Optimal Power Flow of Integrated Renewable Energy System using a Thyristor Controlled SeriesCompensator and a Grey-Wolf Algorithm

Rambabu, M.; Kumar, G. V. Nagesh; Sivanagaraju, S.

doi:10.3390/en12112215

Cited by 14 publications

(6 citation statements)

References 26 publications

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“…A constrained multi-objective population external optimization method in [32] is presented to minimize the fuel cost and emission in the presence of renewable energy sources. A grey wolf optimization algorithm (GWO) in [33] was proposed to tune the parameters of a thyristor controlled series compensator and address OPF, including wind and solar power. A best guided artificial bee colony optimization in [1] was to find the optimal setting of conventional and renewable power generation.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Equilibrium Optimizer Algorithm for Optimal Power Flow with High Penetration of Renewable Energy

Nusair

Alhmoud

2020

Energies

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In recent decades, the energy market around the world has been reshaped to accommodate the high penetration of renewable energy resources. Although renewable energy sources have brought various benefits, including low operation cost of wind and solar PV power plants, and reducing the environmental risks associated with the conventional power resources, they have imposed a wide range of difficulties in power system planning and operation. Naturally, classical optimal power flow (OPF) is a nonlinear problem. Integrating renewable energy resources with conventional thermal power generators escalates the difficulty of the OPF problem due to the uncertain and intermittent nature of these resources. To address the complexity associated with the process of the integration of renewable energy resources into the classical electric power systems, two probability distribution functions (Weibull and lognormal) are used to forecast the voltaic power output of wind and solar photovoltaic, respectively. Optimal power flow, including renewable energy, is formulated as a single-objective and multi-objective problem in which many objective functions are considered, such as minimizing the fuel cost, emission, real power loss, and voltage deviation. Real power generation, bus voltage, load tap changers ratios, and shunt compensators values are optimized under various power systems’ constraints. This paper aims to solve the OPF problem and examines the effect of renewable energy resources on the above-mentioned objective functions. A combined model of wind integrated IEEE 30-bus system, solar PV integrated IEEE 30-bus system, and hybrid wind and solar PV integrated IEEE 30-bus system is performed using the equilibrium optimizer technique (EO) and other five heuristic search methods. A comparison of simulation and statistical results of EO with other optimization techniques showed that EO is more effective and superior and provides the lowest optimization value in term of electric power generation, real power loss, emission index and voltage deviation.

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Section: Literature Reviewmentioning

confidence: 99%

Application of Equilibrium Optimizer Algorithm for Optimal Power Flow with High Penetration of Renewable Energy

Nusair

Alhmoud

2020

Energies

View full text Add to dashboard Cite

show abstract

“…A constrained multi-objective population external optimization method in [33] is presented to minimize the fuel cost and emission in the presence of renewable energy sources. A grey wolf optimization algorithm in [34] was proposed to tune the parameters of a thyristor controlled series compensator and address OPF, including wind and solar power. A gbest guided artificial bee colony optimization in [1] was to find the optimal setting of conventional and renewable power generation.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Operation of Conventional Power Generation with High Penetration of Renewable Energy using Equilibrium Optimizer Technique

Nusair¹,

Alhmoud²

2020

Preprint

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Over the last decades, the energy market around the world has reshaped due to accommodating the high penetration of renewable energy resources. Although renewable energy sources have brought various benefits, including low operation cost of wind and solar PV power plants, and reducing the environmental risks associated with the conventional power resources, they have imposed a wide range of difficulties in power system planning and operation. Naturally, classical optimal power flow (OPF) is a nonlinear problem. Integrating renewable energy resources with conventional thermal power generators escalates the difficulty of the OPF problem due to the uncertain and intermittent nature of these resources. To address the complexity associated with the process of the integration of renewable energy resources into the classical electric power systems, two probability distribution functions (Weibull and lognormal) are used to forecast the voltaic power output of wind and solar photovoltaic, respectively. Optimal power flow, including renewable energy, is formulated as a single-objective and multi-objective problem in which many objective functions are considered, such as minimizing the fuel cost, emission, real power loss, and voltage deviation. Real power generation, bus 13 voltage, load tap changers ratios, and shunt compensators values are optimized under various power systems’ 14 constraints. This paper aims to solve the OPF problem and examines the effect of renewable energy resources 15 on the above-mentioned objective functions. A combined model of wind integrated IEEE 30-bus system, solar 16 PV integrated IEEE 30-bus system, and hybrid wind and solar PV integrated IEEE 30-bus system are performed 17 using the equilibrium optimizer technique (EO) and other five heuristic search methods. A comparison of 18 simulation and statistical results of EO with other optimization techniques showed that EO is more effective 19 and superior.

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“…ey provide straightforward and effective solutions for the OPF problem. e typical algorithms such as particle swarm optimization (PSO) [7], gravitational search algorithm (GSA) [8,9], differential evolutionary (DE) [10], krill herd algorithm (KHA) [11], artificial bee colony (ABC) [12], biogeography-based optimization (BBO) [13], Jaya algorithm [14], harmony search (HS) [15], teaching-learningbased-optimization technique (TLBO) [16], Sine-Cosine (SC) [17], grey wolf optimizer (GWO) [18], and moth swarm approach (MSA) [19]. e objective functions of the OPF problem usually are considered consisting of three different types of fuel functions, namely, quadratic cost, piecewise quadratic cost, quadratic cost curve, and power loss, emission cost and voltage deviation.…”

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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Optimal Power Flow of Integrated Renewable Energy System using a Thyristor Controlled SeriesCompensator and a Grey-Wolf Algorithm

Cited by 14 publications

References 26 publications

Application of Equilibrium Optimizer Algorithm for Optimal Power Flow with High Penetration of Renewable Energy

Application of Equilibrium Optimizer Algorithm for Optimal Power Flow with High Penetration of Renewable Energy

Optimal Operation of Conventional Power Generation with High Penetration of Renewable Energy using Equilibrium Optimizer Technique

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

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