A network reconfiguration approach for power system restoration based on preference-based multiobjective optimization

Sun, Runjia; Liu, Yutian; Zhu, Hui; Azizipanah‐Abarghooee, Rasoul; Terzija, Vladimir

doi:10.1016/j.asoc.2019.105656

Cited by 29 publications

(26 citation statements)

References 32 publications

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“…However, the source of uncertain power supply capability of the transmission grid is complex, which is hard to be expressed by an accurate mathematical model. Online decision-making is more practical than the optimization method for restoration in uncertain problems (Sun et al, 2019b;Sun et al, 2022b), which can modify subsection switch control strategies based on real-time data to decrease the influence of uncertainty. The learning-based method can make decisions within several seconds to guarantee the online implementation of distribution network configuration within one dispatch step.…”

Section: Reinforcement Learning Model Of Distribution Network Configu...mentioning

confidence: 99%

“…Load restoration schemes are applied on either transmission or distribution grids considering security constraints (Liu et al, 2016). Load restoration is usually modeled as a combinational optimization problem, which can be solved either by mathematical programming (Gholami and Aminifar, 2017;Zhao et al, 2019) or evolutionary computation (Sun et al, 2019b;Yang et al, 2021). In order to improve the restoration efficiency, a rolling optimization strategy is proposed for transmission network recovery and load restoration with the help of a wind-storage system (Sun et al, 2022a).…”

Section: Introductionmentioning

confidence: 99%

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An online decision-making method based on multi-agent interaction for coordinated load restoration

et al. 2022

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Load restoration coordinating transmission grid, distribution grid, and microgrids is an effective measure that is taken into consideration while improving the power system resilience in extreme weather conditions. An online decision-making method is proposed to deal with the unexpected nature of power supply issues regarding the re-energization of microgrids and transmission grids. In this research work, an online multi-agent interaction technique is used for coordinated load restoration. The main algorithm comprises of two subsections, namely, a resilience index and a multi-agent-based decision-making system which are used to administer the coordination among the transmission grid, distribution grid, and microgrids. A distributionally robust optimization model is used to evaluate the power supply capability of microgrids on the basis of load restoration parameters. Finally, a step-by-step decision-making method, based on a deep Q-network, is proposed for distribution network reconfiguration considering the uncertainty of power supply capabilities of transmission grid and microgrids. Simulation results demonstrated that the proposed method can perform the online decision-making of substation load restoration, which significantly improves the load restoration efficiency.

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Section: Reinforcement Learning Model Of Distribution Network Configu...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An online decision-making method based on multi-agent interaction for coordinated load restoration

et al. 2022

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“…In order to prove the e ectiveness of the proposed method, Table 4 compares the proposed method and other methods in [13,16,18]. Strategy 1 is obtained by the proposed method.…”

Section: Scenario 1: All Buses Are Available For Restorationmentioning

confidence: 99%

“…e problem of the unit startup sequence is usually formulated as an optimization model. Various methods, e.g., mixed-integer linear programming [15] and genetic algorithm [16], are used to solve it. Based on predesigned partitioning plans, the unit startup sequence of each subsystem can be determined by these methods.…”

Section: Introductionmentioning

confidence: 99%

Online Decision-Making of Parallel Restoration Strategy for Power Systems Based on Susceptible-Infected-Recovered Model

Huang

et al. 2022

International Transactions on Electrical Energy Systems

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Parallel restoration following blackouts can reduce economic and social losses. This paper aims to develop a parallel restoration method coordinating the partitioning scheme of the blackout system and restoration strategies of subsystems. The susceptible-infected-recovered model, i.e., a virus propagation model of complex networks, is used to decide the parallel restoration strategies online. Firstly, various types of viruses are used to represent different subsystems. The probability vector of virus infection is obtained according to the importance level of each bus. Secondly, an immunization strategy is developed based on the faulted buses in the blackout situation. According to the infection rate and the immunization strategy, the virus propagation direction will be changed based on real-time system conditions. The startup characteristics of units and the charging reactive power of restoration paths are considered as constraints to embed in the virus propagation process. Finally, the partitioning scheme and the restorative actions for subsystems are determined based on the infected results of viruses. The effectiveness of the proposed method is validated by case studies on the IEEE 39-bus and the IEEE 118-bus test systems.

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“…For example, the ability of neural networks to predict wind speed 6 or neuro‐FS to predict the magnitude of the next large earthquakes could be considered 7 . There are so many types of SC models that introduced, improved, or evaluated in the literature 8–12 to resolve the complex problems in different fields such as nonlinear delayed systems, 13 transcoding simultaneously acquired MRI data, 14 power system restoration, 15 eye surgery, 16 broiler output energies, 17 intelligent human action recognition, 18 behavior and environmental impacts of waste glass geopolymers, 19 and also for concrete elements 20–23 …”

Section: Introductionmentioning

confidence: 99%

NIC: An innovative supervised machine learning computational framework having network‐based interactional connections

Mirrashid

Shirazi

2021

Computational Intelligence

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Models based on computational intelligence have many applications in various problems. Such systems are generally set up and designed based on a collection of data and information. In some real problems, the implementation of experimental studies or doing tests are costly and time‐consuming. Therefore, a model which requires fewer data than the existing soft computing methods can be useful and applicable. In this article, a network‐based interactional connection system is proposed as a new supervised machine learning computational framework for problems with small data. This model, which is inspired by the connections between neurons of the brain, utilizes the series and parallel structures with interactional connections to determine the best estimation. The proposed approach uses less unknown parameters than existing models and gives a suitable response in a few steps. The learning process starts with one generation and continues until an acceptable prediction is found, and the coefficients are determined. However, it may have more generations in a sequential format for better prediction and providing more accurate answers. To evaluate the performance of the proposed computational system, three engineering problems were investigated as a numerical study. The results also compared with the predicted values of four well‐known techniques.

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A network reconfiguration approach for power system restoration based on preference-based multiobjective optimization

Cited by 29 publications

References 32 publications

An online decision-making method based on multi-agent interaction for coordinated load restoration

An online decision-making method based on multi-agent interaction for coordinated load restoration

Online Decision-Making of Parallel Restoration Strategy for Power Systems Based on Susceptible-Infected-Recovered Model

NIC: An innovative supervised machine learning computational framework having network‐based interactional connections

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