Techno-economic based static and dynamic transmission network expansion planning using improved binary bat algorithm

Mouwafi, Mohamed T.; El‐Ela, Adel A. Abou; El‐Sehiemy, Ragab A.; Al-Zahar, Waleed K.

doi:10.1016/j.aej.2021.06.021

Cited by 23 publications

(10 citation statements)

References 37 publications

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“…From the results in Figure 10b, a large share of the heating demand and heating power consumption of absorption chillers is supplied with the boilers since they have lower investment costs and higher efficiency than the CHPs. However, the share (8)(9), (13)(14)(15)(16)(17)(18)(19)(20) of the CHPs is higher than the heating market in providing the heating demand. The power procurement from the heating market increases in some scenarios, especially in the scenarios with higher heating load demand.…”

Section: Numerical Studymentioning

confidence: 96%

“…It is worth mentioning that the total electrical load of each DSO is the sum of the electrical load at the connection node and the electrical power consumption of the electrical chillers. As observed from Figure 10a, a considerable amount of the electrical load and electrical power consumption of electrical chillers is supplied by FIGURE 8 The wind speed and solar irradiation in different scenarios [69] FIGURE 9 Investment, operation, emission, and total costs of the whole system (TSO+DSOs) in different cases the PV/WT units as well as the CHPs. However, the share of the CHPs is higher compared to other resources.…”

Section: Numerical Studymentioning

confidence: 99%

“…These references have not proposed the coordination between the TS and DSs. (See [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]). Recently, the coordination between the TS and electrical (or heating) DS from the operational point of view has been proposed in some papers.…”

Section: Introductionmentioning

confidence: 99%

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A tri‐level approach for coordinated transmission and distribution system expansion planning considering deployment of energy hubs

Allahvirdizadeh

Shayanfar

Moghaddam

2022

IET Generation Trans & Dist

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This paper presents a tri‐level stochastic coordinated transmission and distribution system expansion (CTDS) planning considering the deployment of energy hubs (EHs) across the distribution systems (DSs). The first level of the proposed optimization approach deals with transmission system expansion planning (TEP), while the second level develops distribution system expansion planning (DEP). Market clearing is done in the third level to update the locational marginal prices (LMPs) for different distribution system operators (DSOs). The EHs include photovoltaic cells (PVs), wind turbines (WTs), combined heating and power units (CHPs), boilers, and electrical and absorption chillers. The proposed tri‐level approach is solved with an iterative algorithm based on the diagonalization approach and tested on the modified IEEE 30‐bus transmission systems (TS) with several DSOs. Each DSO includes 18‐bus electrical, heating, and cooling energy systems. The numerical analysis shows that the total cost of the transmission system operator (TSO) is not changed with implementing the uncoordinated approach. However, the total cost of the whole system is reduced by 6.54% with accommodation of the EHs in the uncoordinated approach. Moreover, the total cost of the TSO as well as the whole system are reduced by 41.11% and 16.96%, respectively, with the proposed (CTDS) planning.

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Section: Numerical Studymentioning

confidence: 96%

Section: Numerical Studymentioning

confidence: 99%

See 1 more Smart Citation

A tri‐level approach for coordinated transmission and distribution system expansion planning considering deployment of energy hubs

Allahvirdizadeh

Shayanfar

Moghaddam

2022

IET Generation Trans & Dist

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“…Mouwafi et al [ 208 ] tackled the problem of static and dynamic transmission network expansion planning (TNEP) using an improved binary bat algorithm (IBBA). The proposed algorithm utilizes a multi-V-shaped transfer function and adaptive search space to improve the quality of the obtained solution.…”

Section: Recent Variants Of Bat-inspired Algorithmmentioning

confidence: 99%

Recent advances of bat-inspired algorithm, its versions and applications

Alyasseri

Alomari

Al-Betar

et al. 2022

Neural Comput & Applic

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Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main idea of BA. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. BA has very impressive characteristics such as its easy-to-use, simple in concepts, flexible and adaptable, consistent, and sound and complete. It has strong operators that incorporate the natural selection principle through survival-of-the-fittest rule within the intensification step attracted by local-best solution. Initially, the growth of the recent solid works published in Scopus indexed articles is summarized in terms of the number of BA-based Journal articles published per year, citations, top authors, work with BA, top institutions, and top countries. After that, the different versions of BA are highlighted to be in line with the complex nature of optimization problems such as binary, modified, hybridized, and multiobjective BA. The successful applications of BA are reviewed and summarized, such as electrical and power system, wireless and network system, environment and materials engineering, classification and clustering, structural and mechanical engineering, feature selection, image and signal processing, robotics, medical and healthcare, scheduling domain, and many others. The critical analysis of the limitations and shortcomings of BA is also mentioned. The open-source codes of BA code are given to build a wealthy BA review. Finally, the BA review is concluded, and the possible future directions for upcoming developments are suggested such as utilizing BA to serve in dynamic, robust, multiobjective, large-scaled optimization as well as improve BA performance by utilizing structure population, tuning parameters, memetic strategy, and selection mechanisms. The reader of this review will determine the best domains and applications used by BA and can justify their BA-related contributions.

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“…Asadamongkol, et al [68] worked in Generalized Benders Decomposition. Mohamed eta l [69] worked in binary bat algorithm. Abou et al [70] worked in System Expansion Planning.…”

Section: Related Studymentioning

confidence: 99%

Novel Extreme Learning Machine and Chaotic in-Built Opposition Based – Quantum Ruddy Turnstone Optimization Algorithms for Real Power Loss Dwindling and Voltage Consistency Enhancement

Lenin

2022

Technol Econ Smart Grids Sustain Energy

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This paper proposes Ruddy turnstone optimization (RTO) algorithm, Extreme Learning Machine based Ruddy turnstone Optimization (ELMRTO) Algorithm, Chaotic based Ruddy turnstone optimization (CRTO) algorithm, Quantum based Ruddy turnstone Optimization (QRTO) Algorithm, Opposition based Ruddy turnstone optimization (ORTO) Algorithm and chaotic in-built Opposition based -Quantum Ruddy turnstone optimization (COQRTO) algorithm for genuine loss lessening. Important goals of the paper are Power fidelity extension, power eccentricity minimization and genuine loss lessening. The leading stimulus is in the sculpting of Repositioning -Peripatetic and argumentative actions of Ruddy turnstone. Ruddy turnstone will guzzle wiretaps, young insect and it subsists in bundling style. The constellation of Ruddy turnstone, which mobile from one place to alternate in the sequence of repositioning and the fresh investigation agent position is to evade the smash amongst their contiguous Ruddy turnstone. In the proposed Extreme Learning Machine based Ruddy turnstone Optimization (ELMRTO) Algorithm, Ruddy turnstone Optimization Algorithm approach enhances Extreme Learning Machine features to determine an optimal skeleton of Extreme Learning Machine for enhanced canons. In Chaotic based Ruddy turnstone optimization (CRTO) algorithm Exploration and Exploitation are augmented. In Quantum based Ruddy turnstone Optimization (QRTO) Algorithm, features emulate the analogous performance with the certain stage as they route in a credible powdered of median. Opposition based Ruddy turnstone optimization (ORTO) Algorithm employs Laplace distribution to enhance the exploration skill. Then examining the prospect to widen the exploration, a new method endorses stimulating capricious statistics used in formation stage regulator factor in Ruddy turnstone Optimization Algorithm. In the projected chaotic in-built Opposition based -Quantum Ruddy turnstone optimization (COQRTO) algorithm, the transaction of erratic figures is completed with the irrational digits enthused by Laplace distribution to amplify the support of the probability of formation level inside the exploration zone. Proposed Ruddy turnstone optimization (RTO) algorithm, Extreme Learning Machine based Ruddy turnstone Optimization (ELMRTO) Algorithm, Chaotic based Ruddy turnstone optimization (CRTO) algorithm, Quantum based Ruddy turnstone Optimization (QRTO) Algorithm, Opposition based Ruddy turnstone optimization (ORTO) Algorithm and chaotic in-built Opposition based -Quantum Ruddy turnstone optimization (COQRTO) algorithm are corroborated in Garver's 6-bus test system, IEEE 30, 57, 118, 300, 354 bus test systems and Practical system -WDN 220 KV (Unified Egyptian Transmission Network (UETN)). Loss lessening, voltage divergence curtailing, and voltage constancy index augmentation has been attained.

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Techno-economic based static and dynamic transmission network expansion planning using improved binary bat algorithm

Cited by 23 publications

References 37 publications

A tri‐level approach for coordinated transmission and distribution system expansion planning considering deployment of energy hubs

A tri‐level approach for coordinated transmission and distribution system expansion planning considering deployment of energy hubs

Recent advances of bat-inspired algorithm, its versions and applications

Novel Extreme Learning Machine and Chaotic in-Built Opposition Based – Quantum Ruddy Turnstone Optimization Algorithms for Real Power Loss Dwindling and Voltage Consistency Enhancement

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