Determination of Local Voltage Control Strategy of Distributed Generators in Active Distribution Networks Based on Kriging Metamodel

Li, Peng; Zhang, Chuanchi; Fu, Xiaopeng; Song, Guanyu; Wang, Chengshan; Wu, Jianzhong

doi:10.1109/access.2019.2904144

Cited by 23 publications

(19 citation statements)

References 37 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the existing literature, the optimal allocation of DG in active distribution network involves consideration of energy storage configuration and reactive power compensation, active operation strategy, demand side management and network reconfiguration, and life cycle theory [15][16][17]. A novel selection strategy for restricted operation scenarios based on the shadow price is proposed to reduce the complexity of scenario selection and ease the computational burden substantially in virtue of the decoupling of ADN operation and planning [15].…”

Section: There Are Two Kinds Of Dg Programming Methods Considering Unmentioning

confidence: 99%

Distributed Generation Planning in Active Distribution Networks Based on Multi-Scene Analysis

Lv¹,

Li²,

Guo³

et al. 2019

Preprint

View full text Add to dashboard Cite

In recent years, distributed generation technology has developed rapidly. Renewable energy, represented by wind energy and solar energy, has been widely studied and utilized. In order to give full play to the advantages of Distributed Generation (DG) and meet the challenges after power grid access, Active Distribution Network (ADN) is considered as the future development direction of traditional distribution network because of its ability of active management. Nowadays, multi-scenario analysis is widely used in the research of optimal allocation of distributed power supply in active distribution network. Aiming at the problems that may arise when using multi-scenario analysis to plan DG with uncertainties in large-scale scenarios, a scenario reduction method based on improved clustering algorithm is proposed. The validity of the scene reduction method is tested, and the feasibility of the method is verified. At present, there are few studies on the optimal allocation of DG in ADN under fault state. In this paper, comprehensive safety indicators are introduced. Considering the timing characteristics of DG and the influence of active management mode, a bi-level programming model is established, which aims at minimizing the investment of annual life cycle and the removal of active power. The bi-level model is a complex mixed integer non-linear programming model. A hybrid algorithm combining cuckoo search algorithm and primal dual interior point method is used to solve the model. Finally, through the simulation of the IEEE-33 node system, the superiority of the scenario reduction method and the comprehensive security index used in this paper to optimize the configuration of DG in ADN is verified.

show abstract

Section: There Are Two Kinds Of Dg Programming Methods Considering Unmentioning

confidence: 99%

Distributed Generation Planning in Active Distribution Networks Based on Multi-Scene Analysis

Lv¹,

Li²,

Guo³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…In this paper, it is assumed that the i-th nominal isolated subsystem of the interconnected system (1) has uniform relative degree ( r i1 , r i2 , · · · , r im i ) and the distribution G i (x i ) is involutive in domain i for i = 1, 2, · · · n. For the convenience of analysis and design, the feedback linearization technique is applied to the system (3).…”

Section: Definition 2 (29) Systemmentioning

confidence: 99%

“…Such a large computational burden may not be attractive to industry as data transfer requirements are large and risks of cyber attack increase. Plant shut down may result from corruption or blockage of data transfer and the system robustness may be compromised . This has motivated the development of decentralized control strategies in which only local subsystem information is needed for local control design.…”

Section: Introductionmentioning

confidence: 99%

Decentralized sliding mode control for a class of nonlinear interconnected systems by static state feedback

Feng

Zhao

Yan

et al. 2020

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

Summary In this paper, a class of interconnected systems is considered, where the nominal isolated systems are fully nonlinear. A robust decentralized sliding mode control based on static state feedback is developed. By local coordinate transformation and feedback linearization, the interconnected system is transformed to a new regular form. A composite sliding surface which is a function of the system state variables is proposed and the stability of the corresponding sliding mode dynamics is analyzed. A new reachability condition is proposed and a robust decentralized sliding mode control is then designed to drive the system states to the sliding surface in finite time and maintain a sliding motion thereafter. Both uncertainties and interconnections are allowed to be unmatched and are assumed to be bounded by nonlinear functions. The bounds on the uncertainties and interconnections have more general forms when compared with existing work. A MATLAB simulation example is used to demonstrate the effectiveness of the proposed method.

show abstract

“…damage states, based on the ranges of associated occurrence probabilities. ̂, for limit state function i using (24)- (29), otherwise, skip the error calculation for limit state function i and the error from the last iteration will be used in future steps. • Step 8: Evaluation of the stopping criterion.…”

Section: Voi Analysis With Adaptive Kriging Frameworkmentioning

confidence: 99%

Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging

2020

View full text Add to dashboard Cite

Large uncertainties in many phenomena have challenged decision making. Collecting additional information to better characterize reducible uncertainties is among decision alternatives. Value of information (VoI) analysis is a mathematical decision framework that quantifies expected potential benefits of new data and assists with optimal allocation of resources for information collection. However, analysis of VoI is computational very costly because of the underlying Bayesian inference especially for equality-type information. This paper proposes the first surrogate-based framework for VoI analysis. Instead of modeling the limit state functions describing events of interest for decision making, which is commonly pursued in surrogate model-based reliability methods, the proposed framework models system responses. This approach affords sharing equality-type information from observations among surrogate models to update likelihoods of multiple events of interest. Moreover, two knowledge sharing schemes called model and training points sharing are proposed to most effectively take advantage of the knowledge offered by costly model evaluations. Both schemes are integrated with an error rate-based adaptive training approach to efficiently generate accurate Kriging surrogate models. The proposed VoI analysis framework is applied for an optimal decision-making problem involving load testing of a truss bridge. While state-of-the-art methods based on importance sampling and adaptive Kriging Monte Carlo simulation are unable to solve this problem, the proposed method is shown to offer accurate and robust estimates of VoI with a limited number of model evaluations. Therefore, the proposed method facilitates the application of VoI for complex decision problems.

show abstract

Determination of Local Voltage Control Strategy of Distributed Generators in Active Distribution Networks Based on Kriging Metamodel

Cited by 23 publications

References 37 publications

Distributed Generation Planning in Active Distribution Networks Based on Multi-Scene Analysis

Distributed Generation Planning in Active Distribution Networks Based on Multi-Scene Analysis

Decentralized sliding mode control for a class of nonlinear interconnected systems by static state feedback

Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging

Contact Info

Product

Resources

About