Stabilized frequency response of a microgrid using a two-degree-of-freedom controller with African vultures optimization algorithm

Swain, Nirojakanta; Sinha, Nidul; Behera, Sadasiva

doi:10.1016/j.isatra.2023.05.009

Cited by 10 publications

(3 citation statements)

References 23 publications

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“…In this article, the tuning of fuzzy PID controller is performed to accommodate the uncertainties of the automatic voltage variation and power management. An African vultures optimization algorithm (AVOA) has been developed in article 31 for the optimization of a novel two-degree of freedom PID (2DOFPID) controller to emulate the virtual inertia and damping into the Microgrid. The performance of the proposed controller has been compared with the other conventional controller to show its effectiveness.…”

Section: Introductionmentioning

confidence: 99%

“…The performance of the proposed controller has been compared with the other conventional controller to show its effectiveness. The developed methodlogy 31 has also been validated on OPAL-RT real time environmental simulator. A slime mold meta-heuristic optimization algorithm for the operation management of Microgrids considering Demand Response Program (DRP) is presented in article 32 .…”

Section: Introductionmentioning

confidence: 99%

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Data-driven optimization for microgrid control under distributed energy resource variability

Mathur,

Kumari,

Meena

et al. 2024

Sci Rep

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The integration of renewable energy resources into the smart grids improves the system resilience, provide sustainable demand-generation balance, and produces clean electricity with minimal leakage currents. However, the renewable sources are intermittent in nature. Therefore, it is necessary to develop scheduling strategy to optimise hybrid PV-wind-controllable distributed generator based Microgrids in grid-connected and stand-alone modes of operation. In this manuscript, a priority-based cost optimization function is developed to show the relative significance of one cost component over another for the optimal operation of the Microgrid. The uncertainties associated with various intermittent parameters in Microgrid have also been introduced in the proposed scheduling methodology. The objective function includes the operating cost of CDGs, the emission cost associated with CDGs, the battery cost, the cost of grid energy exchange, and the cost associated with load shedding. A penalty function is also incorporated in the cost function for violations of any constraints. Multiple scenarios are generated using Monte Carlo simulation to model uncertain parameters of Microgrid (MG). These scenarios consist of the worst as well as the best possible cases, reflecting the microgrid’s real-time operation. Furthermore, these scenarios are reduced by using a k-means clustering algorithm. The reduced procedures for uncertain parameters will be used to obtain the minimum cost of MG with the help of an optimisation algorithm. In this work, a meta-heuristic approach, grey wolf optimisation (GWO), is used to minimize the developed cost optimisation function of MG. The standard LV Microgrid CIGRE test network is used to validate the proposed methodology. Results are obtained for different cases by considering different priorities to the sub-objectives using GWO algorithm. The obtained results are compared with the results of Jaya and PSO (particle swarm optimization) algorithms to validate the efficacy of the GWO method for the proposed optimization problem.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-driven optimization for microgrid control under distributed energy resource variability

Mathur,

Kumari,

Meena

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The success of each optimization approach will be thoroughly assessed by examining comparative metrics, such as total system costs, renewable penetration percentages, pollution reductions, and resilience indices, in order to derive relevant conclusions. [16][17][18][19][20] Anticipated Contributions: This study is expected to provide invaluable insights to both the academic and industrial sectors. The study endeavors to enlighten decision-makers, energy planners, and researchers on the most appropriate methodologies for various microgrid configurations and goals by conducting a comprehensive analysis of optimization strategies inside the realm of hybrid energy microgrids.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Energy Microgrids: A Comparative Study of Optimization Techniques

Nemova,

Rao,

Singh

et al. 2024

E3S Web Conf.

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This study examines the relative effectiveness of Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Linear Programming (LP) in optimizing hybrid energy microgrids. Drawing upon empirical data derived from the study, the research explores many facets, including economic efficacy, environmental viability, and microgrid robustness. The use of GA showcases a significant 10% decrease in overall system expenses, highlighting its efficacy in augmenting economic feasibility. PSO diligently tracks, attaining an 8% decrease, while SA and LP make significant contributions but provide somewhat lesser cost reductions at 7% and 6%, correspondingly. Within the domain of renewable energy integration, GA and PSO have emerged as frontrunners, with remarkable advancements of 12% and 10%, respectively. SA and LP provide commendable contributions, demonstrating their effectiveness in optimizing the usage of renewable energy sources inside the microgrid, as seen by their respective increases of 8% and 7%. The environmental factor, as quantified by the decrease of carbon emissions, highlights the commendable efficacy of GA and PSO, resulting in significant reductions of 15% and 12% respectively. SA and LP provide praiseworthy environmental efforts, achieving reductions of 10% and 8% respectively. The resilience index highlights the strength of GA and PSO in assessing the resilience of the microgrid, with GA showing an increase of 0.05 and PSO showing an increase of 0.04. SA and LP make a significant contribution, with increments of 0.03 and 0.02, underscoring the potential of evolutionary and swarm-based methodologies to bolster the microgrid’s resilience against disturbances. Scenario analysis effectively brings unpredictability into the operational environment of the microgrid, continually showcasing the remarkable flexibility of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) over a wide range of situations. SA and LP demonstrate consistent efficacy but with somewhat reduced flexibility. Statistical evaluations provide compelling evidence confirming the exceptional efficacy of GA and PSO in improving microgrid metrics. Ultimately, this research provides valuable perspectives on the intricate trade-offs between various optimization techniques, empowering decision-makers to choose strategies that align with specific microgrid objectives. Moreover, it contributes to the wider discussion on resilient, sustainable, and economically feasible energy infrastructures.

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Enhanced load frequency control using predictive reduced order generalized active disturbance rejection control under communication delay and cyber-attack

Kumari,

Pan

2024

Electr Eng

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Stabilized frequency response of a microgrid using a two-degree-of-freedom controller with African vultures optimization algorithm

Cited by 10 publications

References 23 publications

Data-driven optimization for microgrid control under distributed energy resource variability

Data-driven optimization for microgrid control under distributed energy resource variability

Hybrid Energy Microgrids: A Comparative Study of Optimization Techniques

Enhanced load frequency control using predictive reduced order generalized active disturbance rejection control under communication delay and cyber-attack

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