On the efficiency of metaheuristics for solving the optimal power flow

Buch, Hitarth; Trivedi, Indrajit N.

doi:10.1007/s00521-018-3382-8

Cited by 11 publications

(4 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Metaheuristics has been recognized as one of the most successful approaches to solving large-scale optimization problems in power systems [57], including OPF problems [51,58,59]. In recent years, to overcome specific limitations of the underlying metaheuristics, a new field in metaheuristics has appeared combining the ideas of two or more metaheuristic method, called a hybrid metaheuristic [60].…”

Section: Short Comments On Metaheuristic Methods For Opfmentioning

confidence: 99%

Optimal Allocation of Static Var Compensators in Electric Power Systems

et al. 2020

View full text Add to dashboard Cite

In the current age, power systems contain many modern elements, one example being Flexible AC Transmission System (FACTS) devices, which play an important role in enhancing the static and dynamic performance of the systems. However, due to the high costs of FACTS devices, the location, type, and value of the reactive power of these devices must be optimized to maximize their resulting benefits. In this paper, the problem of optimal power flow for the minimization of power losses is considered for a power system with or without a FACTS controller, such as a Static Var Compensator (SVC) device The impact of location and SVC reactive power values on power system losses are considered in power systems with and without the presence of wind power. Furthermore, constant and variable load are considered. The mentioned investigation is realized on both IEEE 9 and IEEE 30 test bus systems. Optimal SVC allocation are performed in program GAMS using CONOPT solver. For constant load data, the obtained results of an optimal SVC allocation and the minimal value of power losses are compared with known solutions from the literature. It is shown that the CONOPT solver is useful for finding the optimal location of SVC devices in a power system with or without the presence of wind energy. The comparison of results obtained using CONOPT solver and four metaheuristic method for minimization of power system losses are also investigated and presented.

show abstract

Section: Short Comments On Metaheuristic Methods For Opfmentioning

confidence: 99%

Optimal Allocation of Static Var Compensators in Electric Power Systems

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Ermis et al [18] proposed wind driven optimization (WDO) algorithm to solve the voltage deviation problem in the IEEE 9-bus power system, showing its supremacy in voltage regulation. Many algorithms are applied to improve the power dispatch on IEEE 57,118 bus systems [19]. The moth-flame optimization (MFO) and grey wolf optimizer (GWO) outperformed the other tested algorithms in power loss reduction, voltage deviation, and stability.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for prediction models to mitigate the voltage deviation in photovoltaic-rich distributed network

Baniyounis¹,

Salah²,

Ghaeb³

2023

IJECE

View full text Add to dashboard Cite

<span lang="EN-US">The voltage deviation is one of the most crucial power quality issues that occur in electrical power systems. Renewable energy plays a vital role in electrical distribution networks due to the high economic returns. However, the presence of photovoltaic systems changes the nature of the energy flow in the grid and causes many problems such as voltage deviation. In this work, several predictive models are examined for voltage regulation in the Jordanian Sabha distribution network equipped with photovoltaic farms. The augmented grey wolf optimizer is used to train the different predictive models. To evaluate the performance of models, a value of one for regression factor and a low value for root mean square error, mean square error, and mean absolute error are used as standards. In addition, a comparison between nineteen predictive models has been made. The results have proved the capability of linear regression and the gaussian process to restore the bus voltages in the distribution network accurately and quickly and to solve the shortening in the voltage dynamic response caused by the iterative nature of the heuristic algorithm. </span>

show abstract

“…Instead of single algorithm, there were various hybrid algorithms that have been proposed to solve either single or multi‐objectives OPF such as Moth Swarm Algorithm with GSA (MSA‐GSA) as recommended in Reference 26, Fuzzy Based Hybrid Particle Swarm Optimization as proposed in Reference 27 and 28, Hybrid Particle Swarm and SalpSwarm Optimization as proposed in Reference 29, Hybrid of (PSOGSA) as proposed in Reference 30, Hybrid Modified Imperialist Competitive Algorithm and Sequential Quadratic Programming, (HMICA‐SQP), 31 Hybrid Fuzzy Particle Optimisation and Nelder‐Mead (NM) algorithm (HFPSO‐NM), 28 Hybrid of Adaptive Neuro Fuzzy Interference System (ANFIS) with Advanced SalpSwarm Optimization Algorithm called (ANFASO) 32 and Hybrid SalpSwarm Optimization Algorithm with Particle Swarm Optimization (PSO‐SSO) 29 . The analysis of the metaheuristic approaches into OPF problem has been discussed in Reference 33 where eight different optimization algorithms, that is, MFO, Grey Wolf Optimizer (GWO), Dragonfly Algorithm (DA), Sine‐Cosine Algorithm (SCA), Antlion Optimizer (ALO), Multi‐Verse Optimizer (MVO), Grasshopper Algorithm (GOA) and Ion Motion Algorithm (IMO) have been studied and analysed.…”

Section: Introductionmentioning

confidence: 99%

Optimal power flow with stochastic solar power using barnacles mating optimizer

Sulaiman

Mustaffa

Mohamad

et al. 2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary This work proposes the implementation of recent evolutionary metaheuristic algorithm namely, barnacles mating optimizer (BMO) to solve the Optimal Power Flow (OPF) issue. BMO is inspired by the mating behaviour of barnacles which happened in two ways: by normal copulation and sperm‐cast. The effectiveness of the proposed BMO in solving the OPF is tested on a modified IEEE‐30 bus system that is integrated with solar PV farms for five cases viz (1) cost minimization of the power generation that consists of thermal and stochastic solar power generations, (2) power loss minimization, (3) voltage deviation minimization, (4) emission minimization and (5) combined cost and emission minimization of power generations. To demonstrate the effectiveness and the veracity of the solution obtained by BMO, several recent algorithms that have been reported in the literature will be utilized and compared intensively. In the end, the simulation results demonstrate that the BMO can be effectively becoming an alternative solution for the OPF issue in general.

show abstract

On the efficiency of metaheuristics for solving the optimal power flow

Cited by 11 publications

References 41 publications

Optimal Allocation of Static Var Compensators in Electric Power Systems

Optimal Allocation of Static Var Compensators in Electric Power Systems

Machine learning for prediction models to mitigate the voltage deviation in photovoltaic-rich distributed network

Optimal power flow with stochastic solar power using barnacles mating optimizer

Contact Info

Product

Resources

About