Optimal Power Flow Incorporating FACTS Devices and Stochastic Wind Power Generation Using Krill Herd Algorithm

Abdollahi, Arsalan; Ghadimi, Ali; Miveh, Mohammad Reza; Mohammadi, Fazel

doi:10.3390/electronics9061043

Cited by 36 publications

(22 citation statements)

References 26 publications

(22 reference statements)

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“…Additionally, the significant growth of metaheuristic algorithms has resulted in a trend of solving OPF problems by using population-based metaheuristic algorithms. In the literature, the OPF was solved by using black hole (BO) [89], teaching-learning based optimization (TLBO) [90] algorithms, the krill herd (KH) algorithm [91], the equilibrium optimizer (EO) algorithm [92], and the slime mould algorithm (SMA) [93]. Additionally, some studies used the modified and enhanced version of the canonical swarm intelligence algorithms for solving OPF with different test systems such as the modified shuffle frog leaping algorithm (MSLFA) for multi-objective optimal power flow [94] that added a mutation strategy to overcome the problem of being trapped in local optima.…”

Section: Related Workmentioning

confidence: 99%

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

et al. 2021

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The optimal power flow (OPF) is a vital tool for optimizing the control parameters of a power system by considering the desired objective functions subject to system constraints. Metaheuristic algorithms have been proven to be well-suited for solving complex optimization problems. The whale optimization algorithm (WOA) is one of the well-regarded metaheuristics that is widely used to solve different optimization problems. Despite the use of WOA in different fields of application as OPF, its effectiveness is decreased as the dimension size of the test system is increased. Therefore, in this paper, an effective whale optimization algorithm for solving optimal power flow problems (EWOA-OPF) is proposed. The main goal of this enhancement is to improve the exploration ability and maintain a proper balance between the exploration and exploitation of the canonical WOA. In the proposed algorithm, the movement strategy of whales is enhanced by introducing two new movement strategies: (1) encircling the prey using Levy motion and (2) searching for prey using Brownian motion that cooperate with canonical bubble-net attacking. To validate the proposed EWOA-OPF algorithm, a comparison among six well-known optimization algorithms is established to solve the OPF problem. All algorithms are used to optimize single- and multi-objective functions of the OPF under the system constraints. Standard IEEE 6-bus, IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus test systems are used to evaluate the proposed EWOA-OPF and comparative algorithms for solving the OPF problem in diverse power system scale sizes. The comparison of results proves that the EWOA-OPF is able to solve single- and multi-objective OPF problems with better solutions than other comparative algorithms.

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Section: Related Workmentioning

confidence: 99%

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

et al. 2021

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“…Inam [21] applied the Gray Wolf Optimizer method to solve the OPF problem which combines thermal power, wind energy, and solar energy in IEEE-30 bus and IEEE-57 bus system. Arsalan [22] applied the Krill Herd algorithm to solve OPF problems considering FACTS devices and wind energy generation under uncertainty using Weibull PDF in IEEE-30 bus and IEEE-57 bus systems. Mohd [23] applied the Barnacles Mating Optimizer method to solve the OPF problem with stochastic wind energy in modified IEEE 30-bus and IEEE 57-bus systems.…”

Section: Bus Systemmentioning

confidence: 99%

“…Optimal power flow incorporating FACTS devices and stochastic wind power generation using Krill Herd Algorithm [22] The optimal power flow problem was solved using the Krill Herd Algorithm with FACTS devices and stochastic wind power generation at one scenario for wind generation overestimation and underestimation costs.…”

Section: Yearmentioning

confidence: 99%

Optimal Power Flow Solution of Wind-Integrated Power System Using Novel Metaheuristic Method

et al. 2021

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Wind energy is particularly significant in the power system today since it is a cheap and clean power source. The unpredictability of wind speed leads to uncertainty in devolved power which increases the difficulty in wind energy system operation. This paper presents a stochastic optimal power flow (SCOPF) for obtaining the best scheduled power from wind farms while lowering total operational costs. A novel metaheuristics method called Aquila Optimizer (AO) is used to address the SCOPF problem due to its highly nonconvex and nonlinear nature. Wind speed is represented by the Weibull probability distribution function (PDF), which is used to anticipate the cost of wind-generated power from a wind farm based on scheduled power. Weibull parameters that provide the best match to wind data are estimated using the AO approach. The suggested wind generation cost model includes the opportunity costs of wind power underestimation and overestimation. Three IEEE systems (30, 57, and 118) are utilized to solve optimal power flow (OPF) using the AO method to prove the accuracy of this method, and results are compared with other metaheuristic methods. With six scenarios for the penalty and reverse cost coefficients, SCOPF is applied to a modified IEEE-30 bus system with two wind farms to obtain the optimal scheduled power from the two wind farms which reduces total operation cost.

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“…However, the MG extensively relies on the support provided from the available dispatchable sources, such as battery energy storage systems (BESS), the grid, or any other source integrated within the MG system. Consequently, there will be more stress on these dispatchable sources, especially the BESS, whenever demand exceeds the renewable outputs [6]. Moreover, BESS depletion will be higher in the summer compared to other seasons due to higher ambient temperature [7].…”

Section: Introductionmentioning

confidence: 99%

Techno-Economic Assessment and Operational Planning of Wind-Battery Distributed Renewable Generation System

et al. 2021

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Electrical energy and power demand will experience exponential increase with the rise of the global population. Power demand is predictable and can be estimated based on population and available historical data. However, renewable energy sources (RES) are intermittent, unpredictable, and environment-dependent. Interestingly, microgrids are becoming smarter but require adequate and an appropriate energy storage system (ESS) to support their smooth and optimal operation. The deep discharge caused by the charging–discharging operation of the ESS affects its state of health, depth of discharge (DOD), and life cycle, and inadvertently reduces its lifetime. Additionally, these parameters of the ESS are directly affected by the varying demand and intermittency of RES. This study presents an assessment of battery energy storage in wind-penetrated microgrids considering the DOD of the ESS. The study investigates two scenarios: a standalone microgrid, and a grid-connected microgrid. The problem is formulated based on the operation cost of the microgrid considering the DOD and the lifetime of the battery. The optimization problem is solved using non-linear programming. The scheduled operation cost of the microgrid, the daily scheduling cost of ESS, the power dispatch by distributed generators, and the DOD of the battery storage at any point in time are reported. Performance analysis showed that a power loss probability of less than 10% is achievable in all scenarios, demonstrating the effectiveness of the study.

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Optimal Power Flow Incorporating FACTS Devices and Stochastic Wind Power Generation Using Krill Herd Algorithm

Cited by 36 publications

References 26 publications

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

Optimal Power Flow Solution of Wind-Integrated Power System Using Novel Metaheuristic Method

Techno-Economic Assessment and Operational Planning of Wind-Battery Distributed Renewable Generation System

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