Optimized Self-Healing of Networked Microgrids using Differential Evolution Algorithm

Meenakshi, N.; Kavitha, D.

doi:10.1109/npec.2018.8476779

Cited by 3 publications

(5 citation statements)

References 17 publications

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“…The initial population (Np) is randomly generated from the given range in the first stage between two bounds (𝑋 𝑚𝑖𝑛 , 𝑋 𝑚𝑎𝑥 ). Then, each member of the initial population is evaluated using the fitness function [38,39]. A solution (X) consists of (D) elements, which are the dimensions of the problem (number of parameters to optimize).…”

Section: Differential Evolution Strategymentioning

confidence: 99%

Realization of Robust Frequency Stability in Low-Inertia Islanded Microgrids With Optimized Virtual Inertia Control

Hamanah,

Shafiullah,

Alhems

et al. 2024

IEEE Access

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Section: Differential Evolution Strategymentioning

confidence: 99%

Realization of Robust Frequency Stability in Low-Inertia Islanded Microgrids With Optimized Virtual Inertia Control

Hamanah,

Shafiullah,

Alhems

et al. 2024

IEEE Access

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“…Tere are two positive variables. Tese two variables can be converted from a nonlinear model to a linear model by modeling the value of voltage deviation with the help of equation (3).…”

Section: Formulationmentioning

confidence: 99%

“…Te structure of the smart grid, while implementing the self-healing strategy, is categorized into smart transmission grid, smart distribution grid, and smart MGs [2]. Te method of diferential evolution algorithm is chosen in [3] to optimize the performance of network MGs in self-healing mode. Te evolving search space method for new confgurations is performed by combining graph theory and a heuristic method [4].…”

Section: Introductionmentioning

confidence: 99%

Providing a New Multiobjective Two-Layer Approach for Developing Service Restoration of a Smart Distribution System by Islanding of Faulty Area

Keshavarz Ziarani,

Hosseinian,

Fakharian

2024

International Transactions on Electrical Energy Systems

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One of the essential capabilities of a smart distribution network is to improve network restoration performance using the postfault islanding method. Islanding of the faulty area can be done offline and online. Online islanding will decrease load shedding and operation cost. In this study, a novel two-step mathematical method for system restoration after the fault is presented. A new mathematical model for the optimal arrangement of the system for the faulty area in the first layer is proposed. In this layer, the main objective is to decrease the distribution system’s load shedding and operational costs. In this regard, after the fault event, the boundary of the islanded MGs is determined. Then, in the second layer, the problem of unit commitment in the smart distribution network is addressed. In addition to the load shedding, optimal planning of energy storage systems (ESSs) and nondispatchable distributed generation (DG) resource rescheduling are also determined in this layer. The important advantages of the proposed approach are low execution time and operational costs. A demand response (DR) program has also been used for optimal system restoration. Solving the problem using the multiobjective method with the epsilon-constraint method is another goal of the paper, which simultaneously minimizes the cost and the emissions of the smart distribution network. The proposed model has been tested on an IEEE 33-bus system. Better performance of the proposed model compared to the techniques in the literature has been proven.

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“…Several authors discussed the self-healing property of the smart grids and the techniques used to optimize the operation during interruptions [20], [21]. The techniques that are used to optimize the self-healing system can be divided into three types, namely the heuristic approach [22], soft computing approach (SC) [23]- [25], and mathematical programming approach (MP) [26]- [29]. The heuristic approach, also known as an expert system, relies on the search strategy based on the operator's experience and knowledge to get the best and optimum reconfiguration of the microgrid network.…”

Section: Introductionmentioning

confidence: 99%

“…It is known as a computational intelligence (CI) strategy, as a computer should have the ability to understand a particular function from experimental observation. Thus far, different SC techniques have been applied to find an answer for the restoration problem, such as fuzzy logic [23], particle swarm optimization [24], and clonal selection algorithm [25]. In [26], the authors proposed a differential evolution algorithm technique based on the sharing part of the desired power needed for each microgrid network in a scattered approach.…”

Section: Introductionmentioning

confidence: 99%

A MILP-Based Approach for Virtual Microgrid Restoration

2020

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There has been a sharp increase in the demand for energy in recent years as a result of technological and societal advances, requiring continuous updates to the power system networks. Greater reliance on renewable energy (RE) sources is seen as a viable way to minimize the dependency on conventional sources, which also plays a significant role in increasing system reliability and reducing the environmental pollution. The system resulting from merging RE sources with the distribution system (DS) should have the ability to isolate and restore the power when faults occur. In this paper, we propose a smart restoration technique for the virtual microgrid networks. The motivation of the proposed technique was derived from merging the concept of fault management in the microgrid system and participating in the energy market (EM) in virtual power plant (VPP) networks, and the technique is referred to as the virtual microgrid system. Several factors are considered in the study, including the maximum load demand, the available power supply, and the operation cost. The restoration problem is posed as a Mixed Integer Linear Programming (MILP) problem. The system is implemented as an IEEE 13 bus system and it is assumed to be a smart virtual microgrid system that consists of automated devices, such as automated switches, computer-based remote control, and sensors. INDEX TERMS Distribution system (DS), energy market (EM), mixed-integer linear programming (MILP), renewable energy (RE), virtual power plant (VPP).

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Optimized Self-Healing of Networked Microgrids using Differential Evolution Algorithm

Cited by 3 publications

References 17 publications

Realization of Robust Frequency Stability in Low-Inertia Islanded Microgrids With Optimized Virtual Inertia Control

Realization of Robust Frequency Stability in Low-Inertia Islanded Microgrids With Optimized Virtual Inertia Control

Providing a New Multiobjective Two-Layer Approach for Developing Service Restoration of a Smart Distribution System by Islanding of Faulty Area

A MILP-Based Approach for Virtual Microgrid Restoration

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