Selfish-herd optimisation based frequency regulation in combined solar-thermal and biogas generator based hybrid microgrid

Sahoo, Subash Chandra; Barik, Amar Kumar; Das, Dulal Chandra

doi:10.1109/ic3a48958.2020.233323

Cited by 8 publications

(6 citation statements)

References 16 publications

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“…For the combined LFRBG unit, the linear transfer function model can be expressed as (5). 13

G_{LFR_BG} ((), S) = G_{LFR} ((), S) + G_{BGT} ((), S) ((), \frac{1}{1 + {italicsT}_{SBT}})

…”

Section: Proposed Hybrid Microgrid System Representation and Configur...mentioning

confidence: 99%

“…SPV is a fast‐growing solar energy technology with significant potential to address the global energy problem. SPV uses solar irradiance 13 for energy generation. The photons available in the solar radiation, when absorbed in the active part of the semiconductor, generate free electrons and holes.…”

Section: Proposed Hybrid Microgrid System Representation and Configur...mentioning

confidence: 99%

“…As such battery energy storage system (BESS), particularly lead-acid battery system has been considered widely. 9,13 Further, a flywheel energy storage system (FESS) is also a suitable candidate for such application. 14 Though there are several benefits of RES, climate and weather conditions influence the power output of these generating units besides the load demand, which leads to an imbalance between generation and demand in hμGs.…”

Section: Introductionmentioning

confidence: 99%

“…In the previous works, different meta-heuristic algorithms were used to obtain the control parameters related to frequency control of microgrid/hybrid microgrid. Genetic Algorithm (GA), 11 Sine Cosine Algorithm (SCA), 29 Particle Swarm Optimization (PSO), 30,31 Grasshopper Optimization Algorithm (GOA), 12 Cultural Algorithm (CA), 32 Yellow Saddle Goatfish Algorithm (YSGA), 33 Salp Swarm Algorithm (SSA), 34 Grey Wolf Optimization (GWO), 35 Selfish Herd Optimization (SHO), 13 Mind Blast Algorithm (MBA), 8 and so forth, were found satisfactory for such applications. However, a novel optimization algorithm called Green Leaf-hopper Flame optimization algorithm (GLFOA), based on green leafhopper behavior could provide better performance and worth exploring for the pertinent frequency control work of hμGs.…”

Section: Introductionmentioning

confidence: 99%

“…In this regard, energy storage systems play a vital role in such isolated hμGs. As such battery energy storage system (BESS), particularly lead‐acid battery system has been considered widely 9,13 . Further, a flywheel energy storage system (FESS) is also a suitable candidate for such application 14 …”

Section: Introductionmentioning

confidence: 99%

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A novel Green Leaf‐hopper Flame optimization algorithm for competent frequency regulation in hybrid microgrids

Sahoo

Barik

Das

2022

Int J Numerical Modelling

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A proficient frequency regulation scheme has been explored in this work for two interconnected hybrid microgrids (hμGs) comprised of multiple renewable energy sources, like solar-thermal units, wind turbine generator units, solar photovoltaic, biogas turbine, biodiesel generator, and storage units. A new optimization algorithm has been formulated to replicate the photo-tactic swarming behavior of green leafhoppers named Green Leaf-hopper Flame optimization algorithm (GLFOA) to tune the system controllers (proportional integral (PI)/proportional derivative (PD)/proportional integral derivative (PID) controllers). The superiority of GLFOA is confirmed by comparing the performance with several popular optimization algorithms. To assess the stability of the hμGs several rigorous tests have been conducted considering availability/nonavailability of renewable resources, gusty wind, real-time variation of solar radiation and wind velocities along with the random load disturbances. GLFOA tuned PID controllers were found to be the best to contain the frequency deviations. The maximum peak overshoot, undershoot, and settling time obtained by GLFOA tuned PID controllers are 0.01051 Hz, À0.00764 Hz, and 14.15 s, respectively, which are within an acceptable limit. Further, there is an improvement of 0.15% in the performance index obtained by GLFOA over its counterpart obtained by grey wolf optimization, confirming the superiority of the GLFOA tuned controller.

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“…For the combined LFRBG unit, the linear transfer function model can be expressed as (5). 13

G_{LFR_BG} ((), S) = G_{LFR} ((), S) + G_{BGT} ((), S) ((), \frac{1}{1 + {italicsT}_{SBT}})

…”

Section: Proposed Hybrid Microgrid System Representation and Configur...mentioning

confidence: 99%

Section: Proposed Hybrid Microgrid System Representation and Configur...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A novel Green Leaf‐hopper Flame optimization algorithm for competent frequency regulation in hybrid microgrids

Sahoo

Barik

Das

2022

Int J Numerical Modelling

Self Cite

View full text Add to dashboard Cite

show abstract

A comprehensive review of modern trends in optimization techniques applied to hybrid microgrid systems

Arfeen

Sheikh

Azam

et al. 2020

Concurrency and Computation

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Microgrids have drawn substantial consideration due to high quality and reliable mix sources of electricity. This paper articulates the implication of innovative algorithms for cognitive microgrid. It perceived the algorithms that are backed by artificial intelligence (AI) are quite efficient due to the precision, convergence speed, and less computation time as compared to the conventional heuristic methods. Solar PV/Battery grid‐connected MG is modeled to achieve optimum size, supreme power quality, reduced fluctuations in voltage and frequency, reduced settling time, eliminate short transient currents, seamless power, least annual cost and high reliability as an objective function under wavering weather condition and dynamic load changes. Four broad categorizations of metaheuristic algorithms, that is, evolutionary, swarm intelligence, physics, and human intelligence‐based algorithms are well elaborated in this study. The optimal solution to the fitness function by using a hybrid optimization method also directed in the study. This paper gives deep insight to readers working in the area.

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Demand Side Management-Based Load Frequency Control of Islanded Microgrid Using Direct Load Control

Sahoo

Latif

Naidu

et al. 2021

Modeling, Simulation and Optimization

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Selfish-herd optimisation based frequency regulation in combined solar-thermal and biogas generator based hybrid microgrid

Cited by 8 publications

References 16 publications

A novel Green Leaf‐hopper Flame optimization algorithm for competent frequency regulation in hybrid microgrids

A novel Green Leaf‐hopper Flame optimization algorithm for competent frequency regulation in hybrid microgrids

A comprehensive review of modern trends in optimization techniques applied to hybrid microgrid systems

Demand Side Management-Based Load Frequency Control of Islanded Microgrid Using Direct Load Control

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