Optimal power flow incorporating stochastic wind and solar generation by metaheuristic optimizers

Sulaiman, Mohd Herwan; Mustaffa, Zuriani

doi:10.1007/s00542-020-05046-7

Cited by 32 publications

(24 citation statements)

References 58 publications

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“…It is important to indicate that the cost evaluation for wind generator and solar PV unit are depends on the wind Weibull probability-distribution-functions (PDF) and solar radiation by lognormal PDF respectively [23].…”

Section: Objective Functionsmentioning

confidence: 99%

Novel Design of Slim Mould Optimizer for the Solution of Optimal Power Flow Problems Incorporating Intermittent Sources: A Case Study of Algerian Electricity Grid

2022

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Nowadays, electrical power grids are facing increased penetration of renewable energy sources (RES), which result in increasing level of randomness and uncertainties for its operational quality. In addition, emerging need for efficient solutions to stochastic optimal power flow (OPF) problem has attracted considerable attention to ensure optimal and reliable grid operations in the presence of generation uncertainty and increasing demand. Therefore, this paper proposes an efficient slime mould-inspired algorithm that aims to minimize overall operating cost of main grid by managing the power flow among different generating resources. The problem is formulated as large-scale constrained optimization problem with non-linear characteristics. Its degree of complexity increases with incorporation of intermittent energy sources, making it harder to be solved using conventional optimization techniques. However, could be efficiently resolved by nature-inspired optimization techniques without any modification or approximation into the originalformulation. The objective function is the overall cost of system, including reserve cost for over-estimation and penalty cost for under-estimation of both PV-solar and wind energy. The SMA performance is evaluated on the IEEE 30-bus test system and Algerian power system, DZA 114-bus. The SMA is compared with four optimization algorithms: i) The well-studied meta-heuristics, i.e., Gorilla troops optimizer (GTO), and Orca predation algorithm (OPA), ii) Recently developed meta-heuristics, i.e., Artificial ecosystem optimizer (AEO), Hunger games search (HGS), and Jellyfish search (JS) optimizer, iii) ad high-performance metaheuristics, Success-History based parameter adaptation for differential evolution method. The overall simulation results reveal that the SMA ranked first among the compared algorithms, and so, over and so, over different function landscapes.

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Section: Objective Functionsmentioning

confidence: 99%

Novel Design of Slim Mould Optimizer for the Solution of Optimal Power Flow Problems Incorporating Intermittent Sources: A Case Study of Algerian Electricity Grid

2022

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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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“…It is worth to highlight that the initial works related to the application of BMO in solving OPF with the presence of wind‐solar generations as well as wind‐solar‐small hydro generations have been published in References 37 and 38 respectively. The rest of the paper is organized as follows: Section 2 discusses the OPF formulation followed by the brief description of BMO in Section 3.…”

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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Optimal power flow incorporating stochastic wind and solar generation by metaheuristic optimizers

Cited by 32 publications

References 58 publications

Novel Design of Slim Mould Optimizer for the Solution of Optimal Power Flow Problems Incorporating Intermittent Sources: A Case Study of Algerian Electricity Grid

Novel Design of Slim Mould Optimizer for the Solution of Optimal Power Flow Problems Incorporating Intermittent Sources: A Case Study of Algerian Electricity Grid

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

Optimal power flow with stochastic solar power using barnacles mating optimizer

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