 Original research
 Open Access
 Published:
Load shedding strategy coordinated with storage device and DSTATCOM to enhance the microgrid stability
Protection and Control of Modern Power Systems volume 4, Article number: 22 (2019)
Abstract
Recently microgrids have drawn a potential attraction by fulfilling the environmental demands and the increasing energy demands of the endusers. It is necessary to focus on various protection and control aspects of a microgrid. During the transition between the gridfollowing and gridforming modes, the voltage and the frequency instability due to the power mismatch condition becomes the major point of concern. Therefore, the paper executes a frequencyactive power and voltagereactive power drooping control strategy for the precise powersharing among the distributed power generators. Furthermore, to handle the power deficit scenarios and to maintain the system stability, a system independent and prioritybased adaptive threestage load shedding strategy is proposed. The sensitivity of the strategy depends on the system inertia and is computed according to the varying absolute rateofchangeoffrequency. The strategy incorporates the operation of battery storage system and distributed static compensator (DSTATCOM) in the microgrid, to provide a reliable power supply to the customers for a considerable time instead of a sudden load shedding. The effectiveness of the proposed strategies is investigated on a scaleddown modified IEEE 13bus microgrid system on the podium of MATLAB 2015b through the timedomain simulation.
Introduction
The incorporation of renewable power sources with the power utilities has been a promising solution towards the uninterrupted ecofriendly power demand. As a consequence, the installation and integration of distributed generators (DG) with the utilities appear as a major source of developing the small and mediumsize microgrids [1]. Since the DGs in the conventional microgrid are operationally similar, the effects of disturbance and the amount of power to be shared are equally distributed without overloading any particular DG in the microgrid. But the voltage and the frequency at the point of common coupling (PCC) between the microgrid and utility undergo high distortion [2]. However, with the increased research efforts, the microgrids successively integrate the very differently operating DG (i.e., synchronous machine and inverterbased power sources). This incorporation of high inertia synchronous machines in an inertialess dominated microgrid environment supports the system to ride through during the transient [3].
At the occurrence of any disturbance in the operationally diversified DG integrated microgrid, the powersharing challenge arises. The inertialess inverterbased DG can instantly respond to the fluctuation and vary the voltage according to the power required. Conversely, the sudden changes in the power are not feasible for high inertia based DG (synchronous machine). Further, the issue turns out to be critical, when the power generation of the operating DG reduces significantly due to the climatic condition [4]. Thus, the conjunction of high inertia power sources with the inertialess sources challenges the operational stability. In order to address these issues, powersharing integrated load shedding strategy is executed to enhance the performance of the microgrid [5, 6]. A twophase droop based frequency control and a load shedding strategy are analyzed in [5] to maintain the stability of an autonomous microgrid.
To attain a stable electric system, various researchers have proposed several underfrequency load shedding (UFLS) schemes [7,8,9]. The conventional UFLS technique employs the strategy of shedding a fixed amount of load according to the predefined frequency limits. This conventional strategy uses a trial and error approach to calculate the number and the amount of load to be shed in each step. The strategy possesses a major drawback of shedding either the excessive or insufficient load amount, to attain the system stability [10]. Thus to shed an exact amount of load from an appropriate location brings into the adaptive UFLS scheme. The adaptive load shedding strategy sheds the load by measuring the frequency derivatives [11]. As a result, the load is shed based on the power deficit in each step.
Extensive work has been done in the field of adaptive UFLS. A comparison among the conventional, semi adaptive and adaptive UFLS is presented in [12]. The adaptive load shedding scheme implements rateofchangeoffrequency in [11,12,13] and disturbance magnitude in [14] to shed the load. Further to enhance the shedding strategy, [13, 15] use voltage dependency of the load to shed the loads. In [16, 17] the frequency deviation is monitored in accordance with the inertia constant and follows a pattern of response based event approach. A strategy proposed in [18] implements shedding at the location where the voltage reduction and the frequency deviation is maximum. Integer programming and genetic algorithm are executed in load shedding strategies by [19, 20]. Moreover, certain smartgrid load shedding strategies are introduced by [21, 22]. The strategies proposed till now need to be explored further to achieve efficient performance in a microgrid environment. The microgrid system with multiple numbers and multiple types of power sources, storage devices, and loads, challenges the calculation of exact values for the load shedding parameters. Furthermore, the slightest interruption in an isolated microgrid can affect system stability. A reactive power deficit during the autonomous mode of operation may lead the system towards voltage sag. Subsequently, the voltage sags vary the amount of power drawn by the load. As a result, delay in the measurements of frequency and its derivative may mislead the shedding strategy, because the estimated power shortage will be lesser than the actual deficit. Further, an additional drawback of this strategy is the nonlinear relationship between the amount of load shed and the power deficit.
In order to further enhance the adaptive UFLS scheme in accordance with the transient stability, storage elements like ultracapacitor are incorporated to impart dynamic frequency support by [23]. Further to compensate for the frequency variations until the governor response comes into action, the superconducting storage devices are implemented by [24]. The super magnetic energy storage device to enhance transient stability while shedding the loads are also employed in [25]. However, the implementation of fastresponding storage devices cannot be imposed with the frequency limit and their energy recovery factor. Further, the presence of slowresponding storage devices in the microgrid is yet to be considered while designing the UFLS.
The energy storage system has been an indispensable part of the microgrid. The addition of a storage system is a smart way to curb the power fluctuations and counteract the power imbalances [26]. Besides that, the storage system plays a significant task in improving the system reliability and stability in a microgrid (comprising a major share of renewable sources). Its integration could setback the cost of improving the transmission and distribution capacity to address the increasing power demand [27]. Thus, a battery energy storage (BES) device is integrated into the microgrid system to counteract the power deficits. The operation of BES is incorporated with the load shedding strategy to support the local loads from the instantaneous power cutoff during the unstable system operation.
However, the incorporation of the battery storage devices cannot always ensure a robust transient support system because these storage elements offer low power density with respect to their storage volume. Though these kinds of storage devices increase the relative inertia of the microgrid, but fail during the transient period. In other words, the microgrid lacks the required amount of inertia to withstand the transient fluctuations due to the system disturbances. Therefore, considerable attention has been drawn towards the integration of distributed static compensator (DSTATCOM) in the microgrids.
DSTATCOMs solely perform to improve the load balancing and power factor of a system [28]. The incorporation of DSTATCOMs in the distribution structure has drawn a significant interest because of its novel characteristic of compensating either PCC voltage or the line current [29]. Thus in this paper, the DSTATCOM performs to avoid the instantaneous conflict of powersharing between the two different types of inertial DG by compensating the voltage. The paper also highlights that the DSTATCOM and the high inertia system can efficiently control the system stability for a few cycles until the load shedding is initialized.
The paper proposes a reliable load shedding strategy along with an efficient powersharing among the different inertial DGs. In the hierarchical control of microgrids, the secondary control focuses on powersharing and load shedding. The paper implements a selftuned proportionalintegral strategy with a powersharing approach. The scheme implements the inertialless and high inertial DGs, battery storage system, and DSTATCOM to monitor the voltage and frequency of the microgrid. However, a major emphasis has been given to a system scenario, where the load demand is greater than the power generated. Thus to deal with this scenario, a system independent and prioritybased adaptive threestage load shedding strategy is proposed. The strategy consists of a threestage load shedding along with the consideration of the energy storage system (ESS). The fastresponding DSTATCOM is considered to deal with the transient events and reactive power deficits and the slow responding battery energy storage systems are considered to prolong the system stability by a certain time span. Thus, the major highlights of the proposed work are as follows:
A detailed analysis of a Pf and QV droop control strategy to attain an efficient powersharing among the different inertial DGs.
The paper proposes an independent and prioritybased adaptive threestage load shedding strategy.
To enhance the system stability, the performance of the battery and DSTATCOM present in the microgrid is closely analyzed while executing the load shedding algorithm.
The manuscript is organized as follows: Section II introduces the test system undertaken. Section III describes the controller design for each power source incorporated in the microgrid. The proposed control method for powersharing and load shedding is extensively described in Section IV. The test results of the proposed strategy are analyzed in Section V. A brief discussion and conclusion obtained from the result analysis are discussed in Section VI and Section VII respectively.
Microgrid structure
A balanced threephase 13 bus microgrid system considered in this study is represented in Fig. 1. The scaleddown test system comprises an inertialess photovoltaic (PV) microsource (DG1), a high inertia synchronous machine (DG2) and some distributed controllable loads. The designed microgrid integrates a battery storage device and distribution static compensator. The designed storage system is a scalable system. They are designed such that their characteristics can be studied within a smaller time span. However, for realtime implementation, the system ratings can be varied.
The system parameters are presented in the Appendix. The microgrid covers a distance of 8200 ft. length. The designed microgrid is integrated with the utility via a 115 kV(delta)/25 kV(grounded wye) substation transformer. The Simulink platform of MATLAB 2015b is used to replicate and test the designed microgrid system.
Controllers of power sources
Microsources
The designed inertialess inverter based photovoltaic system generates a maximum of 100 kW at 1000 W/m^{2} solar irradiance. The maximum power point tracking (MPPT) uses the Incremental Conductance method. The duty cycle controlled by the MPPT varies according to the voltage required to maximize the power extraction [30]. The DCAC inverter converts the maximum extracted DC power to AC power using the control based on the dq reference frame. The control is designed such that it varies the DC link voltage according to the voltage variations at the PCC. The extracted power from the PV system is fed to the DC link [31]. Therefore, the V_{dc} controller in the inverter, controls the voltage variations by specifying the daxis current (\( {I}_{d_{ref}} \)) values to balance the power flow in the DC link [32]. Since the system is considered to be operating at unity power factor, the reactive reference current (\( {I}_{q_{ref}} \)) is considered to be zero. Further, the control system implements the phaselocked loop to maintain the inverter voltage in phase with the grid voltage. The dq axis currents when fed to the proportionalintegral (PI) controllers, it helps in obtaining the reference voltages for the sinusoidal pulse width modulator (SPWM). The control design for the inertialess inverterbased DG is shown in Fig. 2a.
The instantaneous voltage of a DG1 connected to 13 bus test feeders can be written as in (1).
here, the i and v terms represent the instantaneous current and voltage respectively. R_{f} and L_{f} signify the resistance and the inductance of the filter respectively.
On using the Park’s transformation on (1) to transform it into the rotating reference frame, the equation is reframed as:
where,
Using the terms v_{dconv} and v_{qconv}, the magnitude and the angle for the SPWM are generated. Hence, the inverter’s switching pattern is signalled using (4).
The major advantages of using a currentcontrol strategy can be stated as:
 1)
It provides protection against the overcurrent.
 2)
It reduces the contribution of fault currents by the unit.
 3)
It limits the converter output current during fault conditions.
On the other hand, the design of a high inertia synchronous system regulates the voltage and the frequency by a different approach. The frequency of the generator is regulated by adjusting the torque on the basis of speed error. Simultaneously the reference speed is adjusted according to the active power measured. Further, the automated voltage regulator (AVR) integral action regulates the voltage when the voltage reference error becomes zero [33]. The control strategy of the generator is presented in Fig. 2b.
The synchronous generator feeds both the active and the reactive power simultaneously up to certain limits of MVA rating according to the prime mover’s capability. These limits can be obtained from the capability curve of the synchronous generator. The curve in Fig. 2c defines the interrelation between the active and the reactive power to be generated by the generator. The shaded portion under the capability curve in Fig. 2c represents the required quantity of reactive power to be generated by the generator. Further, the point of intersection between the armature heating limits and field heating limits specifies the MW and MVAR rating of synchronous generators [34]. In Fig. 2c, it can be analyzed that intersecting point ‘A’ sets the generator active power as \( {P}_{SG}^{rated} \) and reactive power at \( {Q}_{SG}^{rated} \). The reactive power can be fed until it reaches the heating limits (i.e., \( {Q}_{SG}^{rated} \)). However, if the reactive power to be fed is further increased to \( {Q}_{SG}^B \) at point B in Fig. 2c, it is delivered by reducing the active power generation as \( {P}_{SG}^B \) (where \( {P}_{SG}^B \) < \( {P}_{SG}^{rated} \)). The limits on the reactive power generation are set by either the armature heating limits as in point ‘B’ or by the field heating limits as in point ‘C’ [35].
DSTATCOM
DSTATCOM is a shunt connect voltage source converter (VSC) in the distributed power system. The integration of DSTATCOM in the power system is the most effective solution for reactive power compensation. A sudden change in reactive power demand or supply cannot be compensated by a synchronous machine, as fast as the voltage source converter [36]. It regulates the line voltage to control the leading or the lagging reactive power in the system with a response speed of 12 cycles [37]. Thus, the compensating device helps to regulate the voltage within its rated limit by efficient power management. In a gridconnected microgrid, the voltage fluctuations are majorly detected at the load end of the feeder. However, it is not possible to detect the voltage drop and the compensation point in an islanded microgrid. Therefore, the designed DSTATCOM is interconnected in the microgrid with the bus having critical loads. Figure 3 presents the schematic layout of the reactive power compensating DSTATCOM connected to the critical load.
The major role of DSTATCOM is to perform a conversion of input DC voltage to an output threephase AC voltage. The instantaneous input and output power have to be balanced properly. Therefore, the input terminal of VSC is connected to a capacitor, which acts as a passive voltage source. On the other hand, the output of the VSC is connected to a coupling transformer, which acts as a passive current source.
The output current fed by the DSTATCOM can be expressed as in (5).
Here, V_{stat} and E represent the voltage at DSTATCOM and bus respectively, X signifies the system reactance and I_{stat} presents the current fed by the DSTATCOM. On using eq.(5), the reactive power compensated by the DSTATCOM can be stated as:
Analyzing (6), it can be clearly stated that the voltage fed by the DSTATCOM regulates the reactive power of the system [38]. The control over reactive power is attained by keeping the DSTATCOM voltage in phase with the system voltage. Moreover, these in phase voltages do not allow the charged capacitor to supply active power into the system. Further, at zero frequency of the DC capacitor, the reactive power of DSTATCOM is zero. Thus, it can be concluded that the DC capacitor does not play a role in reactive power generation. It proves that the VSC of DSTATCOM is capable of facilitating the free flow of reactive power among the three AC terminals [39]. The DC capacitor only enables the compensation for the instantaneous power mismatch in the system.
Storage device
The storage system considered in this study is the LithiumIon battery model acquired from the Matlab SimPowerSystems library. The battery parameters are set to support the Pf and QV control within the system. Indepth analysis regarding the storage devices presented in [40]. Due to the uncertain fluctuations in renewable power generation and load demands, lithiumion batteries are highly preferable in microgrids. This is due to the fact that these batteries can be exploited up to their maximum capacity [41]. Therefore, to achieve a deep cycle operation, the lithiumion batteries have been modelled with the proper selection of parameters.
The voltage of a completely charged LiIon battery (i.e., E_{battery}) can be expressed as:
The analytical battery model can be presented by (8) and (9) for charging and discharging respectively.
Charge
Discharge
where, V_{battery} and I_{battery} represent the battery voltage and current respectively. K signifies the polarisation constant and R_{int} signifies the internal battery resistance. The battery capacity is represented by Q whereas the actual charge of the battery is presented by Q_{actual}. The constant voltage and the filtered current of the battery are stated as E_{0} and i_{filtered} respectively. To address the exponential zone of amplitude and time constant inverse, under the characteristic curve of the battery are termed as A and B respectively [42].
The assumption made for the undertaken battery system is that it can discharge up to StateofCharge (SoC) being 30%. The battery parameters are set to reduce the rate of discharge after 50% of SoC is attained.
Proposed control method for microgrid
In order to attain a balanced voltage profile and a reduced system loss, efficient powersharing is crucial. Powersharing can be controlled either by implementing a communicationbased centralized control strategy or by decentralized droop strategy. In recent times, communication strategies are avoided because of their economic limitations. Moreover, the interruptions and breakdown in communication are few added up limitations to the communicationbased control strategy. Hence, decentralized droop control strategies have drawn major attention. The droop control facilitates faster stability and an efficient powersharing among the distributed generations in a microgrid.
The droop strategy can be explained using the basic circuit of two AC sources (i.e., V_{1} ∠ δ and V_{2} ∠ 0) connected by reactance (X) dominated impedance line. The power transfer between the two sources can be termed as:
It can be analyzed that the active power transferred is in proportion to δ, as the angle tends to be small during the normal operation of the system. The reactive power transferred also varies proportionally with the difference between the voltage magnitudes at the two ends [43]. Thus, the droop characteristics can be framed as in (12) and (13), and are graphically shown in Fig. 4.
Therefore, the control strategy of DGs present in an inductancedominated microgrid implements active powerfrequency (Pf) and reactive powervoltage (QV) droop control characteristics [44]. The strategy aims to maintain the voltage and the frequency of the microgrid within its limit, along with a precise powersharing between the multiple power generators present in the microgrid.
In spite of an efficient powersharing strategy, the microgrid may undergo an instability while experiencing an overloading condition. In an autonomous mode, the distributed power generators sacrifice their frequency and voltage to increase the power generation. This leads to a reduction in voltage and frequency, which may go beyond the specified threshold limits and cause a system blackout. A selfgoverning microgrid manages the power shortage either by increasing the power generation or by shedding the increased power demand. As a result, the system response due to the increased generation or reduced load demand is incorporated to restore the voltage and frequency stability. Thus, the paper proposes a load shedding strategy along with powersharing.
A system independent and prioritybased adaptive threestage load shedding strategy is proposed. It deals with the power inequality in a microgrid after the occurrence of an islanding event. The rateofchangeoffrequency is considered to compute the amount of load to be shed. At the beginning of the load shedding algorithm, it verifies the presence of a storage system. The stored battery power supports to prolong the load for a certain time. The first stage of the strategy controls the rapid frequency drop by shedding large loads. It also causes the system frequency to settle within the specified operational lower limit (f ≥ 59.3).
As the gridforming microgrid possesses minimum inertial stability, it becomes highly sensitive to the dynamic load demand. This sensitivity increases the system instability with the increment in the frequency deviation. Therefore, in the second and third stages of the proposed load shedding algorithm, it has been observed that the operating frequency tends to maintain the nominal frequency (60 Hz). Thus, to reach the predetermined operating value of the system frequency, the second stage of load shedding strategy triggers and sheds small load in iterative steps. Subsequently, the third stage of load shedding tends the difference between the operating frequency and the nominal frequency to approximately zero by shedding the very small loads. The algorithm for the proposed load shedding scheme is presented in Fig. 6 and each shedding stage is analyzed in the section as follows:
Proposed load shed amount
StageI, stageII, and stageIII of the load shedding scheme shed the load by measuring the frequency, the rateofchangeoffrequency and Δf respectively. However, the amount of shed load differs in each stage. The proposed scheme ensures an iterative shedding process and the sheddable loads from busses are decided using the strategy presented in [45]. The iteration continues until it satisfies the individual criterion. The scheme computes the shedding amount concerning the instantaneous rate of change of frequency measured. From Fig. 5 it can be observed that the rateofchangeoffrequency varies significantly during the transition between the stages. The load to be shed during an iteration is computed using (14).
where,
Here, P_{Load − shed} is the amount of load shed in each iteration, and K denotes the shedding constant obtained from the swing equation of the system. H_{equivalent} denotes the normalized inertia constant.
It can be observed from Fig. 5 that the rateofchangeoffrequency at PCC is significantly high and it is a directly dependent factor of the power deficit. At the occurrence of an islanding instant, the significant difference in rateofchangeoffrequency occurs due to the reduced moment of inertia of the gridforming microgrid. As a result, the amount of load shed is directly proportional to the frequency derivative. Further, Fig. 5 illustrates that, with every step of load shed, the rateofchangeoffrequency reduces with reducing power deficit and tends to attain approximately zero. Thus on realizing eq.(14), it can be analyzed that with the decrease of rateofchangeoffrequency, the amount of load shed decreases. Therefore, this strategy of shedding the load amount in the proposed load shedding scheme presented in Fig. 6 helps to shed the proportional amount of load concerning the power deficit.
Battery and DSTATCOM performance simultaneously with the proposed load shedding strategy
The presence of a storage system in a microgrid adds to the advantages of the proposed load shedding strategy. It provides a certain amount of power to the extra loads by a certain period of time. The battery storage element supports during the islanded microgrid and compensates for the steady part of the power deficit. Conversely, the DSTATCOM compensates the system disturbances during the transients. The batteries supply a steady power at a specified nominal rate of discharge (RoD_{1}) till they reach 50% and at RoD_{2} till they reach 30% of their stateofcharge (SoC). The SoC of a battery is a timescaled factor with respect to the rate of power delivery. In other words, the instantaneous change in power supply does not change the SoC instantaneously. Therefore, considering the deep discharge limit of the battery, the power supplied by the battery is intentionally reduced to RoD_{2} at a specified SoC condition of 50%. It can be observed from Fig. 6 that the algorithm clearly shows the operational performance of the storage device.
StageI of load shedding
The first stage of the proposed load shedding scheme functions with the objective to hold back the normal operating conditions of the microgrid after facing a severe power deficit. In Fig. 6, load shedding steps involved in each stage are highlighted. From the conventional strategy of power networks, it is observed that the rate of decrement of the frequency inversely depends on the cumulative shortcircuit MVA of the entire integrated system [46]. However, in the microgrid the shortcircuit MVA dramatically falls down due to the power electronic interfaces present in the system. Therefore, the microgrid fails to withstand the extra load demand. As a result, at the instant of islanding, system frequency drops down in proportion to the surplus load demand. The analyzed microgrid characteristic can be observed in Fig. 5. From Fig. 5 it can be clearly observed that the rateofchangeoffrequency is proportional to the microgrid demand. So, to regain the rateofchangeoffrequency, a large load is shed using eq.(14) in an iterative process. Thus, the load shedding starts when the system frequency goes beyond 59.3 Hz. The iterative process continues until the frequency regains to the given threshold set value f ≥ 59.3. The instant, when the microgrid reaches the set frequency limit for operational stability, the stageII triggers.
StageII of load shedding
The loads in this stage are shed by considering the rateofchangeoffrequency \( \left(\raisebox{1ex}{$ df$}\!\left/ \!\raisebox{1ex}{$ dt$}\right.\right) \). Smaller values of load shed in each step tends the frequency towards the operating nominal frequency of 60 Hz. The proposed algorithm in Fig. 6 illustrates that the iterative shedding process in this stage continues until the rateofchangeoffrequency is less than 0.03.
StageIII of load shedding
Now, as soon as the rateofchangeoffrequency attains its 0.03 limit, the third stage of load shedding starts. In this stage, the Δf (i.e., difference between the operating frequency and the nominal frequency (60 Hz)) is calculated to shed small loads. The intention of this stage is to make Δf = ± 0.05, by shedding small load iteratively. The stageIII of the load shedding strategy sheds the loads in a very small amount and makes the system stable. The stage reaches an end on achieving the Δf to be approximately zero.
The amount of load shed in each iteration and in each stage of the algorithm is based on the absolute rateofchangeoffrequency of the microgrid. Hence, the shedding scheme is independent of the power generation. As a result, the climatic challenges or the operational challenges will not affect the load shedding strategy. Thus, the load shedding controls the microgrid to attain stability during the gridforming mode of the microgrid. Further, the powersharing scheme subsequent to load shedding strategy also operates to maintain a stiff system synchronization.
Result analysis
This section presents the efficacy of the proposed approach under the system undertaken. About four different cases are simulated to analyze the performance of the system. Initially, the performance of the compensating devices present in the test system is analyzed individually and subsequently the proposed load shedding approach. Case1 highlights the powersharing performance within the microgrid integrated only with a battery storage device under 5% of overloading. Case2 is an extension of the first case. It integrates DSTATCOM into the system and analyses its effect on the distributed generators present in the system. Highlighting the powersharing strategy in Case1 and Case2, an emphasis has been given to investigate the powersharing and load shedding strategy together in the subsequent cases. Case3 verifies the system stability using load shedding strategy under 30% of active power and 10% of reactive power overloading condition. Further, in Case4 the load shedding strategy is examined under a power deficit scenario, where the power generation at the DG end reduces significantly.
Case1
The case analyses the efficient powersharing with the operating storage system present in the microgrid. The test system is formulated with 5% of overloading with the absence of DSTATCOM. It can be analyzed from SoC and battery power, that the battery charges itself while present in a gridconnected mode. On the detection of an islanding instance (i.e.,1 s), the battery system discharges itself as shown in Fig. 7a. Thus the battery power and the synchronous generator handles the small active and the reactive power overloading of 5%.
At time 4.55 s, when the battery attains 50% of SoC, the power to be fed is reduced. On attaining the 30% of SoC at 7.49 s, the power fed by the battery is approximately zero. The active power fed by a battery based on the SoC can be studied in Fig. 7b. In the meantime, when the battery power reduces, the microgrid load demand is compensated by the synchronous generator of the microgrid. The photovoltaics do not increase its power generation as it is an MPPT based system and always supplies the maximum power in the system. The DG2 increments the power in two steps at time 4.55 s and 7.49 s as illustrated in Fig. 7c. The reactive power fluctuations are negligible, as only the active power of the battery is compensated by the DG2 in this case study. This negligible reactive power variation of DG2 is presented in Fig. 7d. The increase in power generation by DG2 is attained by reducing the DG voltage and frequency. Thus, the drop in voltage and frequency corresponding to the power increment can be analyzed from Fig. 7e and f respectively.
Case2
Case2 is an extension of the case1. The system is considered with a similar overloading condition of 5% with the integrated power sources, storage device and a DSTATCOM. The powersharing is efficient in Case1 but the reactive power overloading is solely controlled by the synchronous generator from the instance of islanding. In order to support the reactive power compensation, the DSTATCOM is integrated and its effect on the system is examined in Case2. The DSTATCOM is assumed to be triggered at the detection of an autonomous mode of operation.
The voltage control and reactive power compensated by the DSTATCOM can be examined through Fig. 8a and b respectively. It can be studied that with the reduction in battery power and an increase in synchronous generation, the DSTATCOM simultaneously increases the compensation level. Such that 60% of extra reactive power demand is fed by DG2 and the remaining 40% of overloading is fed by the DSTATCOM. This helps in avoiding sudden stress on the synchronous generator to generate extra power beyond its generation limit.
The presence of DSTATCOM has highly enhanced the transient stability of the system and has reduced the fluctuations in the system parameters during the steadystate operation. This can be analyzed by considering a comparative study of the system responses with and without DSTATCOM. Figure 9ad and Fig. 10ad illustrates the effects of DSTATCOM on DG1 and DG2 respectively. Further, in a comparative approach, Table 1 highlights the suppression of peak overshoot and rise time during transient conditions. The table also emphasizes the enhancement of the settling time in both the DGs integrated with the microgrid.
The integration of DSTATCOM suppresses the transient peak overshoot in active power by 1.938% and 21.71% in DG1 and DG2 respectively. It can be observed that the suppression in DG1 is negligible, as it operates at the maximum power point. The reactive power transient fluctuation is suppressed by 21.71% and 22.35% in both DG1 and DG2 respectively.
Case3
This case is stimulated at 0.86 power factor, to test the system performance under 30% of active power and 10% of reactive power overloading condition. The loads L11 to L16 are incorporated in the microgrid to simulate the overloading scenario. The case is designed such that it highlights the performance of powersharing and load shedding strategy together. The islanding is assumed to have occurred at the time 1 s. The battery charges itself while present in gridconnected mode, and from the instance of islanding, the battery starts discharging as shown in Fig. 11a. The active power fed by the storage device is demonstrated in Fig. 11b. However, due to an extreme loading condition, the voltage and the frequency at the PCC tend to violate the threshold limit of frequency and voltage. The transient variations in voltage are compensated by the DSTATCOM as shown in Fig. 11c. The DSTATCOM also feeds an instantaneous reactive power into the system as illustrated in Fig. 11d.
The load demand in the microgrid remains too high to make the system unstable. The frequency and the voltage of the DGs tending towards the instability can be analyzed from Fig. 12a and b respectively. Thus, the violation of the system parameter’s limit at PCC triggers the load shedding strategy. The loads shed by the proposed shedding strategy are calculated using (14) and the approximate similar load values with the least priority are shed as shown in Table 2.
The proposed load shedding strategy initializes the stageI of the strategy and sheds the load in three iterations at time 1.4 s, 1.8 s, and 2.2 s. The effects of the load shedding on voltage and frequency are illustrated in Fig. 12a and b respectively. The MPP controlled DG1 being an inertialess power source responds with an insignificant time delay to the load shed. Whereas the DG2 being a high inertia system shows a sluggish response and consumes a certain time to retain itself within the stability zone. The time consumed to retain the stability is compensated by the DSTATCOM present in the microgrid. The stageI of the load shedding strategy tends the microgrid to attain the voltage and the frequency within the operating limits as shown in Fig. 12a and b.
Further, when the \( \frac{df}{dt} \) condition satisfies, the stageII of the load shedding strategy is triggered. Though the system parameters are operating within the stability limit, but to achieve the system to be operating at a standard operating point, a small load is shed at a time about 3.2 s and the stageIII of the load shedding scheme sheds another small load at 3.9 s. The variations in active and reactive power generation of DG1 and DG2, corresponding to the load shed can be analyzed from Fig. 12c and Fig. 12d.
Case4
In order to examine the proposed approach under a reduced power generation, the Case4 is simulated. The power generation by DG1 suddenly reduces from 100 kW to 80 kW of generation. The drop of 20 kW of power from DG1 at time 0.5 s can be analyzed in Fig. 13. The battery operates in the discharge stageI instantly and feeds the maximum possible power of 15 kW into the system. The remaining 5 kW of power deficit is managed and fed by DG2.
At time 2 s, the battery attains 50% of SoC and tends to operate in the discharge stageII of the battery as shown in Fig. 14a. Thus the battery reduces the rate of discharge and feeds the power of about 9 kW as in Fig. 14b. As a result, the surplus load demand of 11 kW is to be fed by DG2 depicted in Fig. 13a. But from Fig. 13c and d it can be observed that the increase in power deficit leads the voltage and frequency towards instability. Therefore the stageII of the load shedding is triggered to shed the loads. So, a load is shed at 2.18 s.
This shedding of load helps the voltage and the frequency of the DGs to attain the operating point. Further, as the battery reaches 30% of its SoC, two small loads are shed by the stageIII of the load shedding strategy after 3.3 s. These shedding lead the system towards the specified operating nominal value as shown in Fig. 13 and the amount and location where the load is shed in this examined case are shown in Table 3. The DSTATCOM present in this scenario only responds to suppress the transient variation occurring in the system during the reduction in power generation and load shedding as shown in Fig. 14c and d.
Discussion
The proposed threestage operational strategy is tested under different scenarios to verify the efficacy. The case studies are analyzed such that the performance of ESS and DSTATCOM is significantly highlighted. The major proposal of the load shedding strategy incorporates the performance of battery storage power during the shedding process, which has been verified in the result analysis. Moreover, an efficient powersharing within the microgrid has been analyzed to attain the system stability. The incorporation of DSTATCOM has significantly controlled the transient instances. The conventional strategies do not analyze the powersharing while shedding loads, but the paper closely analyzes the load shedding along with an efficient powersharing among the DGs. The most sensitive scenarios of excessive loading and decrement in power generation have been closely analyzed by the proposed strategy. In Table 4 a comparative study of the proposed strategy with the state of art has been presented to analyze the efficacy of the proposed approach.
Conclusion
The article aims to address the microgrid stability while operating in an autonomous mode of operation. The microgrid system undertaken integrates both inertialess and high inertia power sources along with the storage system and the reactive power compensating device. The control strategy for each unit is elaborated in Section III of the manuscript. Each operating unit uses the Pf and QV powersharing control strategy to maintain system stability. To deal with the worstpower deficit scenario, a load shedding strategy is proposed. The shedding strategy incorporates the microgrid storage system to prolong the loads for a certain time after attaining the frequency within the operational stability and limit. The DSTATCOM present in the system helps to avoid the instantaneous conflict of powersharing between the two different types of inertial DG by compensating the voltage, and efficiently control the system stability for few cycles until the load shedding is initialized. The designed droop based load shedding strategy is only dependent on the system inertia. The strategy sheds load to control the reducing rateofchangeoffrequency. The precise shedding process is faster compared to the conventional schemes and confirms the difference between the operating frequency and nominal frequency to be approximately zero proving its robustness to act under a wide range of operating conditions.
Availability of data and materials
Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
Abbreviations
 AVR:

Automated voltage regulator
 BES:

Battery energy storage
 DG:

Distributed generators
 DSTATCOM:

Distributed static compensator
 ESS:

Energy storage system
 MPPT:

Maximum power point tracking
 PCC:

Point of common coupling
 PI:

Proportionalintegral
 PV:

Photovoltaic
 ROD:

Rate of discharge
 SOC:

State of charge
 SPWM:

Sinusoidal pulse width modulator
 UFLS:

Underfrequency load shedding
 VSC:

Voltage source converter
References
Roy, N. K., Hossain, M. J., & Pota, H. R. (2011). Voltage profile improvement for distributed wind generation using DSTATCOM. In 2011 IEEE Power and Energy Society General Meeting (pp. 1–6). IEEE; Detroit.
Divshali, P. H., Alimardani, A., Hosseinian, S. H., & Abedi, M. (2012). Decentralized cooperative control strategy of microsources for stabilizing autonomous VSCbased microgrids. IEEE Transactions on Power Systems, 27(4), 1949–1959.
Paquette, A. D., Reno, M. J., Harley, R. G., & Divan, D. M. (2012). Transient load sharing between inverters and synchronous generators in islanded microgrids. In 2012 IEEE Energy Conversion Congress and Exposition (ECCE) (pp. 2735–2742). IEEE; Raleigh.
Majumder, R., Ghosh, A., Ledwich, G., & Zare, F. (2009). Power sharing and stability enhancement of an autonomous microgrid with inertial and noninertial DGs with DSTATCOM. In 2009 International Conference on Power Systems (pp. 1–6). IEEE; Kharagpur.
Raghami, A., Ameli, M. T., & Hamzeh, M. (2013). Primary and secondary frequency control in an autonomous microgrid supported by a loadshedding strategy. In 4th Annual International Power Electronics, Drive Systems and Technologies Conference (pp. 282–287). IEEE; Tehran.
Bakar, N. N. A., Hassan, M. Y., Sulaima, M. F., Na’im Mohd Nasir, M., & Khamis, A. (2017). Microgrid and load shedding scheme during islanded mode: A review. Renewable and Sustainable Energy Reviews, 71, 161–169.
Joe, A., & Krishna, S. (2015). An underfrequency load shedding scheme with minimal knowledge of system parameters. International Journal of Emerging Electric Power Systems, 16(1), 33–46.
Rudez, U., & Mihalic, R. (2015). Predictive underfrequency load shedding scheme for islanded power systems with renewable generation. Electric Power Systems Research, 126, 21–28.
ElZonkoly, A. (2015). Application of smart grid specifications to overcome excessive load shedding in Alexandria, Egypt. Electric Power Systems Research, 124, 18–32.
Lokay, H. E., & Burtnyk, V. (1968). Application of underfrequency relays for automatic load shedding. IEEE Transactions on Power Apparatus and Systems, 3, 776–783.
Anderson, P. M., & Mirheydar, M. (1992). An adaptive method for setting underfrequency load shedding relays. IEEE Transactions on Power Systems, 7(2), 647–655.
Delfino, B., Massucco, S., Morini, A., Scalera, P., & Silvestro, F. (2001). Implementation and comparison of different under frequency loadshedding schemes. In 2001 Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No. 01CH37262) (Vol. 1, pp. 307–312). IEEE; Vancouver.
Rudez, U., & Mihalic, R. (2009). Analysis of underfrequency load shedding using a frequency gradient. IEEE Transactions on Power Delivery, 26(2), 565–575.
Terzija, V. V. (2006). Adaptive underfrequency load shedding based on the magnitude of the disturbance estimation. IEEE Transactions on Power Systems, 21(3), 1260–1266.
Rudez, U., & Mihalic, R. (2011). A novel approach to underfrequency load shedding. Electric Power Systems Research, 81(2), 636–643.
Shekari, T., Aminifar, F., & SanayePasand, M. (2015). An analytical adaptive load shedding scheme against severe combinational disturbances. IEEE Transactions on Power Systems, 31(5), 4135–4143.
Karimi, M., Wall, P., Mokhlis, H., & Terzija, V. (2016). A new centralized adaptive underfrequency load shedding controller for microgrids based on a distribution state estimator. IEEE Transactions on Power Delivery, 32(1), 370–380.
Marzband, M., Moghaddam, M. M., Akorede, M. F., & Khomeyrani, G. (2016). Adaptive load shedding scheme for frequency stability enhancement in microgrids. Electric Power Systems Research, 140, 78–86.
CejaGomez, F., Qadri, S. S., & Galiana, F. D. (2012). Underfrequency load shedding via integer programming. IEEE Transactions on Power Systems, 27(3), 1387–1394.
Luan, W. P., Irving, M. R., & Daniel, J. S. (2002). Genetic algorithm for supply restoration and optimal load shedding in power system distribution networks. IEE ProceedingsGeneration, Transmission and Distribution, 149(2), 145–151.
Mullen, S., & Onsongo, G. (2010). Decentralized agentbased underfrequency load shedding. Integrated ComputerAided Engineering, 17(4), 321–329.
Chuvychin, V., & Petrichenko, R. (2013). Development of smart underfrequency load shedding system. Journal of Electrical Engineering, 64(2), 123–127.
Delille, G., Francois, B., & Malarange, G. (2012). Dynamic frequency control support by energy storage to reduce the impact of wind and solar generation on isolated power system's inertia. IEEE Transactions on Sustainable Energy, 3(4), 931–939.
Zhang, L., Liu, Y., & Crow, M. L. (2005, 2005). Coordination of UFLS and UFGC by application of DSMES. In IEEE Power Engineering Society General Meeting (pp. 1064–1070). IEEE; San Francisco.
Hsu, C. T. (2002). Enhancement of transient stability of an industrial cogeneration system with superconducting magnetic energy storage unit. IEEE Transactions on Energy Conversion, 17(4), 445–452.
Divya, K. C., & Østergaard, J. (2009). Battery energy storage technology for power systems—An overview. Electric Power Systems Research, 79(4), 511–520.
Tan, X., Li, Q., & Wang, H. (2013). Advances and trends of energy storage technology in microgrid. International Journal of Electrical Power & Energy Systems, 44(1), 179–191.
Freitas, W., Morelato, A., Xu, W., & Sato, F. (2005). Impacts of AC generators and DSTATCOM devices on the dynamic performance of distribution systems. IEEE Transactions on Power Delivery, 20(2), 1493–1501.
Majumder, R., Ghosh, A., Ledwich, G., & Zare, F. (2009). Enhancing the stability of an autonomous microgrid using DSTATCOM. International Journal of Emerging Electric Power Systems, 10(5). https://doi.org/10.2202/1553779X.2227.
Giroux, P., Sybille, G., Osorio, C., & Chandrachood, S. (2012). 100kW gridconnected PV array demo detailed model. In MathWorks File Exchange.
Chandak, S., Bhowmik, P., & Rout, P. (2019). Dualstage cascaded control to resynchronize an isolated microgrid with the utility. IET Renewable Power Generation. https://doi.org/10.1049/ietrpg.2019.0062.
Vahedi, H., Noroozian, R., Jalilvand, A., & Gharehpetian, G. B. (2011). A new method for islanding detection of inverterbased distributed generation using DClink voltage control. IEEE Transactions on Power Delivery, 26(2), 1176–1186.
Chandak, S., Bhowmik, P., Mishra, M., & Rout, P. K. (2018). Autonomous microgrid operation subsequent to an antiislanding scheme. Sustainable Cities and Society, 39, 430–448.
Bhattacharya, K., & Zhong, J. (2001). Reactive power as an ancillary service. IEEE Transactions on Power Systems, 16(2), 294–300.
Canizares, C. A., Bhattacharya, K., ElSamahy, I., Haghighat, H., Pan, J., & Tang, C. (2010). Redefining the reactive power dispatch problem in the context of competitive electricity markets. IET Generation Transmission and Distribution, 4(2), 162–177.
Majumder, R. (2013). Some aspects of stability in microgrids. IEEE Transactions on Power Systems, 28(3), 3243–3252.
Fujita, H., & Akagi, H. (2007). Voltageregulation performance of a shunt active filter intended for installation on a power distribution system. IEEE Transactions on Power Electronics, 22(3), 1046–1053.
Benhabib, M. C., & Saadate, S. (2005). New control approach for fourwire active power filter based on the use of synchronous reference frame. Electric Power Systems Research, 73(3), 353–362.
Montero, M. I. M., Cadaval, E. R., & González, F. B. (2007). Comparison of control strategies for shunt active power filters in threephase fourwire systems. IEEE Transactions on Power Electronics PE, 22(1), 229.
Tremblay, O., & Dessaint, L. A. (2009). Experimental validation of a battery dynamic model for EV applications. World Electric Vehicle Journal, 3(2), 289–298.
Adhikari, S., & Li, F. (2014). Coordinated Vf and PQ control of solar photovoltaic generators with MPPT and battery storage in microgrids. IEEE Transactions on Smart Grid, 5(3), 1270–1281.
Bhowmik, P., Chandak, S., & Rout, P. K. (2019). State of charge and state of power management of the hybrid energy storage system in an architecture of microgrid. Journal of Renewable and Sustainable Energy, 11(1), 014103.
Pogaku, N., Prodanovic, M., & Green, T. C. (2007). Modeling, analysis and testing of autonomous operation of an inverterbased microgrid. IEEE Transactions on Power Electronics, 22(2), 613–625.
Tabatabaee, S., Karshenas, H. R., Bakhshai, A., & Jain, P. (2011). Investigation of droop characteristics and X/R ratio on smallsignal stability of autonomous microgrid. In 2011 2nd Power Electronics, Drive Systems and Technologies Conference (pp. 223–228). IEEE; Tehran.
Reddy, C. P., Chakrabarti, S., & Srivastava, S. C. (2013). A sensitivitybased method for underfrequency loadshedding. IEEE Transactions on Power Systems, 29(2), 984–985.
Wang, Y., Zhou, R., & Wen, C. (1993). Robust loadfrequency controller design for power systems. In IEE proceedings C (generation, transmission and distribution) (140, 1, pp. 1116). IET Digital Library. https://doi.org/10.1049/ipc.1993.0003.
Acknowledgments
The authors acknowledge the financial support provided by the Council of Scientific and Industrial Research (CSIR), Government of India.
Funding
Financial funding by Council of Scientific and Industrial Research (CSIR), Government of India.
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SC and PB carried out the design of the proposed study and performed the statistical analysis. PKR perceived the study, and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.
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Appendix
Appendix
System Rating:  
Operating frequency  60 Hz 
Nominal Rated Voltage  25 kV 
Base Power  150kVA 
DG1  100 kW 
DG2  50kVA (p.f = 0.86) 
m_{p}  5e5 Hz/W 
m_{q}  0.1667 V/Var 
T1  115 kV/ 25 kV 
T2  260 V/ 25 kV 
H_{equivalent}  312.67 s 
Battery Rating:  
Battery Rating (Q)  0.226 Ah 
Rated V_{battery}  300 V 
E_{0}  305 V 
Rated I_{battery}  120A 
R_{int}  40mΩ 
T_{battery}  300 V/25 kV 
DSTATCOM:  
DC Capacitance  520 μF 
Maximum Compensation  10kVAR 
Series Reactance  4.8 μH 
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Chandak, S., Bhowmik, P. & Rout, P.K. Load shedding strategy coordinated with storage device and DSTATCOM to enhance the microgrid stability. Prot Control Mod Power Syst 4, 22 (2019). https://doi.org/10.1186/s4160101901380
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DOI: https://doi.org/10.1186/s4160101901380
Keywords
 Microgrid
 Powersharing
 Load shedding
 High inertia distributed generators
 Inertialess distributed generators
 Battery storage system
 DSTATCOM