Enhancing risk control ability of distribution network for improved renewable energy integration through flexible DC interconnection

Xiao, Hao; Pei, Wei; Deng, Wei; Ma, Tengfei; Zhang, Shizhong; Kong, Li

doi:10.1016/j.apenergy.2020.116387

Cited by 15 publications

(4 citation statements)

References 36 publications

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“…e risk evolution analysis is based on the research of risk impacts that come from different levels and dimensions. And then, decision makers can decide the order of risk indicators based on the needs of stakeholders and the characteristics of the multistation integrated operation [21,22]. In addition, they consider each risk level by level to clarify the focus of decision-making.…”

Section: Literature Reviewmentioning

confidence: 99%

Operation Risk Structural Analysis and Evaluation System Construction for Multistation Integration Project

Cui

Liu

2022

Mathematical Problems in Engineering

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Digitalization in the power grid sector will become a strong support in the future for the upgrading of the power industry. And, multistation integration has become a leading project in the process. However, there are some hidden risks to its security with the boom of new technologies. Risk analysis and evolution of the multistation integration have an important role in safe operation. This paper analyzes the relationship among risk factors by using both the structural equation modeling (SEM) and the chi-squared automatic interaction detector (CHAID) approach, which is more objective than subjective judgment. The result shows the following: (1) The operational risks come from 10 risk-derived sources which therefore can be qualitatively divided into 2 risk categories with 11 risk indicators. (2) Concerning the structural analysis, the main risk source of multistation integration comes from endogenous risks. (3) In terms of procedural analysis, cultural risk, information risk, and accidental risk are the most critical external risks, while performance risk and location risk are the most critical internal ones. The results of models provide a reference for engineering management in digitalization in the power grid sector.

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Section: Literature Reviewmentioning

confidence: 99%

Operation Risk Structural Analysis and Evaluation System Construction for Multistation Integration Project

Cui

Liu

2022

Mathematical Problems in Engineering

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“…As power electronics technology advances, building an AC/DC hybrid distribution network has gained popularity [1]. The direct current (DC) distribution network provides several benefits over an AC distribution network, such as strong system stability, reduced transmission loss, variable control modes, and a large power supply capacity [2]. At present, when the traditional AC distribution network evolves into an AC/DC hybrid distribution network [3,4], the most important problem to be solved is how to schedule the hybrid distribution network to achieve the optimal operation of the distribution network [5].…”

Section: Introductionmentioning

confidence: 99%

A Non-Iterative Coordinated Scheduling Method for a AC-DC Hybrid Distribution Network Based on a Projection of the Feasible Region of Tie Line Transmission Power

Dai,

Gao,

Goh

et al. 2024

Energies

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AC-DC hybrid distribution grids realize power transmission through tie lines. Accurately characterizing the power exchange capacity between regional grids while ensuring safe grid operation is the basis for the coordinated scheduling of resources in interconnected distribution grids. However, most of the current AC/DC hybrid models are linear, and it is challenging to ensure the accuracy criteria of the obtained feasible regions. In this paper, a two-stage multi-segment boundary approximation method is proposed to characterize the feasible region of hybrid distribution grid tie line operation. Information such as security operation constraints are mapped to the feasible region of the boundary tie line to accurately characterize the transmission exchange capacity of the tie line. To avoid the limitations of linear models, the method uses a nonlinear model to iteratively search for boundary points of the feasible region. This ensures high accuracy in approximating the real feasible region shape and capacity limitations. A convolutional neural network (CNN) is then utilized to map the given boundary and cost information to obtain an estimated equivalent operating cost function for the contact line, overcoming the inability of previous methods to capture nonlinear cost relationships. This provides the necessary cost information in a data-driven manner for the economic dispatch of hybrid AC-DC distribution networks. Numerical tests demonstrate the effectiveness of the method in improving coordination accuracy while preserving regional grid privacy. The key innovations are nonlinear modeling of the feasible domain of the contact line and nonlinear cost fitting for high-accuracy dispatch.

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“…The presence of DG brings new characteristics and control complexity to network [2]. As renewable generation in the power system increases, microgrids-related technologies are attracting increasing attention [3]. Networked microgrids (NMGs) interconnect multiple microgrids and operate them in the form of clusters.…”

Section: Introductionmentioning

confidence: 99%

Three-phase optimal power flow for networked microgrids based on semidefinite programming convex relaxation

Huang

et al. 2022

Applied Energy

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Enhancing risk control ability of distribution network for improved renewable energy integration through flexible DC interconnection

Cited by 15 publications

References 36 publications

Operation Risk Structural Analysis and Evaluation System Construction for Multistation Integration Project

Operation Risk Structural Analysis and Evaluation System Construction for Multistation Integration Project

A Non-Iterative Coordinated Scheduling Method for a AC-DC Hybrid Distribution Network Based on a Projection of the Feasible Region of Tie Line Transmission Power

Three-phase optimal power flow for networked microgrids based on semidefinite programming convex relaxation

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