An improved equilibrium optimizer for optimal placement of photovoltaic systems in radial distribution power networks

Nguyen, Thang Trung; Nguyen, Thuan Thanh; Duong, Minh Quan

doi:10.1007/s00521-021-06779-w

Cited by 28 publications

(10 citation statements)

References 66 publications

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“…For example, equilibrium optimizer (EO) [90] is one of these novel and successful algorithms that gained the attention of researchers, and it was used in many papers such as [116][117][118][119][120].…”

Section: Neural Computing and Applicationsmentioning

confidence: 99%

“…For fractional-order proportional-integral-derivative plus derivative (FOPIDD) controller [118], a self-adaptive hybrid equilibrium optimizer (SAHEO) for distributed jobshop rescheduling problem [119], enhanced equilibrium optimization (E. 2 O) to application engineering [116], stock market prediction [120], a modified EO for optimal allocation of PVs in radial distribution systems [117] According to the no free lunch (NFL) theorem, there is no perfect algorithm for solving all optimization problems. Consequently, novel and more efficient algorithms are needed.…”

Section: Neural Computing and Applicationsmentioning

confidence: 99%

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A novel metaheuristic algorithm inspired by COVID-19 for real-parameter optimization

Mohammadi

Nazarpour

Beiraghi

2023

Neural Comput & Applic

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In this modern world, we are encountered with numerous complex and emerging problems. The metaheuristic optimization science plays a key role in many fields from medicine to engineering, design, etc. Metaheuristic algorithms inspired by nature are among the most effective and fastest optimization methods utilized to optimize different objective functions to minimize or maximize one or more specific objectives. The use of metaheuristic algorithms and their modified versions is expanding every day. However, due to the abundance and complexity of various problems in the real world, it is always necessary to select the most proper metaheuristic method; hence, there is a strong need to create new algorithms to achieve our desired goal. In this paper, a new and powerful metaheuristic algorithm, called the coronavirus metamorphosis optimization algorithm (CMOA), is proposed based on metabolism and transformation under various conditions. The proposed CMOA algorithm has been tested and implemented on the comprehensive and complex CEC2014 benchmark functions, which are functions based on real-world problems. The results of the experiments in a comparative study under the same conditions show that the CMOA is superior to the newly-developed metaheuristic algorithms including AIDO,

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Section: Neural Computing and Applicationsmentioning

confidence: 99%

Section: Neural Computing and Applicationsmentioning

confidence: 99%

A novel metaheuristic algorithm inspired by COVID-19 for real-parameter optimization

Mohammadi

Nazarpour

Beiraghi

2023

Neural Comput & Applic

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“…The current of 𝑖 𝑡ℎ line at any time, 𝐼 𝑖 𝑡 , must be less than the maximum current capacity of this branch, 𝐼 𝑖 𝑚𝑎𝑥 , and it represents as follows [39].…”

Section: Termal Constraintmentioning

confidence: 99%

Optimal Allocation of Renewable Distributed Generations Using Heuristic Methods to Minimize Annual Energy Losses and Voltage Deviation Index

Pürlü

Türkay

2022

IEEE Access

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In this paper, two metaheuristic methods, genetic algorithm and particle swarm optimization, are proposed to determine the optimal locations, sizes and power factors of single and double distributed generation units. In line with the 2050 carbon neutral goal, the aim was to integrate renewable distributed energy sources such as photovoltaic panels and wind turbines into the distribution system with a high penetration level. In contrast to most studies based on constant loads and dispatchable generations, an application considering the seasonal uncertainties of generation and consumption was performed to minimize the annual energy losses and voltage deviations of the distribution network. In addition, dispatchable, controllable and fuel-based conventional resources were allocated to compare the contributions of renewable resources. These seasonal case studies with various constraints were applied to IEEE 33-bus radial distribution network. To verify the feasibility and robustness of the proposed algorithms, case studies for peak loads were created and compared with the literature studies. While all distributed generation sources were operated at both unity and optimum power factor in all case studies, zero power factor and leading power factor scenarios were examined for a peak load only. Photovoltaic applications without energy storage technologies have not been very efficient because of the uneven daily distribution of solar irradiance, especially insufficient irradiation in the evening and excessive irradiation at noon. However, wind energy applications are more reliable and feasible, because the wind speed distribution is relatively more uniform than that of solar irradiation, both seasonally and daily. In all cases, operating distributed generation sources at the optimal power factor provided better results than those operating at unity power factor. As a result, wind turbines operating at optimal power factors have been found to contribute better than photovoltaic systems and are almost as good as conventional sources with controllable power output. While both proposed algorithms yielded better results than those in the literature, particle swarm optimization was better than genetic algorithm in terms of providing the best solution, faster convergence and shorter running time.

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“…As reported, the total power losses in DPNs could equal 13% of the total generated power [ 1 ], and high voltage drops at peak hours with a high-power demand of loads were serious [ 2 ]. To avoid high power losses and voltage drops, the solution is to reduce the line's operating current and resistance [ 3 ] by placing capacitors [ 4 ], distributed generators (DGs) [ 5 ], WDGs [ 6 ], SDGs [ 7 ], and DGs based on biomass energy (BMDGs) [ 8 ], implementing network reconfiguration (NR) [ 9 ], SOPs devices [ 10 ], and so on. In the study, the placement of electric components, such as capacitors, soft open points (SOPs), distributed generators based on wind energy (WDGs) and distributed generators based on solar radiation (SDGs), are considered to reach the economic and technical benefits for DPNs.…”

Section: Introductionmentioning

confidence: 99%

Optimal placement and operation of soft open points, capacitors, and renewable distributed generators in distribution power networks to reduce total one-year energy loss

Van Tran,

Truong,

Phan

et al. 2024

Heliyon

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An improved equilibrium optimizer for optimal placement of photovoltaic systems in radial distribution power networks

Cited by 28 publications

References 66 publications

A novel metaheuristic algorithm inspired by COVID-19 for real-parameter optimization

A novel metaheuristic algorithm inspired by COVID-19 for real-parameter optimization

Optimal Allocation of Renewable Distributed Generations Using Heuristic Methods to Minimize Annual Energy Losses and Voltage Deviation Index

Optimal placement and operation of soft open points, capacitors, and renewable distributed generators in distribution power networks to reduce total one-year energy loss

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