydro Energy Storage Integrated Power System Using Meta-Heuristic Algorithm

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