Adaptive and Fuzzy PI Controllers Design for Frequency Regulation of Isolated Microgrid Integrated With Electric Vehicles

Abstract

The number of electric vehicles and renewable energy resources integrated into the power system is increasing day by day. The objective behind the development of electric vehicles and renewable energy sources is to build a sustainable and green power system. The renewables either don't possess system inertia or have less system inertia, therefore, they don't effectively respond to the load variations. The battery storage system of electric vehicles is used as the first line of defense to counter the effect of load/frequency variations and make the system stable. As active power is inversely proportional to the system frequency, for this purpose electric vehicles are included in the microgrid environment. In this paper, an isolated microgrid having a reheat turbine system, wind turbine system, photovoltaic system, and electric vehicles is studied. The output of the renewables is not controlled to utilize its maximum output power. Therefore, adaptive droop control and fuzzy PI control mechanisms are implemented to cater to the frequency variations of the isolated microgrid; the former regulates the power of electric vehicles while maintaining the energy needs of each EV and the later controls the output power of reheat turbine system according to the frequency variation. Furthermore, the genetic algorithm optimization toolbox is utilized to optimize the parameters of the adaptive and fuzzy PI controllers. The proposed model is developed in MATLAB/Simulink which shows that these control techniques effectively sustained the system frequency of isolated microgrid in the desired limits. INDEX TERMS Adaptive droop control, electric vehicles, frequency regulation, fuzzy PI control, GA optimization technique, renewable energy sources, reheat turbine system.

Optimum Modified Fractional Order Controller for Future Electric Vehicles and Renewable Energy-Based Interconnected Power Systems

“…Optimized design for FLC systems for EVs in multi-area power systems has been presented in [43] using the ICA optimizer. Another adaptive droop controller with the fuzzy-PI controller has been optimized using the genetic algorithm optimizer (GA) [44]. A modified fuzzy-PID controller optimized with the tribe-DE optimizer (TDE) has been introduced in [45].…”

Frequency Regulation of an Isolated Microgrid With Electric Vehicles and Energy Storage System Integration Using Adaptive and Model Predictive Controllers

“…More specifically, the three folded benefits of EVBs are: (a) they effectively manage the MG frequency by regulating their storage according to system load variations, (b) they provide additional storage to the MG resulting in the decrease in the expanse/cost of consumption by an extra energy storage system, and (c) they also provide economic benefits to the owner by selling power during peak hours to MG. The detailed model is explained in [28], [36], is shown in Figure(4). The K , C EV and T EV is the droop, rated capacity and time constant of EV battery, respectively.…”

“…The authors proposed droop and modified droop control techniques to regulate the EVs battery according to system frequency deviations [26], [27]. Moreover, in [28] fuzzy PI and adaptive droop control are used to cater to the frequency in isolated MG.…”

“…The above authors either use ESS or EVs to address the frequency issues in MG, they didn't consider the combined effect of both ESS or EVs fleets in isolated MG. Moreover, in [28], [29] the control effort to regulate the controller output is too large to be applied in a specific control application.…”

Frequency coordinated control strategy based on sliding mode method for a microgrid with hybrid energy storage system

“…In ref. [11], an adaptive and fuzzy proportional‐integral (PI) controller is designed for frequency regulation of isolated microgrid. The parameters of PI controller are optimized by taking the frequency change and the frequency change rate as the input of FLC.…”