A Probabilistic Multi-Objective Approach for Power Flow Optimization in Hybrid Wind-Based Power Systems Using Grasshopper Optimization Algorithm

2020 ◽  
Vol 11 (4) ◽  
pp. 61-86
Author(s):  
Barun Mandal ◽  
Provas Kumar Roy
Keyword(s):  
Power Systems ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Thermal Power ◽  
Optimal Power ◽  
Valve Point Effect ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Practical Constraints

This article introduces a grasshopper optimization algorithm (GOA) to efficiently prove its superiority for solving different objectives of optimal power flow (OPF) based on a mixture thermal power plant that incorporates uncertain wind energy (WE) sources. Many practical constraints of generators, valve point effect, multiple fuels, and the various scenarios incorporating several configurations of WEs are considered for both singles along with multi-objectives for the OPF issue. Within the article, the considered method is verified on two common bus experiment systems, i.e. IEEE 30-bus as well as the IEEE 57-bus. Here, the fuel amount minimization and emission minimization are studied as the primary purposes of a GOA-based OPF problem. However, the proposed algorithm yields a reasonable conclusion about the better performance of the wind turbine. Computational results express the effectiveness of the proposed GOA approach for the secure and financially viable of the power system under various uncertainties. The comparison is tabulated with the existing algorithms to provide superior results.

scholarly journals Optimal Power Flow Controller for Grid-Connected Microgrids using Grasshopper Optimization Algorithm

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 111 ◽  
Author(s):  
Touqeer Jumani ◽  
Mohd Mustafa ◽  
Madihah Rasid ◽  
Nayyar Mirjat ◽  
Mazhar Baloch ◽  
...  
Keyword(s):  
Dynamic Response ◽  
Power Quality ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Harmonic Distortion ◽  
Optimal Power ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Power Flow Controller

Despite the vast benefits of integrating renewable energy sources (RES) with the utility grid, they pose stability and power quality problems when interconnected with the existing power system. This is due to the production of high voltages and current overshoots/undershoots during their injection or disconnection into/from the power system. In addition, the high harmonic distortion in the output voltage and current waveforms may also be observed due to the excessive inverter switching frequencies used for controlling distributed generator’s (DG) power output. Hence, the development of a robust and intelligent controller for the grid-connected microgrid (MG) is the need of the hour. As such, this paper aims to develop a robust and intelligent optimal power flow controller using a grasshopper optimization algorithm (GOA) to optimize the dynamic response and power quality of the grid-connected MG while sharing the desired amount of power with the grid. To validate the effectiveness of proposed GOA-based controller, its performance in achieving the desired power sharing ratio with optimal dynamic response and power quality is compared with that of its precedent particle swarm optimization (PSO)-based controller under MG injection and abrupt load change conditions. The proposed controller provides tremendous system’s dynamic response with minimum current harmonic distortion even at higher DG penetration levels.

Multi-Objective Optimal Power Flow of Integrated Renewable Systems Using a Novel Evolutionary Algorithm

2020 ◽  
pp. 160-194
Author(s):  
Barun Mandal ◽  
Provas Kumar Roy
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Thermal Power ◽  
Power Generators ◽  
Optimal Dispatch ◽  
Multi Objective ◽  
Optimal Power ◽  
Previous Decade ◽  
Generation Costs ◽  
Grasshopper Optimization

This chapter introduces an approach to explain optimal power flow (OPF) for stochastic wind and conventional thermal power generators-based system. In this chapter, grasshopper optimization algorithm (GOA) is implemented to efficiently prove its superiority for solving wind-based OPF problem. Diminishing carbon emissions is a significant goal for the entire world; a tremendous penetration of unpredictable wind energy can assist in reducing emissions. In the previous decade, the access of renewable energy opening for energy production has improved significantly. WE has become an important source that has begun to be used for energy all over the world in recent years. The optimal dispatch between thermal and wind units to minimize the total generating costs and emission are considered as multi-objective (MO) model. In MO optimization, whole electrical energy generation costs and burning emissions are concurrently minimized. The performance of aforesaid approach is exercised and it proves itself as a superior technique as compared to other algorithms revealed in the literature.

scholarly journals Optimal Power Flow of Power Systems Including Distributed Generation Units Using Sunflower Optimization Algorithm

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 109289-109300 ◽  
Author(s):  
Mohamed A. M. Shaheen ◽  
Hany M. Hasanien ◽  
S. F. Mekhamer ◽  
Hossam E. A. Talaat
Keyword(s):  
Power Systems ◽  
Distributed Generation ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Optimal Power

scholarly journals Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

2021 ◽  
Vol 13 (16) ◽  
pp. 8703
Author(s):  
Andrés Alfonso Rosales-Muñoz ◽  
Luis Fernando Grisales-Noreña ◽  
Jhon Montano ◽  
Oscar Danilo Montoya ◽  
Alberto-Jesus Perea-Moreno
Keyword(s):  
Objective Function ◽  
Successive Approximation ◽  
Direct Current ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Problem ◽  
Distributed Generators ◽  
Optimal Power ◽  
Power Flow Problem

This paper addresses the optimal power flow problem in direct current (DC) networks employing a master–slave solution methodology that combines an optimization algorithm based on the multiverse theory (master stage) and the numerical method of successive approximation (slave stage). The master stage proposes power levels to be injected by each distributed generator in the DC network, and the slave stage evaluates the impact of each power configuration (proposed by the master stage) on the objective function and the set of constraints that compose the problem. In this study, the objective function is the reduction of electrical power losses associated with energy transmission. In addition, the constraints are the global power balance, nodal voltage limits, current limits, and a maximum level of penetration of distributed generators. In order to validate the robustness and repeatability of the solution, this study used four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: ant lion optimization, particle swarm optimization, continuous genetic algorithm, and black hole optimization algorithm. All of them employed the method based on successive approximation to solve the load flow problem (slave stage). The 21- and 69-node test systems were used for this purpose, enabling the distributed generators to inject 20%, 40%, and 60% of the power provided by the slack node in a scenario without distributed generation. The results revealed that the multiverse optimizer offers the best solution quality and repeatability in networks of different sizes with several penetration levels of distributed power generation.

scholarly journals Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods

2020 ◽  
Vol 34 (01) ◽  
pp. 630-637 ◽  
Author(s):  
Ferdinando Fioretto ◽  
Terrence W.K. Mak ◽  
Pascal Van Hentenryck
Keyword(s):  
Deep Learning ◽  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Electrical Power ◽  
Practical Applications ◽  
Fundamental Building Block ◽  
Optimal Power

The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems. It is nonlinear and nonconvex and computes the generator setpoints for power and voltage, given a set of load demands. It is often solved repeatedly under various conditions, either in real-time or in large-scale studies. This need is further exacerbated by the increasing stochasticity of power systems due to renewable energy sources in front and behind the meter. To address these challenges, this paper presents a deep learning approach to the OPF. The learning model exploits the information available in the similar states of the system (which is commonly available in practical applications), as well as a dual Lagrangian method to satisfy the physical and engineering constraints present in the OPF. The proposed model is evaluated on a large collection of realistic medium-sized power systems. The experimental results show that its predictions are highly accurate with average errors as low as 0.2%. Additionally, the proposed approach is shown to improve the accuracy of the widely adopted linear DC approximation by at least two orders of magnitude.

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