A load frequency control using a PSO-based ANN for micro-grids in the presence of electric vehicles

Safari, Amin; Babaei, Farshad; Farrokhifar, Meisam

doi:10.1080/01430750.2018.1563811

Cited by 74 publications

(43 citation statements)

References 36 publications

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“…To reach this purpose, the authors in [22] proposed a novel multi-objective FO controller. The PID controller adjusted by particle swarm optimisation (PSO) stand on the artificial neural network (ANN) technique is a great choice which is utilised as the LFC controller in an island microgrid [23]. In the LFC system, communication networks send control instructions to EVs.…”

Section: Literature Reviewmentioning

confidence: 99%

Salp swarm algorithm‐based fractional‐order PID controller for LFC systems in the presence of delayed EV aggregators

Babaei

Lashkari

Safari

et al. 2020

IET Electrical Systems in Transportation

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Electric vehicles (EVs) battery has the ability to enhance the balance between the load demand and power generation units. Random behaviour of owners and EVs low capacity are some factors that develop the EV aggregator notion to increase the EV participation in the ancillary services market. In the presence of EVs aggregator, the frequency control service is capable of causing time-varying delay in load frequency control (LFC) systems. Owing to the dependency of controller effectiveness on its parameters, these parameters should be designed optimally in order to have a better result in an LFC system in the presence of time-varying delay. Therefore, a salp swarm algorithm (SSA) is utilised to adjust the fractional-order proportional integral derivative (PID) (FOPID) controller coefficients. Also, some evaluations are performed about the proposed LFC performance by an integral absolute error, integral time absolute error and mean absolute error indicators. In this study, both single and two-area LFC systems containing EVs aggregator with time-varying delay are simulated and analysed. The results indicate that the proposed controller has fewer frequency variations in contrast to other controllers presented in the case studies. Moreover, it is concluded that the FOPID controller could significantly decrease the overshoot and settling time of the frequency variation signal.

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Section: Literature Reviewmentioning

confidence: 99%

Salp swarm algorithm‐based fractional‐order PID controller for LFC systems in the presence of delayed EV aggregators

Babaei

Lashkari

Safari

et al. 2020

IET Electrical Systems in Transportation

Self Cite

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“…The latest research is focused on the utilization of artificial intelligence techniques for PFC. In this context [25], incorporated particle swarm optimization (PSO) through artificial neural networks (ANNs) adjust the proportional integral derivative (PID) controller parameters in microgrid architecture. To this end, the simulation results exhibit an increased stability in the system by PSO-based techniques.…”

Section: Introductionmentioning

confidence: 99%

V2G Strategy for Primary Frequency Control of an Industrial Microgrid Considering the Charging Station Operator

et al. 2020

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Electric vehicles (EVs) have been receiving greater attention as a tool for frequency control due to their fast regulation capability. The proliferation of EVs for primary frequency regulation is hampered by the need to simultaneously maintain industrial microgrids dispatch and EV state of charge levels. The current research aims to examine the operative and dominating role of the charging station operator, along with a vehicle to grid strategy; where, indeterminate tasks are executed in the microgrid without the EVs charging/discharging statistics. The role of the charging station operator in regulation is the assignment of the job inside the primary frequency control capacity of electric vehicles. Real-time rectification of programmed vehicle to grid (V2G) power ensures electric vehicles’ state of charge at the desired levels. The proposed V2G strategy for primary frequency control is validated through the application of a two-area interconnected industrial micro-grid and another microgrids with renewable resources. Regulation specifications are communicated to electric vehicles and charging station operators through an electric vehicle aggregator in the proposed strategy. At the charging station operator, V2G power at the present time is utilized for frequency regulation capacity calculation. Subsequently, the V2G power is dispatched in light of the charging demand and the frequency regulation. Furthermore, V2G control strategies for distribution of regulation requirement to individual EVs are also developed. In summary, the article presents a novel primary frequency control through V2G strategy in an industrial microgrid, involving effective coordination of the charging station operator, EV aggregator, and EV operator.

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“…The flatness-based approach was applied to a three-area power system with a high penetration of wind energy to reduce frequency deviations [64]. With the penetration of electric vehicles, a PSO-based ANN technique [65] and model-free control, based on the sliding mode technique [66], were used for the secondary frequency control. An adaptive-VSG was used in [67] to improve the frequency response of a permanent magnet synchronous generator-based WECS.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Optimization of Virtual Inertia Control in Grid-Connected Wind Energy Conversion Systems for Frequency Support in a Restructured Environment

et al. 2020

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In the work reported in this paper, a novel application of the artificial bee colony algorithm is used to implement a virtual inertia control strategy for grid-connected wind energy conversion systems. The proposed control strategy introduces a new heuristic optimization technique that uses the artificial bee colony (ABC) algorithm to calculate the optimal gain value of an additional derivative control loop added to the control scheme of the machine side converter in a wind energy system to enable wind farms to participate in frequency control as specified by recent grid codes. This helps to minimize the frequency deviations, reduce active power deviation in the system, and increase the penetration level of wind energy in power systems. The study was performed in a restructured power system environment. The proposed control scheme and its robustness were evaluated using load–frequency analysis for three real-life transaction scenarios that can occur in an interconnected open-energy market and the validation was carried out using eigenvalue analysis. The results in this study show that the optimal gain of the proposed controller reduces the frequency deviations and improves stability and overall performance of the system.

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A load frequency control using a PSO-based ANN for micro-grids in the presence of electric vehicles

Cited by 74 publications

References 36 publications

Salp swarm algorithm‐based fractional‐order PID controller for LFC systems in the presence of delayed EV aggregators

Salp swarm algorithm‐based fractional‐order PID controller for LFC systems in the presence of delayed EV aggregators

V2G Strategy for Primary Frequency Control of an Industrial Microgrid Considering the Charging Station Operator

Heuristic Optimization of Virtual Inertia Control in Grid-Connected Wind Energy Conversion Systems for Frequency Support in a Restructured Environment

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