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A Novel Controller Design for Small-Scale Islanded Microgrid Integrated with Electric Vehicle-Based Energy Storage Management

A Novel Controller Design for Small-Scale Islanded Microgrid Integrated with Electric... Hindawi International Transactions on Electrical Energy Systems Volume 2022, Article ID 5059215, 19 pages https://doi.org/10.1155/2022/5059215 Research Article A Novel Controller Design for Small-Scale Islanded Microgrid Integrated with Electric Vehicle-Based Energy Storage Management Nihal Omar ,AnilKumar Tiwari ,K. Seethalekshmi ,andNitin AnandShrivastava Department of Electrical Engineering, Institute of Engineering and Technology, Lucknow 226021, UP, India Correspondence should be addressed to K. Seethalekshmi; seethalekshmik@ietlucknow.ac.in and Nitin Anand Shrivastava; nitinshrivastava@ietlucknow.ac.in Received 4 February 2022; Accepted 28 March 2022; Published 29 April 2022 Academic Editor: Kamran Iqbal Copyright © 2022 Nihal Omar et al. +is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Small-scale islanded microgrid technology has the potential to utilize renewable sources for electrification in extreme areas where conventional transmission of power is difficult. But due to the absence of a utility grid in the islanded operation, voltage and frequency control becomes the major concern. Instead of using a centralized large battery storage system, electric vehicle- (EV-) based distributed energy storage may provide a dynamic and much cheaper energy storage solution for small-scale systems in the long run. +e only issue is to effectively manage the dynamics of EV penetration for achieving the overall control in the islanded system. To address the same issue, this paper proposes a novel segregation-based inverter control structure for an islanded microgrid while employing EVs as an energy storage system, thus eliminating the need for centralized storage. +e designed integrated control structure simultaneously attends to the voltage and frequency regulation need of the system, overcoming the issue of control lag, along with energy storage and management aspects in the microgrid operation for controlling the power flow under various practical scenarios. Extensive experimental studies are performed in MATLAB/SIMULINK environment, which indicates that the proposed integrated control structure gives a satisfactory performance under various scenarios encompassing load variation, renewable energy uncertainties, and EV dynamics in comparison with conventional control schemes employed in the islanded microgrid. disturbances, profile variation, connection or disconnection 1. Introduction status of the RES, proper energy storage and management +e major problem with renewable energy sources (RES) is (ESM) in the system, voltage maintenance, frequency reg- their intermittent and unpredictable nature. So to utilize ulation, and also ensuring good power quality. +us, energy RES, a large and complex control is required. For electri- storage is used in MG [8, 9]. +e use of electric vehicles fication of remote rural areas, the islanded system is es- (EVs) in MG for ESM is a promising area of research because sentially required [1–4]. RES-based microgrid (MG) requires of its fast charging and discharging capabilities. +e bidi- a power electronics-based converter in the system; thus, it rectional power flow in EV is called vehicle-to-grid (V2G) may adversely affect the overall power quality of the system. for discharging operation and grid-to-vehicle (G2V) for Further, any islanded MG itself suffers from the lack of charging operation of the vehicle [10, 11]. system inertia because RES are integrated through power +e inverter control is essential in MG for its proper electronic controllers, which gets manifested into issues in functioning and operation [12, 13]. However, the reported frequency regulation [5–7]. Having an integrated DC bus in work in [12, 13] does not consider the integration of RES and the system may improve this issue a little bit. EVs. As the system starts including more and more intrinsic +e proper functioning of an MG includes complete mechanisms and also approaches towards more practical control of the system under load variations, system scenarios, conventional control starts failing to operate. lag which occurs due to complex computational To overcome this, various papers have investigated the functioning of MG with RES [5, 14–17], energy storage techniques. systems [18–20], some advanced ESM strategies designed (iii) Consideration of intrinsic mechanisms and non- with dynamic programming [21] and use of EVs in charging linear characteristics of converters interfacing RES stations and V2G design [22–29]. A few works are reported and EVs in the study. in advanced inverter control structures like adaptive droop (iv) Seamless coordination among proposed SBIC control [10, 30], proportional-integral (PI) controller [4], module, converter interfaced renewable generation fuzzy logic [20, 31, 32], model predictive controllers [33] for and EV control module, and ESM module. MG control, and so on. In [31, 34–38], nonconventional optimization algorithms, genetic algorithms, butterfly op- +e outline of this paper is as follows. Section 2 discusses timization, and so on are applied to tune the controllers. the detailed description of MG system modeling used for +ese control techniques are effective and adaptive; how- problem formulation. Section 3 consists of a detailed dis- ever, they require a lot of iterative computational memory cussion over the proposed inverter control strategy and ESM which may create hurdles in real-time operation and are strategy. Section 4 shows the simulation results and dis- prone to be stuck at local optimum [39, 40]. +is can cussion over considered case scenarios. Finally, Section 5 generate an unintentional control lag that may hinder the provides conclusions. controller from working at its global optimum operation. With this background, the main problem, which is 2. System Modeling considered in this research, is to make the MG independent from centralized storage, external inertia support, and its +e schematic of islanded MG system is shown in Figure 1. control being free from the iterative computation. Most of MG consists of a DC bus, integrated with RES, and an AC the reported literature considers grid support or large- bus which is connected to the load. An inverter filter in- battery storage system (LBSS) [18, 30, 33] support for sta- terface is used to connect both buses. +e inverter used is a bilizing the system. Some of the works consider only uni- grid forming voltage source inverter, for isolated MG op- directional power flow for EV charging stations [23–27, 29]. erations. EV modules are integrated at both DC and AC Reheat turbine system support and approximated solar and buses. +e proposed main inverter controller and ESM al- EV models are considered in [31] without including their gorithm module are shown with input and output signals for intrinsic characteristics. None of these works has explored a overall control and proper functioning of MG. standalone MG system with renewable generation and EV integration without the support of a power grid, LBSS, and turbine systems. Also, the nonlinearity of converters in- 2.1. Renewable Generation Integration. RES (solar and wind) terfacing RES and EVs is avoided in most of the studies just are integrated into the DC bus of the microgrid. Mathe- by replacement from a first-order transfer function [39–41] matical modeling and respective control mechanisms of to avoid certain complexities. +is became the motivation solar and wind plants, connected to the DC bus, are detailed for this work to include the same in the study. in this section. A novel self-adaptive segregation-based inverter control (SBIC) with a dynamic band margin- (DBM-) based hys- teresis controller, which uses the voltage signals for gen- 2.1.1. Solar Plant. +e solar power plant is integrated into erating pulse width modulation (PWM) signals, is designed. the DC bus of a microgrid by the means of a boost converter Controller derives its adaptive features, through the segre- that is being controlled by the incremental conductance gation of its dynamic and fundamental components, of maximum power point tracking (IC-MPPT) control algo- feedback. +e main advantage of using SBIC is that it oc- rithm. For dynamic modeling of PV array, a panel of 10 cupies very little memory, produces no control lag due to series modules and 47 parallel strings is chosen. Mathe- coordination delay, and is neither prone to be stuck at local matical modeling of solar PV characteristics is reported in optimum as the controller is not driven from an iterative [5, 17]. algorithm or member function-based strategy. A propor- Mathematically, IC-MPPT can be described in both tional-resonant (PR) controller is used for high speed and discrete sample systems (DSS) and continuous differential improved steady-state performance. Also, bidirectional systems (CDS), as shown in (1)–(7). converters are designed for EV charging and discharging with small modifications controlled from an ESM scheme. P(j) � P(j + 1) − P(j), P(j) � V ∗ I , (1) j j +e major research contributions of this paper are as follows: 􏽘 P(j) � V ∗ I − V ∗ I � 0, (2) (i) Voltage and frequency regulation of islanded n n 1 1 j�1 microgrid through EVs (using as energy storage) without any LBSS or external inertia support. n k n (ii) A novel, self-adaptive discrete sample coordinated 􏽘 P(j) � 􏽘 P(j) + 􏽘 P(j) � 0, (3) SBIC scheme to overcome the problem of control j�1 j�1 j�k+1 DC bus of MG shaft torque (p.u.) Base torque PWW EV system side 6 PWM Pulses International Transactions on Electrical Energy Systems 3 V DC V1 (abc) I1 (abc) DC BUS AC BUS V (abc), I (abc) MPPT algorithm Bi-directional Switching Vehicle Solar DC Power & Boost Circuit AC-DC conv. Systems Plant SUB AC BUS converter LCL Inverter AC side EV Module AC side filter Wind Uncuntrolled DDMT Dynamic Plant rectifirer AC power SOC control Residential Voltage and measurement Load current measurement VSI Gate Driver and Isolation EV Vehicle Bi-directional Switching Circuit Circuit systems DC-DC conv. Switching Signals Reference MAIN Inputs INVERTER CONTROLLER DC Side EV Module Energy Storage Management (ESM) DC Bus Voltage SOC Algorithm measurement Figure 1: Schematic of the proposed microgrid system. gen. speed (p.u.) k n DDMT 􏽘 P(j) � − 􏽘 P(j). (4) Wind wind torque Pitch beta Gear j�1 turbine (p.u.) j�k+1 Controller Box wind speed rotor speed (p.u.) In CDS, the derivative approach is used to find out the rotor speed (p.u.) condition for the point of maximum power (PMP), as shown To uncontrolled PMSG in (5) and (6). +e condition of PMP in CDS is given in (7). rectifire P � V∗ I, (5) Figure 2: Schematic diagram of DDMT control. zP zI (6) � I + V � 0, zV zV + PI PI – + V DC (ref.) V2 zV zI + + (7) � − . V I Boost S + 2.1.2. Wind Plant. +e permanent magnet synchronous – – V1 generator (PMSG) based wind power plant is integrated into V EV s/s (ref.) + – PI + – PI the DC bus with the help of differential drive mass train (DDMT) control [15] and with an uncontrolled rectifier Figure 3: Schematic of bidirectional DC-DC converter with circuit. Mathematical modeling of the wind power plant is control. reported in [31]. DDMT control is a gearbox-based shaft speed control, a schematic diagram of which is shown in 2.2. Electric Vehicle (EV) Integration. In MG operation, EV Figure 2. Mathematical modeling is shown in (8)–(11). modules are utilized as flexible loads for ESM and power flow maintenance and regulation of MG system via V2G and P � ω T � ωT , (8) r s m G2V operations [42]. During these operations, EV converter modules are in operation; hence, the design process focuses dω (9) 2H � T − T , t m s on the same. Converter and its control design affect the V2G dt and G2V process for both DC and AC sides of the MG. Some advanced converter designs are described in [27, 43]. 1 dθ sa � ω − ω , (10) ω dt es 2.2.1. Converter Design for DC Side EV System. DC side EV dθ sa (11) T � K θ + D . system modules are integrated into the DC bus of the MG s s sa t dt with the help of a common bidirectional DC↔ DC PWW Buck I1 R C 4 International Transactions on Electrical Energy Systems abc to PWM V + – + – dq0 to + – PI PI V DC EV ref. dq0 abc PI Vdq0 idq0 V MG (AC) ref. 6 pulse DC 2 AC LCL EV system AC bus S1 bridge filter side of MG converter abc to Vabc, iabc PI Circulating dq0 curr. protection I PWM S2 I2 MOSFET Uncontrolled based bridge Buck 3 phase rectifire converter breaker Figure 4: Schematic of designed bidirectional AC-DC converter. Ref. Id signal Inner PI Microgrid Edq0 Vref Ref. I/P - V DC PR Ref. I/P 6 PWM error dq0 to abc Coordination Hysteresis current System ref Controller Delay Band PWM Pulses Signal Controller Model –– feedback feedback 5 samples delay feedback h (t) Vabc I (dq0)F Ts = 1e-06 s DC Bus Voltage V DC abc to dq0 abc to dq0 Wt ref V (abc)F I (abc)F I (abc)F I1 (abc)F Vabc, V1abc V (abc)D Dynamic Dynamic Band Margin feedback Segregation Generation signls 1abc, I1abc Frequency Ref. Frequency Integrator Ref. Gen. W0 = 2*pi*f Figure 5: Schematic design of proposed SBIC-based main inverter controller. converter, where V2G is a discharging, closed-loop boost 2.2.2. Converter Design for AC Side EV System. AC side EV operation control and G2V is a charging, closed-loop buck modules are integrated into the AC bus of MG with the help operation control, with internal current control, schematic of a modified bidirectional AC↔ DC converter design in- design of which is shown in Figure 3. cluding a circulating current protection circuit within the +e closed-loop control is described in (13)-(14). Voltage converter. V2G discharging operation is carried out with relations for V2G and G2V operations are given in (15)-(16). DC ⟶ AC conversion and G2V charging operation is In (12), PI controller in discrete domain is denoted as PI. carried out with AC ⟶ DC conversion. +is AC ⟶ DC conversion requires an uncontrolled AC ⟶ DC rectifier k T with a DC ⟶ DC buck converter connected in a cascaded i s PI[z] � k + , manner. +e schematic design of the proposed AC ↔ DC Z − 1 (12) bidirectional converter with its control is shown in Figure 4. n− 1 Circulating current protection circuit logic is given in PI[n] � 􏼠 􏼡􏽮PI[z]∗ z 􏽯dz, 2πj (17)–(20). S � 1(on)∀I � 0, (17) 1 2 D [n] � PI[n]⊛􏼐PI[n]⊛􏽮V − V 􏽯 − i􏼑, (13) 1 ev,ref 1 S � 0(off )∀I ≠ 0, (18) 1 2 D [n] � PI[n]⊛􏼐PI[n]⊛􏽮V − V 􏽯 − I􏼑, (14) 2 DC,ref 2 S � 1(on)∀I � 0, (19) 2 1 V � , (15) 1 − D S � 0(off )∀I ≠ 0. (20) 2 1 V � D V . (16) 1 1 2 PI controllers are the same as in (12). feedback Wt ref Wt ref – International Transactions on Electrical Energy Systems 5 e command or switching signals, for connection or disconnection of both DC side EV systems and AC side EV systems, are generated by the ESM scheme, which is dis- Inner PI Integrator cussed in Section 3. error current Ref.I/P - V DC ref signal Controller (W2)/Kr –– 3. Proposed Control Strategy Feedback PR Controller is section describes the following: DC Bus Voltage Integrator V DC (a) Proposed novel SBIC structure for the main inverter Figure 6: Schematic diagram of used PR controller. interfacing the DC and AC bus of MG. (b) ESM scheme for meeting the energy requirements to ensure the proper functioning of MG. (c) Coordination among SBIC module, ESM scheme, and control module of renewable generation and EVs. G (D) G (F) G (F) G (F) feedback Digital filter signals model 3.1. Main Inverter Controller. e proposed controller is Figure 7: Schematic diagram of dynamic segregation block. shown in Figure 5. Inputs, given to the controller, are DC bus voltage and system frequency reference. Feedback voltage and current signals are also being supplied. PWM pulses are taken as output signals. As there is no external Fundamental components of feedback signals, before inertia support of power grid, LBSS, or turbine system in the injecting into ICC and MG system model, are segregated, MG system (Figure 1), it is essential to have a robust and self- through digital Œlter operations, as shown in Figure 7. is adaptive controller design to ensure the proper functioning segregation is used to achieve coordination among high of MG under any kind of disturbances and with high bandwidth PR controller output signals, low bandwidth penetration of converter interfaced RES and EV systems. feedback signals, and dynamics of high bandwidth PI To ensure the same, dynamic segregation and dynamic controller, same as (12), used in ICC. band margin (DBM) generation blocks are introduced in the For any given signal G, this can be expressed as controller, which modiŒes the feedback signals before injecting them into the inner current controller (ICC) and G  G(F)+ G(D). (24) hysteresis band PWM blocks. e novel segregation feature, e digital Œlter model used to segregate the funda- which segregates the fundamental component from feed- mental G(F) is as follows. back, makes the controller robust and the DBM makes the Consider a discrete form of signal G as G[n], which is controller self-adaptive. formulated as (25). A delay element is introduced in the controller for ef- fective coordination and to avoid control lag. PR controller 2πn 6πn − (n/N) G[n]  y e + y ∗ sin + θ + y ∗ sin + θ , is used for improving the steady-state performance and 0 1 1 3 3 N N space vector frame transformation is used to reduce the (25) complexity of the control structure. A schematic of the PR controller [44] is shown in 2 3 n n n − (n/N) Figure 6, which is used for DC bus voltage control and the (26) e  1 − + − + ·· ·, 2 3 2N 6N output of the PR controller is supplied to ICC as referenced- ∗ ∗ axis current i and reference q-axis current is taken as i  0. d q 2πn e transfer function of the PR controller is given in (21), k  , while i is expressed in (23). (27) y  y cos θ , 1c 1 1 ak a + k p r n− 1 y  y sin θ . PR[z] , PR[n]  PR[z] ∗ z dz, 1s 1 1 2 2 2πj a + ω Using (27), (21) 2πn y ∗ sin + θ  y ∗ sin k cos θ + cos k sin θ 1 1 1 n 1 n 1 z − 1 a  , (28) (22) y sin k + y cos k . 1c n 1s n i [n] PR[n]⊛V − V . (23) DC,ref DC Substituting (26) and (28) in (25) yields Kr Kp 6 International Transactions on Electrical Energy Systems Vcc 3 Pulses Vref (abc) Hysteresis Band Vref (abc) - h (t) to Vref (abc) + h (t) From MG s/s In Band Relay 3 Pulses model based Hysteresis Controller Logical NOT 6 PWM Hysteresis Vabc Dynamic band Comparator Pulses feedback 3 Pulses margin h (t) Vcc 3 Pulses Figure 8: Schematic diagram of hysteresis controller. Start Table 1: System parameters. Parameter Value Calculate - VDC, P (SW), V 600 V DC,ref SOC (EV)_dc, f, P (Load), f 50 Hz SOC (EV)_ac V 400 V ACL− L f 48.5 Hz min 12 hours of 12 hours of f 51.5 Hz max Day Night V 570 V min V 630 V max VDC > Vmax | | P (SW) > f 50 kHz sw P (Load) | | f> f (max) S 1000 W/m ref T 25 C ref Yes Yes V 363 V oc− boost No At Day C 3.267 mF boost SOC (EV)_dc SOC (EV)_ac < L 1.43 mH boost < SOCmax SOCmax At Night P 100 kW solar− rated No P 30 kW wind− rated Yes Yes u 12 m/s wind Charge Charge H 4 No EVs (DC) EVs (AC) K 0.3 D 1 Disconnect Yes P (sw) = Yes Disconnect SOC 40% min EV (DC) P (Load) EV (AC) SOC 95% max No No L 500 μH Disconnect No C 100 μF EV (DC) At Day SOC (EV)_ac R 0.02Ω SOC (EV)_dc > SOCmin > SOCmin Disconnect At Night Having n � 1, 2, 3, . . . , N samples, y , y , y , y , y No 0 1c 1s 3c 3s EV (AC) Yes Yes are computed from the following matrix. Discharge Discharge EVs (DC) EVs (AC) G[1] j sin k 􏼁 cos k 􏼁 sin 3k 􏼁 cos 3k 􏼁 ⎡ ⎢ ⎤ ⎥ ⎢ ⎥ 1 1 1 1 1 ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎢ ⎥ ⎥ ⎢ ⎥ ⎡ ⎢ ⎤ ⎥ ⎡ ⎢ ⎤ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ y ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ 1c ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎢ ⎥ ⎢ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎥ ⎢ ⎥ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ Figure 9: Flowchart of energy storage management scheme. ⎢ G[2] ⎥ ⎢ j sin k 􏼁 cos k 􏼁 sin 3k 􏼁 cos 3k 􏼁 ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ 2 2 2 2 2 ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎢ ⎥ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ � ⎢ ⎥⎢ ⎥. ⎢ ⎥ ⎢ ⎥⎢ y ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ 1s ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎢ ⎥ ⎥ ⎢ ⎢ ⎥ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ . . . ⎥ ⎢ . . . . . . . . . . . . . . . ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎣ ⎦ ⎣ ⎦⎢ ⎢ ⎥ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ y ⎥ ⎢ ⎥ ⎢ 3c ⎥ ⎣ ⎦ G[N] j sin k cos k sin 3k cos 3k 􏼁 􏼁 􏼁 􏼁 N N N N N 3s 2 3 n n n G[n] � y 1 − + − 􏼠 􏼡 (31) 2 3 2N 6N Inversing the matrix, we get (29) + y sin k + y cos k 􏼁 􏼁 1c n 1s n − 1 ⎡ ⎢ ⎤ ⎥ j sin k 􏼁 cos k 􏼁 sin 3k 􏼁 cos 3k 􏼁 G[1] ⎢ ⎥ ⎢ ⎥ 1 1 1 1 1 ⎢ ⎢ ⎥ ⎢ ⎥ ⎥ ⎢ ⎥ ⎢ ⎥ ⎡ ⎢ ⎤ ⎥ ⎢ ⎥ ⎢ ⎥ ⎡ ⎢ ⎤ ⎥ ⎢ y ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ ⎢ 1c ⎥ ⎥ ⎢ ⎥ ⎥ ⎢ ⎥ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ + y sin 3k 􏼁 + y cos 3k 􏼁 , ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ 3c n 3s n ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ j sin k 􏼁 cos k 􏼁 sin 3k 􏼁 cos 3k 􏼁 ⎥ ⎢ G[2] ⎥ ⎢ ⎢ ⎥ ⎥ ⎢ ⎢ ⎥ ⎥ ⎢ ⎢ ⎥ ⎥ ⎢ ⎥ ⎢ 2 2 2 2 2 ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎢ y ⎥ � ⎢ ⎢ ⎥ ⎥ ⎢ ⎢ ⎥ ⎥. ⎢ ⎥ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ 1s ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ ⎥ ⎥ ⎢ . . . . . . . . . . . . . . . ⎥ ⎥ ⎢ . . . ⎥ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ 2 3 ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ ⎢ ⎢ ⎥ ⎣ ⎦ ⎢ ⎥ ⎥ ⎢ y ⎥ n n n ⎢ ⎥ ⎢ 3c ⎥ ⎢ ⎥ ⎣ ⎦ 􏼠1 − + − 􏼡 � j . j sin k 􏼁 cos k 􏼁 sin 3k 􏼁 cos 3k 􏼁 G[N] N N N N N 2 3 2N 6N 3s (32) (30) Le = Day Time Scheme Right = Night Time Scheme International Transactions on Electrical Energy Systems 7 Voltage variation with RES integration 0.26 0.28 0.3 0.32 0.34 0.36 0.38 Time (s) DC bus voltage with Solar Plant (a) 0.26 0.28 0.3 0.32 0.34 0.36 0.38 Time (s) DC bus voltage with Solar and wind Plant (b) -200 3-phase AC bus Voltage with solar and wind plant -400 0.26 0.28 0.3 0.32 0.34 0.36 0.38 Time (s) (c) Figure 10: Voltage profiles. (a) DC bus voltage robustness of MG with solar plant integration. (b) DC bus voltage sensitivity of MG with both solar and wind plants. (c) +ree-phase AC bus voltage of MG with solar and wind plant integrated. 􏽶����������� From (32), the functions G(F)[n] and G(D)[n] can be 􏽴 computed as follows. 3s √� (37) x [n] � y + 􏼠 􏼡 , s 1s 2πn 3 G(F)[n] � x[n]sin 􏼒 + δ􏼓 G(D)[n] � G[n] − G(F)[n]. (38) 2πn 2πn � x[n]∗ 􏼔sin 􏼒 􏼓cos(δ) + cos􏼒 􏼓sin(δ)􏼕, N N (33) +us, same as the above equations, feedback voltages and current signals can be segregated as given in (39)–(41) and x[n]cos(δ) � x [n], will be supplied wherever necessary. x[n]sin(δ) � x [n], V � V (F) + V (D), (39) abc abc abc (34) 2πn 2πn i � i (F) + i (D), (40) abc abc abc G(F)[n] � x [n]sin 􏼒 􏼓 + x [n]cos 􏼒 􏼓, (35) c s N N 􏽶����������� i1 � i1 (F) + i1 (D). (41) abc abc abc 2 3c Modeling of ICC is given in (44)-(45) through frame √� (36) x [n] � y + , 􏼠 􏼡 c 1c transformation as given in (42)-(43). Voltage (V) Voltage (V) Voltage (V) 8 International Transactions on Electrical Energy Systems Frequency control with RES connect and disconnect 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.60 0.65 0.70 Time (s) (a) Wind Plant disconnect at 0.4s 0.36 0.38 0.40 0.42 0.44 0.46 Time (s) f (mesured) f (Ref.) (b) Solar Plant disconnect at 0.653 s Wind again connect at 0.613 s 0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.70 0.71 Time (s) (c) Figure 11: Load frequency robustness waveforms (a) shows complete time scale, (b) zoom version of (a) From t � 0.36 s to 0.47 s, (c) zoom version of (a) From t � 0.61 s to 0.71 s. i (F) � [T] i (F) , 􏼂 􏼃 (42) dV (F) dq 0 abc q C � i (F) − i1 (F) − ω CV (F), q q 0 d dt √�� � (49) i (F) � i (F) + ji (F), j � − 1, (43) dV (F) dq 0 d q q C � E . dt ∗ ∗ V (F) � PI[n]⊛􏼈i [n] − i (F)􏼉, (44) d d d For linear control with the fundamental component, the system satisfies the relation given in (50). ∗ ∗ V (F) � − PI[n]⊛i (F), i � 0. (45) q q q V (F) V (F) T ∗ ∗ q d s Reference inputs V and V , generated from ICC, are � � . (50) d q E E C(z − 1) supplied to the MG system model which represents the LCL d q filter modeling as given in (48)–(50). In the AC frame, the output of the MG system V is ref,abc i1 (F) � [T] i1 (F) , 􏼂 􏼃 (46) given in (52). dq 0 abc E + jE � E , (51) i1 (F) � i1 (F) + ji1 (F), (47) d q dq 0 dq 0 d q − 1 dV (F) V � [T] E . (52) 􏽨 􏽩 d ∗ ref ,abc dq 0 C � i (F) − i1 (F) + ω CV (F), d d 0 dt (48) Actual feedback V and reference V are supplied abc ref ,abc dV (F) C � E , to a hysteresis controller through a delay element of 5 dt Frequency (Hz) Frequency (Hz) Frequency (Hz) International Transactions on Electrical Energy Systems 9 Control With RES profile variation ×10 30 kW power only due to 12 m/s wind speed 25 deg. Celsius Temprature 130 kW power at 1 kW/m2 solar irradiation and 12 m/s wind speed 0.4 0.5 0.6 0.7 0.8 0.9 1 Time (s) Active Power (a) AC bus currents due to contineous solar irradiation variation –200 –400 0.4 0.5 0.6 0.7 0.8 0.9 1 Time (s) (b) 0.4 0.5 0.6 0.7 0.8 0.9 1 Time (s) DC bus voltage Ref. DC bus voltage (c) Figure 12: Control under continuous solar irradiation linear variation. (a) +e power output at AC bus with wind plant (at base speed 12 m/s) also connected to MG. (b) +e variation in AC bus three-phase currents waveforms. (c) Robustness in DC bus voltage waveform. samples to synchronize the DBM, denoted as h(t), with With the above calculation, the hysteresis band will feedback signals and eliminate the control lag. range from V − h(t) to V + h(t). +e schematic ref ,abc ref ,abc Dynamic band margin (DBM) is generated using diagram of the used hysteresis band-based PWM generation feedback voltage dynamic component (anything other than technique is shown in Figure 8. +e detailed working of a a fundamental component in the signal) as evaluated in (53). conventional current hysteresis controller is well known as given in [45, 46]. V (D) � V − V (F). (53) abc abc abc Consecutive 5 samples of V (D) are used to generate abc 3.2. Energy Storage Management (ESM) Scheme. +e flow- an autovarying DBM. +is varying DBM makes the hys- chart for the algorithm used in the ESM scheme, for teresis band self-changing, which makes the hysteresis command or signal generation for connection or discon- controller self-adaptive. For any discrete function f[n], nection of both EV systems, is given in Figure 9. ESM sam 􏼈f[n]􏼉 represents the consecutive 5 samples as given in scheme is designed for twenty-four hours of the day. It is sam 􏼈f[n]􏼉 � 􏼈f[n − 4], f[n − 3], f[n − 2], f[n − 1], f[n]􏼉. assumed that two separate fleets of EVs are present on the DC and AC sides, for 24 hours of the day, for energy (54) management in the microgrid. Under the normal operating Considering f[n] � V (D), DBM can be calculated as condition, wherein the supply from RES can meet the system abc follows. demand, given in (69), both EV systems remain discon- nected; otherwise, connection and disconnection take place h(t) � max sam V (D) − min sam V (D) . 􏼂 􏼈 􏼈 􏼉􏼉 􏼈 􏼈 􏼉􏼉􏼃 according to the scheme to regulate the system parameters. n abc n abc +e inputs given to the algorithm are P , P , SOC input PV W (55) for both the EV modules, P , V , AC bus voltage and L DC 1.1 1.1 1.1 AC currents (Amp.) Voltage (V) Power (W) 10 International Transactions on Electrical Energy Systems Load Demand variation control 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Time (s) Supplied Active Power Load demand/phase (a) 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Time (s) Supplied Reactive Power (b) 3-phase AC bus currents –50 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Time (s) (c) Figure 13: Load side power demand variations. (a) Active power per phase demand and supplied power per phase in watt. (b) Reactive power supplied/phase in VAR. (c) +ree-phase AC bus currents during power demand variation. currents, reference inputs SOC and SOC , and Charge present at a given time in Ah min max SOC (%) � 100∗ . 􏼠 􏼡 f , f , V , andV . Rated charge of a vehicle battery in Ah min max min max SOC for every vehicle SOC present in any of EV (59) modules must satisfy As an indicative parameter of EV modules, the average SOC < SOC < SOC . (56) min V max SOC for overall modules SOC and SOC can be EV,DC EV,AC calculated as follows. While the connection with MG the power supplied or and P taken by DC and AC EV systems are P n1 EV,DC EV,AC SOC (%) iV which are given, respectively, as follows: (60) SOC (%) � 􏽘 , EV,DC n1 i�1 n1 P � 􏽘 P , (57) EV,DC iV n2 SOC (%) i�1 iV (61) SOC (%) � 􏽘 . EV,AC n2 i�1 n2 P � 􏽘 P . (58) EV,AC iV Combined RES power can be written as P , given as SW i�1 P � P + P . (62) SW PV W At any time, SOC (in percentage) of any vehicle can be calculated as Power flow management is given in (63)–(66). Reactive power/phase (VAR) Power/phase (W) Ac currents (Amp.) International Transactions on Electrical Energy Systems 11 4 Control wioth RES switching and profile variation ×10 Same solar profile, wind speed 0 to 1m/sec Wind plant disconnected, only solar plant with 700 kW Solar irradiation 700 kW/m2, 25 deg. Celcius, wind speed 12 m/s to 15 m/s 0.6 0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.76 0.78 0.8 Time (s) Active power at AC bus (a) –5000 0.6 0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.76 0.78 0.8 Time (s) Reactive power at AC bus (b) three-phase AC bus currents –500 0.6 0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.76 0.78 0.8 Time (s) (c) Figure 14: (a) Active power at the load bus. (b) Reactive power at the load bus. (c) +ree-phase AC bus currents. Case 1. P > P (charging) SW L P � P + P � P . (69) SW PV W L Day time P − P � P , (63) SW L EV,DC Extreme positive or negative case scenarios of extremely low load demand (need of generation curtailment/feed-in Night time P − P � P . (64) SW L EV,AC management) or requirement of load shedding are not considered during this study and thus not included in the ESM scheme. Case 2. P < P (discharging) SW L Day time P + P � P , (65) SW EV,DC L 3.3. Coordination among SBIC Module, ESM Scheme, and Control Module of Renewable Generation and EVs Night time P + P � P . (66) SW EV,AC L (i) Control logic, used in renewable generation con- verters, is for extracting the maximum amount of DC bus voltage and frequency of AC bus f must AC satisfy the following conditions. power from solar and wind plants having nonlinear characteristics. V < V < V , (67) min DC max (ii) Control logic, used in EV bidirectional converters, is to maintain power flow in the right direction using f < f < f . (68) the constant voltage charging method and also min AC max eliminate the risk of circulating current in the +e normal operating condition of MG when no EV converters. Switching of EV modules (connection system will be connected is given in (69) along with (67), and disconnection from MG) is decided by the ESM (68). scheme through digital switching signals. Reactive power (VAR) AC currents (Amp.) Active power (W) 12 International Transactions on Electrical Energy Systems Under unbalanced three phase load conditions Phase A Phase B Phase C -500 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Time (s) (a) Phase A Phase B Phase C -100 -200 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Time (s) (b) ×10 2.5 Another 3 phase unbalanced load connected 1.5 Phase A 0.5 Phase B Phase C 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Time (s) (c) Figure 15: (a) AC bus three-phase voltages. (b) AC bus three-phase currents. (c) Active power for each phase. (iii) ESM scheme only decides on switching of EV 4.1. Controller Performance and Sensitivity Analysis. To modules (from conditions specified in Section 3.2). validate the performance and robustness/sensitivity of the EV control regulated the power flow direction but controller under disturbances of the MG system, some cases along with the fact that stabilization of MG pa- have been considered without ESM scheme and EV inte- rameters to their reference values is also essential gration, while maintaining the power balance between which is being governed by the SBIC module. generation and demand. (iv) First of all, renewable control deals with extracting maximum power which is continuously varying 4.1.1. Voltage Control with RES Integration. +e solar plant which triggers the ESM scheme to decide for is integrated with the DC bus of microgrid and the voltage of connection or disconnection of EV modules. Si- the DC bus remains well within permissible limits due to the multaneously, SBIC control tries to stabilize the MG controller as shown in Figure 10(a). When the wind plant is system. +us, overall coordination is established integrated with already having a solar plant on the DC bus, among all the control modules employed in the the voltage becomes more stable because of wind plant system. inertia aiding to DC bus of MG as shown in Figure 10(b). +ree-phase AC bus voltage is shown in Figure 10(c) with 4. Simulation Results and Discussions solar and wind plant integrated to DC bus of MG. +e proposed MG system as shown in Figure 1 is modeled in the MATLAB/SIMULINK platform. Data for load demand 4.1.2. Frequency Regulation and Robustness under Switching variation, RES profile variation are created by authors of RES. In the isolated microgrid, initially, both RES (solar and themselves. +e system parameters used for simulation are wind) modules are connected to the DC bus of MG. At t � 0.4 s, shown in Table 1. wind plant is disconnected; again at t � 0.613 s, wind plant is For analyzing the performance of the controller and connected to the DC bus of MG, and then at t � 0.653 s, solar ESM scheme, various case scenarios have been taken, which plant is disconnected from the MG. +e variation in the fre- are given below. quency due to these switching is shown in Figure 11. +e Currens (Amp.) Voltage (V) Active Power (W) International Transactions on Electrical Energy Systems 13 6 6 ×10 ×10 Conventional control Conventional control Wind conn.at 0.2 s proposed control proposed control 130 kW 30 kW Solar discon.at 0.4 s –1 Solar conn.at 0.45 s 130 kW –2 –1 –3 Solar discon.at 0.4 s Solar discon.at 0.4 s –2 –4 0.2 0.3 0.4 0.5 0.6 0.7 0.2 0.3 0.4 0.5 0.6 0.7 Time (s) Time (s) (a) (b) conven. control proposed control Ref. DC Voltage Solar discon.at 0.4 s Solar discon.at 0.45 s Wind conn. at 0.2 s 600 V 0.2 0.3 0.4 0.5 0.6 0.7 Time (s) (c) Figure 16: Failure of conventional control and comparison with proposed control while RES connections and disconnections. Till t � 0.2 s, only solar plant is supplying 100 kW of power at 600 V DC bus reference voltage; at t � 0.2 s, wind plant is connected, and supplied power rises to 130 kW, at t � 0.4 s, solar plant disconnected and again connected at t � 0.45 s. (a) Active power. (b) Reactive power. (c) DC bus voltage. controller is effective in maintaining the frequency within the the MG was contributed by the solar plant and disconnection permissible limits specified. Variations after t � 0.653 s are high of the same leads to higher fluctuations; however, due to the relative to that of t � 0.4 s because the major power supply to proposed controller, frequency is regulated. Active power (W) Voltage (V) Reactive power (VAR) 14 International Transactions on Electrical Energy Systems Renewable Power, Load Demand, DC side EV supply wind disconnect Solar Gen. start wind plant connect –2000 0 0.5 1.5 3 3.5 4.5 1 2 2.5 4 5 ×10 Time (s) P (solar + wind) EV Power flow reverses Power Load demand Power from DC side EV module (a) DC side Avarage SOC indication Renewable gen.> Load demand Only EV feeding the load EV charging no Renew. gen. EV discharging 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 Time (s) ×10 (b) Switching signal for EV module EV connected Renew. gen. = Load demand –1 EV disconnect –2 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 ×10 Time (s) (c) Figure 17: Twelve hours, daytime simulation with DC side EV module. (a) Solar and wind power generation with wind plant connect and disconnect, load power demand, and power from DC side EV module. (b) Average SOC indication parameter in percentage for DC side EV module. (c) Switching signal from ESM algorithm for DC side EV module. 4.1.3. Control and Robustness of MG System under RES phase by inverter, and three-phase AC bus current wave- Profile Variation. +e solar irradiation is decreased linearly forms are shown in Figure 13. +e controller is effectively from 1 kW/m to complete disconnection level from t � 0.4 s to supplying switching signals to the inverter for sustaining 0.7 s. Further, the plant is connected back and solar irradiation disturbances. is linearly increased from 0 to 1 kW/m at t � 0.8 s to 1 s. Temperature is kept at a reference value of 25 C. +e wind plant, working at a base wind speed of 12 m/s is connected to 4.1.5. Control and Robustness of MG under RES Profile Variation and Switching. Active and reactive power control the MG. From t � 0.7 s to 0.8 s, only wind plant power of 30 kW is available at AC bus. +e controller is effective in maintaining is validated with large and sudden variation in wind profile and also with disconnection of wind plant completely. As the sinusoidal nature of the three-phase AC bus currents and keeps the DC bus voltage within permissible limits during the shown in Figure 14, solar irradiation and temperature in this case are kept constant at 700 kW/m and 25 C, respectively. variations as shown in Figure 12. Till t � 0.663 s, wind speed was hovering between 12 m/s to 15 m/s and the total generation was from 103 kW to 105 kW. 4.1.4. Control and Robustness of MG with Load Power De- After t � 0.663 s, wind speed suddenly drops down to 0 to mand Variation. +e load active power, as well as reactive 1 m/s and generation falls to 72 kW to 73 kW at t � 0.68 s; the power demand, is varied step-wise at t � 0.4 s, 0.8 s, 1.2 s, wind plant is disconnected from the DC bus; thus, only solar 1.6 s, and 2.0 s to check the inverter capability to sustain the generation of 70 kW is left. For these variations, active power load demand variation. Active power per phase demand and profile is shown in Figure 14(a), reactive power profile is supplied power by the inverter, supplied reactive power per hovering around 0 kVAR as the load used in this case is Power (W) ON (1)/OFF (0) SOC (%) International Transactions on Electrical Energy Systems 15 Renewable Power, Load Demand, DC side EV supply Wind going down Renew. gen. stops, EV supply rising EV mod. feeding the load Solar gen. stop, wind supplying load. –2000 5.5 6 6.5 7 7.5 8 8.5 ×10 Time (s) P (solar + wind) Power Load demand Power EV mod. AC side (a) AC side avarage SOC indication AC side EV mod. discharging Niether charging or discharging 5.5 6 6.5 7 7.5 8 8.5 ×10 Time (s) (b) Switching signal for EV module Renew. gen. = Load demand 1 EV isolated. Renew. gen. < Load demand EV connected. –1 5.5 6 6.5 7 7.5 8 8.5 ×10 Time (s) (c) Figure 18: Twelve-hour nighttime simulation with AC side EV module. (a) Solar and wind power generation with solar plant disconnect, load power demand, and power from AC side EV module. (b) Average SOC indication parameter in percentage for AC side EV module. (c) Switching signal from ESM algorithm for AC side EV module. Figure 15(a), three-phase AC bus currents are shown in purely resistive. Small fluctuations are there in the reactive power profile (Figure 14(b)) due to the filter circuit and Figure 15(b), and the active power of each phase is shown in other converters elements present in the system. +ree- Figure 15(c). phase AC bus currents are shown in Figure 14(c) for the same variation. 4.1.7. Comparison with the Conventional Control Technique. While applying conventional control [4, 5, 9] in the pro- 4.1.6. Controller Robustness under Unbalanced Loading posed model with solar and wind plant connection and Condition. Due to the self-adaptive nature of the proposed disconnection, conventional control (CC) fails to converge controller, it can work with three-phase unbalanced loading as reported in Figure 16. In conventional control, the DC bus voltage control is generally separated from inverter control; conditions, and on switching extra random load, it can supply power to the system. Since unbalanced loads voltage thus, on switching the generation plants such large spikes appear in the results while in proposed control, the control and current magnitude of every phase are not identical but are having sinusoidal nature and on increasing the load at of DC bus voltage is integrated with inverter controller; thus, t � 0.25 s, only three-phase current rises and voltage mag- it can withstand the disturbances as shown in Figure 16(c). nitudes remain at the same level. Controller, having self-adaptive nature due to the virtue of Initially load demand of phases A, B, and C is 10.7 kW, dynamic band margin, is also able to control active and 10.5 kW, and 9.3 kW, respectively. At t � 0.25 s, another load reactive power while conventional control shows a very poor is switched on which increases the load demand of phases A, response as can be seen in Figures 16(a) and 16(b). Even for B, and C to 21 kW, 18 kW, and 15 kW, respectively. For these wind plant connection, a permanent voltage dip appears in variations, the three-phase AC bus voltage is shown in the system with conventional control while in the proposed Power (W) SOC (%) ON (1)/OFF (0) 16 International Transactions on Electrical Energy Systems control strategy MG system can withstand all kinds of RES 5. Conclusion connections and disconnections. In this article, a control scheme for an islanded microgrid, which is independent of centralized storage, external inertia support, and relying only on distributed EV-based 4.2. Performance with Energy Storage Management (ESM) storage for ESM, is proposed. +e proposed control Scheme. +e effectiveness of the proposed ESM scheme has scheme is free from iterative computational algorithms; been tested through a few cases to check its ability to stabilize thus, it occupies relatively less memory, produces no the system under switching instructions provided by the control lag, and is free from being stuck at local optimum ESM algorithm and to check on any control lag due to the operation. Intrinsic mechanisms and nonlinearities of ESM strategy. Phasor-based simulation is performed in this interfacing power electronic converters are also consid- case. ered in the system while designing the control as shown in Section 2.2. 4.2.1. Twelve-Hour Daytime Simulation with DC Side Electric +e proposed scheme incorporates a novel self-adaptive discrete sample type segregation-based inverter control Vehicle (EV) Modules. Continuously varying RES data is considered with wind plant connection and disconnection structure, which generates dynamic band margin, integrated with a voltage hysteresis controller and a proportional- and varying load power demands are taken with daytime consideration, and DC side EV modules, as per the ESM resonant controller. +e proposed scheme establishes a proper orchestration among the main control module, ESM algorithm. DC side EV module is connected with 120 ve- hicles with a 500 Ah rating and charging voltage of 240 V. module, and RES, EVs converter control module as given in Results for the twelve-hour daytime (4 am to 4 pm�50000 s) Section 4.2. +e proposed controller is robust against load variation, unbalanced loading, renewable energy uncer- simulation have been shown in Figure 17. RES power generation with wind plant connection and disconnection, tainties, and RES switching as shown in Section 4.1. +is scheme, while RES switching, shows way better performance power demand by the load, power supplied or taken by the EV modules is shown in Figure 17(a). Negative values of than conventional PI controller, as can be seen in Figure 16. +e system retains its performance intact in the whole range power indicate that EVs are charging and zero power in- dicates the disconnection of the EV module. +e indication from rated solar irradiation to plant completely cut off from the system and also from rated wind speed to wind plant parameter of the average SOC of the module (59) is shown in Figure 17(b). It can be easily seen SOC of EV modules is well cutoff from microgrid thus highly robust against input within the range specified. A negative slope indicates that the parameter variation as shown during sensitivity analysis in EV module is discharging, the positive slope for charging, Figures 12 and 14. and no slope for isolation. +e switching signal from the As a future directive, a small hydro and fuel cell-based renewable generation can be integrated to test the coordi- ESM algorithm is shown in Figure 17(c). Value 1 specifies connection and 0 specifies disconnection. +e proposed nation between the control scheme and ESM module (a complex and advanced design will be required) under control technique is in good coordination with the ESM scheme. multiple power generation modules for optimized power flow in the system. 4.2.2. Twelve-Hour Nighttime Simulation with AC Side Nomenclature Electric Vehicle (EV) Module. While considering nighttime (4p.m. to 4a.m.�45000 s) simulation solar plant discon- (1) Solar plant nection is considered, and continuously varying generation j , j , j : +ermal and irradiation constants 1 2 3 and load demand are considered. AC side EV modules are P(j), P(j), V , I : Power difference, power, voltage, and j j integrated and controlled with the ESM algorithm. In the EV current at jth sample module, 210 vehicles are considered with a 500 Ah rating and n, k, j: Total number of samples, the sample of charging voltage of 240 V. +e complete formulation is maximum power, and sample count shown in Figure 18. RG power output, solar disconnection, L , C : Solar boost converter inductance and boost boost wind generation decrement, load demand which is dy- capacitance namically supplied by both RES and EV module, and power P : Solar output power solar− rated flow from AC side EV modules are shown in Figure 18(a). V : Open-circuit solar panel voltage or input oc− boost +e average SOC indication parameter which shows isola- of boost converter tion or discharging is shown in Figure 18(b). +e average f : Switching frequency of boost converter sw SOC of the overall module is well within specified limits during the nighttime. Switching signals for connection or (2) Wind plant disconnection of AC side EV module from ESM scheme are H , K , D : Inertia constant, shaft stiffness, and damping t s t shown in Figure 18(c). Value 1 specifies connection and 0 coefficient specifies disconnection. +e proposed control technique is ω , θ : Base electrical speed and shaft twist angle es sa in good coordination with the ESM scheme without any ω, ω : Angular velocity of wind turbine and permanent control lag. magnet synchronous generator International Transactions on Electrical Energy Systems 17 T , T : Wind mechanical torque and generator shaft Feedback currents of sub- m s torque AC bus and AC bus in abc frame (3) DC and AC side electric vehicle system sam V (D): Array of five samples of n abc V , Reference voltages of electric vehicle module ev,ref V (D) from (n − 4)th abc V : and DC bus of microgrid DC,ref sample to current nth D , D : +e duty cycle for buck and boost operation 1 2 sample V , V : Measured voltages of electric vehicle module 1 2 L, C: LCL filter inductance and and DC bus of microgrid capacitance S , S : Switching signals for protection switches of 1 2 V : Nominal RMS line to line ACL− L DC-AC and AC-DC conversion circuits AC bus voltage i, I: Measured currents of electric vehicle module and DC bus of microgrid (5) Energy storage management scheme parameters V , V : Minimum and maximum permissible min max limits of DC bus voltage (4) Control parameters f : Measured frequency of AC bus of , k , k : Proportional, integral, and AC p i r microgrid resonant constants P , P : Net power supplied or consumed by DC i , i1 , V , V1 : Feedback currents and EV,DC EV,AC dq 0 dq 0 dq 0 dq 0 and AC side vehicle system modules voltages of sub-AC bus and f , f : Minimum and maximum permissible AC bus in dq 0 frame min max limits of AC bus frequency T , Z: Sample time of the system P : Power of ith vehicle attached either of DC and Z-transform parameter iV or AC side vehicle module in the discrete domain P , P , P : Instantaneous power of the solar plant, i , i , i1 , i1 , V , V , V1 , V1 : Feedback currents and PV W L d q d q d q d q wind plant, and load demand voltages of sub-AC bus and SOC , Minimum and maximum permissible AC bus in d-axis and q-axis min SOC : limits of vehicle state of charge of dq 0frame max P : Instantaneous power of solar and wind abc, dq 0: +ree-phase AC frame and SW plant rotational time-invariant SOC : Measured state of charge for ith vehicle DC frame iV SOC , State of charge indication parameters for V (F), V1 (F), Fundamental components EV,DC abc abc SOC : DC and AC side vehicle module. i (F), i1 (F), of feedback voltages and EV,AC abc abc V (F), V1 (F), currents in abc and dq 0 dq 0 dq 0 i (F), i1 (F): frame: dq 0 dq 0 Data Availability − 1 [T], [T] : Transformation matrices from abc to dq 0 frame and +e data used to support the findings of this study are in- dq 0 to abc frame cluded in the article. V (D), V1 (D), Dynamic components of abc abc i (D), i1 (D), feedback voltages and abc abc Conflicts of Interest V (D), V1 (D), currents in abc and dq 0 dq 0 dq 0 i (D), i1 (D): frame dq 0 dq 0 +e authors declare that they have no conflicts of interest ω , f: Reference angular regarding the publication of this paper. frequency and given nominal frequency References i (F), i (F), i1 (F), Fundamental components d q d i1 (F), V (F), V (F), of currents and voltages in q d q [1] C. J. Mozina, “Impact of green power distributed generation,” V1 (F), V1 (F): d-axis and q-axis of dq 0 IEEE Industry Applications Magazine, vol. 16, no. 4, pp. 55–62, d q frame [2] O. Singh, A. Iqbal, S. Kumar, and S. Rajput, “Hybrid re- V : Feedback DC bus voltage DC newable energy system integration in the micro-grid: Indian E , E , E , V : Output voltages of d q dq 0 ref ,abc context,” in Proceedings of the International Conference on microgrid system model in Control, Computing, Communication and Materials d-axis, q-axis, in dq 0 frame, (ICCCCM), pp. 1–5, Allahabad, India, October 2016. and abc frame [3] L. Ahmethodzic and Mustafa Music, “Comprehensive review V , V1 : Feedback voltages of sub- abc abc of trends in microgrid control,” Renewable Energy Focus, AC bus and AC bus in abc vol. 38, pp. 84–96, 2021. frame [4] L. Fan, “Frequency and voltage control in microgrids,” in h(t): Dynamic band margin of Control and Dynamics in Power System and Microgrids, hysteresis controller pp. 147–169, CRC Press, New York, NY, USA, 1st edition, i , i1 : 2017. abc abc 18 International Transactions on Electrical Energy Systems [5] E. Kabalci, H. Irgan, and Y. Kabalci, “Hybrid microgrid [19] Y. Varetsky, V. Konoval, M. Saheda, and P. Olena, “Studying system design with renewable energy sources,” in Proceedings voltage fluctuations in microgrid with hybrid renewable en- of the 2018 IEEE 18th International Power Electronics and ergy system,” in Proceedings of the IEEE 6th International Motion Control Conference (PEMC), pp. 387–392, Budapest, Conference on Energy Smart Systems (ESS), pp. 239–242, Kyiv, Hungary, August 2018. Ukraine, April 2019. [6] T. Kerdphol, S. R. Fathin, W. Masayuki et al., “Enhanced [20] M. Jafari, M. Zahra, J. Zhu, and M. K. Hassan, “A novel predictive fuzzy logic-based energy management system for virtual inertia control based on derivative technique to em- ulate simultaneous inertia and damping properties for grid-connected and off-grid operation of residential smart microgrid frequency regulation,” IEEE Access, vol. 7, microgrids,” IEEE Journal of Emerging and Selected Topics in pp. 14422–14433, 2019. Power Electronics, vol. 8, no. 2, pp. 1391–1404, 2020. [7] X. Hou, Y. Sun, Z. Xin, J. H. Lu, W. Peng, and M. G. Josep, [21] M. Jafari, Z. Malekjamshidi, and J. Zhu, “A magnetically “Improvement of frequency regulation in VSG-based AC coupled multi-port, multi-operation-mode micro-grid with a microgrid via adaptive virtual inertia,” IEEE Transactions on predictive dynamic programming-based energy management Power Electronics, vol. 35, no. 2, pp. 1589–1602, 2020. for residential applications,” International Journal of Electrical [8] A. Tavakoli, S. Saha, M. T. Arif, E. Haque, M. Nishad, and Power and Energy Systems, vol. 104, no. 1, pp. 784–796, 2019. M. T. Aman, “Impacts of grid integration of solar PV and [22] A. Khan, S. Memon, and T. P. Sattar, “Analyzing integrated electric vehicle on grid stability, power quality and energy renewable energy and smart-grid systems to improve voltage quality and harmonic distortion losses at electric-vehicle economics: a review,” IET Energy Systems Integration, vol. 2, no. 3, pp. 243–260, 2020. charging stations,” IEEE Access, vol. 6, pp. 26404–26415, 2018. [9] J. Rocabert, A. Luna, B. Frede, and R. Pedro, “Control of [23] A. Hassouneet, M. Khafallah, A. Mesbahi, and T. Bouragba, power converters in AC microgrids,” IEEE Transactions on “Power management strategies of electric vehicle charging Power Electronics, vol. 27, no. 11, pp. 4734–4749, 2012. station based grid tied PV-battery system,” International [10] K.-W. Lao, W. Y. Deng, J. P. Shang, and N. Dai, “PQ-coupling Journal of Renewable Energy Resources, vol. 8, no. 2, strategy for droop control in grid-connected capacitive- pp. 851–860, 2018. coupled inverter,” IEEE Access, vol. 7, pp. 31663–31671, 2019. [24] A. Hassouneet, M. Khafallah, A. Mesbahi, and T. Bouragba, [11] T. Wu, G. Bao, Y. Y. Chen, and J. P. Shang, “A review for “Improved control strategies of electric vehicles charging control strategies in microgrid,” in Proceedings of the 2018 station based on grid tied PV/battery system,” International 37th Chinese Control Conference (CCC), pp. 30–35, Wuhan, Journal of Advanced Computer Science and Applications, China, July 2018. vol. 11, no. 3, pp. 116–124, 2020. [12] J. Wang, A. Pratt, and M. Baggu, “Integrated synchronization [25] A. Hassouneet, M. Khafallah, A. Mesbahi, and T. Bouragba, control of grid-forming inverters for smooth microgrid “Experimental analysis of control in electric vehicle charging transition,” in Proceedings of the IEEE Power and Energy station based grid tied photovoltaic-battery system,” Inter- Society General Meeting (PESGM), pp. 1–5, Atlanta, GA, USA, national Journal of Electrical and Computer Engineering, August 2019. vol. 13, no. 5, pp. 276–284, 2019. [13] Z. Liu, J. Liu, and Y. Zhao, “A unified control strategy for [26] S. Singh, S. Jagota, and M. Singh, “Energy management and three-phase inverter in distributed generation,” IEEE Trans- voltage stabilization in an islanded microgrid through an actions on Power Electronics, vol. 29, no. 3, pp. 1176–1191, electric vehicle charging station,” Sustainable Cities and So- 2014. ciety, vol. 41, pp. 679–694, 2018. [14] M. E. Haque, M. Negnevitsky, and K. M. Muttaqi, “A novel [27] A. Hassouneet, K. Mohamed, A. Mesbahi, N. Ayoub, and control strategy for a variable-speed wind turbine with a B. Tarik, “Experimental implementation of a smart battery permanent-magnet synchronous generator,” IEEE Transac- charger for electric vehicles charging station,” International tions on Industry Applications, vol. 46, no. 1, pp. 331–339, Journal of Power Electronics and Drive Systems, vol. 11, no. 4, 2010. pp. 1689–1699, 2020. [15] T. Rashid, A. Routh, R. Rana, A. Ferdous, and R. Sayed, “A [28] S. Iqbal, M. U. Jan, X. Ai et al., “V2G strategy for primary novel approach to maximize performance and reliability of frequency control of an industrial microgrid considering the PMSG based wind turbine: Bangladesh perspective,” Amer- charging station operator,” Electronics, vol. 9, no. 4, ican Journal of Engineering Research (AJER), vol. 7, no. 6, pp. 549–570, 2020. pp. 17–26, 2018. [29] K. Sayed, A. G. Abo-Khalil, and A. S. Alghamdi, “Optimum [16] N. +odsaporn and V. Kinnares, “Wind turbine simulator resilient operation and control dc microgrid based electric equipped with real-time monitoring and user-friendly pa- vehicles charging station powered by renewable energy rameter setup controlled by C2000 microcontroller,” in Pro- sources,” Energies, vol. 12, no. 22, pp. 4240–4263, 2019. ceedings of the 2020 17th International Conference on Electrical [30] E. K. Belal, D. M. Yehia, and A. M. Azny, “Adaptive droop Engineering/Electronics, Computer, Telecommunications and control for balancing SOC estimation of distributed batteries Information Technology (ECTI-CON), pp. 713–716, Phuket, in DC microgrids,” IET Generation, Transmission and Dis- +ailand, June 2020. tribution, vol. 13, no. 20, pp. 4667–4676, 2019. [31] M. U. Jan, X. Ai, A. A. Mohamed, U. R. Haseeb, and S. Iqbal, [17] Z. Gao, J. Li, M. Zhao, C. Zhou, and L. Kai, “Design and simulation of 500 kw wind-solar complementary microgrid,” “Adaptive and fuzzy PI controllers design for frequency in Proceedings of the 2020 Chinese Control and Decision regulation of isolated microgrid integrated with electric ve- Conference (CCDC), pp. 5499–5505, Hefei, China, August hicles,” IEEE Access, vol. 8, pp. 87621–87632, 2020. 2020. [32] H. Karimi, T. Mohammad, H. Beheshti, R. Amin, and [18] P. S. Kumar, R. P. S. Chandrasena, V. Ramu, G. N. Srinivas, H. Zareipour, “Intelligent control of islanded AC microgrids and K. V. S. M. Babu, “Energy management system for small based on adaptive neuro-fuzzy inference system,” Interna- scale hybrid wind solar battery based microgrid,” IEEE Access, tional Journal of Electrical Power and Energy Systems, vol. 133, vol. 8, pp. 8336–8345, 2020. Article ID 107161, 2021. International Transactions on Electrical Energy Systems 19 [33] J. Hu, Y. Shan, J. M. Guerrero, I. Adrian, K. W. Chan, and [46] P. A. Dahono, “New hysteresis current controller for single phase full bridge inverter,” IET Power Electronics, vol. 2, no. 5, J. Rodriguez, “Model predictive control of microgrids—an overview,” Renewable and Sustainable Energy Reviews, pp. 585–594, 2009. vol. 136, Article ID 110422, 2021. [34] P. Jampeethong and S. Khomfoi, “Coordinated control of electric vehicles and renewable energy sources for frequency regulation in microgrids,” IEEE Access, vol. 8, pp. 141967– 141976, 2020. [35] S. Ranjan, A. Latif, D. C. Das, and N. Sinha, “LFC for au- tonomous hybrid micro grid system of 3 unequal renewable areas using mine blast algorithm,” International Journal of Renewable Energy Resources, vol. 8, no. 3, pp. 1297–1308, 2018. [36] A. Latif, D. C. Das, K. B. Amar, and S. Ranjan, “Maiden coordinated load frequency control strategy for ST-AWEC- GEC-BDDG-based independent three-area interconnected microgrid system with the combined effect of diverse energy storage and DC link using BOA-optimised PFOID control- ler,” IET Renewable Power Generation, vol. 13, no. 14, pp. 2634–2646, 2019. [37] A. Latif, D. C. Das, K. B. Amar, and S. Ranjan, “Illustration of demand response supported coordinated system performance evaluation of YSGA optimized dual stage PIFOD-(1 + PI) controller employed with wind-tidal-biodiesel based inde- pendent two-area interconnected microgrid system,” IET Renewable Power Generation, vol. 14, no. 6, pp. 1074–1086, [38] K. V. Hessam, F. D. Mohammadi, V. Verma, J. Solanki, and S. K. Solanki, “Hierarchical multi-agent based frequency and voltage control for a microgrid power system,” International Journal of Electrical Power and Energy Systems, vol. 135, Article ID 107535, 2022. [39] M. U. Jan, A. Xin, H. U. Rehman, M. A. Abdelbaky, S. Iqbal, and M. Aurangzeb, “Frequency regulation of an isolated microgrid with electric vehicles and energy storage system integration using adaptive and model predictive controllers,” IEEE Access, vol. 9, pp. 14958–14970, 2021. [40] A. K. Barik and D. C. Das, “Coordinated regulation of voltage and load frequency in demand response supported bio- renewable cogeneration-based isolated hybrid microgrid with quasi-oppositional selfish herd optimisation,” International Transactions on Electrical Energy Systems, vol. 30, Article ID e12176, 2020. [41] R. Mandal and K. Chatterjee, “Frequency control and sen- sitivity analysis of an isolated microgrid incorporating fuel cell and diverse distributed energy sources,” International Journal of Hydrogen Energy, vol. 45, no. 23, pp. 13009–13024, 2020. [42] T. UttamraoSolanke, V. K. Ramachandaramurthy, Y. Y. Jia, J. Pasupuleti, P. Kasinathan, and A. Rajagopalan, “A review of strategic charging-discharging control of grid-connected electric vehicles,” Journal of Energy Storage, vol. 28, Article ID 101193, 2020. [43] N. Sujitha and S. Krithiga, “Grid tied PV-electric vehicle battery charger using bidirectional converter,” International Journal of Renewable Energy Resources, vol. 9, no. 4, pp. 1873–1881, 2019. [44] A. R. Dash, B. C. Babu, K. B. Mohanty, and D. Rahul, “Analysis of PI and PR controllers for distributed power generation system under unbalanced grid faults,” in Pro- ceedings of the International Conference on Power and Energy Systems, pp. 1–6, Chennai India, December 2011. [45] H. Mao, X. Yang, Z. Chen, and Z. Wang, “A hysteresis current controller for single phase three level voltage source in- verters,” IEEE Transactions on Power Electronics, vol. 27, no. 7, http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Transactions on Electrical Energy Systems Hindawi Publishing Corporation

A Novel Controller Design for Small-Scale Islanded Microgrid Integrated with Electric Vehicle-Based Energy Storage Management

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Copyright © 2022 Nihal Omar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Hindawi International Transactions on Electrical Energy Systems Volume 2022, Article ID 5059215, 19 pages https://doi.org/10.1155/2022/5059215 Research Article A Novel Controller Design for Small-Scale Islanded Microgrid Integrated with Electric Vehicle-Based Energy Storage Management Nihal Omar ,AnilKumar Tiwari ,K. Seethalekshmi ,andNitin AnandShrivastava Department of Electrical Engineering, Institute of Engineering and Technology, Lucknow 226021, UP, India Correspondence should be addressed to K. Seethalekshmi; seethalekshmik@ietlucknow.ac.in and Nitin Anand Shrivastava; nitinshrivastava@ietlucknow.ac.in Received 4 February 2022; Accepted 28 March 2022; Published 29 April 2022 Academic Editor: Kamran Iqbal Copyright © 2022 Nihal Omar et al. +is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Small-scale islanded microgrid technology has the potential to utilize renewable sources for electrification in extreme areas where conventional transmission of power is difficult. But due to the absence of a utility grid in the islanded operation, voltage and frequency control becomes the major concern. Instead of using a centralized large battery storage system, electric vehicle- (EV-) based distributed energy storage may provide a dynamic and much cheaper energy storage solution for small-scale systems in the long run. +e only issue is to effectively manage the dynamics of EV penetration for achieving the overall control in the islanded system. To address the same issue, this paper proposes a novel segregation-based inverter control structure for an islanded microgrid while employing EVs as an energy storage system, thus eliminating the need for centralized storage. +e designed integrated control structure simultaneously attends to the voltage and frequency regulation need of the system, overcoming the issue of control lag, along with energy storage and management aspects in the microgrid operation for controlling the power flow under various practical scenarios. Extensive experimental studies are performed in MATLAB/SIMULINK environment, which indicates that the proposed integrated control structure gives a satisfactory performance under various scenarios encompassing load variation, renewable energy uncertainties, and EV dynamics in comparison with conventional control schemes employed in the islanded microgrid. disturbances, profile variation, connection or disconnection 1. Introduction status of the RES, proper energy storage and management +e major problem with renewable energy sources (RES) is (ESM) in the system, voltage maintenance, frequency reg- their intermittent and unpredictable nature. So to utilize ulation, and also ensuring good power quality. +us, energy RES, a large and complex control is required. For electri- storage is used in MG [8, 9]. +e use of electric vehicles fication of remote rural areas, the islanded system is es- (EVs) in MG for ESM is a promising area of research because sentially required [1–4]. RES-based microgrid (MG) requires of its fast charging and discharging capabilities. +e bidi- a power electronics-based converter in the system; thus, it rectional power flow in EV is called vehicle-to-grid (V2G) may adversely affect the overall power quality of the system. for discharging operation and grid-to-vehicle (G2V) for Further, any islanded MG itself suffers from the lack of charging operation of the vehicle [10, 11]. system inertia because RES are integrated through power +e inverter control is essential in MG for its proper electronic controllers, which gets manifested into issues in functioning and operation [12, 13]. However, the reported frequency regulation [5–7]. Having an integrated DC bus in work in [12, 13] does not consider the integration of RES and the system may improve this issue a little bit. EVs. As the system starts including more and more intrinsic +e proper functioning of an MG includes complete mechanisms and also approaches towards more practical control of the system under load variations, system scenarios, conventional control starts failing to operate. lag which occurs due to complex computational To overcome this, various papers have investigated the functioning of MG with RES [5, 14–17], energy storage techniques. systems [18–20], some advanced ESM strategies designed (iii) Consideration of intrinsic mechanisms and non- with dynamic programming [21] and use of EVs in charging linear characteristics of converters interfacing RES stations and V2G design [22–29]. A few works are reported and EVs in the study. in advanced inverter control structures like adaptive droop (iv) Seamless coordination among proposed SBIC control [10, 30], proportional-integral (PI) controller [4], module, converter interfaced renewable generation fuzzy logic [20, 31, 32], model predictive controllers [33] for and EV control module, and ESM module. MG control, and so on. In [31, 34–38], nonconventional optimization algorithms, genetic algorithms, butterfly op- +e outline of this paper is as follows. Section 2 discusses timization, and so on are applied to tune the controllers. the detailed description of MG system modeling used for +ese control techniques are effective and adaptive; how- problem formulation. Section 3 consists of a detailed dis- ever, they require a lot of iterative computational memory cussion over the proposed inverter control strategy and ESM which may create hurdles in real-time operation and are strategy. Section 4 shows the simulation results and dis- prone to be stuck at local optimum [39, 40]. +is can cussion over considered case scenarios. Finally, Section 5 generate an unintentional control lag that may hinder the provides conclusions. controller from working at its global optimum operation. With this background, the main problem, which is 2. System Modeling considered in this research, is to make the MG independent from centralized storage, external inertia support, and its +e schematic of islanded MG system is shown in Figure 1. control being free from the iterative computation. Most of MG consists of a DC bus, integrated with RES, and an AC the reported literature considers grid support or large- bus which is connected to the load. An inverter filter in- battery storage system (LBSS) [18, 30, 33] support for sta- terface is used to connect both buses. +e inverter used is a bilizing the system. Some of the works consider only uni- grid forming voltage source inverter, for isolated MG op- directional power flow for EV charging stations [23–27, 29]. erations. EV modules are integrated at both DC and AC Reheat turbine system support and approximated solar and buses. +e proposed main inverter controller and ESM al- EV models are considered in [31] without including their gorithm module are shown with input and output signals for intrinsic characteristics. None of these works has explored a overall control and proper functioning of MG. standalone MG system with renewable generation and EV integration without the support of a power grid, LBSS, and turbine systems. Also, the nonlinearity of converters in- 2.1. Renewable Generation Integration. RES (solar and wind) terfacing RES and EVs is avoided in most of the studies just are integrated into the DC bus of the microgrid. Mathe- by replacement from a first-order transfer function [39–41] matical modeling and respective control mechanisms of to avoid certain complexities. +is became the motivation solar and wind plants, connected to the DC bus, are detailed for this work to include the same in the study. in this section. A novel self-adaptive segregation-based inverter control (SBIC) with a dynamic band margin- (DBM-) based hys- teresis controller, which uses the voltage signals for gen- 2.1.1. Solar Plant. +e solar power plant is integrated into erating pulse width modulation (PWM) signals, is designed. the DC bus of a microgrid by the means of a boost converter Controller derives its adaptive features, through the segre- that is being controlled by the incremental conductance gation of its dynamic and fundamental components, of maximum power point tracking (IC-MPPT) control algo- feedback. +e main advantage of using SBIC is that it oc- rithm. For dynamic modeling of PV array, a panel of 10 cupies very little memory, produces no control lag due to series modules and 47 parallel strings is chosen. Mathe- coordination delay, and is neither prone to be stuck at local matical modeling of solar PV characteristics is reported in optimum as the controller is not driven from an iterative [5, 17]. algorithm or member function-based strategy. A propor- Mathematically, IC-MPPT can be described in both tional-resonant (PR) controller is used for high speed and discrete sample systems (DSS) and continuous differential improved steady-state performance. Also, bidirectional systems (CDS), as shown in (1)–(7). converters are designed for EV charging and discharging with small modifications controlled from an ESM scheme. P(j) � P(j + 1) − P(j), P(j) � V ∗ I , (1) j j +e major research contributions of this paper are as follows: 􏽘 P(j) � V ∗ I − V ∗ I � 0, (2) (i) Voltage and frequency regulation of islanded n n 1 1 j�1 microgrid through EVs (using as energy storage) without any LBSS or external inertia support. n k n (ii) A novel, self-adaptive discrete sample coordinated 􏽘 P(j) � 􏽘 P(j) + 􏽘 P(j) � 0, (3) SBIC scheme to overcome the problem of control j�1 j�1 j�k+1 DC bus of MG shaft torque (p.u.) Base torque PWW EV system side 6 PWM Pulses International Transactions on Electrical Energy Systems 3 V DC V1 (abc) I1 (abc) DC BUS AC BUS V (abc), I (abc) MPPT algorithm Bi-directional Switching Vehicle Solar DC Power & Boost Circuit AC-DC conv. Systems Plant SUB AC BUS converter LCL Inverter AC side EV Module AC side filter Wind Uncuntrolled DDMT Dynamic Plant rectifirer AC power SOC control Residential Voltage and measurement Load current measurement VSI Gate Driver and Isolation EV Vehicle Bi-directional Switching Circuit Circuit systems DC-DC conv. Switching Signals Reference MAIN Inputs INVERTER CONTROLLER DC Side EV Module Energy Storage Management (ESM) DC Bus Voltage SOC Algorithm measurement Figure 1: Schematic of the proposed microgrid system. gen. speed (p.u.) k n DDMT 􏽘 P(j) � − 􏽘 P(j). (4) Wind wind torque Pitch beta Gear j�1 turbine (p.u.) j�k+1 Controller Box wind speed rotor speed (p.u.) In CDS, the derivative approach is used to find out the rotor speed (p.u.) condition for the point of maximum power (PMP), as shown To uncontrolled PMSG in (5) and (6). +e condition of PMP in CDS is given in (7). rectifire P � V∗ I, (5) Figure 2: Schematic diagram of DDMT control. zP zI (6) � I + V � 0, zV zV + PI PI – + V DC (ref.) V2 zV zI + + (7) � − . V I Boost S + 2.1.2. Wind Plant. +e permanent magnet synchronous – – V1 generator (PMSG) based wind power plant is integrated into V EV s/s (ref.) + – PI + – PI the DC bus with the help of differential drive mass train (DDMT) control [15] and with an uncontrolled rectifier Figure 3: Schematic of bidirectional DC-DC converter with circuit. Mathematical modeling of the wind power plant is control. reported in [31]. DDMT control is a gearbox-based shaft speed control, a schematic diagram of which is shown in 2.2. Electric Vehicle (EV) Integration. In MG operation, EV Figure 2. Mathematical modeling is shown in (8)–(11). modules are utilized as flexible loads for ESM and power flow maintenance and regulation of MG system via V2G and P � ω T � ωT , (8) r s m G2V operations [42]. During these operations, EV converter modules are in operation; hence, the design process focuses dω (9) 2H � T − T , t m s on the same. Converter and its control design affect the V2G dt and G2V process for both DC and AC sides of the MG. Some advanced converter designs are described in [27, 43]. 1 dθ sa � ω − ω , (10) ω dt es 2.2.1. Converter Design for DC Side EV System. DC side EV dθ sa (11) T � K θ + D . system modules are integrated into the DC bus of the MG s s sa t dt with the help of a common bidirectional DC↔ DC PWW Buck I1 R C 4 International Transactions on Electrical Energy Systems abc to PWM V + – + – dq0 to + – PI PI V DC EV ref. dq0 abc PI Vdq0 idq0 V MG (AC) ref. 6 pulse DC 2 AC LCL EV system AC bus S1 bridge filter side of MG converter abc to Vabc, iabc PI Circulating dq0 curr. protection I PWM S2 I2 MOSFET Uncontrolled based bridge Buck 3 phase rectifire converter breaker Figure 4: Schematic of designed bidirectional AC-DC converter. Ref. Id signal Inner PI Microgrid Edq0 Vref Ref. I/P - V DC PR Ref. I/P 6 PWM error dq0 to abc Coordination Hysteresis current System ref Controller Delay Band PWM Pulses Signal Controller Model –– feedback feedback 5 samples delay feedback h (t) Vabc I (dq0)F Ts = 1e-06 s DC Bus Voltage V DC abc to dq0 abc to dq0 Wt ref V (abc)F I (abc)F I (abc)F I1 (abc)F Vabc, V1abc V (abc)D Dynamic Dynamic Band Margin feedback Segregation Generation signls 1abc, I1abc Frequency Ref. Frequency Integrator Ref. Gen. W0 = 2*pi*f Figure 5: Schematic design of proposed SBIC-based main inverter controller. converter, where V2G is a discharging, closed-loop boost 2.2.2. Converter Design for AC Side EV System. AC side EV operation control and G2V is a charging, closed-loop buck modules are integrated into the AC bus of MG with the help operation control, with internal current control, schematic of a modified bidirectional AC↔ DC converter design in- design of which is shown in Figure 3. cluding a circulating current protection circuit within the +e closed-loop control is described in (13)-(14). Voltage converter. V2G discharging operation is carried out with relations for V2G and G2V operations are given in (15)-(16). DC ⟶ AC conversion and G2V charging operation is In (12), PI controller in discrete domain is denoted as PI. carried out with AC ⟶ DC conversion. +is AC ⟶ DC conversion requires an uncontrolled AC ⟶ DC rectifier k T with a DC ⟶ DC buck converter connected in a cascaded i s PI[z] � k + , manner. +e schematic design of the proposed AC ↔ DC Z − 1 (12) bidirectional converter with its control is shown in Figure 4. n− 1 Circulating current protection circuit logic is given in PI[n] � 􏼠 􏼡􏽮PI[z]∗ z 􏽯dz, 2πj (17)–(20). S � 1(on)∀I � 0, (17) 1 2 D [n] � PI[n]⊛􏼐PI[n]⊛􏽮V − V 􏽯 − i􏼑, (13) 1 ev,ref 1 S � 0(off )∀I ≠ 0, (18) 1 2 D [n] � PI[n]⊛􏼐PI[n]⊛􏽮V − V 􏽯 − I􏼑, (14) 2 DC,ref 2 S � 1(on)∀I � 0, (19) 2 1 V � , (15) 1 − D S � 0(off )∀I ≠ 0. (20) 2 1 V � D V . (16) 1 1 2 PI controllers are the same as in (12). feedback Wt ref Wt ref – International Transactions on Electrical Energy Systems 5 e command or switching signals, for connection or disconnection of both DC side EV systems and AC side EV systems, are generated by the ESM scheme, which is dis- Inner PI Integrator cussed in Section 3. error current Ref.I/P - V DC ref signal Controller (W2)/Kr –– 3. Proposed Control Strategy Feedback PR Controller is section describes the following: DC Bus Voltage Integrator V DC (a) Proposed novel SBIC structure for the main inverter Figure 6: Schematic diagram of used PR controller. interfacing the DC and AC bus of MG. (b) ESM scheme for meeting the energy requirements to ensure the proper functioning of MG. (c) Coordination among SBIC module, ESM scheme, and control module of renewable generation and EVs. G (D) G (F) G (F) G (F) feedback Digital filter signals model 3.1. Main Inverter Controller. e proposed controller is Figure 7: Schematic diagram of dynamic segregation block. shown in Figure 5. Inputs, given to the controller, are DC bus voltage and system frequency reference. Feedback voltage and current signals are also being supplied. PWM pulses are taken as output signals. As there is no external Fundamental components of feedback signals, before inertia support of power grid, LBSS, or turbine system in the injecting into ICC and MG system model, are segregated, MG system (Figure 1), it is essential to have a robust and self- through digital Œlter operations, as shown in Figure 7. is adaptive controller design to ensure the proper functioning segregation is used to achieve coordination among high of MG under any kind of disturbances and with high bandwidth PR controller output signals, low bandwidth penetration of converter interfaced RES and EV systems. feedback signals, and dynamics of high bandwidth PI To ensure the same, dynamic segregation and dynamic controller, same as (12), used in ICC. band margin (DBM) generation blocks are introduced in the For any given signal G, this can be expressed as controller, which modiŒes the feedback signals before injecting them into the inner current controller (ICC) and G  G(F)+ G(D). (24) hysteresis band PWM blocks. e novel segregation feature, e digital Œlter model used to segregate the funda- which segregates the fundamental component from feed- mental G(F) is as follows. back, makes the controller robust and the DBM makes the Consider a discrete form of signal G as G[n], which is controller self-adaptive. formulated as (25). A delay element is introduced in the controller for ef- fective coordination and to avoid control lag. PR controller 2πn 6πn − (n/N) G[n]  y e + y ∗ sin + θ + y ∗ sin + θ , is used for improving the steady-state performance and 0 1 1 3 3 N N space vector frame transformation is used to reduce the (25) complexity of the control structure. A schematic of the PR controller [44] is shown in 2 3 n n n − (n/N) Figure 6, which is used for DC bus voltage control and the (26) e  1 − + − + ·· ·, 2 3 2N 6N output of the PR controller is supplied to ICC as referenced- ∗ ∗ axis current i and reference q-axis current is taken as i  0. d q 2πn e transfer function of the PR controller is given in (21), k  , while i is expressed in (23). (27) y  y cos θ , 1c 1 1 ak a + k p r n− 1 y  y sin θ . PR[z] , PR[n]  PR[z] ∗ z dz, 1s 1 1 2 2 2πj a + ω Using (27), (21) 2πn y ∗ sin + θ  y ∗ sin k cos θ + cos k sin θ 1 1 1 n 1 n 1 z − 1 a  , (28) (22) y sin k + y cos k . 1c n 1s n i [n] PR[n]⊛V − V . (23) DC,ref DC Substituting (26) and (28) in (25) yields Kr Kp 6 International Transactions on Electrical Energy Systems Vcc 3 Pulses Vref (abc) Hysteresis Band Vref (abc) - h (t) to Vref (abc) + h (t) From MG s/s In Band Relay 3 Pulses model based Hysteresis Controller Logical NOT 6 PWM Hysteresis Vabc Dynamic band Comparator Pulses feedback 3 Pulses margin h (t) Vcc 3 Pulses Figure 8: Schematic diagram of hysteresis controller. Start Table 1: System parameters. Parameter Value Calculate - VDC, P (SW), V 600 V DC,ref SOC (EV)_dc, f, P (Load), f 50 Hz SOC (EV)_ac V 400 V ACL− L f 48.5 Hz min 12 hours of 12 hours of f 51.5 Hz max Day Night V 570 V min V 630 V max VDC > Vmax | | P (SW) > f 50 kHz sw P (Load) | | f> f (max) S 1000 W/m ref T 25 C ref Yes Yes V 363 V oc− boost No At Day C 3.267 mF boost SOC (EV)_dc SOC (EV)_ac < L 1.43 mH boost < SOCmax SOCmax At Night P 100 kW solar− rated No P 30 kW wind− rated Yes Yes u 12 m/s wind Charge Charge H 4 No EVs (DC) EVs (AC) K 0.3 D 1 Disconnect Yes P (sw) = Yes Disconnect SOC 40% min EV (DC) P (Load) EV (AC) SOC 95% max No No L 500 μH Disconnect No C 100 μF EV (DC) At Day SOC (EV)_ac R 0.02Ω SOC (EV)_dc > SOCmin > SOCmin Disconnect At Night Having n � 1, 2, 3, . . . , N samples, y , y , y , y , y No 0 1c 1s 3c 3s EV (AC) Yes Yes are computed from the following matrix. Discharge Discharge EVs (DC) EVs (AC) G[1] j sin k 􏼁 cos k 􏼁 sin 3k 􏼁 cos 3k 􏼁 ⎡ ⎢ ⎤ ⎥ ⎢ ⎥ 1 1 1 1 1 ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎢ ⎥ ⎥ ⎢ ⎥ ⎡ ⎢ ⎤ ⎥ ⎡ ⎢ ⎤ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ y ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ 1c ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎢ ⎥ ⎢ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎥ ⎢ ⎥ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ Figure 9: Flowchart of energy storage management scheme. ⎢ G[2] ⎥ ⎢ j sin k 􏼁 cos k 􏼁 sin 3k 􏼁 cos 3k 􏼁 ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ 2 2 2 2 2 ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎢ ⎥ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ � ⎢ ⎥⎢ ⎥. ⎢ ⎥ ⎢ ⎥⎢ y ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ 1s ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎢ ⎥ ⎥ ⎢ ⎢ ⎥ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ . . . ⎥ ⎢ . . . . . . . . . . . . . . . ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎣ ⎦ ⎣ ⎦⎢ ⎢ ⎥ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ y ⎥ ⎢ ⎥ ⎢ 3c ⎥ ⎣ ⎦ G[N] j sin k cos k sin 3k cos 3k 􏼁 􏼁 􏼁 􏼁 N N N N N 3s 2 3 n n n G[n] � y 1 − + − 􏼠 􏼡 (31) 2 3 2N 6N Inversing the matrix, we get (29) + y sin k + y cos k 􏼁 􏼁 1c n 1s n − 1 ⎡ ⎢ ⎤ ⎥ j sin k 􏼁 cos k 􏼁 sin 3k 􏼁 cos 3k 􏼁 G[1] ⎢ ⎥ ⎢ ⎥ 1 1 1 1 1 ⎢ ⎢ ⎥ ⎢ ⎥ ⎥ ⎢ ⎥ ⎢ ⎥ ⎡ ⎢ ⎤ ⎥ ⎢ ⎥ ⎢ ⎥ ⎡ ⎢ ⎤ ⎥ ⎢ y ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ ⎢ 1c ⎥ ⎥ ⎢ ⎥ ⎥ ⎢ ⎥ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ + y sin 3k 􏼁 + y cos 3k 􏼁 , ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ 3c n 3s n ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ j sin k 􏼁 cos k 􏼁 sin 3k 􏼁 cos 3k 􏼁 ⎥ ⎢ G[2] ⎥ ⎢ ⎢ ⎥ ⎥ ⎢ ⎢ ⎥ ⎥ ⎢ ⎢ ⎥ ⎥ ⎢ ⎥ ⎢ 2 2 2 2 2 ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎢ y ⎥ � ⎢ ⎢ ⎥ ⎥ ⎢ ⎢ ⎥ ⎥. ⎢ ⎥ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ 1s ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ ⎥ ⎥ ⎢ . . . . . . . . . . . . . . . ⎥ ⎥ ⎢ . . . ⎥ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ 2 3 ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ ⎢ ⎢ ⎥ ⎣ ⎦ ⎢ ⎥ ⎥ ⎢ y ⎥ n n n ⎢ ⎥ ⎢ 3c ⎥ ⎢ ⎥ ⎣ ⎦ 􏼠1 − + − 􏼡 � j . j sin k 􏼁 cos k 􏼁 sin 3k 􏼁 cos 3k 􏼁 G[N] N N N N N 2 3 2N 6N 3s (32) (30) Le = Day Time Scheme Right = Night Time Scheme International Transactions on Electrical Energy Systems 7 Voltage variation with RES integration 0.26 0.28 0.3 0.32 0.34 0.36 0.38 Time (s) DC bus voltage with Solar Plant (a) 0.26 0.28 0.3 0.32 0.34 0.36 0.38 Time (s) DC bus voltage with Solar and wind Plant (b) -200 3-phase AC bus Voltage with solar and wind plant -400 0.26 0.28 0.3 0.32 0.34 0.36 0.38 Time (s) (c) Figure 10: Voltage profiles. (a) DC bus voltage robustness of MG with solar plant integration. (b) DC bus voltage sensitivity of MG with both solar and wind plants. (c) +ree-phase AC bus voltage of MG with solar and wind plant integrated. 􏽶����������� From (32), the functions G(F)[n] and G(D)[n] can be 􏽴 computed as follows. 3s √� (37) x [n] � y + 􏼠 􏼡 , s 1s 2πn 3 G(F)[n] � x[n]sin 􏼒 + δ􏼓 G(D)[n] � G[n] − G(F)[n]. (38) 2πn 2πn � x[n]∗ 􏼔sin 􏼒 􏼓cos(δ) + cos􏼒 􏼓sin(δ)􏼕, N N (33) +us, same as the above equations, feedback voltages and current signals can be segregated as given in (39)–(41) and x[n]cos(δ) � x [n], will be supplied wherever necessary. x[n]sin(δ) � x [n], V � V (F) + V (D), (39) abc abc abc (34) 2πn 2πn i � i (F) + i (D), (40) abc abc abc G(F)[n] � x [n]sin 􏼒 􏼓 + x [n]cos 􏼒 􏼓, (35) c s N N 􏽶����������� i1 � i1 (F) + i1 (D). (41) abc abc abc 2 3c Modeling of ICC is given in (44)-(45) through frame √� (36) x [n] � y + , 􏼠 􏼡 c 1c transformation as given in (42)-(43). Voltage (V) Voltage (V) Voltage (V) 8 International Transactions on Electrical Energy Systems Frequency control with RES connect and disconnect 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.60 0.65 0.70 Time (s) (a) Wind Plant disconnect at 0.4s 0.36 0.38 0.40 0.42 0.44 0.46 Time (s) f (mesured) f (Ref.) (b) Solar Plant disconnect at 0.653 s Wind again connect at 0.613 s 0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.70 0.71 Time (s) (c) Figure 11: Load frequency robustness waveforms (a) shows complete time scale, (b) zoom version of (a) From t � 0.36 s to 0.47 s, (c) zoom version of (a) From t � 0.61 s to 0.71 s. i (F) � [T] i (F) , 􏼂 􏼃 (42) dV (F) dq 0 abc q C � i (F) − i1 (F) − ω CV (F), q q 0 d dt √�� � (49) i (F) � i (F) + ji (F), j � − 1, (43) dV (F) dq 0 d q q C � E . dt ∗ ∗ V (F) � PI[n]⊛􏼈i [n] − i (F)􏼉, (44) d d d For linear control with the fundamental component, the system satisfies the relation given in (50). ∗ ∗ V (F) � − PI[n]⊛i (F), i � 0. (45) q q q V (F) V (F) T ∗ ∗ q d s Reference inputs V and V , generated from ICC, are � � . (50) d q E E C(z − 1) supplied to the MG system model which represents the LCL d q filter modeling as given in (48)–(50). In the AC frame, the output of the MG system V is ref,abc i1 (F) � [T] i1 (F) , 􏼂 􏼃 (46) given in (52). dq 0 abc E + jE � E , (51) i1 (F) � i1 (F) + ji1 (F), (47) d q dq 0 dq 0 d q − 1 dV (F) V � [T] E . (52) 􏽨 􏽩 d ∗ ref ,abc dq 0 C � i (F) − i1 (F) + ω CV (F), d d 0 dt (48) Actual feedback V and reference V are supplied abc ref ,abc dV (F) C � E , to a hysteresis controller through a delay element of 5 dt Frequency (Hz) Frequency (Hz) Frequency (Hz) International Transactions on Electrical Energy Systems 9 Control With RES profile variation ×10 30 kW power only due to 12 m/s wind speed 25 deg. Celsius Temprature 130 kW power at 1 kW/m2 solar irradiation and 12 m/s wind speed 0.4 0.5 0.6 0.7 0.8 0.9 1 Time (s) Active Power (a) AC bus currents due to contineous solar irradiation variation –200 –400 0.4 0.5 0.6 0.7 0.8 0.9 1 Time (s) (b) 0.4 0.5 0.6 0.7 0.8 0.9 1 Time (s) DC bus voltage Ref. DC bus voltage (c) Figure 12: Control under continuous solar irradiation linear variation. (a) +e power output at AC bus with wind plant (at base speed 12 m/s) also connected to MG. (b) +e variation in AC bus three-phase currents waveforms. (c) Robustness in DC bus voltage waveform. samples to synchronize the DBM, denoted as h(t), with With the above calculation, the hysteresis band will feedback signals and eliminate the control lag. range from V − h(t) to V + h(t). +e schematic ref ,abc ref ,abc Dynamic band margin (DBM) is generated using diagram of the used hysteresis band-based PWM generation feedback voltage dynamic component (anything other than technique is shown in Figure 8. +e detailed working of a a fundamental component in the signal) as evaluated in (53). conventional current hysteresis controller is well known as given in [45, 46]. V (D) � V − V (F). (53) abc abc abc Consecutive 5 samples of V (D) are used to generate abc 3.2. Energy Storage Management (ESM) Scheme. +e flow- an autovarying DBM. +is varying DBM makes the hys- chart for the algorithm used in the ESM scheme, for teresis band self-changing, which makes the hysteresis command or signal generation for connection or discon- controller self-adaptive. For any discrete function f[n], nection of both EV systems, is given in Figure 9. ESM sam 􏼈f[n]􏼉 represents the consecutive 5 samples as given in scheme is designed for twenty-four hours of the day. It is sam 􏼈f[n]􏼉 � 􏼈f[n − 4], f[n − 3], f[n − 2], f[n − 1], f[n]􏼉. assumed that two separate fleets of EVs are present on the DC and AC sides, for 24 hours of the day, for energy (54) management in the microgrid. Under the normal operating Considering f[n] � V (D), DBM can be calculated as condition, wherein the supply from RES can meet the system abc follows. demand, given in (69), both EV systems remain discon- nected; otherwise, connection and disconnection take place h(t) � max sam V (D) − min sam V (D) . 􏼂 􏼈 􏼈 􏼉􏼉 􏼈 􏼈 􏼉􏼉􏼃 according to the scheme to regulate the system parameters. n abc n abc +e inputs given to the algorithm are P , P , SOC input PV W (55) for both the EV modules, P , V , AC bus voltage and L DC 1.1 1.1 1.1 AC currents (Amp.) Voltage (V) Power (W) 10 International Transactions on Electrical Energy Systems Load Demand variation control 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Time (s) Supplied Active Power Load demand/phase (a) 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Time (s) Supplied Reactive Power (b) 3-phase AC bus currents –50 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Time (s) (c) Figure 13: Load side power demand variations. (a) Active power per phase demand and supplied power per phase in watt. (b) Reactive power supplied/phase in VAR. (c) +ree-phase AC bus currents during power demand variation. currents, reference inputs SOC and SOC , and Charge present at a given time in Ah min max SOC (%) � 100∗ . 􏼠 􏼡 f , f , V , andV . Rated charge of a vehicle battery in Ah min max min max SOC for every vehicle SOC present in any of EV (59) modules must satisfy As an indicative parameter of EV modules, the average SOC < SOC < SOC . (56) min V max SOC for overall modules SOC and SOC can be EV,DC EV,AC calculated as follows. While the connection with MG the power supplied or and P taken by DC and AC EV systems are P n1 EV,DC EV,AC SOC (%) iV which are given, respectively, as follows: (60) SOC (%) � 􏽘 , EV,DC n1 i�1 n1 P � 􏽘 P , (57) EV,DC iV n2 SOC (%) i�1 iV (61) SOC (%) � 􏽘 . EV,AC n2 i�1 n2 P � 􏽘 P . (58) EV,AC iV Combined RES power can be written as P , given as SW i�1 P � P + P . (62) SW PV W At any time, SOC (in percentage) of any vehicle can be calculated as Power flow management is given in (63)–(66). Reactive power/phase (VAR) Power/phase (W) Ac currents (Amp.) International Transactions on Electrical Energy Systems 11 4 Control wioth RES switching and profile variation ×10 Same solar profile, wind speed 0 to 1m/sec Wind plant disconnected, only solar plant with 700 kW Solar irradiation 700 kW/m2, 25 deg. Celcius, wind speed 12 m/s to 15 m/s 0.6 0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.76 0.78 0.8 Time (s) Active power at AC bus (a) –5000 0.6 0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.76 0.78 0.8 Time (s) Reactive power at AC bus (b) three-phase AC bus currents –500 0.6 0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.76 0.78 0.8 Time (s) (c) Figure 14: (a) Active power at the load bus. (b) Reactive power at the load bus. (c) +ree-phase AC bus currents. Case 1. P > P (charging) SW L P � P + P � P . (69) SW PV W L Day time P − P � P , (63) SW L EV,DC Extreme positive or negative case scenarios of extremely low load demand (need of generation curtailment/feed-in Night time P − P � P . (64) SW L EV,AC management) or requirement of load shedding are not considered during this study and thus not included in the ESM scheme. Case 2. P < P (discharging) SW L Day time P + P � P , (65) SW EV,DC L 3.3. Coordination among SBIC Module, ESM Scheme, and Control Module of Renewable Generation and EVs Night time P + P � P . (66) SW EV,AC L (i) Control logic, used in renewable generation con- verters, is for extracting the maximum amount of DC bus voltage and frequency of AC bus f must AC satisfy the following conditions. power from solar and wind plants having nonlinear characteristics. V < V < V , (67) min DC max (ii) Control logic, used in EV bidirectional converters, is to maintain power flow in the right direction using f < f < f . (68) the constant voltage charging method and also min AC max eliminate the risk of circulating current in the +e normal operating condition of MG when no EV converters. Switching of EV modules (connection system will be connected is given in (69) along with (67), and disconnection from MG) is decided by the ESM (68). scheme through digital switching signals. Reactive power (VAR) AC currents (Amp.) Active power (W) 12 International Transactions on Electrical Energy Systems Under unbalanced three phase load conditions Phase A Phase B Phase C -500 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Time (s) (a) Phase A Phase B Phase C -100 -200 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Time (s) (b) ×10 2.5 Another 3 phase unbalanced load connected 1.5 Phase A 0.5 Phase B Phase C 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Time (s) (c) Figure 15: (a) AC bus three-phase voltages. (b) AC bus three-phase currents. (c) Active power for each phase. (iii) ESM scheme only decides on switching of EV 4.1. Controller Performance and Sensitivity Analysis. To modules (from conditions specified in Section 3.2). validate the performance and robustness/sensitivity of the EV control regulated the power flow direction but controller under disturbances of the MG system, some cases along with the fact that stabilization of MG pa- have been considered without ESM scheme and EV inte- rameters to their reference values is also essential gration, while maintaining the power balance between which is being governed by the SBIC module. generation and demand. (iv) First of all, renewable control deals with extracting maximum power which is continuously varying 4.1.1. Voltage Control with RES Integration. +e solar plant which triggers the ESM scheme to decide for is integrated with the DC bus of microgrid and the voltage of connection or disconnection of EV modules. Si- the DC bus remains well within permissible limits due to the multaneously, SBIC control tries to stabilize the MG controller as shown in Figure 10(a). When the wind plant is system. +us, overall coordination is established integrated with already having a solar plant on the DC bus, among all the control modules employed in the the voltage becomes more stable because of wind plant system. inertia aiding to DC bus of MG as shown in Figure 10(b). +ree-phase AC bus voltage is shown in Figure 10(c) with 4. Simulation Results and Discussions solar and wind plant integrated to DC bus of MG. +e proposed MG system as shown in Figure 1 is modeled in the MATLAB/SIMULINK platform. Data for load demand 4.1.2. Frequency Regulation and Robustness under Switching variation, RES profile variation are created by authors of RES. In the isolated microgrid, initially, both RES (solar and themselves. +e system parameters used for simulation are wind) modules are connected to the DC bus of MG. At t � 0.4 s, shown in Table 1. wind plant is disconnected; again at t � 0.613 s, wind plant is For analyzing the performance of the controller and connected to the DC bus of MG, and then at t � 0.653 s, solar ESM scheme, various case scenarios have been taken, which plant is disconnected from the MG. +e variation in the fre- are given below. quency due to these switching is shown in Figure 11. +e Currens (Amp.) Voltage (V) Active Power (W) International Transactions on Electrical Energy Systems 13 6 6 ×10 ×10 Conventional control Conventional control Wind conn.at 0.2 s proposed control proposed control 130 kW 30 kW Solar discon.at 0.4 s –1 Solar conn.at 0.45 s 130 kW –2 –1 –3 Solar discon.at 0.4 s Solar discon.at 0.4 s –2 –4 0.2 0.3 0.4 0.5 0.6 0.7 0.2 0.3 0.4 0.5 0.6 0.7 Time (s) Time (s) (a) (b) conven. control proposed control Ref. DC Voltage Solar discon.at 0.4 s Solar discon.at 0.45 s Wind conn. at 0.2 s 600 V 0.2 0.3 0.4 0.5 0.6 0.7 Time (s) (c) Figure 16: Failure of conventional control and comparison with proposed control while RES connections and disconnections. Till t � 0.2 s, only solar plant is supplying 100 kW of power at 600 V DC bus reference voltage; at t � 0.2 s, wind plant is connected, and supplied power rises to 130 kW, at t � 0.4 s, solar plant disconnected and again connected at t � 0.45 s. (a) Active power. (b) Reactive power. (c) DC bus voltage. controller is effective in maintaining the frequency within the the MG was contributed by the solar plant and disconnection permissible limits specified. Variations after t � 0.653 s are high of the same leads to higher fluctuations; however, due to the relative to that of t � 0.4 s because the major power supply to proposed controller, frequency is regulated. Active power (W) Voltage (V) Reactive power (VAR) 14 International Transactions on Electrical Energy Systems Renewable Power, Load Demand, DC side EV supply wind disconnect Solar Gen. start wind plant connect –2000 0 0.5 1.5 3 3.5 4.5 1 2 2.5 4 5 ×10 Time (s) P (solar + wind) EV Power flow reverses Power Load demand Power from DC side EV module (a) DC side Avarage SOC indication Renewable gen.> Load demand Only EV feeding the load EV charging no Renew. gen. EV discharging 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 Time (s) ×10 (b) Switching signal for EV module EV connected Renew. gen. = Load demand –1 EV disconnect –2 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 ×10 Time (s) (c) Figure 17: Twelve hours, daytime simulation with DC side EV module. (a) Solar and wind power generation with wind plant connect and disconnect, load power demand, and power from DC side EV module. (b) Average SOC indication parameter in percentage for DC side EV module. (c) Switching signal from ESM algorithm for DC side EV module. 4.1.3. Control and Robustness of MG System under RES phase by inverter, and three-phase AC bus current wave- Profile Variation. +e solar irradiation is decreased linearly forms are shown in Figure 13. +e controller is effectively from 1 kW/m to complete disconnection level from t � 0.4 s to supplying switching signals to the inverter for sustaining 0.7 s. Further, the plant is connected back and solar irradiation disturbances. is linearly increased from 0 to 1 kW/m at t � 0.8 s to 1 s. Temperature is kept at a reference value of 25 C. +e wind plant, working at a base wind speed of 12 m/s is connected to 4.1.5. Control and Robustness of MG under RES Profile Variation and Switching. Active and reactive power control the MG. From t � 0.7 s to 0.8 s, only wind plant power of 30 kW is available at AC bus. +e controller is effective in maintaining is validated with large and sudden variation in wind profile and also with disconnection of wind plant completely. As the sinusoidal nature of the three-phase AC bus currents and keeps the DC bus voltage within permissible limits during the shown in Figure 14, solar irradiation and temperature in this case are kept constant at 700 kW/m and 25 C, respectively. variations as shown in Figure 12. Till t � 0.663 s, wind speed was hovering between 12 m/s to 15 m/s and the total generation was from 103 kW to 105 kW. 4.1.4. Control and Robustness of MG with Load Power De- After t � 0.663 s, wind speed suddenly drops down to 0 to mand Variation. +e load active power, as well as reactive 1 m/s and generation falls to 72 kW to 73 kW at t � 0.68 s; the power demand, is varied step-wise at t � 0.4 s, 0.8 s, 1.2 s, wind plant is disconnected from the DC bus; thus, only solar 1.6 s, and 2.0 s to check the inverter capability to sustain the generation of 70 kW is left. For these variations, active power load demand variation. Active power per phase demand and profile is shown in Figure 14(a), reactive power profile is supplied power by the inverter, supplied reactive power per hovering around 0 kVAR as the load used in this case is Power (W) ON (1)/OFF (0) SOC (%) International Transactions on Electrical Energy Systems 15 Renewable Power, Load Demand, DC side EV supply Wind going down Renew. gen. stops, EV supply rising EV mod. feeding the load Solar gen. stop, wind supplying load. –2000 5.5 6 6.5 7 7.5 8 8.5 ×10 Time (s) P (solar + wind) Power Load demand Power EV mod. AC side (a) AC side avarage SOC indication AC side EV mod. discharging Niether charging or discharging 5.5 6 6.5 7 7.5 8 8.5 ×10 Time (s) (b) Switching signal for EV module Renew. gen. = Load demand 1 EV isolated. Renew. gen. < Load demand EV connected. –1 5.5 6 6.5 7 7.5 8 8.5 ×10 Time (s) (c) Figure 18: Twelve-hour nighttime simulation with AC side EV module. (a) Solar and wind power generation with solar plant disconnect, load power demand, and power from AC side EV module. (b) Average SOC indication parameter in percentage for AC side EV module. (c) Switching signal from ESM algorithm for AC side EV module. Figure 15(a), three-phase AC bus currents are shown in purely resistive. Small fluctuations are there in the reactive power profile (Figure 14(b)) due to the filter circuit and Figure 15(b), and the active power of each phase is shown in other converters elements present in the system. +ree- Figure 15(c). phase AC bus currents are shown in Figure 14(c) for the same variation. 4.1.7. Comparison with the Conventional Control Technique. While applying conventional control [4, 5, 9] in the pro- 4.1.6. Controller Robustness under Unbalanced Loading posed model with solar and wind plant connection and Condition. Due to the self-adaptive nature of the proposed disconnection, conventional control (CC) fails to converge controller, it can work with three-phase unbalanced loading as reported in Figure 16. In conventional control, the DC bus voltage control is generally separated from inverter control; conditions, and on switching extra random load, it can supply power to the system. Since unbalanced loads voltage thus, on switching the generation plants such large spikes appear in the results while in proposed control, the control and current magnitude of every phase are not identical but are having sinusoidal nature and on increasing the load at of DC bus voltage is integrated with inverter controller; thus, t � 0.25 s, only three-phase current rises and voltage mag- it can withstand the disturbances as shown in Figure 16(c). nitudes remain at the same level. Controller, having self-adaptive nature due to the virtue of Initially load demand of phases A, B, and C is 10.7 kW, dynamic band margin, is also able to control active and 10.5 kW, and 9.3 kW, respectively. At t � 0.25 s, another load reactive power while conventional control shows a very poor is switched on which increases the load demand of phases A, response as can be seen in Figures 16(a) and 16(b). Even for B, and C to 21 kW, 18 kW, and 15 kW, respectively. For these wind plant connection, a permanent voltage dip appears in variations, the three-phase AC bus voltage is shown in the system with conventional control while in the proposed Power (W) SOC (%) ON (1)/OFF (0) 16 International Transactions on Electrical Energy Systems control strategy MG system can withstand all kinds of RES 5. Conclusion connections and disconnections. In this article, a control scheme for an islanded microgrid, which is independent of centralized storage, external inertia support, and relying only on distributed EV-based 4.2. Performance with Energy Storage Management (ESM) storage for ESM, is proposed. +e proposed control Scheme. +e effectiveness of the proposed ESM scheme has scheme is free from iterative computational algorithms; been tested through a few cases to check its ability to stabilize thus, it occupies relatively less memory, produces no the system under switching instructions provided by the control lag, and is free from being stuck at local optimum ESM algorithm and to check on any control lag due to the operation. Intrinsic mechanisms and nonlinearities of ESM strategy. Phasor-based simulation is performed in this interfacing power electronic converters are also consid- case. ered in the system while designing the control as shown in Section 2.2. 4.2.1. Twelve-Hour Daytime Simulation with DC Side Electric +e proposed scheme incorporates a novel self-adaptive discrete sample type segregation-based inverter control Vehicle (EV) Modules. Continuously varying RES data is considered with wind plant connection and disconnection structure, which generates dynamic band margin, integrated with a voltage hysteresis controller and a proportional- and varying load power demands are taken with daytime consideration, and DC side EV modules, as per the ESM resonant controller. +e proposed scheme establishes a proper orchestration among the main control module, ESM algorithm. DC side EV module is connected with 120 ve- hicles with a 500 Ah rating and charging voltage of 240 V. module, and RES, EVs converter control module as given in Results for the twelve-hour daytime (4 am to 4 pm�50000 s) Section 4.2. +e proposed controller is robust against load variation, unbalanced loading, renewable energy uncer- simulation have been shown in Figure 17. RES power generation with wind plant connection and disconnection, tainties, and RES switching as shown in Section 4.1. +is scheme, while RES switching, shows way better performance power demand by the load, power supplied or taken by the EV modules is shown in Figure 17(a). Negative values of than conventional PI controller, as can be seen in Figure 16. +e system retains its performance intact in the whole range power indicate that EVs are charging and zero power in- dicates the disconnection of the EV module. +e indication from rated solar irradiation to plant completely cut off from the system and also from rated wind speed to wind plant parameter of the average SOC of the module (59) is shown in Figure 17(b). It can be easily seen SOC of EV modules is well cutoff from microgrid thus highly robust against input within the range specified. A negative slope indicates that the parameter variation as shown during sensitivity analysis in EV module is discharging, the positive slope for charging, Figures 12 and 14. and no slope for isolation. +e switching signal from the As a future directive, a small hydro and fuel cell-based renewable generation can be integrated to test the coordi- ESM algorithm is shown in Figure 17(c). Value 1 specifies connection and 0 specifies disconnection. +e proposed nation between the control scheme and ESM module (a complex and advanced design will be required) under control technique is in good coordination with the ESM scheme. multiple power generation modules for optimized power flow in the system. 4.2.2. Twelve-Hour Nighttime Simulation with AC Side Nomenclature Electric Vehicle (EV) Module. While considering nighttime (4p.m. to 4a.m.�45000 s) simulation solar plant discon- (1) Solar plant nection is considered, and continuously varying generation j , j , j : +ermal and irradiation constants 1 2 3 and load demand are considered. AC side EV modules are P(j), P(j), V , I : Power difference, power, voltage, and j j integrated and controlled with the ESM algorithm. In the EV current at jth sample module, 210 vehicles are considered with a 500 Ah rating and n, k, j: Total number of samples, the sample of charging voltage of 240 V. +e complete formulation is maximum power, and sample count shown in Figure 18. RG power output, solar disconnection, L , C : Solar boost converter inductance and boost boost wind generation decrement, load demand which is dy- capacitance namically supplied by both RES and EV module, and power P : Solar output power solar− rated flow from AC side EV modules are shown in Figure 18(a). V : Open-circuit solar panel voltage or input oc− boost +e average SOC indication parameter which shows isola- of boost converter tion or discharging is shown in Figure 18(b). +e average f : Switching frequency of boost converter sw SOC of the overall module is well within specified limits during the nighttime. Switching signals for connection or (2) Wind plant disconnection of AC side EV module from ESM scheme are H , K , D : Inertia constant, shaft stiffness, and damping t s t shown in Figure 18(c). Value 1 specifies connection and 0 coefficient specifies disconnection. +e proposed control technique is ω , θ : Base electrical speed and shaft twist angle es sa in good coordination with the ESM scheme without any ω, ω : Angular velocity of wind turbine and permanent control lag. magnet synchronous generator International Transactions on Electrical Energy Systems 17 T , T : Wind mechanical torque and generator shaft Feedback currents of sub- m s torque AC bus and AC bus in abc frame (3) DC and AC side electric vehicle system sam V (D): Array of five samples of n abc V , Reference voltages of electric vehicle module ev,ref V (D) from (n − 4)th abc V : and DC bus of microgrid DC,ref sample to current nth D , D : +e duty cycle for buck and boost operation 1 2 sample V , V : Measured voltages of electric vehicle module 1 2 L, C: LCL filter inductance and and DC bus of microgrid capacitance S , S : Switching signals for protection switches of 1 2 V : Nominal RMS line to line ACL− L DC-AC and AC-DC conversion circuits AC bus voltage i, I: Measured currents of electric vehicle module and DC bus of microgrid (5) Energy storage management scheme parameters V , V : Minimum and maximum permissible min max limits of DC bus voltage (4) Control parameters f : Measured frequency of AC bus of , k , k : Proportional, integral, and AC p i r microgrid resonant constants P , P : Net power supplied or consumed by DC i , i1 , V , V1 : Feedback currents and EV,DC EV,AC dq 0 dq 0 dq 0 dq 0 and AC side vehicle system modules voltages of sub-AC bus and f , f : Minimum and maximum permissible AC bus in dq 0 frame min max limits of AC bus frequency T , Z: Sample time of the system P : Power of ith vehicle attached either of DC and Z-transform parameter iV or AC side vehicle module in the discrete domain P , P , P : Instantaneous power of the solar plant, i , i , i1 , i1 , V , V , V1 , V1 : Feedback currents and PV W L d q d q d q d q wind plant, and load demand voltages of sub-AC bus and SOC , Minimum and maximum permissible AC bus in d-axis and q-axis min SOC : limits of vehicle state of charge of dq 0frame max P : Instantaneous power of solar and wind abc, dq 0: +ree-phase AC frame and SW plant rotational time-invariant SOC : Measured state of charge for ith vehicle DC frame iV SOC , State of charge indication parameters for V (F), V1 (F), Fundamental components EV,DC abc abc SOC : DC and AC side vehicle module. i (F), i1 (F), of feedback voltages and EV,AC abc abc V (F), V1 (F), currents in abc and dq 0 dq 0 dq 0 i (F), i1 (F): frame: dq 0 dq 0 Data Availability − 1 [T], [T] : Transformation matrices from abc to dq 0 frame and +e data used to support the findings of this study are in- dq 0 to abc frame cluded in the article. V (D), V1 (D), Dynamic components of abc abc i (D), i1 (D), feedback voltages and abc abc Conflicts of Interest V (D), V1 (D), currents in abc and dq 0 dq 0 dq 0 i (D), i1 (D): frame dq 0 dq 0 +e authors declare that they have no conflicts of interest ω , f: Reference angular regarding the publication of this paper. frequency and given nominal frequency References i (F), i (F), i1 (F), Fundamental components d q d i1 (F), V (F), V (F), of currents and voltages in q d q [1] C. J. Mozina, “Impact of green power distributed generation,” V1 (F), V1 (F): d-axis and q-axis of dq 0 IEEE Industry Applications Magazine, vol. 16, no. 4, pp. 55–62, d q frame [2] O. Singh, A. Iqbal, S. Kumar, and S. Rajput, “Hybrid re- V : Feedback DC bus voltage DC newable energy system integration in the micro-grid: Indian E , E , E , V : Output voltages of d q dq 0 ref ,abc context,” in Proceedings of the International Conference on microgrid system model in Control, Computing, Communication and Materials d-axis, q-axis, in dq 0 frame, (ICCCCM), pp. 1–5, Allahabad, India, October 2016. and abc frame [3] L. Ahmethodzic and Mustafa Music, “Comprehensive review V , V1 : Feedback voltages of sub- abc abc of trends in microgrid control,” Renewable Energy Focus, AC bus and AC bus in abc vol. 38, pp. 84–96, 2021. frame [4] L. Fan, “Frequency and voltage control in microgrids,” in h(t): Dynamic band margin of Control and Dynamics in Power System and Microgrids, hysteresis controller pp. 147–169, CRC Press, New York, NY, USA, 1st edition, i , i1 : 2017. abc abc 18 International Transactions on Electrical Energy Systems [5] E. Kabalci, H. Irgan, and Y. Kabalci, “Hybrid microgrid [19] Y. Varetsky, V. Konoval, M. Saheda, and P. Olena, “Studying system design with renewable energy sources,” in Proceedings voltage fluctuations in microgrid with hybrid renewable en- of the 2018 IEEE 18th International Power Electronics and ergy system,” in Proceedings of the IEEE 6th International Motion Control Conference (PEMC), pp. 387–392, Budapest, Conference on Energy Smart Systems (ESS), pp. 239–242, Kyiv, Hungary, August 2018. Ukraine, April 2019. [6] T. Kerdphol, S. R. Fathin, W. Masayuki et al., “Enhanced [20] M. Jafari, M. Zahra, J. Zhu, and M. K. Hassan, “A novel predictive fuzzy logic-based energy management system for virtual inertia control based on derivative technique to em- ulate simultaneous inertia and damping properties for grid-connected and off-grid operation of residential smart microgrid frequency regulation,” IEEE Access, vol. 7, microgrids,” IEEE Journal of Emerging and Selected Topics in pp. 14422–14433, 2019. Power Electronics, vol. 8, no. 2, pp. 1391–1404, 2020. [7] X. Hou, Y. Sun, Z. Xin, J. H. Lu, W. Peng, and M. G. Josep, [21] M. Jafari, Z. Malekjamshidi, and J. Zhu, “A magnetically “Improvement of frequency regulation in VSG-based AC coupled multi-port, multi-operation-mode micro-grid with a microgrid via adaptive virtual inertia,” IEEE Transactions on predictive dynamic programming-based energy management Power Electronics, vol. 35, no. 2, pp. 1589–1602, 2020. for residential applications,” International Journal of Electrical [8] A. Tavakoli, S. Saha, M. T. Arif, E. Haque, M. Nishad, and Power and Energy Systems, vol. 104, no. 1, pp. 784–796, 2019. M. T. Aman, “Impacts of grid integration of solar PV and [22] A. Khan, S. Memon, and T. P. Sattar, “Analyzing integrated electric vehicle on grid stability, power quality and energy renewable energy and smart-grid systems to improve voltage quality and harmonic distortion losses at electric-vehicle economics: a review,” IET Energy Systems Integration, vol. 2, no. 3, pp. 243–260, 2020. charging stations,” IEEE Access, vol. 6, pp. 26404–26415, 2018. [9] J. Rocabert, A. Luna, B. Frede, and R. Pedro, “Control of [23] A. Hassouneet, M. Khafallah, A. Mesbahi, and T. Bouragba, power converters in AC microgrids,” IEEE Transactions on “Power management strategies of electric vehicle charging Power Electronics, vol. 27, no. 11, pp. 4734–4749, 2012. station based grid tied PV-battery system,” International [10] K.-W. Lao, W. Y. Deng, J. P. Shang, and N. Dai, “PQ-coupling Journal of Renewable Energy Resources, vol. 8, no. 2, strategy for droop control in grid-connected capacitive- pp. 851–860, 2018. coupled inverter,” IEEE Access, vol. 7, pp. 31663–31671, 2019. [24] A. Hassouneet, M. Khafallah, A. Mesbahi, and T. Bouragba, [11] T. Wu, G. Bao, Y. Y. Chen, and J. P. Shang, “A review for “Improved control strategies of electric vehicles charging control strategies in microgrid,” in Proceedings of the 2018 station based on grid tied PV/battery system,” International 37th Chinese Control Conference (CCC), pp. 30–35, Wuhan, Journal of Advanced Computer Science and Applications, China, July 2018. vol. 11, no. 3, pp. 116–124, 2020. [12] J. Wang, A. Pratt, and M. Baggu, “Integrated synchronization [25] A. Hassouneet, M. Khafallah, A. Mesbahi, and T. Bouragba, control of grid-forming inverters for smooth microgrid “Experimental analysis of control in electric vehicle charging transition,” in Proceedings of the IEEE Power and Energy station based grid tied photovoltaic-battery system,” Inter- Society General Meeting (PESGM), pp. 1–5, Atlanta, GA, USA, national Journal of Electrical and Computer Engineering, August 2019. vol. 13, no. 5, pp. 276–284, 2019. [13] Z. Liu, J. Liu, and Y. Zhao, “A unified control strategy for [26] S. Singh, S. Jagota, and M. Singh, “Energy management and three-phase inverter in distributed generation,” IEEE Trans- voltage stabilization in an islanded microgrid through an actions on Power Electronics, vol. 29, no. 3, pp. 1176–1191, electric vehicle charging station,” Sustainable Cities and So- 2014. ciety, vol. 41, pp. 679–694, 2018. [14] M. E. Haque, M. Negnevitsky, and K. M. Muttaqi, “A novel [27] A. Hassouneet, K. Mohamed, A. Mesbahi, N. Ayoub, and control strategy for a variable-speed wind turbine with a B. Tarik, “Experimental implementation of a smart battery permanent-magnet synchronous generator,” IEEE Transac- charger for electric vehicles charging station,” International tions on Industry Applications, vol. 46, no. 1, pp. 331–339, Journal of Power Electronics and Drive Systems, vol. 11, no. 4, 2010. pp. 1689–1699, 2020. [15] T. Rashid, A. Routh, R. Rana, A. Ferdous, and R. Sayed, “A [28] S. Iqbal, M. U. Jan, X. Ai et al., “V2G strategy for primary novel approach to maximize performance and reliability of frequency control of an industrial microgrid considering the PMSG based wind turbine: Bangladesh perspective,” Amer- charging station operator,” Electronics, vol. 9, no. 4, ican Journal of Engineering Research (AJER), vol. 7, no. 6, pp. 549–570, 2020. pp. 17–26, 2018. [29] K. Sayed, A. G. Abo-Khalil, and A. S. Alghamdi, “Optimum [16] N. +odsaporn and V. Kinnares, “Wind turbine simulator resilient operation and control dc microgrid based electric equipped with real-time monitoring and user-friendly pa- vehicles charging station powered by renewable energy rameter setup controlled by C2000 microcontroller,” in Pro- sources,” Energies, vol. 12, no. 22, pp. 4240–4263, 2019. ceedings of the 2020 17th International Conference on Electrical [30] E. K. Belal, D. M. Yehia, and A. M. Azny, “Adaptive droop Engineering/Electronics, Computer, Telecommunications and control for balancing SOC estimation of distributed batteries Information Technology (ECTI-CON), pp. 713–716, Phuket, in DC microgrids,” IET Generation, Transmission and Dis- +ailand, June 2020. tribution, vol. 13, no. 20, pp. 4667–4676, 2019. [31] M. U. Jan, X. Ai, A. A. Mohamed, U. R. Haseeb, and S. Iqbal, [17] Z. Gao, J. Li, M. Zhao, C. Zhou, and L. Kai, “Design and simulation of 500 kw wind-solar complementary microgrid,” “Adaptive and fuzzy PI controllers design for frequency in Proceedings of the 2020 Chinese Control and Decision regulation of isolated microgrid integrated with electric ve- Conference (CCDC), pp. 5499–5505, Hefei, China, August hicles,” IEEE Access, vol. 8, pp. 87621–87632, 2020. 2020. [32] H. Karimi, T. Mohammad, H. Beheshti, R. Amin, and [18] P. S. Kumar, R. P. S. Chandrasena, V. Ramu, G. N. Srinivas, H. Zareipour, “Intelligent control of islanded AC microgrids and K. V. S. M. Babu, “Energy management system for small based on adaptive neuro-fuzzy inference system,” Interna- scale hybrid wind solar battery based microgrid,” IEEE Access, tional Journal of Electrical Power and Energy Systems, vol. 133, vol. 8, pp. 8336–8345, 2020. Article ID 107161, 2021. International Transactions on Electrical Energy Systems 19 [33] J. Hu, Y. Shan, J. M. Guerrero, I. Adrian, K. W. Chan, and [46] P. A. Dahono, “New hysteresis current controller for single phase full bridge inverter,” IET Power Electronics, vol. 2, no. 5, J. Rodriguez, “Model predictive control of microgrids—an overview,” Renewable and Sustainable Energy Reviews, pp. 585–594, 2009. vol. 136, Article ID 110422, 2021. [34] P. Jampeethong and S. Khomfoi, “Coordinated control of electric vehicles and renewable energy sources for frequency regulation in microgrids,” IEEE Access, vol. 8, pp. 141967– 141976, 2020. [35] S. Ranjan, A. Latif, D. C. Das, and N. Sinha, “LFC for au- tonomous hybrid micro grid system of 3 unequal renewable areas using mine blast algorithm,” International Journal of Renewable Energy Resources, vol. 8, no. 3, pp. 1297–1308, 2018. [36] A. Latif, D. C. Das, K. B. Amar, and S. Ranjan, “Maiden coordinated load frequency control strategy for ST-AWEC- GEC-BDDG-based independent three-area interconnected microgrid system with the combined effect of diverse energy storage and DC link using BOA-optimised PFOID control- ler,” IET Renewable Power Generation, vol. 13, no. 14, pp. 2634–2646, 2019. [37] A. Latif, D. C. Das, K. B. Amar, and S. Ranjan, “Illustration of demand response supported coordinated system performance evaluation of YSGA optimized dual stage PIFOD-(1 + PI) controller employed with wind-tidal-biodiesel based inde- pendent two-area interconnected microgrid system,” IET Renewable Power Generation, vol. 14, no. 6, pp. 1074–1086, [38] K. V. Hessam, F. D. Mohammadi, V. Verma, J. Solanki, and S. K. Solanki, “Hierarchical multi-agent based frequency and voltage control for a microgrid power system,” International Journal of Electrical Power and Energy Systems, vol. 135, Article ID 107535, 2022. [39] M. U. Jan, A. Xin, H. U. Rehman, M. A. Abdelbaky, S. Iqbal, and M. Aurangzeb, “Frequency regulation of an isolated microgrid with electric vehicles and energy storage system integration using adaptive and model predictive controllers,” IEEE Access, vol. 9, pp. 14958–14970, 2021. [40] A. K. Barik and D. C. Das, “Coordinated regulation of voltage and load frequency in demand response supported bio- renewable cogeneration-based isolated hybrid microgrid with quasi-oppositional selfish herd optimisation,” International Transactions on Electrical Energy Systems, vol. 30, Article ID e12176, 2020. [41] R. Mandal and K. Chatterjee, “Frequency control and sen- sitivity analysis of an isolated microgrid incorporating fuel cell and diverse distributed energy sources,” International Journal of Hydrogen Energy, vol. 45, no. 23, pp. 13009–13024, 2020. [42] T. UttamraoSolanke, V. K. Ramachandaramurthy, Y. Y. Jia, J. Pasupuleti, P. Kasinathan, and A. Rajagopalan, “A review of strategic charging-discharging control of grid-connected electric vehicles,” Journal of Energy Storage, vol. 28, Article ID 101193, 2020. [43] N. Sujitha and S. Krithiga, “Grid tied PV-electric vehicle battery charger using bidirectional converter,” International Journal of Renewable Energy Resources, vol. 9, no. 4, pp. 1873–1881, 2019. [44] A. R. Dash, B. C. Babu, K. B. Mohanty, and D. Rahul, “Analysis of PI and PR controllers for distributed power generation system under unbalanced grid faults,” in Pro- ceedings of the International Conference on Power and Energy Systems, pp. 1–6, Chennai India, December 2011. [45] H. Mao, X. Yang, Z. Chen, and Z. Wang, “A hysteresis current controller for single phase three level voltage source in- verters,” IEEE Transactions on Power Electronics, vol. 27, no. 7,

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International Transactions on Electrical Energy SystemsHindawi Publishing Corporation

Published: Apr 29, 2022

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