Multi-Objective Mixed-Integer Dynamic Optimization Method Applied to Optimal Allocation of Dynamic Var Sources of Power Systems

Deng, Zhuoming; Liu, Mingbo; Ouyang, Yifeng; Lin, Shunfu; Xie, Ming

doi:10.1109/tpwrs.2017.2724058

Cited by 42 publications

(26 citation statements)

References 28 publications

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“…Rostami, Neri, and Epitropakis () proposed a novel algorithm by employing a progressive preference sector approach to conduct the decision. The preference sector approach (also referred to as the utopia point method) has been successfully applied in several fields such as the electric power network problem (see Aghaei, Baharvandi, Rabiee, & Akbari, ; Deng, Liu, Ouyang, Lin, & Xie, ; Ning & You, ), energy harvesting problem (see Wang, Liu, Yuan, & Chen, ), and shortest path problem (Granat & Guerriero, ). However, this approach has not been applied in the network design problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Strategic network expansion of urban rapid transit systems: A bi‐objective programming model

Wei

Jin

Yang

et al. 2018

Computer aided Civil Eng

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With the development of urbanization and the extension of city boundaries, the expansion of rapid transit systems based on the existing lines becomes an essential issue in urban transportation systems. In this study, the network expansion problem is formulated as a bi-objective programming model to minimize the construction cost and maximize the total travel demand covered by the newly introduced transit lines. To solve the bi-objective mixed-integer linear program, an approach called minimum distance to the utopia point is applied. Thus, the specific trade-off is suggested to the decision makers instead of a series of optimal solutions. A real-world case study based on the metro network in Wuxi, China, is conducted, and the results demonstrate the effectiveness and efficiency of the proposed model and solution method. It is found that the utopia method can not only provide a reasonable connecting pattern of the network expansion problem but also identify the corridors with high priority under the limited budget condition.

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Section: Literature Reviewmentioning

confidence: 99%

Strategic network expansion of urban rapid transit systems: A bi‐objective programming model

Wei

Jin

Yang

et al. 2018

Computer aided Civil Eng

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“…The simulation executes the optimization tasks to compute minimal amount of reactive power provision of both devices, so that to calculate the minimal size of devices. The objective functions of optimization tasks for both cases are formulated as (3) and (4).…”

Section: B Criterion For Comparisonmentioning

confidence: 99%

Comparison of voltage support services by means of STATCOM and smart transformer in medium voltage grid

Gao

Zhu

Carne

et al. 2018

2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)

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Due to the increasing demand of electrical energy, especially in urban areas, the traditional distribution power grid reaches its limitations of operation (e.g. large current flow or voltage deviation). The distribution grid needs to be upgraded to ensure stability margin of operation. Since the cost of space is expensive and there is also resistance against installation of extra equipment from local resident, to install new equipment in distribution grid becomes difficult. The choice of new device to support the grid must consider not only the services but also the cost of equipment and space. The devices based on power electronics (STATCOM, smart transformer (ST)) are able to provide ancillary services such as voltage support to medium voltage (MV) distribution grid. In this paper, the ST is analyzed and compared with the STATCOM in terms of cost and performance. By means of literature review and simulation, the performance of ST has been analyzed. The result shows that the ST provides more services to assist the control in distribution grid than STATCOM. In order to achieve the same performance of voltage support, the investment of ST is less. Index Terms-Smart Transformer, voltage support in distribution grid, reactive power control, MV grid control, STATCOM.

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“…Consequently, evolutionary algorithms (EAs) [2] have been widely applied to address DMOPs. EAs used to solve DMOPS are called dynamic multi-objective evolutionary algorithms (DMOEAs), and they have been used in problems such as scheduling [3], management [4,5], control [6], distribution feeder reconfiguration [7] and network routing [2].…”

Section: Introductionmentioning

confidence: 99%

Solving dynamic multi-objective problems with an evolutionary multi-directional search approach

Zheng

et al. 2020

Knowledge-Based Systems

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Multi-Objective Mixed-Integer Dynamic Optimization Method Applied to Optimal Allocation of Dynamic Var Sources of Power Systems

Cited by 42 publications

References 28 publications

Strategic network expansion of urban rapid transit systems: A bi‐objective programming model

Strategic network expansion of urban rapid transit systems: A bi‐objective programming model

Comparison of voltage support services by means of STATCOM and smart transformer in medium voltage grid

Solving dynamic multi-objective problems with an evolutionary multi-directional search approach

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