Imperialist competitive algorithm optimized cascade controller for load frequency control of multi-microgrid system

Nayak, Pratap Chandra; Prusty, Umesh Chandra; Prusty, Ramesh Chandra; Panda, Sidhartha

doi:10.1080/15567036.2021.1897710

Cited by 28 publications

(11 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Diverse test functions have been discussed for the purpose of confirming the CBMO. To demonstrate the algorithm's effectiveness, the obtained results would be compared with some prominent metaheuristic algorithms, such as the crow search algorithm, 37 the teaching–learning‐based optimization (TLBO), 38 the whale optimization algorithm, 39 and the basic BMO 33 . In addition, the population size for each algorithm is assumed to be 100 to provide the comparison.…”

Section: Converged Barnacles Mating Optimizermentioning

confidence: 99%

Joint scheduling optimization of a microgrid with integration of renewable energy sources and electric vehicles considering energy and reserve minimization

Tao

Zhou

Zain

et al. 2023

Energy Science & Engineering

View full text Add to dashboard Cite

To lower operational costs as well as emissions when wind and solar resources are available in a microgrid (MG), this study discusses the scheduling of electric vehicles (EVs) and responsive demands simultaneously. To mitigate the effects associated with undispatchable energy sources such as wind and solar, the proposed system makes use of EVs for peak shaving and load curve changes, while responsive demands provide the reserves required to do so. In addition, a two‐stage model is provided to evaluate MG's planned running costs (energy and reserve). Costs related to generating and reserving electricity are minimized in Stage 1, while costs related to adjusting unit scheduling to account for fluctuations in wind and photovoltaic output are minimized in Stage 2. Converged barnacles mating optimizer (CBMO) is a highly effective and powerful optimization tool that is used to handle the resultant objective optimization issue. An MG consisting of multiple dispersed generations is used to implement the proposed model. It is worth mentioning that three scenarios have been defined to analyze the impact of joint scheduling of EVs and controllable loads on the MG's day‐ahead operation. The three cost terms, that is, the generation cost, the reserve cost, and the startup cost of units in this scenario, are derived as $745.6913, $10.5278, and $6.35, respectively, remarkably less than the values reported in Scenarios 1 and 2. In Scenario 1, the CBMO algorithm yielded a lower MG operational cost than methods by a margin of 843.2 $/day. Costs per day of operation in Scenario 2 are derived to be $819.3 using the CBMO technique, whereas in Scenario 3, they are determined to be $743.1.

show abstract

Section: Converged Barnacles Mating Optimizermentioning

confidence: 99%

Joint scheduling optimization of a microgrid with integration of renewable energy sources and electric vehicles considering energy and reserve minimization

Tao

Zhou

Zain

et al. 2023

Energy Science & Engineering

View full text Add to dashboard Cite

show abstract

“…In [22], the author proposed FTIDF controller tuned by competition over resources algorithm. The author discussed in [23] about cascaded controller optimized by using Imperialist competitive algorithm. The author employed genetic algorithm for load frequency control of a MMG system discussed in [24].…”

Section: Literature Surveymentioning

confidence: 99%

Marine predator algorithm based PD‐(1+PI) controller for frequency regulation in multi‐microgrid system

Padhy

Sahu

Panda

et al. 2022

IET Renewable Power Gen

Self Cite

View full text Add to dashboard Cite

Renewable generation uncertainty, dynamic load change, and system parameter variation play a significant role in the performance degradation of non-linear multi-microgrid (MMG) systems. As a result, intelligent control becomes the need of the hour for assisting superlative attribute-based consistent electric power. The application of marine predator algorithm (MPA)-based cascaded PD-(1+PI) controller for Automatic Generation Control (AGC) of MMG system is a novel work. A maiden attempt of the MPA is proposed to optimize the parameters of the cascaded PD-(1+PI) controller using the integral time absolute error criterion. To demonstrate its superiority, the proposed algorithm is compared to the genetic algorithm, differential evolution, and grey wolf optimization. MPA then applied to conventional controller PID, cascaded PD-PI controller and proposed PD-(1+PI) controller for frequency control in multi-microgrid system. The robustness of the suggested controller is verified over PID and PD-PI controller by taking step and random load perturbation and integrating the renewable sources like solar and wind with their uncertain nature. The simulation of the investigated interconnected microgrid is carried out in MATLAB/SIMULINK environment. Finally, detailed simulation and hardware in the loop experimental results are presented to confirm the practicality of the proposed approach.

show abstract

“…The presence of dead time between fuel input and output torque makes the system nonlinear. After linearizing the system, a simple 1st order transfer function is considered and is expressed as (3) 27 :

G_{italicDEG} = \frac{K_{DEG}}{1 + {italicsT}_{italicDEG}}

Here

K_{italicDEG}

and

T_{italicDEG}

are gain and time constant of wind turbine generator, respectively.…”

Section: Model Descriptionmentioning

confidence: 99%

“…In the meanwhile, the transition from the exploration phase to the exploitation phase is achieved. The position of the dragonflies is updated as (27):…”

Section: Exploitation and Exploration Phase Operatormentioning

confidence: 99%

See 1 more Smart Citation

Hybrid dragonfly algorithm and pattern search optimized adaptive fuzzy PID controller for frequency regulation of multi microgrid system using Henkel matrix based reduced‐order model

Mishra

Nayak

Prusty

et al. 2022

Int J Numerical Modelling

Self Cite

View full text Add to dashboard Cite

This paper proposes frequency regulation of multi microgrid systems. The system under study consists of wind energy, solar energy and energy storage system operating in dissimilar combinations to establish equilibrium among the production and consumer demand. In this proposed work, stability analysis of the concerned model is studied using the Henkel matrix approach of the reduced‐order model. Again, an attempt is made to apply the adaptive fuzzy proportional integral and derivative (AFPID) controller for frequency control of the system. The optimal gains of the controllers are tuned by reducing the integral time absolute error using a hybrid dragonfly algorithm supported by the pattern search (hDAPS) algorithm. The efficacy of the proposed adaptive fuzzy controller is examined with fuzzy PID and PID for different cases of the concern system. The efficacy of the hDAPS algorithm is justified over the genetic algorithm, dragonfly algorithm and particle swarm optimization. The robustness study also reveals that the systems are offering reduced overshoot and undershoot under different scenarios of load and source environment. To demonstrate expediency of the proposed controller sensitivity analysis was carried out under different loading conditions. In addition, to show the efficiency of the hDAPS tuned AFPID controller, the outcomes were associated with other artificial intelligence methods reported in the literature.

show abstract

Imperialist competitive algorithm optimized cascade controller for load frequency control of multi-microgrid system

Cited by 28 publications

References 40 publications

Joint scheduling optimization of a microgrid with integration of renewable energy sources and electric vehicles considering energy and reserve minimization

Joint scheduling optimization of a microgrid with integration of renewable energy sources and electric vehicles considering energy and reserve minimization

Marine predator algorithm based PD‐(1+PI) controller for frequency regulation in multi‐microgrid system

Hybrid dragonfly algorithm and pattern search optimized adaptive fuzzy PID controller for frequency regulation of multi microgrid system using Henkel matrix based reduced‐order model

Contact Info

Product

Resources

About