Load frequency control of multi-area power system incorporated renewable energy considering electrical vehicle effect using modified cascaded controller tuned by BESSO algorithm
2024; Elsevier BV; Volume: 10; Issue: 11 Linguagem: Inglês
10.1016/j.heliyon.2024.e31840
ISSN2405-8440
AutoresRehana Ghafoor, Lyu-Guang Hua, Muhammad Majid Gulzar, Rasmia Irfan, Mohammed H. Alqahtani, Muhammad Khalid,
Tópico(s)Wind Turbine Control Systems
ResumoIn power systems, load frequency control (LFC) matters significantly to achieve stability. Dealing with the fluctuations in the frequency of a multi-area power system becomes more challenging by incorporating additional energy resources. In this research, a multi-area power system is built by integrating thermal power systems with photovoltaic (PV) cells, wind turbines, and electric vehicles (EV). The addition of an electric vehicle "to a thermal power system which is integrated with a renewable energy source (RES)" increases the system productivity but also increases the system complexity, making it more problematic for LFC. Looking at the stability criteria for LFC, frequencies in two areas (Area-1 & Area-2) and tie-line power are considered for measurements. For the tuning of the proposed cascaded (1+PI)-PID controller, a new approach Bald Eagle Sparrow Search Optimization (BESSO) algorithm is implemented which is strongly inspired by nature. BESSO is a combination of bald eagle and sparrow searching techniques and performs comparatively better for fast convergence due to their strong food-seeking natural behavior to find the best solution for controller gains. Controller effects on multi-area systems are compared with the addition of PV, wind, and EV and resulting measurements meet the stability criteria with high accuracy even with the complexity of the system and also undertake a stability analysis to prove the performance by minimizing undershoot, overshoot, steady-state error and settling time for system frequencies and tie-line power. Simulation results are examined at different load-changing conditions. In contrast with similar combinations of PID controller with proposed cascaded (1+PI)-PID controller, it is claimed that the effect of the proposed controller is much finer and more reliable, even with electric vehicles to avoid system blackout caused by frequency fluctuations in interconnected power system. In power systems, load frequency control (LFC) matters significantly to achieve stability. Dealing with the fluctuations in the frequency of a multi-area power system becomes more challenging by incorporating additional energy resources. In this research, a multi-area power system is built by integrating thermal power systems with photovoltaic (PV) cells, wind turbines, and electric vehicles (EV). The addition of an electric vehicle "to a thermal power system which is integrated with a renewable energy source (RES)" increases the system productivity but also increases the system complexity, making it more problematic for LFC. Looking at the stability criteria for LFC, frequencies in two areas (Area-1 & Area-2) and tie-line power are considered for measurements. For the tuning of the proposed cascaded (1+PI)-PID controller, a new approach Bald Eagle Sparrow Search Optimization (BESSO) algorithm is implemented which is strongly inspired by nature. BESSO is a combination of bald eagle and sparrow searching techniques and performs comparatively better for fast convergence due to their strong food-seeking natural behavior to find the best solution for controller gains. Controller effects on multi-area systems are compared with the addition of PV, wind, and EV and resulting measurements meet the stability criteria with high accuracy even with the complexity of the system and also undertake a stability analysis to prove the performance by minimizing undershoot, overshoot, steady-state error and settling time for system frequencies and tie-line power. Simulation results are examined at different load-changing conditions. In contrast with similar combinations of PID controller with proposed cascaded (1+PI)-PID controller, it is claimed that the effect of the proposed controller is much finer and more reliable, even with electric vehicles to avoid system blackout caused by frequency fluctuations in interconnected power system. Tabled 1List of Abbreviations and TermsRESRenewable Energy Sources2Change in Frequency at area-2PVPhotovoltaic tie-lineChange in Power of Tie-LineEVElectric VehicleFAFirefly AlgorithmITAEIntegral Time Absolute ErrorGWOGray Wolf OptimizationACEArea Control ErrorCBOChaotic Butterfly Optimization1/ DroopReference Power1Change in Frequency at Area-1BESSOBald Eagle Sparrow Search OptimizationKpsPower System GainTpsPower System Time ConstantFrequency Bais Factor in Aea-1Frequency Bais Factor in Aea-1T12Synchronizing CoefficientReference Power Open table in a new tab Energy demand has increased manifold by growing industry in the past decades. After the industrialization of the whole world, in the last century, the source of energy was by thermal means such as oil, coal and nuclear energy, etc. These are the conventional or non-renewable energy sources which have fatally affected the global environment creating catastrophic health issues both for human and animals. Carbon dioxide is one of the major environmental issues. To mitigate these issues, the world has turned to renewable energy sources which have zero carbon footprint and low maintenance cost as 14% of energy is obtained from renewable energy sources [1Bevrani G.L.H. Ghosh A. Renewable energy sources and frequency regulation: survey and new perspectives.IET Renew. Power Gener. 2021; 4: 438-457Crossref Scopus (491) Google Scholar]. Although implementation of these renewable energy sources (RESs) with conventional power system is beneficial but it is also quite challenging due to their volatile nature [2M Gulzar M. Tehreem H. Khalid M. Modified Finite Time Sliding Mode Controller for Automatic Voltage Regulation under Fast-Changing Atmospheric Conditions in Grid-Connected Solar Energy Systems.International Journal of Intelligent Systems. 2023; 2023Google Scholar]. When renewable energy resources are connected with thermal systems, there are certain fluctuations occur, which are frequency and voltage. These frequency fluctuations are due to sudden load changes, create imbalance in power generation and demand, and ultimately stability impacted [3Ghosh A. Ray A.K. Nurujjaman M. Jamshidi M. Voltage and frequency control in conventional and PV integrated power systems by a particle swarm optimized Ziegler–Nichols based PID controller.SN Appl. Sci. 2021; 3: 1-13Crossref Scopus (29) Google Scholar]. Electrical vehicles are extensively used to increase the system productivity for future demand due its storage capacity but adding up Electric vehicles (EVs) to renewable energy resources (RES) makes the system more complicated and obviously LFC issue needs to tackle with more accuracy [4Gulzar Muhammad Majid. Designing of Robust Frequency Stabilization Using Optimized MPC-($1+\text {PIDN} $) Controller for High Order Interconnected Renewable Energy Based Power Systems.Protection and Control of Modern Power Systems. 2023; 8: 1-14Crossref Scopus (16) Google Scholar]. Stability in power system is a big concern for the synchronization of interconnected systems to deliver smooth power supply with minimum power loss. Managing load frequency is a key parameter, for the reliability of a power generation system in term of stability. Load frequency control involves steady state error and automatic gain control (AGC) to avoid frequency variations and make the system stable [5Sibtain D. Murtaza A.F. Ahmed N. Sher H.A. Gulzar M.M. Multi control adaptive fractional order PID control approach for PV/wind connected grid system.International Transactions on Electrical Energy Systems. 2020; Google Scholar]. So, there is crucial need to handle such issue by introducing a novel controlling technique which is capable to manage the rapid load variations in power system and ultimately limits frequency variations to a certain range. Latest power systems are complicated due to their rapid transformation from conventional to nonconventional structures. With the advancement of design, system stability set off more difficult. Therefore, researches have been worked a lot to tackle the instability issue in power system with using simply designed controllers as well as advanced controllers tuned by algorithm. For automatic gain control (AGC), PID and PI are the most commonly used controllers to cope LFC in power systems due to simplicity and easy to modify [6Rameshar V. Sharma G. Bokoro P.N. Çelik E. Frequency Support Studies of a Diesel–Wind Generation System Using Snake Optimizer-Oriented PID with UC and RFB.Energies. 2023; 16: 3417Crossref Google Scholar]. In some studies, PID controller is used with combination of other controllers to improve its outcomes such as in [7Doley R. Ghosh S. Application of PID and fuzzy based controllers for load frequency control of a single - Area and double - Area power systems.2019 5th Int. Conf. Adv. Electr. Eng. ICAEE. 2019; (2019): 479-484https://doi.org/10.1109/ICAEE48663.2019.8975568Crossref Scopus (3) Google Scholar] fuzzy logic controller with PID is implemented for two area power systems to automatic generation control (AGC) but this approach is limited to only PV and thermal system. Derivative filter-based optimal PID controller [8Patel N.C. Debnath M.K. Bagarty D.P. Das P. Load frequency control of a non-linear power system with optimal PID controller with derivative filter.IEEE Int. Conf. Power, Control. Signals Instrum. Eng. ICPCSI 2017. 2018; : 1515-1520Google Scholar] and robust PID controller with MPC [9Kumar N. Singh A. Load frequency control for multiarea power system using secondary controllers.Trends in Sciences. 2022; 19 (2044-2044)Crossref Scopus (6) Google Scholar] are used implemented for AGC. A robust observer sliding mode controller with Lyapunov method of stability is executed to deal with uncertainties and chattering is reduced to deviate frequency with high robustness [10Huynh V.V. Minh B.L.N. Amaefule E.N. Tran A.T. Tran &P.T. Highly robust observer sliding mode based frequency control for multi area power systems with renewable power plant.Electronics. 2021; 10: 274Crossref Scopus (12) Google Scholar]. Novel based model predictive controller (MPC) [11Yang J. Sun X. Liao K. He Z. Cai L. Model predictive control-based load frequency control for power systems with wind-turbine generators.IET Renew. Power Gener. 2019; 13: 2871-2879https://doi.org/10.1049/iet-rpg.2018.6179Crossref Scopus (43) Google Scholar] and a distributed model predictive controller (DMPC) [12Jia, Y., Zhou, J., Yong, P., & Guo, J. (2022, December). Data-Driven Distributed MPC for Load Frequency Control of Networked Nonlinear Power Systems. In 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV) (pp. 802-807), IEEE, 2022.Google Scholar] are also utilized for AGC using real-time predictions basis on sampled error but in limited areas. A modified form controller, cascaded fractional MPC with FOPID [13Gulzar M.M. Sibtain D. Khalid M. Cascaded Fractional Model Predictive Controller for Load Frequency Control in Multiarea Hybrid Renewable Energy System with Uncertainties.International Journal of Energy Research. 2023; Crossref Scopus (16) Google Scholar] is designed to handle the frequencies and tie-line power in a limit rang. In literature, mostly authors used conventional methods for integrated systems. However, according to requirements of power system designs and their controlling ways are continuously shifting toward advancements with complexity by adding multi energy resource [14Daraz A. Malik S.A. Basit A. Aslam S. Zhang G. Modified FOPID Controller for Frequency Regulation of a Hybrid Interconnected System of Conventional and Renewable Energy Sources.Fractal Fract. 2023; 7: 89Crossref Scopus (31) Google Scholar]. It is patent to move from traditional ways to intelligent mechanism to avoid system blackout due to frequency degradation in interconnected power systems because only controllers are not enough to deal such real time problems, implementation of optimization techniques with controllers extend the way to advancement for handling frequency variations and tie-line power with fast response. Several optimization techniques have been implemented in power systems such as Combination of PIDD2-PD is implemented using algorithm of wild horse optimization (WHO) [15Khudhair M. Ragab M. AboRas K.M. Abbasy N.H. Robust control of frequency variations for a multi-area power system in smart grid using a newly wild horse optimized combination of PIDD2 and PD controller.Sustainability. 2022; 14: 8223Crossref Scopus (14) Google Scholar], another form of PID, fractional order PID tuned metaheuristic algorithm [16Saikia LC S.N. Load Frequency Control of a Multi Area System Incorporating Distributed Generation resources, Gate Controlled Series Capacitor Along with High Voltage Direct Current Link Using Hybrid ALO-Pattern Search Optimized Fractional Order Controller.IET Renew. Power Gener. 2018; Google Scholar], gravitational search and hybrid arithmetic logic optimization [17R. Fatunmbi, R. Belkacemi, F. K. Ariyo, and G. Radman, "Genetic algorithm based optimized load frequency control for storageless photo voltaic generation in a two area multi-agent system," 2017 North Am. Power Symp. NAPS 2017, 2017.Google Scholar]. Bacterial Foraging optimization technique [18Abd-Elazim S.M. Ali E.S. Load frequency controller design of a two-area system composing of PV grid and thermal generator via firefly algorithm.Neural Comput. Appl. 2018; 30: 607-616Crossref Scopus (172) Google Scholar] and Flower Pollination Algorithm (FPA) [19Jagatheesan K. Anand B. Samanta S. Dey N. Santhi V. Ashour A.S. Balas V.E. Application of flower pollination algorithm in load frequency control of multi-area interconnected power system with nonlinearity.Neural Computing and applications. 2017; 28: 475-488Crossref Scopus (74) Google Scholar] are too executed based on their natural working to tune controllers. Further, Sine Cosine algorithm [20Kamel, S., Elkasem, A. H., Korashy, A., & Ahmed, M. H. Sine Cosine Algorithm for Load Frequency Control Design of Two Area Interconnected Power System with DFIG Based Wind Turbine. In 2019 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE) (pp. 1-5). IEEE, 2019.Google Scholar], Improved Black Widow optimization algorithm [21Khalid Muhammad et al.A novel computational paradigm for scheduling of hybrid energy networks considering renewable uncertainty limitations.Energy Reports. 2024; 11: 1959-1978Crossref Scopus (0) Google Scholar], Constrained population extremal optimization [22Mustafa Faizan E. et al.An adaptive metaheuristic optimization approach for Tennessee Eastman process for an industrial fault tolerant control system.Plos one. 2024; 19e0296471Crossref Scopus (0) Google Scholar], Harries Hawks Optimizer (HHO) [23Yousri D. Babu T.S. Fathy A. Recent methodology based Harris Hawks optimizer for designing load frequency control incorporated in multi-interconnected renewable energy plants.Sustainable Energy, Grids and Networks. 2020; 2210035Crossref Scopus (90) Google Scholar], swarm search algorithm [24Ahmed Ijaz et al.Adaptive salp swarm algorithm for sustainable economic and environmental dispatch under renewable energy sources.Renewable Energy. 2024; 223119944Crossref Scopus (5) Google Scholar], Sine Cosine Algorithm [25Gulzar M.M. Irfan R. Shahid K. Raza M.T. Javed I. Frequency and Voltage Stabilization of Multi area Hybrid Power System by Considering the Impact of Communication Time Delay.Electric Power Components and Systems. 2023; : 1-15Crossref Scopus (0) Google Scholar], Manta Ray Foraging optimization (MRFO) [26El-Sousy F.F. Alqahtani M.H. Aljumah A.S. Aly M. Almutairi S.Z. Mohamed E.A. Design Optimization of Improved Fractional-Order Cascaded Frequency Controllers for Electric Vehicles and Electrical Power Grids Utilizing Renewable Energy Sources.Fractal and Fractional. 2023; 7: 603Crossref Scopus (4) Google Scholar], the Artificial Electric Field Algorithm (AEFA) [27Kalyan C.H. Rao G.S. Coordinated SMES and TCSC Damping Controller for Load Frequency Control of Multi Area Power System with Diverse Sources.International Journal on Electrical Engineering & Informatics. 2020; 12Google Scholar], Path Finder Algorithm (FPA) [28Priyadarshani S. Subhashini K.R. Satapathy J.K. Pathfinder algorithm optimized fractional order tilt-integral-derivative (FOTID) controller for automatic generation control of multi-source power system.Microsystem Technologies. 2021; 27: 23-35Crossref Scopus (63) Google Scholar], Improved Mayfly Optimization [29Subramani P. Mani S. Lai W.C. Ramamurthy D. Sustainable Energy Management and Control for Variable Load Conditions Using Improved Mayfly Optimization.Sustainability. 2022; 14: 6478Crossref Scopus (5) Google Scholar], Grasshopper Optimization Algorithm (GOA) [30Lal D.K. Barisal A.K. Grasshopper algorithm optimized fractional order fuzzy PID frequency controller for hybrid power systems.Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering). 2019; 12: 519-531Crossref Scopus (4) Google Scholar], Squirrel Search Algorithm [31Maden D. Çelik E. Houssein E.H. Sharma D. Squirrel search algorithm applied to effective estimation of solar PV model parameters: a real-world practice.Neural Computing and Applications. 2023; 35: 13529-13546Crossref Scopus (10) Google Scholar] and Marine Predator Algorithm (MPA) [32Yakout A.H. Sabry W. Abdelaziz A.Y. Hasanien H.M. AboRas K.M. Kotb H. Enhancement of frequency stability of power systems integrated with wind energy using marine predator algorithm based PIDA controlled STATCOM.Alexandria Engineering Journal. 2022; 61: 5851-5867Crossref Scopus (16) Google Scholar] are recently worked. With the advancements in optimization techniques, combinations of algorithm also been used for LFC issues such as gray wolf and Cuckoo search (CS) algorithms with TID [33Khadanga R.K. Kumar A. Panda S. A modified Grey Wolf Optimization with Cuckoo Search Algorithm for load frequency controller design of hybrid power system.Applied soft computing. 2022; 124109011Crossref Scopus (49) Google Scholar]. These advanced controlling strategies showed better performance in term of settling time steady-state error, undershoot and settling time for the reduction of frequency oscillations but with untimely convergence and time computation complexity. Taking into account the discussed literature, it is essential to introduce more advance technique to deal with system stability with a balance integration of power system and multiple energy resources and must be covered the problem within minimum time frame smoothly and precisely. For this objective, a novel metaheuristic strategy named Bald Eagle Sparrow Search Optimization (BESSO) is stimulated to enhance the stability, reliability, productivity and most important the sustainability of power system integrated with sustainable energy resource. BESSO is a combination of Bald Eagle Search (BaESO) and Sparrow search (SpSOA) to make it intelligent and highly effective than other algorithms. Its dual optimizing nature differs it from other swarming techniques and quickly resulted to avoid the local minima trap. Mathematical measurements from benchmark make it more effective and superior [34Raj T.D. Kumar C. Kotsampopoulos P. Fayek, H H.H. Load Frequency Control in Two-Area Multi-Source Power System Using Bald Eagle-Sparrow Search Optimization Tuned PID Controller.Energies. 2014; 16: 2023Google Scholar]. Contribution and novelty of this research basis on above aspects are:•Development of conventional (Thermal power system) and renewable energy resources (PV, wind) in two areas separated by tie-line.•Modified form of PID named Cascaded (1+PI)-PID Controller is designed to resolve LFC problem. Novel nature inspired Bald Eagle Sparrow Search Optimization (BESSO) algorithm is implemented to get controller parameters.•Analyzed the participation of electric vehicle aggregator in both areas.•Examined the controller performance at different load variations and Comparative analysis is carried out with most identical controllers. Organization of paper is done in following way: In Section 2, system modeling is explained with the discussion of thermal power system, PV, Wind and EV. Section 3 is about the state space modelling of the system. Designing of proposed controller with tuning algorithm BESSO is discussed in Section 4. Simulation results are analyzed considering different load conditions and comparison of similar controllers are discussed in Section 5. Conclusion of research is given in Section 6. Working methodology is followed by a block diagram in Figure 1. Where each area is constructed having four different systems thermal, PV, Wind and EV and further both areas are connected through tie line and load frequency control (LFC) is examined using controller. A thermal power system consists of a governor with dead band, turbine and re-heater. Governor used to control the valve and the speed of that system at different load conditions. Re-heater regulates the temperature to the equal level of governor's temperature. These components of thermal power system are defined by transfer functions for their modeling as shown in Figure 2. There are two inputs of governor as mentioned in equation (1), which are frequency change ( ) and reference power ( ), where 1/ represent the droop.(1) Transfer function of governor with dead and (Gg) is given in equation (2), where governor time constant is denoted by Tgr, turbine (Gt) and re-heater (Gr).(2) Transfer function of turbine is denoted by Gt is given in equation (3), Kt represent the gain of turbine and Tt represent the time constant of turbine.(3) In equation (4), transfer function of re-heater is illustrated with Kr, and Tr that represent the gain of re-heater and time constant of re-heater respectively.(4) Parametric values of the transfer function are taken from [35Gulzar M.M. Sibtain D. Murtaza A.F. Murawwat S. Saadi M. Jameel A. Adaptive fuzzy based optimized proportional-integral controller to mitigate the frequency oscillation of multi-area photovoltaic thermal system.Int. Trans. Electr. Energy Syst. 2021; 31: 1-20https://doi.org/10.1002/2050-7038.12643Crossref Scopus (26) Google Scholar]. Area control error (ACE) for area 1 & area 2 are given in equation ((5), (6)) respectively, where biasing frequency factor is denoted by B and tie line power difference in denoted by Ptie-line.(5) (6) Photo voltaic (PV) has become an important part of power generation due to its local availability, which converts the sun light into energy. The efficiency of generated power by PV cell is affected by weather conditions that are temperature variation and irradiation. In other words, we can say that the input of PV in uncertain many other factors such as panel degradation, inverter efficiency, dust and dirt accumulation on the panels, shading, angle and orientation of panels degrades its performance. Furthermore, the non-linear nature of PV system can be seen in graph provided in figure 3 where current and voltage (I-V) and power-voltage (P-V) curve is shown. So, to cope up with all these issues of PV system, it is important to operate PV at maximum power point (MPP) for better efficiency. For maximum power point tracking (MPPT), PV is designed by considering irradiance of 1000 W/m2 at 250 C and using PV, 150W to 30 kW power generated [35Gulzar M.M. Sibtain D. Murtaza A.F. Murawwat S. Saadi M. Jameel A. Adaptive fuzzy based optimized proportional-integral controller to mitigate the frequency oscillation of multi-area photovoltaic thermal system.Int. Trans. Electr. Energy Syst. 2021; 31: 1-20https://doi.org/10.1002/2050-7038.12643Crossref Scopus (26) Google Scholar]. The block diagram of the model is given in Figure 4.Figure 4Block diagramof PV systemView Large Image Figure ViewerDownload Hi-res image Download (PPT) In equation (7), G represent the gain between DC voltage (VDC) and AC voltage (VAC).(7) Boost converter gain is given in equation (9) is obtained from equation (8).(8) (9) Here, I1 and I2 are currents and Gc shown in equation (9) is the gain of converter mentioned in [35Gulzar M.M. Sibtain D. Murtaza A.F. Murawwat S. Saadi M. Jameel A. Adaptive fuzzy based optimized proportional-integral controller to mitigate the frequency oscillation of multi-area photovoltaic thermal system.Int. Trans. Electr. Energy Syst. 2021; 31: 1-20https://doi.org/10.1002/2050-7038.12643Crossref Scopus (26) Google Scholar]. AC current is given by equation (10).(10) By taking the Laplace transform of (10), transfer function of IAC is obtained [s2 / (s2 + w2)] and I2 = 1/s. So, the transfer function of inverter gain Gi get in equation (11).(11) Transfer function of instantaneous power is given by equation (12), where impedance is shown by VmIm.(12) Here, VmIm shows the impedance and = 2 f = 2 (50) = 314.12 rad/s. Transfer function of instantaneous power gain is given by equation (13) and average power is stated in equation (14).(13) (14) Gain of average power is given in equation (15):(15) The obtained transfer functions [35Gulzar M.M. Sibtain D. Murtaza A.F. Murawwat S. Saadi M. Jameel A. Adaptive fuzzy based optimized proportional-integral controller to mitigate the frequency oscillation of multi-area photovoltaic thermal system.Int. Trans. Electr. Energy Syst. 2021; 31: 1-20https://doi.org/10.1002/2050-7038.12643Crossref Scopus (26) Google Scholar] of inverter gain (Gi), instantaneous power gain Gp(inst) and average power gain Gp(avg) are used to design PV model in MATLAB/Simulink. Wind turbine converts wind energy into mechanical energy which used to drive the rotor. Wind turbine consists of drive train and rotor blades but due to noise and mechanical strength concerns, blades of turbine rotate at low speed. A gear box is used in turbine to increase the speed of blades. In excessively strong winds, a break is also used in turbine to rotate its blades and a step-up transformer is used to generator maximum power. There are two types of wind turbines, which are vertical axis turbine and horizontal axis turbine. Their power generation is from 10kW to 8000kW for different turbines. The output power of wind turbine is derived in equation (16) [36Oshnoei S. Oshnoei A. Mosallanejad A. Haghjoo F. Novel load frequency control scheme for an interconnected two-area power system including wind turbine generation and redox flow battery.International Journal of Electrical Power & Energy Systems. 2021; 130107033Crossref Scopus (62) Google Scholar].(16) Here, Pw is power of wind turbine, p is air density, Ap is swept area of rotor, vw is speed of wind turbine, Cp is coefficient of aerodynamic power and , are tip speed ratio and blade pitch angle respectively. It is further explained in equation (17), where Cp is expressed as:(17) (18) Equation (18), is about the nonlinear function speed ratio, where, t is the speed of blade and R is the radius of rotor. Wind power is the input of power system and in case of random output of wind turbine, output oscillations are multiplied with wind speed. These oscillations are obtained from Simulink blocks of white noise and low pass filter. Wind turbine output power (PW) is derived in (16). The complete Simulink model for wind system is shown in Figure 5. EVs play a vital role in power systems while creating much differences using them with renewable energy resources in case of LFC. Electric vehicle (EV) is used with two area thermal-PV-Wind systems to minimize frequency oscillations with more accuracy while changing active power from one state to another state. The Electric vehicle model combined to the specified system to meet the power constraints, which is supposed to be a charging and discharging power at continuous rate. First order Electric vehicle model used in [37C. Chen, C. Guo, Z. Man, & X. Tong, "Control strategy research on frequency regulation of power system considering Electric vehicles," IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) (pp. 2101-2105), october 2016.Google Scholar] shows the reflecting power of electric vehicle (EV). Here the power equation is shown in equation (19).(19) Where and are the constant of electric vehicle battery. is the gain of participation factor equal to -4 and is the time constant equal to 0.008 sec, the value of participation factor is negative and the value of time constant, is also considered very small. Block diagram shows the response of controlling strategy for electric vehicle having some important assumptions regarding EV's:•Ignoring state of charge (SOC) of EVs.•Ignoring the issues of battery life. When EVs are used to serve primary frequency regulations, losses are not much important so they are not considered in this model of electric vehicle. Block diagram of EV model for regulating the frequency is shown in figure 6. Moreover, the complete transfer function model of investigated system is shown in figure 7.Figure 7Transfer fuction model of complete systemView Large Image Figure ViewerDownload Hi-res image Download (PPT) In this section the state-space modelling is performed for the studied power system model considering PV system penetration is described in equations ((20), (21)):(20) (21) From the equation (20), denotes the state vector, the control vector, where the disturbance vector is represented by and output system vector . System vectors are defined as: The state space model of the system is mentioned in equations ((22), (23)).(22) (23) Moreover, the single line diagram
Referência(s)