A new coordinated design of sectionalizing scheme and load restoration process considering reliability of transmission system

Esmaili, Mohammad Reza; Khodabakhshian, Amin; Hooshmand, Rahmat‐Allah; Siano, Pierluigi

doi:10.1016/j.ijepes.2018.04.017

Cited by 9 publications

(19 citation statements)

References 33 publications

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“…The failure of any equipment shown in Figure makes the start‐up process of NBSUs not be successfully done. Therefore, the proper operation of any equipment is essential during the black start phase and the modeling of the network reliability should be established in the worst conditions . In other words, the failure probability of CBs during the closing process has a large value and the failure rate of cranking transmission path ( λ line ), that is related to its length, is determined for the worst weather condition such as lightning .…”

Section: Formulation Of Proposed Mopmentioning

confidence: 99%

“…In other words, the failure probability of CBs during the closing process has a large value and the failure rate of cranking transmission path ( λ line ), that is related to its length, is determined for the worst weather condition such as lightning . The lowest amount of RQI t among all islands is taken to be the worst island quality and the third fitness function is to enhance the worst quality among k islands which is shown in Equation (14) . According to Equations (12) to (14), the third objective function is defined as a nonlinear problem.

({, \begin{array}{l} Max ({}, {Of}_{3} = \underset{K}{Min} ((), {RQI}_{normalt}^{normalK})) \\ S . t : \\ φ ((), I_{normalk}) > 0 \end{array})

In (14), K is the total number of subsystems.…”

Section: Formulation Of Proposed Mopmentioning

confidence: 99%

“…Also, the constraint φ ( I k ) shows the total imbalance amount of generation capacity and demand for all buses. This constraint must be a positive value and is acquired for each subsystem after each load pick‐up stage . Finally, the optimal interconnecting lines between nearby subsystems, that is, optimal subsystems, will be obtained by optimizing Equation (14).…”

Section: Formulation Of Proposed Mopmentioning

confidence: 99%

“…18 The lowest amount of RQI t among all islands is taken to be the worst island quality and the third fitness function is to enhance the worst quality among k islands which is shown in Equation (14). 20 According to Equations (12) to (14), the third objective function is defined as a nonlinear problem.…”

Section: Optimizing the Quality Of Islandsmentioning

confidence: 99%

“…21 Therefore, the best compromise solution may not be obtained easily. 20 In order to solve this drawback, this paper uses the Pareto optimization, which considers all objective functions simultaneously and gives the best compromise solution. In addition, there are many heuristic optimization algorithms to search the best solution during the multi-objective optimization process such as genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), and other algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Power system observability enhancement for parallel restoration of subsystems considering renewable energy resources

Khodabakhshian

Esmaili

Hooshmand

et al. 2019

Int Trans Electr Energ Syst

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Summary The observability of power systems during the parallel restoration of subsystems is one of the most important issues for system operators to accomplish the restoration task as quick as possible. Thus, this article proposes a coordinated optimal plan to solve the observability and sectionalizing problems by determining the locations of phasor measurement units (PMUs) and subsystems. Also, the impact of renewable energy resources on power system sectionalizing and the reliability value of power generation are taken into account in the proposed model. The objective functions that are considered in the optimization problem are the cost of wide‐area measurement system (WAMS), the worst observability index among all subsystems and the lowest value of quality among all subsystems based on the reliability of subsystems. Since there are three contradictory objective functions, a multi‐objective problem (MOP) is proposed as a mixed‐integer nonlinear problem (MINLP). The Pareto curve of the proposed MOP is extracted by using a particle swarm optimization (PSO) algorithm. Two standard power grids are considered to validate the suggested technique. The outcomes of simulations confirm that the observability value of all sections is enhanced during the parallel restoration of the system. Also, the results show that the quality of subsystems in the presence of renewable energy resources is enhanced.

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Section: Formulation Of Proposed Mopmentioning

confidence: 99%