Optimal power flow with stochastic solar power using barnacles mating optimizer

Sulaiman, Mohd Herwan; Mustaffa, Zuriani; Mohamad, Ahmad Johari; Saari, Mohd Mawardi; Mohamed, Mohd Rusllim

doi:10.1002/2050-7038.12858

Cited by 29 publications

(15 citation statements)

References 40 publications

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“…This section demonstrates the P‐OPF simulations and validation performed by the proposed methods discussed in the literature [34–69]. The P‐OPF problem in electric power systems considers the integration of RE sources such as WT and PV.…”

Section: Discussionmentioning

confidence: 99%

“…The effectiveness of the DEEPSO method was evaluated by comparing it with MSA, BSA, and DS methods. The authors in the paper [56,57] addressed the emphasis of the OPF problem while integrating solar power [56] and wind-solar-small hydro power [57]. A new method called Barnacles Mating Optimizer (BMO) was proposed, which effectively solves the P-OPF problem in the power system.…”

Section: Systematic Literature On Advanced Studies For P-opfmentioning

confidence: 99%

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Advanced studies for probabilistic optimal power flow in active distribution networks: A scientometric review

Ullah

Wang

et al. 2022

IET Generation Trans & Dist

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In the active distribution networks (ADNs), the emerging trend of high penetration of sustainable energy sources such as wind turbines, photovoltaics, and the forthcoming integration of energy storage devices and additional dynamic loads like plug-in-electric vehicles (PEVs) influences to change the nature of electric distribution networks. Consequently, it deteriorates power quality and reliability in terms of unacceptable voltage rise-drop, harmonic distortion, voltage unbalance, and excessive power loss. In this context of transition into the active distribution network, the probabilistic optimal power flow (P-OPF) in the ADN is considered an important tool that helps the distribution grid operators for defining the optimal settings of the system's control variables. However, the optimal solution of P-OPF becomes highly complex while considering the multiple variables, timevarying characteristics and operational uncertainties associated with sustainable resources, consumer behaviour and electricity market prices. This review aims at promoting the research and discussion of P-OPF studies in the context of ADNs characteristics and challenges. First, we perform a data-driven and visualized scientometric analysis of 1988 quality documents retrieved from the Scopus during the last ten years using VOSviewer software. Second, an in-depth investigation of the recent and emerging techniques for solving the P-OPF problem is presented. Third, a set of recommendations and highlights of future challenges for P-OPF in ADNs are offered. The review findings showed high interest among the scientific community in sustainable energy source integration, given the recent rise in publications and citations. Also, there is still a lack of innovative optimization techniques that can adequately handle the current issues associated with P-OPF and objectives with fair computational efficiency and accuracy. Finally, this paper offers a guide for P-OPF researchers to navigate the recent studies considering the sustainable sources, spot the new research frontiers, and identify the most impactful countries and publishers.

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Section: Discussionmentioning

confidence: 99%

Section: Systematic Literature On Advanced Studies For P-opfmentioning

confidence: 99%

Advanced studies for probabilistic optimal power flow in active distribution networks: A scientometric review

Ullah

Wang

et al. 2022

IET Generation Trans & Dist

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“…Step 7. Update candidates in the balance group using equations ( 32) and (33) Step 8. Update the respective candidate for the best solution X Best � Xeq1.…”

Section: Improved Equilibrium Optimizer Algorithm (Ieo)mentioning

confidence: 99%

“…In [32], authors have been proposed differential evolutionary particle swarm optimization to deal with optimal power flow of power systems with controllable wind-photovoltaic energy systems. A barnacle mating optimizer (BMO) has been proposed to solve the optimal power flow (OPF) problem with and without renewable energy sources [33]. e 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.…”

Section: Introductionmentioning

confidence: 99%

An Improved Equilibrium Optimizer Algorithm for Solving Optimal Power Flow Problem with Penetration of Wind and Solar Energy

Nguyen

et al. 2022

International Transactions on Electrical Energy Systems

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The paper is proposed an improved equilibrium optimizer (IEO) algorithm to solve the optimal power flow (OPF) problem with the participation of a renewable energy source (RES). In the proposed IEO method, the “exponential term” is replaced by a function that does not dependent on the number of iterations. This modification of the IEO algorithm increases exploration ability compared to EO algorithm. In addition, the exploration of the proposed IEO algorithm will not decrease according to the number of iterations which avoids to get stuck at the local optimal solution. The IEO algorithm is tested on two IEEE 30-bus and IEEE 118-bus systems with three different objective functions. The performance of the proposed IEO method is compared with equilibrium optimizer (EO), artificial ecosystem optimization (AEO), cuckoo search algorithm (CSA), teaching-learning-based optimization (TLBO), artificial bee colony (ABC), and many other existing methods. Besides, a simple probabilistic formula for calculating RES output power based on the Monte-Carlo simulation model is proposed in this paper to reduce the computation time for the OPF problem with RES. The simulation results obtained show that the proposed IEO algorithm has better quality of the solution as well as stability level compared to the original EO algorithm and other algorithm. Thus, the proposed IEO algorithm is also one of effective and reliable algorithms for handling OPF problem with RES.

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“…In [3], the authors used the SHADE algorithm with the SF method for arriving at the solution to OPF with RESs. Similarly, in [4][5][6][7][8] the authors proposed several metaheuristic optimization methods for solving OPF with RESs. However, these are formulated as single-objective optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimal Power Flow including Wind and Solar Generation Uncertainty Using New Hybrid Evolutionary Algorithm with Efficient Constraint Handling Method

Avvari

2022

International Transactions on Electrical Energy Systems

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A new hybrid decomposition-based multiobjective evolutionary algorithm is proposed for optimal power flow (OPF) including wind and solar generation uncertainty. This study recommends a novel constraint-handling method, which adaptively adds the penalty function and eliminates the parameter dependency on penalty function evaluation. The summation-based sorting and improved diversified selection methods are utilized to enhance the diversity of multiobjective optimization algorithms. The OPF problem is modeled as a multiobjective optimization problem with four objectives such as minimizing (i) total fuel cost (TC) including the cost of renewable energy source (RES), (ii) total emission (TE), (iii) active power loss (APL), and (iv) voltage magnitude deviation (VMD). The impact of RESs such as wind and solar energy sources on integration is considered in optimal power flow cost analysis. The costs of RESs are considered in the OPF problem to minimize the overall cost so that the impact of intermittence and uncertainty of renewable sources is studied in terms of cost and operation wise. The uncertainty of wind and solar energy sources is described using probability distribution functions (PDFs) such as Weibull and lognormal distributions. The efficiency of the algorithm is tested on IEEE 30-, IEEE 57-, and IEEE 118-bus systems for all possible conditions of renewable sources using Monte Carlo simulations.

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Optimal power flow with stochastic solar power using barnacles mating optimizer

Cited by 29 publications

References 40 publications

Advanced studies for probabilistic optimal power flow in active distribution networks: A scientometric review

Advanced studies for probabilistic optimal power flow in active distribution networks: A scientometric review

An Improved Equilibrium Optimizer Algorithm for Solving Optimal Power Flow Problem with Penetration of Wind and Solar Energy

Multi-Objective Optimal Power Flow including Wind and Solar Generation Uncertainty Using New Hybrid Evolutionary Algorithm with Efficient Constraint Handling Method

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