Optimal, Reliable and Cost-Effective Framework of Photovoltaic-Wind-Battery Energy System Design Considering Outage Concept Using Grey Wolf Optimizer Algorithm—Case Study for Iran

Naderipour, Amirreza; Abdul‐Malek, Zulkurnain; Vahid, Masoud Zahedi; Seifabad, Zahra Mirzaei; Hajivand, Mohammad; Nowdeh, Saber Arabi

doi:10.1109/access.2019.2958964

Cited by 44 publications

(16 citation statements)

References 23 publications

(39 reference statements)

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“…This paper proposes a powerful modern algorithm called phasor particle swarm optimization (PPSO) to solve various MAED optimization problems, which can be highlighted as the second big contribution of the current study. In mathematics, this algorithm is inspired by the phasor theory and proposes substituting PSO control parameters with variable functions to make PSO a non-parametric algorithm with simpler calculations [31]. The obtained results of this study reveal the effectiveness of the proposed algorithm.…”

Section: Introductionmentioning

confidence: 73%

An Effective Algorithm for MAED Problems with a New Reliability Model at the Microgrid

et al. 2021

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This paper proposes a new framework for multi-area economic dispatch (MAED) in which the cost associated with the reliability consideration is taken into account together with the common operational and emission costs using expected energy not supplied (EENS) index. To improve the reliability level, the spinning reserve capacity is considered in the model as well. Furthermore, the MAED optimization problem and non-smooth cost functions are taken into account as well as other technical limitations such as tie-line capacity restriction, ramp rate limits, and prohibited operating zones at the microgrid. Considering all the above practical issues increases the complexity in terms of optimization, which, in turn, necessitates the use of a powerful optimization tool. A new successful algorithm inspired by phasor theory in mathematics, called phasor particle swarm optimization (PPSO), is used in this paper to address this problem. In PPSO, the particles’ update rules are driven by phase angles to essentially ensure a spread of variants across the population so that exploitation and exploration can be balanced. The optimal results obtained via simulations confirmed the capability of the proposed PPSO algorithm to find suitable optimal solutions for the proposed model.

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Section: Introductionmentioning

confidence: 73%

An Effective Algorithm for MAED Problems with a New Reliability Model at the Microgrid

et al. 2021

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“…The heuristic methods also include intelligent-based algorithms under computational intelligence (Goswami and Chatterjee, 2016; Ghasemi et al, 2016 , Naderipour et al, 2021 , Naderipour et al, 2019 ). In these methods, using special rules and methods, the configuration with less loss is determined.…”

Section: Literature Reviewmentioning

confidence: 99%

An improved corona-virus herd immunity optimizer algorithm for network reconfiguration based on fuzzy multi-criteria approach

Naderipour

Abdullah

Marzbali

et al. 2022

Expert Systems with Applications

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Reconfiguration of the distribution network to determine its optimal configuration is a technical and low-cost method that can improve different characteristics of the network based on multi-criteria optimization. In this paper reconfiguration of unbalanced distribution networks is presented with the objective of power loss minimization, voltage unbalance minimization, voltage sag improvement, and minimizing energy not supplied by the customers based on fuzzy multi-criteria approach (FMCA) using new improved corona-virus herd immunity optimizer algorithm (ICHIOA). The voltage unbalances and voltage sag is power quality criteria and also the ENS refers to the reliability index. Conventional CHIOA is inspired based on herd immunity against COVID-19 disease via social distancing and is improved using nonlinearly decreasing inertia weight strategy for global and local exploration improvement. The methodology is implemented as single and multi-objective optimization on 33 and 69 bus IEEE standard networks. Moreover, the performance of the ICHIOA in problem-solving is compared with some well-known algorithms such as particle swarm optimization (PSO), grey wolf optimizer (GWO), moth flame optimizer (MFO), ant lion optimizer (ALO), bat algorithm (BA) and also conventional CHIOA. The simulation results based on the FMCA showed that all criteria are improved with reconfiguration due to compromising between them while in single-objective optimization, some criteria may be weakened. Also, the obtained results confirmed the superiority of the ICHIOA in comparison with the other algorithms in achieving better criteria with lower convergence tolerance and more convergence accuracy. Moreover, the results cleared that the ICHIOA based on FMCA is capable to determine the best network configuration optimally to improve the power loss, voltage sag, voltage unbalance, and ENS in different loading conditions.

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“…Jahannoosh et al [24] proposed a new metaheuristic algorithm for reliable and cos-effective designing of energy systems. Naderipour et al [25] used grey wolf optimizer algorithm for optimal energy system design. Firefly and harmony search algorithms are also used for optimal power damping [26].…”

Section: Evolutionary Algorithms Based Neural Architecture Searchmentioning

confidence: 99%

Image Classification Based on Automatic Neural Architecture Search Using Binary Crow Search Algorithm

et al. 2020

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Neural architectures have accelerated the advancement in various domains by enabling automatic pattern detection, image classification, audio recognition, and face recognition etc. However, they are computationally expensive to design and expert knowledge in various domains is required. In this paper, a swarm intelligence algorithm is proposed to search for novel architectures without human intervention that can achieve comparable performance to those of human-designed architectures. This work is inspired by current neural architecture search approaches based on reinforcement learning and genetic algorithm. However, not much attention is paid towards swarm intelligence metaheuristics-based neural architecture search. A framework is proposed for automatically designing neural architectures based on a swarm intelligence metaheuristic: Crow Search Algorithm. First, Crow Search Algorithm is integrated with binary network representation. To make it compatible for Neural Architecture Search, the original distance metric is replaced with hamming distance-based similarity measure. Second, the tuning parameters of Crow Search Algorithm are reduced by replacing the static flight length parameter with our dynamic flight length distribution algorithm. Third, the target selection method (random selection) is replaced by tournament select method. The proposed framework is used to search for architectures on MNIST, CIFAR10, and CIFAR100 datasets and achieved 0.18%, 3.48%, and 15.64% test error, respectively. Furthermore, smallscale transfer experiments are conducted to search architectures for Tiny ImageNet and achieved 34.43% test error. Nonparametric statistical analysis is performed to validate the impact of each modification in improving the quality of search space exploration. The proposed framework has achieved comparable performance with the state-of-the-art approaches, with a comparatively simpler approach and minimum human intervention. The proposed framework can be used to develop completely automated systems for designing architectures for various data-based classification applications.

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Optimal, Reliable and Cost-Effective Framework of Photovoltaic-Wind-Battery Energy System Design Considering Outage Concept Using Grey Wolf Optimizer Algorithm—Case Study for Iran

Cited by 44 publications

References 23 publications

An Effective Algorithm for MAED Problems with a New Reliability Model at the Microgrid

An Effective Algorithm for MAED Problems with a New Reliability Model at the Microgrid

An improved corona-virus herd immunity optimizer algorithm for network reconfiguration based on fuzzy multi-criteria approach

Image Classification Based on Automatic Neural Architecture Search Using Binary Crow Search Algorithm

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