An advanced Grey Wolf Optimization Algorithm and its application to planning problem in smart grids

Ahmadi, Bahman; Younesi, Soheil; Ceylan, Oğuzhan; Özdemir, Aydoğan

doi:10.1007/s00500-022-06767-9

Cited by 21 publications

(39 citation statements)

References 60 publications

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“…In the backward sweep step, the branch currents are calculated using the KCL method, while in the forward sweep step, the busses voltage is calculated according to KVL. Industrial, residential, commercial [32] and a real data of distribution system with mixed load type [33] model curves have been adopted for the study. This load curves show in Figure 1.…”

Section: Domain Name System (Dns) Distributed Generator (Dg) and Stat...mentioning

confidence: 99%

Determining the Pareto front of distributed generator and static VAR compensator units placement in distribution networks

Ahmadi

Çağlar

2022

IJECE

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<span>The integration of distributed generators (DGs), which are based on renewable energy sources, energy storage systems, and static VAR compensators (SVCs), requires considering more challenging operational cases due to the variability of DG production contributed by different characteristics for different time sequences. The size, quantity, technology, and location of DG units have major effects on the system to benefit from the integration. All these aspects create a multi-objective scope; therefore, it is considered a multi-objective mixed-integer optimization problem. This paper presents an improved multi-objective salp swarm optimization algorithm (MOSSA) to obtain multiple Pareto efficient solutions for the optimal number, location, and capacity of DGs and the controlling strategy of SVC a radial distribution system. MOSSA is a bio-inspired optimizer based on swarm intelligence techniques and it is used in finding the optimal solution for a global optimization problem. Two sets of objective functions have been formulated minimizing DGs and SVC cost, voltage violation, energy losses, and system emission cost. The usefulness of the proposed MOSSA has been tested with the 33-bus and 141-bus radial distribution systems and the qualitative comparisons against two well-known algorithms, multiple objective evolutionary algorithms based on decomposition (MOEA/D), and multiple objective particle swarm optimization (MOPSO) algorithm.</span>

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Section: Domain Name System (Dns) Distributed Generator (Dg) and Stat...mentioning

confidence: 99%

Determining the Pareto front of distributed generator and static VAR compensator units placement in distribution networks

Ahmadi

Çağlar

2022

IJECE

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“…Recently, various research has been performed to find optimal voltage control strategies to control devices in DNs. A Local coordination technique for PVs and ESDs [3], various single and multi-objective optimization techniques [4], [5], neural network applications [6], and decentralized and curtailment control strategy techniques [7] are studied by researchers. Hereby, the voltage control methods can be classified into centralized and distributed methods.…”

Section: B Relevant Literaturementioning

confidence: 99%

“…Coordinating DGs, OLTCs, and SCs using a centralized control method for dayahead coordination is investigated in [8]. The same method is used for managing the active and reactive power of DGs [4], and for voltage rise mitigation by controlling the charge and discharge powers of ESDs [9]. Centralized control strategies may be able to find optimal control strategies.…”

Section: B Relevant Literaturementioning

confidence: 99%

A Decentralized Control Strategy for Voltage Regulators and Energy Storage Devices in Active Unbalanced Distribution Systems

Ahmadi

Giraldo

Hoogsteen

et al. 2022

2022 International Conference on Smart Energy Systems and Technologies (SEST)

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This paper studies a multi-objective optimization problem that optimizes unbalanced distribution networks with high penetration of photovoltaic power generation whereby the aim is to find the optimal tap positions of voltage regulators (VRs) and locally optimized charging and discharging power profiles for energy storage devices (ESDs) based on a decentralized coordination process. The corresponding objective function is based on the voltage profile of the network and the lifetime improvement of the VRs and ESDs. The Advanced Arithmetic Optimizer (AAO) algorithm combined with the profile steering approach is used to find a near-optimal solution for the problem. The decentralized process is compared to a centralized approach for the IEEE 13 and 123 bus systems. Results show that the proposed decentralized method is able to resolve all voltage problems and find good quality solutions in lower computational time compared to the centralized approach and other optimization algorithms.

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“…where 𝑥𝑥 𝑑𝑑 𝑡𝑡+1 is a new position in binary form in the d dimension at t iteration. A random number is selected between 0 and 1 taken from a uniform distribution, and sigmoid (a) is explained as below: Applied for diagnosis of cardiovascular disease and obtain another version of binary GWO Al-Tashi et al [37] 2018 UCI datasets Proposed Combination GWO with SVM as a classifier Al-Tashi et al [6] 2020 UCI datasets Proposed binary version of GWO uses sigmoidal functions and ANN as the classifier for application to a multi-objective problem B Ahmadi et al [38] 2022 UCI datasets proposed new transfer functions and a new updating scheme for parameters called advanced GWO…”

Section: Binary Multi-objective Grey Wolf Optimization (Bmogwo-s)mentioning

confidence: 99%

“…Based on the observations, the initial population with several features ranging from 1 to half of the total features in each dataset has created. The feature's position is determined, based on the population factor strategy as introduced by [38].…”

Section: A Initialization Of Grey Wolf Populationmentioning

confidence: 99%

Population Initialization Factor in Binary Multi-Objective Grey Wolf Optimization for Features Selection

Albashah

Rais

2022

IEEE Access

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Features selection methods not only reduce the dimensionality, but also improve significantly the classification results. In this study, the effect of the initialization population using the population factor has been explored. There are twenty wolves obtained by the population initialization method in binary multiobjective grey wolf optimization for features selection. There are two objectives function that will be minimized i.e. number of features and error rate. The proposed method has been compared with the previous study Binary Multi-Objective Grey Wolf Optimization (BMOGWO-S) using UCI datasets, oil and gas datasets. The results reflect that the proposed method outperforms all existence methods in terms of reducing feature numbers and error rates.

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An advanced Grey Wolf Optimization Algorithm and its application to planning problem in smart grids

Cited by 21 publications

References 60 publications

Determining the Pareto front of distributed generator and static VAR compensator units placement in distribution networks

Determining the Pareto front of distributed generator and static VAR compensator units placement in distribution networks

A Decentralized Control Strategy for Voltage Regulators and Energy Storage Devices in Active Unbalanced Distribution Systems

Population Initialization Factor in Binary Multi-Objective Grey Wolf Optimization for Features Selection

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