A New Optimal Load Frequency Control  Based  on Hybrid Genetic  Algorithm and Particle Swarm Optimization

Kouba, Nour El Yakine; Menaa, Mohamed; Hasni, Mourad; Boudour, Mohamed

doi:10.15676/ijeei.2017.9.3.1

Cited by 18 publications

(4 citation statements)

References 20 publications

(46 reference statements)

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“…Despite their irregular use, these existing auxiliary generators are not both economically and environmentally sustainable. Therefore, replacing the existing auxiliary generators with renewable energy generators, or ESS, is prevailing [42].…”

Section: ) Voltage Supportmentioning

confidence: 99%

Energy Storage Technologies for Modern Power Systems: A Detailed Analysis of Functionalities, Potentials, and Impacts

Sahoo,

Timmann

2023

IEEE Access

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Power systems are undergoing a significant transformation around the globe. Renewable energy sources (RES) are replacing their conventional counterparts, leading to a variable, unpredictable, and distributed energy supply mix. The predominant forms of RES, wind, and solar photovoltaic (PV) require inverter-based resources (IBRs) that lack inherent synchronous inertia desired for the grid and thereby warrant additional interventions for maintaining grid stability by organizing various contingency planning. Such scenarios become more pertinent in the wake of rapid decarbonization objectives adopted by different countries, stringent grid code compliance, and improved grid resilience milestones. Energy storage technologies can potentially address these concerns viably at different levels. This paper reviews different forms of storage technology available for grid application and classifies them on a series of merits relevant to a particular category. The varied maturity level of these solutions is discussed, depending on their adaptability and their notion towards pragmatic implementations. Some specific technologies that require particular mention are -hydrogen (H 2 ) storage with fuel cells (FC) as the reconversion medium, molten metal, and gravity batteries due to their highly scalable and siteable characteristics participating in load shifting; batteries and H 2 FC due to their high flexibility for peak shaving; and flywheels and supercapacitors for quick response applications, such as frequency regulation and voltage support. Various performance metrics are critically evaluated by comparing them on their usability scale, thus helping readers make a subjective judgment on a particular technology while being aware of the forthcoming limitations. Finally, the paper delves into some emerging trends that decide the selection of a particular technology based on life cycle assessment, economic viability, and commercial and environmental considerations that are presented under the given circumstances. The paper is believed to offer a broad overview of possible directions for the electric grid business, eventually emphasizing the need for more hybrid solutions with opportunities for short and long-term storage options.INDEX TERMS Energy storage, compressed air, hydrogen fuel cells, frequency regulation, gravity battery, molten metal, li-ion, peak shaving, power systems, pumped hydro, renewable energy, sustainability, voltage support. I. INTRODUCTIONElectricity plays a central theme in all walks of life in modern society. Because of its importance, issues related to the availability and cost of energy for electricity and its environmentalThe associate editor coordinating the review of this manuscript and approving it for publication was Vitor Monteiro . impact are never far from the daily news. Currently, most energy worldwide is generated from fossil-based sources. The increasing trend in energy production and consumption, as well as visible negative impacts thereof, in the form of air quality, health, and environment ha...

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Section: ) Voltage Supportmentioning

confidence: 99%

Energy Storage Technologies for Modern Power Systems: A Detailed Analysis of Functionalities, Potentials, and Impacts

Sahoo,

Timmann

2023

IEEE Access

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“…Genetic Algorithm and Particle Swarm Optimization are considered as the most utilized optimization techniques. They have the ability to solve linear and nonlinear optimization problems [21].…”

Section: Optimized Pid Controller Using Genetic Algorithm and Particlmentioning

confidence: 99%

“…PSO algorithm is a new approach based on the movement and intelligence of swarms. This method was developed by James Kennedy and Russell Eberhart as an optimization techniques in 1995 [21]. Particles are utilized to move in the search space searching for the solutions that have best values.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Robust Load Frequency Control Schemes in Power System Using Optimized PID and Model Predictive Controllers

et al. 2018

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Robust control methodology for two-area load frequency control model is proposed in this paper. The paper presents a comparative study between the performance of model predictive controller (MPC) and optimized proportional–integral–derivative (PID) controller on different systems. An objective function derived from settling time, percentage overshoot and percentage undershoot is minimized to obtain the gains of the PID controller. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to tune the parameters of the PID controller through performance optimization of the system. System performance characteristics were compared to another controller designed based on MPC. Detailed comparison was performed between the performances of the MPC and optimized PID. The effectiveness and robustness of the proposed schemes were verified by the numerical simulation in MATLAB environment under different scenarios such as load and parameters variations. Moreover, the pole-zero map of each proposed approach is presented to investigate their stability.

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“…As a result of this challenge, many researchers have come up with different techniques for solving LFC problems by using classical PID controllers [9]- [12]. The control parameters tuning was achieved by using a swarm artificial intelligent technique, such as Genetic Algorithm (GA) [9], [11], particle Swarm optimization technique [10], firefly algorithm (FA) [13], and Bacteria Forging Optimization Algorithm [12] and hybrid approach combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) [14]. Nevertheless, the controllers that have been designed do not yield exceptional values for settling time, peak overshoot, or peak undershoot.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multistage Controller for Load Frequency Control of a Multi-Area Network Integrated with Renewable Energy Sources

Abah,

Shehu,

Abubakar

et al. 2023

Preprint

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Recently, renewable energy sources (RESs) are strongly being integrated into power systems due to environmental, and economic benefits due to the deregulation of the energy market. The stochastic behavior of the load and the intermittent nature of RES creates frequency variation in interconnected hybrid power systems. This paper proposed the use of a novel multistage controller, proportional derivative filter type with a 0.85 proportional integral controller PDF+(0.85 + PI) for Load frequency control (LFC). The controller has two sections, the first section deals with a filter-connected PD controller, while the second stage deals with a 0.85 + PI controller employed to maintain the speed of response by overcoming steady-state error and establishing system stability. The PDF+(0.85 + PI) controller gains are optimized using the Grasshopper optimization algorithm. Three different scenarios were investigated. In the first scenario, all system nonlinearities including the Governor DeadBand (GDB) and Generation rate constraint (GRC) were disabled to compare the performance of the suggested controller to other heuristic-based controllers. In the second scenario, the system nonlinearities are activated and the performance with other technique are compared. Finally, the system RESs were included to test the robustness of the controller. The first and second scenarios are compared with the Salp swarm algorithm-based-PID (SSA-PID) controller and the Genetic algorithm-based-PID (GA-PID) controller whilst in the third scenario the dynamic response fluctuation is within the allowable bounds of 0.2Hz. The proposed controller provides superior dynamic system response with the significant performance of an average value of 72.35%, 36.07%, and 49.68% at scenario 1 and 28.16%, 20.31%, and 38.9% at scenario 2 in terms of settling time in change in frequency deviation at area 1, 2, and power tie-line.

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A New Optimal Load Frequency Control Based on Hybrid Genetic Algorithm and Particle Swarm Optimization

Cited by 18 publications

References 20 publications

Energy Storage Technologies for Modern Power Systems: A Detailed Analysis of Functionalities, Potentials, and Impacts

Energy Storage Technologies for Modern Power Systems: A Detailed Analysis of Functionalities, Potentials, and Impacts

Robust Load Frequency Control Schemes in Power System Using Optimized PID and Model Predictive Controllers

A Novel Multistage Controller for Load Frequency Control of a Multi-Area Network Integrated with Renewable Energy Sources

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