Kriging Based Surrogate Modeling for Fractional Order Control of Microgrids

ABSTRACTAn autonomous power generation system (APGS) contains units such as diesel energy generator, solar photovoltaic units, wind turbine generator and fuel cells along with energy-storing units such as the flywheel energy storage system and battery energy storage system. The components either run at lower/higher power output or may turn on/off at different instants of their operation. Due to this, the conventional controllers will not provide desired performance under varied load conditions. This paper proposes an adaptive fuzzy logic PID (AFPID) controller for load frequency control. In order to achieve an improved performance, a modified whale optimization algorithm (mWOA) was also proposed in this paper for tuning of the AFPID parameters. The proposed algorithm was first evaluated using standard test functions and compared with other recent algorithms to authenticate the competence of algorithm. The proposed mWOA algorithm outperforms PSO, GSA, DE and FEP algorithms in five out of seven unimodal test functions and four out of six multimodal test functions. The effectiveness of the AFPID compared with the conventional PID and the proposed AFPID provides better performance. Reduction of 39.13% in error criteria (objective function) compared with WOA-PID controller. The proposed approach was also compared with some recently proposed frequency control approaches in a widely used two-area test system.

This paper addresses a control frequency scheme of the microgrid system using a fractional order PID controller. The proposed Microgrid system is consisted of a Photovoltaic System, Wind Turbine Generator, Diesel Engine Generator, Fuel Cell, and different storage systems like Battery Energy Storage Systems, and Flywheel Energy Storage Systems. The principal objective of the present paper is to limit the frequency and power deviations by the application of the proposed controller which has five parameters to be determined through optimization techniques. Krill Herd algorithm is used for determining the optimum fractional order PID controller parameters using the Integral of Squared Error. A comparison between the Genetic Algorithm and Krill Herd is done, and the obtained simulation results presents that the investigated controller-based Krill Herd outperforms the Genetic Algorithm in terms of fewer fluctuations in power and frequency deviation.

Performance comparison of wind turbine based doubly fed induction generator system using fault tolerant fractional and integer order controllers

In this paper single area fuel cell based load frequency control (LFC) system is examined. This system has a battery energy storage system (BESS) and flywheel energy storage system (FESS). Due to communication components, communication time delay is often occurred in the LFC system in practice. For this reason, this delay is added to system model. In order to improve the system performances, fractional order proportional-integral-derivative PI <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">λ</sup> D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">µ</sup> ) controller is used and it is tuned via recently developed Equilibrium Optimizer (EO). The present study, three different analyzes are performed. First, effectiveness of proposed controller and method are compared with the literature. Second, impact of variations of communication time delays are analyzed. Finally, the impact of changing of system time constants are examined. Obtained results are shown that system performances have been improved. In addition, the effects of variations in time delay and time constants on the system are shown comparatively.

This paper presents a novel technique for tuning PID controllers for Load Frequency Control (LFC) in an autonomous hybrid microgrid using Biogeography Based Optimization (BBO). The hybrid system consists of Wind Turbine Generators (WTG), Solar Photo Voltaic generators (SPV), Diesel Engine Generators (DEG), Fuel Cells (FC), Battery Energy Storage System (BESS), Flywheel Energy Storage System (FESS) and Aqua Electrolyser (AE). In this work, BBO algorithm is employed to search for the optimal controller parameters and to achieve the best coordinated control of the hybrid system. Further, the dynamic performance analysis of the hybrid system is done by subjecting the system to various disturbance conditions such as sudden variation of wind power, solar irradiation and load. The simulation results obtained are compared with conventional method. The result shows that the PID controller tuned using BBO is superior in both the transient as well as steady state performances in comparison with conventional method.

This paper presents the frequency control of the microgrid system using Fractional Order PID controller (FOPID). This microgrid is composed of wind and SPG generators, Diesel Engine Generator (DEG), Aqua Electrolyse (AE), Fuel Cell (FC) and Energy Storage Systems (Battery Energy Storage Systems (BESS) and Flywheel Energy Storage Systems (FESS)). Genetic Algorithm is used to optimize the parameters of the Fractional Order PID controller. The main of this study is to reduce the frequency and power deviation. The results give that the Fractional Order PID controller based on GA gives better performance in comparing with PID controller using GA.

This paper presents a comparison between fractional order controllers and integer order controllers. The well-known PID is the integer order controller chosen whereas the generalized PID and the CRONE (“Commande Robuste d'Ordre Non Entier” which stands for fractional order robust controller) are the fractional order controllers studied. Another big difference between these controllers is that the structure of the PID and the generalized PIλDμ is a priori fixed whereas the structure of the CRONE is a posteriori deduced from the loop shaping. Results show that the fractional order controllers are more robust when small variations appear in the plant and their behavior is more suitable regarding the user specifications requirements.

Discussion on: “Simple Fractional Order Model Structures and their Applications in Control System Design”

The flywheel energy storage (FES) system presented in this paper is composed of four parts: the flywheel, the bearing, the motor/generator and the power converter. The control methods and strategy of the FES system in power network are introduced in detail. During the storage period and generating times, the waveform of the power converter output currents is controlled to be sinusoidal by using sinusoidal pulse width modulation (SPWM) control method, so the efficiency of the energy conversion is high and the torque ripple of the motor/generator is minimized. The control methods of starting and operating large moment of inertia of the flywheel are simulated and studied for the power converter. The latest experimental results of the FES system are introduced, and the control validity is verified through these results.

Compared with the battery energy storage system, the flywheel energy storage system (FESS) applied in the power grid has many advantages, such as faster dynamic response, longer service life, unlimited charge/discharge times, and high power density, etc. However, the control strategy for grid integration of the FESS is critical in practical grid application. Aimed to participate in high frequently power regulation of the grid power using the FESS, this paper studies the charge/discharge process of the FESS and proposes a two-stage control strategy for grid integration of the FESS through the back-to-back converters. The grid side converter controls the DC voltage and the reactive power output to the power grid. The machine side converter controls the rotor speed of the flywheel in the start-up state and then switches to control the active power of the flywheel machine after the rotor speed up to the set value. The grid integrated simulation results of the FESS based on the PSCAD software verify the correctness and effectiveness of the proposed control strategy.

This paper investigates the contribution of energy storage systems to the system frequency response in a large meshed transmission system. The case studies compare the contribution of two types of energy storage technologies, i.e., battery energy storage system (BESS) and flywheel energy storage system (FES), to improve frequency response in the test network. Case studies examine the effect of different type of disturbance on frequency response in the presence of each type of storage technology. The effect of one bulk energy storage and distributed energy storage systems of the same capacity on frequency response of the power system is also investigated.

Output power smoothing control approaches for wind and photovoltaic generation systems: A review

Transient performance improvement of grid connected hydro system using distributed generation and capacitive energy storage unit

This paper analyses the influence of Battery Energy Storage System (BESS) on the electrical system in a Hybrid Micro Grid consisting of two 200 kW diesel generators (DGs), one 200 kW wind turbine generator (WTG) and 200 kW photovoltaic (PV) Plant. The BESS in such a system can have a significant role in covering the power fluctuations in the network. The BESS can cover the power unbalance between the supplied power and the load of the grid by storing the excess energy or supplying the residual demand. An advantage of implementing the BESS is faster provision of power compared to the DGs which can also help improve a system stability. In this paper, the influence of BESS is analyzed by conducting Generation Adequacy analysis and Transient Stability studies using DIgSILENT Power Factory software. Generation Adequacy analysis is carried out using Monte Carlo simulation technique. Typical factors such as Loss of Load Probability (LOLP) and Expected Demand Not Supplied (EDNS) are calculated. Furthermore, the size of BESS required to cover for the intermittent nature of renewables is determined. Dynamic models of DGs, WTG, PV and BESS are developed for the purpose of transient stability studies. Contingencies such as three phase fault, tripping of DG and cloud covering PV field are simulated with and without BESS in order to clearly determine its influence on system stability.

This paper presents a robust frequency control in an islanded ac microgrid (MG). The microgrid considered in this paper consists of distributed energy sources (DES). These include micro-turbine (MT), diesel engine generator (DEG), fuel cell (FC), renewable energy sources like wind turbine (WT), photovoltaic panels (PV), and energy storage systems like battery energy storage system (BESS) and flywheel energy storage system (FESS). The load fluctuation, wind power fluctuation and solar irradiation fluctuation often influence the frequency of the MG. So to tackle this problem, a proportional-integral-derivative controller (PID) is designed and optimized by using the stochastic fractal search (SFS) algorithm. The proposed controller is very effective and able to eliminate the frequency deviation in a short time. The time-domain simulation results obtained by SFS based PID controller are compared with the results obtained by SFS based PI controller to show the effectiveness of the proposed controller. From the comparison, it can be observed that proposed controller gives better frequency stability and hence improves the frequency of the MG significantly.