Coordinated DG-Tie planning in distribution networks based on temporal scenarios

Xiang, Yue; Zhou, Lili; Su, Yunche; Liu, Jichun; Huang, Yuan; Liu, Junyong; Lei, Xia; Sun, Zhanquan; Xu, Weiting; Zhang, Wentao

doi:10.1016/j.energy.2018.06.159

Cited by 10 publications

(7 citation statements)

References 23 publications

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“…It is supposed that a tie-line can be constructed between two buses m and n only if they are in neighbours (adjacent) (30). Finally, (31) assure that the total cost of tie-line installation would be less than the distribution company's total budget for tie-line planning. Also, (32) guarantees that the total number of constructed tie-lines cannot exceed the predefined amount (Ψ tie ).…”

Section: Tie-line Planning Constraintsmentioning

confidence: 99%

Tie‐line planning for resilience enhancement in unbalanced distribution networks

Taheri

Safdarian

2021

IET Generation Trans & Dist

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Over the past decades, there has been a dramatic increase in the frequency of natural disasters, which are the leading causes of large-scale power outages. This paper, therefore, assesses the significance and role of optimal tie-line construction in improving the service restoration performance of unbalanced power distribution systems in the aftermath of high-impact low-probability incidents. In doing so, a restoration process aware stochastic mixed-integer linear programming model is developed to find the optimal locations for new tie-line construction in unbalanced three-phase distribution systems. In particular, the restoration process of distribution systems, including the fault isolation and system reconfiguration, is contemplated to place tie-lines in the most proper locations to enhance the manoeuvring capability of distribution systems and reducing the customer interruption time. Furthermore, the model is a stochastic one wherein uncertainty associated with potential damages alongside demand uncertainty is captured via a set of likely scenarios. To validate the effectiveness of the proposed stochastic mixed-integer linear programming model, it is tested and verified on the IEEE 13-and 123-bus test systems.

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Section: Tie-line Planning Constraintsmentioning

confidence: 99%

Tie‐line planning for resilience enhancement in unbalanced distribution networks

Taheri

Safdarian

2021

IET Generation Trans & Dist

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“…However, as an intelligent discrete optimisation algorithm, genetic algorithm (GA) could be easier to be implemented and integrated with simulations to obtain an effective solution. Thus, an improved GA is adopted in this paper [25].…”

Section: Reliability‐oriented Multi‐objective Optimal Planning Modelmentioning

confidence: 99%

Reliability‐oriented optimal planning of charging stations in electricity–transportation coupled networks

Xiang

Meng

Huo

et al. 2020

IET Renewable Power Generation

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With the increasing application of electric vehicles (EVs), the deployment of charging stations is vital for supporting the charging and navigation for EVs, which also affects the reliability of the corresponding electricity network. To achieve a balanced reliability in both the electricity network and EV trips, an optimal planning method for charging stations in the electricity-transportation coupled networks is proposed. Firstly, an integrated management strategy of EVs is demonstrated to model the reliability enhancement of the two networks. Thereafter, a coupled network reliability assessment method based on the Quasi-sequential Monte Carlo simulation is proposed in this paper. This paper proposes a novel reliability-oriented multi-objective optimal planning model for charging stations planning problem, which is resolved by implementing the optimal planning scheme from the candidate nodes. Additionally, correlation analysis is performed to ensure that the proposed model can be solved quickly for large-scale electricity network with complex transportation flow input data. A test case is presented and simulated to validate the feasibility and effectiveness of the proposed method.

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“…Intelligent optimization algorithms [15]- [17], such as genetic algorithms (GA) [15]- [16] and particle swarm optimization (PSO) [5] [17], were used for solving the optimal capacity problem. However, existing intelligent optimization algorithms require many iterations to find the optimum and cannot deeply mine the complex coupling correlation between evaluation and operation to increase the convergence speed.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Connectable Capacity of Distributed Wind Generation in Distribution Networks Through a Bayesian Integrated Optimization Method

Chen

Xiang

Sun

et al. 2022

IEEE Systems Journal

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The effective evaluation of the connectable capacity of renewable energy plays a vital role in the development of a sustainable distribution network. Quantifying the capacity while considering network security and the local renewable accommodation policy is a challenge. This paper proposes a scenario-based bi-level mathematical model using a Bayesian integrated optimization method to evaluate and quantify the connectable capacity of distributed wind generation in distribution networks, which effectively integrates the characteristics of wind power and the local accommodation policy. The constraint on the generation curtailment ratio (CR) is innovatively designed to represent the renewable accommodation policy and integrated with network security constraints to coordinatively quantify the capacity. The model is solved by the Bayesian integrated optimization method. The regression-based algorithm greatly reduces the complexity of alternating iteration and improves the calculation efficiency. Practical cases are used to verify the effectiveness of the proposed method. Results indicate that the method is more efficient than traditional optimization algorithms, and CR integration ensures that the connectable capacity fits local renewable energy development policies well.

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Coordinated DG-Tie planning in distribution networks based on temporal scenarios

Cited by 10 publications

References 23 publications

Tie‐line planning for resilience enhancement in unbalanced distribution networks

Tie‐line planning for resilience enhancement in unbalanced distribution networks

Reliability‐oriented optimal planning of charging stations in electricity–transportation coupled networks

Evaluating Connectable Capacity of Distributed Wind Generation in Distribution Networks Through a Bayesian Integrated Optimization Method

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