Solving constrained optimal power flow with renewables using hybrid modified imperialist competitive algorithm and sequential quadratic programming

Hmida, Jalel Ben; Chambers, Terrence L.; Lee, Jim

doi:10.1016/j.epsr.2019.105989

Cited by 43 publications

(29 citation statements)

References 31 publications

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“…In the last decade, some studies also have been documented [43][44][45][46][47][48][49][50][51][52][53] to find optimal solution to the OPF problems in the thermal, wind, and solar energy sources-based hybrid power systems. In recent studies [43][44][45][46][47][48][49][50][51][52][53], different metaheuristic approaches including grey wolf optimizer (GWO) [43], fuzzy membership function based PSO (FMF-PSO) [44], improved adaptive DE (IADE) [45], modified imperialist competitive algorithm based on sequential quadratic programming (MICA-SQP) [46], modified JAYA [47], hybrid of phasor PSO and GSA [48], barnacles mating optimization (BMO) [49], PSO [50], Hybrid of DE and PSO [51], MBFA [52], and sunflower optimization (SFO) [53] have been proposed for solving the OPF problems in hybrid power systems.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…In studies [43][44][45][46][47][48][49][50][51][52][53], mostly Lognormal PDF and Weibull PDF have been applied for modeling uncertainty of the stochastic solar irradiance and wind speed, respectively.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…Table 2 represents the summary of all of the aforementioned studies [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53] have been conducted on thermal and wind or thermal, wind, and solar energy sources-based hybrid power systems. As specified in Table 2, OPF problem primary objective -quadratic fuel cost ξ q (electricity generation cost) minimization is considered in all studies.…”

Section: B Literature Reviewmentioning

confidence: 99%

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A Bio-Inspired Heuristic Algorithm for Solving Optimal Power Flow Problem in Hybrid Power System

Javaid¹,

Niaz

Almogren

et al. 2021

IEEE Access

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In recent studies, emphasis has been placed on optimal power flow (OPF) problems in traditional thermal, wind, and solar energy sources-based hybrid power systems. Various metaheuristic algorithms have been proposed to find optimal solutions to the OPF problems in the hybrid power system. The OPF, due to the quadratic nature of its primary objective function, is a nonlinear, nonconvex, and quadratic optimization problem. In this study, we have proposed a bio-inspired bird swarm algorithm (BSA) to find an optimal solution to the OPF problem in the hybrid power system because it performs well in the case of optimizing the well-known Rastrigin quadratic benchmark function. In this study, uncertainty of utility load demand and stochastic electricity output from renewable energy resources (RESs) including wind and solar are incorporated into the hybrid power system for achieving accuracy in operations and planning of the system. We have used a modified IEEE-30 bus test system to verify and measure the performance of BSA and a comparison is made with well-known evolutionary metaheuristic algorithms. The proposed BSA consistently achieves more accurate and stable results than other metaheuristic algorithms. Simulation-based optimization results have shown the superiority of BSA approach to solve the OPF problems by satisfying all constraints and minimum power generation cost 863.121 $/h is achieved in case study 1. Simulation-based experiment results have indicated that by imposing the carbon tax (ton/h) the power generation from RESs was increased. In case study 2, the proposed BSA approach has also outperformed and minimum electricity cost 890.728 $/h is achieved as compared to other algorithms.INDEX TERMS Deterministic optimal power flow, Uncertainty of utility load demand, Bio-inspired bird swarm algorithm, Stochastic solar and wind power.

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Section: B Literature Reviewmentioning

confidence: 99%

Section: B Literature Reviewmentioning

confidence: 99%

Section: B Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A Bio-Inspired Heuristic Algorithm for Solving Optimal Power Flow Problem in Hybrid Power System

Javaid¹,

Niaz

Almogren

et al. 2021

IEEE Access

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“…In 2020, Hossein Saberi et al [2], worked on the SCOPF issue with DC load flow equations was comprehensive to regard as the transient stability margin of the generating units as a heuristic decomposition method. In 2019, Jalel Ben Hmida et al [3], proposed an ICA to resolve the OPF predicaments. In 2019, Thang Trung Nguyen [4], developed an ISSO to solve the OPF issues by optimizing power loss, fuel price, voltage deviation and contaminated emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Salp Swarm and Differential Evolution for Optimal Power Flow

Kumar¹

2020

JCMPS

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Optimal Power Flow (OPF) issue is a non-linear optimization problem that was extensively exploited in power system operations. As a consequence of these characteristics, resolving the OPF issue is a well-liked and demanding task in optimizing power systems. Recently, a lot of developed optimization algorithms are used to pact with the OPF crisis. Nevertheless, the majority of the techniques are unimpeded. In this work, Hybrid Salp Swarm and differential evolution with the self-adaptive penalty (HSSDE-SP) is introduced to attain the optimum solution for the power flow issue. To confirm the efficiency of the developed technique, simulations were carried out on the IEEE 30-bus test system that integrates solar energy and wind energy with thermal generators. The experimentation outcomes show the superiority of the developed technique.

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“…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

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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.

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Solving constrained optimal power flow with renewables using hybrid modified imperialist competitive algorithm and sequential quadratic programming

Cited by 43 publications

References 31 publications

A Bio-Inspired Heuristic Algorithm for Solving Optimal Power Flow Problem in Hybrid Power System

A Bio-Inspired Heuristic Algorithm for Solving Optimal Power Flow Problem in Hybrid Power System

Hybrid Salp Swarm and Differential Evolution for Optimal Power Flow

Optimal power flow with stochastic solar power using barnacles mating optimizer

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