Bat Search Algorithm for Solving Multi-objective Optimal Power Flow Problem

Gupta, Saket; Kumar, Narendra; Srivastava, Laxmi

doi:10.1007/978-981-13-6772-4_30

Cited by 12 publications

(7 citation statements)

References 28 publications

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“…In addition, Figure 6 shows the comparisons of the outcomes of Scenario 1 with numerous alternative techniques without considering the TCSC devices. e described results are reported to be related to social spider optimization (SSO) [42], real-coded biogeography-based optimization (RCBBO) [25], improved Salp Swarm Algorithm (ISSA) [65], jellyfish search (JFS) [66], quasi-reflection jellyfish search (QRJFS) [66], Salp Swarm Algorithm (SSA) [67], differential search algorithm (DSA) [68], heap-based algorithm (HBA) [69], improved heapbased algorithm (IHBA) [69], bat search algorithm (BSA) [70], genetic algorithm (GA) [45], improved genetic algorithm (IGA) [45], social network search algorithm (SNSA) [71], electromagnetic field optimization (EFO) [72], and modified imperialist competitive algorithm (MICA) [73]. As shown, the proposed GTA provides the superior performance in minimizing the fuel costs with the least value of 41668.02 $/h.…”

Section: Minimization Of the Fuel Costs (Scenario 1)mentioning

confidence: 99%

Optimal Power Flow Incorporating Thyristor-Controlled Series Capacitors Using the Gorilla Troops Algorithm

Ginidi

Elattar

Shaheen

et al. 2022

International Transactions on Electrical Energy Systems

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The optimal power flow issue (OPFI) can be solved in this work using the recently developed algorithm, gorilla troops algorithm (GTA). The goal of OPFI is to reduce numerous functions such as minimizing fuel costs, emissions, and power losses and improving the voltage stability related to electric power networks (EPNs). The GTA is inspired by gorillas’ social habits, which include migration to a strange region, migration toward a specified spot, traveling to other gorillas, competing for adult females, and escorting the silverback. The developed GTA is tested with and without the inclusion of the Thyristor-Controlled Series Capacitor (TCSC) devices in the system. The proposed GTA is applied on a practical Egyptian West Delta-EPN (WD-EPN) and the standard IEEE 57-bus EPN and with and without the inclusion of the TCSC devices to appraise the GTA algorithm’s performance in the OPFI. In addition, the proposed GTA is applied on a large-scale IEEE 118 bus system with higher outperformance compared to particle swarm optimization. The results illustrate that the fuel costs, emissions, voltage stability, and power losses are reduced for the standard IEEE 57-bus EPN with and without TCSC devices by a percentage of (18.847% and 18.818%), (59% and 58.97%), (13.405% and 11.507%), and (64.337% and 65.178%), respectively, while fuel costs, emissions, voltage stability, and power losses are reduced for WD-EPN with and without TCSC devices by a percentage of (8.547%, 8.565%), (13.641%, 13.6%), and (61.949%, 61.954%), respectively. A comparison study is conducted to demonstrate the GTA’s effectiveness when compared with other recently developed algorithms such as improved Salp Swarm Algorithm, quasi-reflection jellyfish search, Salp Swarm Algorithm, improved heap-based algorithm, bat search algorithm, social network search algorithm, electromagnetic field optimization, and other well-known algorithms as well. According to the comparison with these algorithms, the GTA demonstrates the best results among the attained results.

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Section: Minimization Of the Fuel Costs (Scenario 1)mentioning

confidence: 99%

Optimal Power Flow Incorporating Thyristor-Controlled Series Capacitors Using the Gorilla Troops Algorithm

Ginidi

Elattar

Shaheen

et al. 2022

International Transactions on Electrical Energy Systems

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“…Primarily, the total fuel costs (TFCs), in $/h, are the sum of the fuel costs of each generator. Therefore, TFCs minimization objective function is the first one (M 1 ) that can be mathematically modelled in (1) [50,51]:…”

Section: Problem Objectivesmentioning

confidence: 99%

Enhanced Teaching Learning-Based Algorithm for Fuel Costs and Losses Minimization in AC-DC Systems

et al. 2022

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The Teaching Learning-Based Algorithm (TLBA) is a powerful and effective optimization approach. TLBA mimics the teaching-learning process in a classroom, where TLBA’s iterative computing process is separated into two phases, unlike standard evolutionary algorithms and swarm intelligence algorithms, and each phase conducts an iterative learning operation. Advanced technologies of Voltage Source Converters (VSCs) enable greater active and reactive power regulation in these networks. Various objectives are addressed for optimal energy management, with the goal of attaining economic and technical advantages by decreasing overall production fuel costs and transmission power losses in AC-DC transmission networks. In this paper, the TLBA is applied for various sorts of nonlinear and multimodal functioning of hybrid alternating current (AC) and multi-terminal direct current (DC) power grids. The proposed TLBA is evaluated on modified IEEE 30-bus and IEEE 57-bus AC-DC networks and compared to other published methods in the literature. Numerical results demonstrate that the proposed TLBA has great effectiveness and robustness indices over the others. Economically, the reduction percentages of 13.84 and 21.94% are achieved for the IEEE 30-bus and IEEE 57-bus test systems when the fuel costs are minimized. Technically, significant improvement in the transmission power losses with reduction 28.01% and 69.83%, are found for the IEEE 30-bus and IEEE 57-bus test system compared to the initial case. Nevertheless, TLBA has faster convergence, higher quality for the final optimal solution, and more power for escaping from convergence to local optima compared to other published methods in the literature.

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“…For this system, 30 runs were performed using Rao algorithms to solve the different objective functions of the OPF problem and the best results out of 30 independent trials are given in this paper. Here, the first objective of the OPF problem is to reduce the total cost of generation or the fuel cost [8]. This function can be described as Equation (20):…”

Section: Ieee 30-bus Test Systemmentioning

confidence: 99%

“…The system voltage stability can be increased by reducing the L-index [8]. In this paper, fuel cost minimization and voltage stability enhancement were considered using the two-fold objective function as in Equation ( 23):…”

Section: Case 3: Voltage Stability Enhancement (Vse)mentioning

confidence: 99%

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A Robust Optimization Approach for Optimal Power Flow Solutions Using Rao Algorithms

et al. 2021

Self Cite

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This paper offers three easy-to-use metaphor-less optimization algorithms proposed by Rao to solve the optimal power flow (OPF) problem. Rao algorithms are parameter-less optimization algorithms. As a result, algorithm-specific parameter tuning is not required at all. This quality makes these algorithms simple to use and able to solve various kinds of complex constrained optimization and engineering problems. In this paper, the main aim to solve the OPF problem is to find the optimal values of the control variables in a given electrical network for fuel cost minimization, real power losses minimization, emission cost minimization, voltage profile improvement, and voltage stability enhancement, while all the operating constraints are satisfied. To demonstrate the efficacy of Rao algorithms, these algorithms have been employed in three standard IEEE test systems (30-bus, 57-bus, and 118-bus) to solve the OPF problem. The OPF results of Rao algorithms and the results provided by other swarm intelligence (SI)/evolutionary computing (EC)-based algorithms published in recent literature have been compared. Based on the outcomes, Rao algorithms are found to be robust and superior to their competitors.

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Bat Search Algorithm for Solving Multi-objective Optimal Power Flow Problem

Cited by 12 publications

References 28 publications

Optimal Power Flow Incorporating Thyristor-Controlled Series Capacitors Using the Gorilla Troops Algorithm

Optimal Power Flow Incorporating Thyristor-Controlled Series Capacitors Using the Gorilla Troops Algorithm

Enhanced Teaching Learning-Based Algorithm for Fuel Costs and Losses Minimization in AC-DC Systems

A Robust Optimization Approach for Optimal Power Flow Solutions Using Rao Algorithms

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