A high-performance crisscross search based grey wolf optimizer for solving optimal power flow problem

Meng, Anbo; Zeng, Cong; Wang, Peng; Chen, De; Zhou, Tianmin; Zheng, Xiaoying; Yin, Hao

doi:10.1016/j.energy.2021.120211

Cited by 85 publications

(34 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The equality constraints are typically described as the power‐flow between the generation and consumer sides in a network. The total active and reactive power,

\sum_{g = 1}^{G} P_{g}

, and,

\sum_{g}^{G} Q_{g}

, are described by Equations () and (), respectively, for all available thermal and renewable energy resources generations units 1,3,13‐20 as

\sum_{g = 1}^{G} P_{g} = P_{L} + P_{los},

\sum_{g = 1}^{G} Q_{g} = Q_{L} + Q_{los},

where

P_{L}

and

Q_{L}

are active and reactive of load demand (consumer sides), G is the total number of generation units,

P_{g}

and

Q_{g}

are the total active and reactive generated from generation unit g,

P_{los}

and

Q_{los}

are the total active and reactive power loss over all buses and lines in the network, respectively. Furthermore, the power‐flow constraints at the level of a single generation unit in the power network can be described as follows 3 :

P_{g} - {P_{L}}_{g} = V_{g} \sum_{n = 1}^{N} V_{n} ((), C_{gn} \cos θ_{gn} + B_{gn} \sin θ_{gn}),

…”

Section: Opf Problem Description: Mathematical Formulation and Modellingmentioning

confidence: 99%

“…Therefore, mathematical approaches showed low performance in solving OPF problems for modern power networks equipped with renewable energy sources 4,15 . An alternative optimization method, heuristic algorithms such as swarm intelligence and support vector machines, for mathematical methods is presented in References 16,17. The heuristic algorithms are easy to develop without requiring a re‐programming for including new OPF constraints.…”

Section: Introductionmentioning

confidence: 99%

“…This optimization approach is an approximation optimization method, which can be used to solve complex OPF problems with and without renewable energy sources 1,6 . For example, MFO, 14 GWO, 16 PSO, 17,18 Moth Swarm Optimization (MSO), 19 Teaching‐Learning‐Based Optimization (TLBO), 3 Golden Ratio Optimization Method (GROM), 3 evolutionary algorithms 20,21 Adaptive Differential Evolution (SHADE) 22 have been developed to solve single and multiple objective function problem for a power network system with or without renewable energy sources. As discussed in Section 1.1, FACTS devices play a significant role in modern power networks such as reducing the power losses and generation costs and improving the voltage stability by controlling transmission lines parameters (series and shunt impedance, and phase angle).…”

Section: Introductionmentioning

confidence: 99%

“…The renewable energy resource profiles (PV and wind) are generated based on the stochastic probability prediction model. To the author's knowledge, the new proposed metaheuristic optimization techniques in this paper (MPA, JS, SMA, and AEO) have not used on solving OPF and energy optimization problems, unlike the previous studies 9,14‐18,23‐28 that only used and applied one optimization method and compared to common methods. In addition, the new proposed techniques in this paper are compared to a common and powerful metaheuristic optimization method, PSO, and other common methods from literature in solving different OPF problems and under different power operation scenarios.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimal placement ofFACTSdevices and power‐flow solutions for a power network system integrated with stochastic renewable energy resources using new metaheuristic optimization techniques

et al. 2021

View full text Add to dashboard Cite

Summary Flexible AC transmission systems (FACTS) and optimal power‐flow (OPF) solutions play an important role in solving power operation problems. The volatile nature of the power generation profiles from renewable energy sources, solar and wind systems, and determining the optimal locations and sizes of FACTS devices increase the complexity of the OPF problems in modern power network models, such as transmission power loss, power generation operation cost and voltage deviation, as a highly nonlinear‐nonconvex optimization problem. Therefore, this article introduces and employs four new independent, reliable and efficient optimization algorithms inspired by nature and biological nature, namely: Slime Mould Algorithm (SMA), Artificial Ecosystem‐based Optimization (AEO), Marine Predators Algorithm (MPA) and Jellyfish Search (JS), for solving both multi‐ and single‐OPF objective problems for a power network incorporating FACTS and stochastic renewable energy sources. The proposed new metaheuristic optimization techniques are compared to the common and available alternatives in the literature, Particle Swarm Optimization (PSO), Moth Flame Optimization (MFO) and Grey Wolf Optimizer (GWO), using IEEE 30‐bus test system. To consider and address the challenges of the OPF in modern power network models, the proposed optimization techniques tested under different operation cases such as an increasing in the load, with and without FCTAS and renewable energy sources, different renewable energy sources locations on the network. The result showed that the MPA, SMA, JS and AEO algorithms are more effective solvers for the OPF problems cases compared to the PSO, GWO and MFO algorithms. For example, the AEO obtained 0.0844 p.u. in case of minimizing the voltage deviation compared to 0.1155 p.u. for PSO, which means that the AEO algorithm improved the voltage deviation term by 27% compared to the PSO algorithm.

show abstract

“…The equality constraints are typically described as the power‐flow between the generation and consumer sides in a network. The total active and reactive power,

\sum_{g = 1}^{G} P_{g}

, and,

\sum_{g}^{G} Q_{g}

, are described by Equations () and (), respectively, for all available thermal and renewable energy resources generations units 1,3,13‐20 as

\sum_{g = 1}^{G} P_{g} = P_{L} + P_{los},

\sum_{g = 1}^{G} Q_{g} = Q_{L} + Q_{los},

where

P_{L}

and

Q_{L}

are active and reactive of load demand (consumer sides), G is the total number of generation units,

P_{g}

and

Q_{g}

are the total active and reactive generated from generation unit g,

P_{los}

and

Q_{los}

P_{g} - {P_{L}}_{g} = V_{g} \sum_{n = 1}^{N} V_{n} ((), C_{gn} \cos θ_{gn} + B_{gn} \sin θ_{gn}),

…”

Section: Opf Problem Description: Mathematical Formulation and Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal placement ofFACTSdevices and power‐flow solutions for a power network system integrated with stochastic renewable energy resources using new metaheuristic optimization techniques

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Alomoush et al [30] proposed Harmony Search with Grey Wolf Optimizer (GWO-HS) Algorithm to solve global optimization problems by using opposing learning strategies. Meng et al [31] proposed a hybrid Crisscross Search-Based Grey Wolf Optimizer (CS-GWO) algorithm, which used two crossover operators to improve the global search ability of α, β and δ wolves while maintaining the population diversity, but the algorithm convergence occurred too soon.…”

Section: Introductionmentioning

confidence: 99%

Ship Track Prediction Based on DLGWO-SVR

Chen

Shuzi

Suo

et al. 2021

Scientific Programming

View full text Add to dashboard Cite

To improve the accuracy of ship track prediction, the improved Grey Wolf Optimizer (GWO) and Support Vector Regression (SVR) models are incorporated for ship track prediction. The hunting strategy of dimensional learning was used to optimize the move search process of GWO and balance exploration and exploitation while maintaining population diversity. Selection and updating procedures keep GWO from being stuck in locally optimal solutions. The optimal parameters obtained by modified GWO were substituted into the SVR model to predict ship trajectory. Dimension Learning Grey Wolf Optimizer and Support Vector Regression (DLGWO-SVR), Grey Wolf Optimized Support Vector Regression (GWO-SVR), and Differential Evolution Grey Wolf Optimized Support Vector Regression (DEGWO-SVR) model trajectory prediction simulations were carried out. A comparison of the results shows that the trajectory prediction model based on DLGWO-SVR has higher prediction accuracy and meets the requirements of ship track prediction. The results of ship track prediction can not only improve the efficiency of marine traffic management but also prevent the occurrence of traffic accidents and maintain marine safety.

show abstract

Study of wind‐solar based combined heat and power economic dispatch problem using quasi‐oppositional‐based whale optimization technique

Paul

Roy

Mukherjee

2021

Optim Control Appl Methods

View full text Add to dashboard Cite

Source of fossil fuel is impoverishing in the upcoming future. Renewable energy sources (RESs) are becoming challenging conventional energy substitutes in the present scenario. In this article, an attempt has been made to utilize RESs such as wind and solar energy with combined heat and power economic dispatch problems. The intention of this presentation is to minimize conflict objectives such as fuel cost accomplished with load demand along with transmission losses while satisfying all the constraints. A new optimization technique, namely a quasi-oppositionalbased whale optimization algorithm (QOWOA) is adopted to cope up with the non-linearities of the chosen systems. The proposed technique is tested on two different nonlinear realistic power systems to achieve the satisfactory performances. The superiority of the proposed QOWOA algorithm is judged by comparing it with some recently developed metaheuristic optimization techniques. K E Y W O R D Scombined heat and power economic dispatch (CHPED), quasi-oppositional based whale optimization algorithm (QOWOA), solar energy, whale optimization algorithm (WOA), wind energy

show abstract

A high-performance crisscross search based grey wolf optimizer for solving optimal power flow problem

Cited by 85 publications

References 48 publications

Optimal placement ofFACTSdevices and power‐flow solutions for a power network system integrated with stochastic renewable energy resources using new metaheuristic optimization techniques

Optimal placement ofFACTSdevices and power‐flow solutions for a power network system integrated with stochastic renewable energy resources using new metaheuristic optimization techniques

Ship Track Prediction Based on DLGWO-SVR

Study of wind‐solar based combined heat and power economic dispatch problem using quasi‐oppositional‐based whale optimization technique

Contact Info

Product

Resources

About