Optimal Voltage–Frequency Regulation in Distributed Sustainable Energy-Based Hybrid Microgrids with Integrated Resource Planning

This work is the earliest attempt to propose an integrated resource planning for distributed hybrid microgrids considering virtual-inertia support (VIS) and demand-response support (DRS) systems. Initially, three-distributed sustainable energy-based unequal hybrid microgrids are envisioned with the availability of solar/wind/bioenergy resources. In order to overcome the effects of intermittency in renewable resources and low inertia, each microgrid is incorporated with DRS and VIS units for demandand supply-side management, respectively. The proposed system is simulated in MATLAB considering real-time recorded solar/wind data with realistic loading for 12 months. A novel quasi-oppositional chaotic selfish-herd optimization (QCSHO) algorithm is proposed by hybridizing quasi-opposition-based learning and chaotic linear search techniques into the selfishherd optimization, for optimal regulation of voltage and frequency in microgrids. Then, the system responses are compared with 7 algorithms and 5 error functions to tune PID controllers’ gains, which confirmed the superiority of QCSHO over others. Then, the study proceeds to investigate the voltage, frequency, and tie-line power coordination in 5 extreme scenarios of source and load variations in the proposed system without retuning the controllers. Finally, the system responses are analyzed for 10 different possible allocation of VIS and DRS units in different microgrids to find the most suitable combinations, and the results are recorded.


Introduction
The mounting global demand of electric power and depleting fossil resources for conventional generation are hammering to hunt the alternative source of sustainable energy. However, the most promising sustainable resources such as solar and wind are intermittent in nature with weather dependency, and the harnessed power from single source with available technology is too small to meet the demand. Hence, multi-resource-based sustainable generators are incorporated in local hybrid microgrids to improve reliability by coordinated generation [1]. The energy management in interconnected multi-microgrids are probed in [2]. The future power demand could be met by interconnecting such microgrids including locally available waste-to-energy-based bioenergy generator system (BEGS) along with solar/wind-based renewable energy systems (RES). The community waste could be collected, segregated, and preprocessed for power generation with suitable BEGS such as Biodiesel engine generator (BDEG) unit, Biogas turbine generator (BGTG) unit, waste-water-driven micro-hydro turbine generator (MHTG) unit, and biomass fired combined heat and power (BCHP) unit to support the RES units such as solar photovoltaic (SPV) arrays, solar thermal power (STP) plants, and wind turbine generator (WTG) units [1].
Conversely, these microgrids comprising small capacity renewable generators, have low inertia to handle larger load variations as well as renewable penetrations [3]. The change in consumer lifestyle with sophisticated electronic appliances/devices have also influenced the pattern of demand variation in recent days. Hence, it crafts a great challenge to supply quality power with adequate inertia support to microgrids involving intermittent renewable resources whose power generations are climate dependent.
Recent works have reported several methods to handle similar challenges. The adjustable [4] and derivative [5][6][7] technique-based virtual inertia control units are considered with supercapacitors [4][5][6] and super-magnetic energy storage [7] for frequency regulation of microgrids. The detail overview of virtual synchronous generators integrated to microgrids and power grids are discussed in [3] with their applications. A fast-acting inverter and storage-based VSG is developed to damp the oscillation of a DG in [8]. An extended virtual synchronous generator is proposed for load frequency control (LFC) of microgrids [9]. The inertia of AC microgrid with renewable penetrations is estimated by frequency response measurement using curve-fitting method in [10]. Most of the similar literature have designed VSG with energy storage system (ESS) and fast-acting bidirectional converter to improve the inertia and damping effect of microgrids. This motivates to propose a virtual inertia support (VIS) system with suitable ESS for supply-side management (SSM)-based IRP in distributed microgrids as illustrated in Figure 1 [11].
Conversely, these microgrids comprising small capacity renewable generators, have low inertia to handle larger load variations as well as renewable penetrations [3]. The change in consumer lifestyle with sophisticated electronic appliances/devices have also influenced the pattern of demand variation in recent days. Hence, it crafts a great challenge to supply quality power with adequate inertia support to microgrids involving intermittent renewable resources whose power generations are climate dependent.
Recent works have reported several methods to handle similar challenges. The adjustable [4] and derivative [5][6][7] technique-based virtual inertia control units are considered with supercapacitors [4][5][6] and super-magnetic energy storage [7] for frequency regulation of microgrids. The detail overview of virtual synchronous generators integrated to microgrids and power grids are discussed in [3] with their applications. A fast-acting inverter and storage-based VSG is developed to damp the oscillation of a DG in [8]. An extended virtual synchronous generator is proposed for load frequency control (LFC) of microgrids [9]. The inertia of AC microgrid with renewable penetrations is estimated by frequency response measurement using curve-fitting method in [10]. Most of the similar literature have designed VSG with energy storage system (ESS) and fast-acting bidirectional converter to improve the inertia and damping effect of microgrids. This motivates to propose a virtual inertia support (VIS) system with suitable ESS for supply-side management (SSM)-based IRP in distributed microgrids as illustrated in Figure 1 [11]. Similarly, the intermittency of RES units in microgrids could be managed by temporarily shifting their power consumptions with combination of some non-essential loads such as hybrid electric vehicles (HEV), heat pumps, water heaters, and freezers contributing the demand responses focused on demand-side management (DSM) [12]. The frequency regulation of multi-area power systems using demand response strategies are discussed in [13]. The emergency DSM program-based real-time voltage regulation is reported in [14]. The regulation of frequency in microgram with minimum manipulated loading proposing extensive central DSM algorithm is reported [15]. DSM strategies considering storage system [16] and HEV charging stations are successfully applied in isolated and interconnected microgrids [17,18]. This motivates to propose a demand response support (DRS) system with suitable DR strategy for DSM-based IRP in distributed microgrids as shown in Figure 1 [11]. Hence, there is a scope to design and allocate suitable VIS/DRS system for optimal regulation of voltage and frequency in distributed microgrids as very few/no works have been reported so far.
The power quality of microgrids could be improved by regulating the system voltage and frequency simultaneously incorporating automatic voltage regulator (AVR) along with automatic load-frequency control (ALFC) in isolated microgrid [1] and in distributed Similarly, the intermittency of RES units in microgrids could be managed by temporarily shifting their power consumptions with combination of some non-essential loads such as hybrid electric vehicles (HEV), heat pumps, water heaters, and freezers contributing the demand responses focused on demand-side management (DSM) [12]. The frequency regulation of multi-area power systems using demand response strategies are discussed in [13]. The emergency DSM program-based real-time voltage regulation is reported in [14]. The regulation of frequency in microgram with minimum manipulated loading proposing extensive central DSM algorithm is reported [15]. DSM strategies considering storage system [16] and HEV charging stations are successfully applied in isolated and interconnected microgrids [17,18]. This motivates to propose a demand response support (DRS) system with suitable DR strategy for DSM-based IRP in distributed microgrids as shown in Figure 1 [11]. Hence, there is a scope to design and allocate suitable VIS/DRS system for optimal regulation of voltage and frequency in distributed microgrids as very few/no works have been reported so far.
The power quality of microgrids could be improved by regulating the system voltage and frequency simultaneously incorporating automatic voltage regulator (AVR) along with automatic load-frequency control (ALFC) in isolated microgrid [1] and in distributed system [19]. The RES such as WTG, SPV, and linear Fresnel reflector (LFR) type STP units are integrated with waste-to-energy-based BEGS such as BDEG, BGTG, MHTG, and BCHP units in this microgrid [1]. Some works applied battery energy storage (BES) units for ALFC of isolated [20] and interconnected [21,22] microgrids.
Encouraged with all these recent literatures and their scopes, this work has proposed a combined SSM-/DSM-based IRP for 3 unequal interconnected microgrids considering the limitations on availability/accessibility of different resources to replicate dispersed generation (DG), where, each of the microgrids is encompassed with a distinct RES-BGES unit pair to supply the tentative demand including BDEG unit as backup support and VIS/DRS unit for system stability. The system performances are analyzed using the proposed QC-SHO tuned PID controllers in 5 different scenarios of extreme source/load variations, designing the linearized Simulink model of distributed microgrids. The nomenclature and design values of system parameters considered in this work are listed in Table 1. The key contributions are:   Inter-grid power transfer ratios The rest part of this work is organized as follows. The component-wise mathematical modelling of the proposed distributed microgrids is deliberated in Section 2. The projected QCSHO algorithm is illustrated with detailed steps in Section 3. The simulated results for all possible scenarios are discussed in Section 4, and Section 5 briefly concludes the work.

Modeling of Distributed Microgrid System
The entire work is investigated by proposing community-based autonomous microgrids with available sustainable resources of the locality and expected to use all the bio-waste of the locality for energy generation in suitable BEGS considering optimal usage of available RES as illustrated in Figure 2a [1]. However, all these resources may not be available in same place, thereby encouraging DG [16,20]. Hence, three-interconnected sustainable energy-based unequal microgrids are proposed here, comprising LFR type STP/BCHP/BDEG1 units in the first microgrid (hµG1), WTG/BGTG/BDEG2 units in the second microgrid (hµG2), and SPV/MHTG/BDEG3 units in the third microgrid (hµG3), with combined SSM-/DSM-based IRP as illustrated in Figure 2b. The linearized system model of the complete proposed microgrids is shown in Figure 3a. The prime objective of the work is to maintain the system voltage-frequency (440 v-50 Hz) within accessible limit irrespective of the RES penetration and load disparities. The component-wise linear model of this microgrids is concisely conferred in this section considering design values of parameters listed in Table 1 [1,17].

Frequency Regulation System
The system frequency of each microgrid is regulated by ALFC unit combined of primary (droop) control and secondary (PID) control. The effect of ALFC with PID controllers (ΔXi1) are expressed in (1) for i th microgrid and the effective tie-line power exchange on i th microgrid(ΔPtii) considering frequency bias (Bi) is expressed in (2) ( )

Frequency Regulation System
The system frequency of each microgrid is regulated by ALFC unit combined of primary (droop) control and secondary (PID) control. The effect of ALFC with PID controllers (∆X i1 ) are expressed in (1) for ith microgrid and the effective tie-line power exchange on ith microgrid(∆P tii ) considering frequency bias (B i ) is expressed in (2)

Voltage Regulation System
The system voltage of ith microgrid is regulated by dedicated AVR units as modelled in Figure 3b. The detailed modelling of AVR unit is discussed and successfully operated in both isolated [1] and interconnected [19] microgrids. This work has employed separate PID controllers for each AVR units with control actions (∆X i2 ) assessed in (3). The power exchange (∆P EQ ) for AVR and voltage variation (∆u i ) for ith microgrids are specified in (4)-(5), respectively [1].

Virtual Inertia Support System
The larger load variations or higher renewable penetrations are mostly responsible for system frequency instability in autonomous microgrids due to small inertia [3,5]. Therefore, it is vital to provide inertia support to those microgrids, virtually [3][4][5] with suitable ESS and bidirectional power convertors considering SSM-based IRP. The inertia power (H) of the microgrid could be estimated by (6) considering apparent powerŜ, mechanical (T m and P m ), and electrical (T e and P e ) torques/powers measuring the system frequency Hence, most of the recent works [5][6][7] were applied derivative technique-based virtual inertia control in accordance to (6) by estimating an inertia gain times derivative of measured frequency error (∆f ), which replicates a derivative controller. However, the derivative controllers used alone will intensify the steady state error as well as the noise signals and cause saturation effects [29]. In order to overcome these issues, this work includes PI-controller parallel to derivative controller (combined to replicate the PID controller) to process ∆f as modelled in Figure 3c.
Recent works have successfully used supercapacitors [4][5][6] and super-magnetic coils [7] as ESS in VIS. However, higher penetration of RES from WTG/SPV should be absorbed to counter their adverse effects on system frequency, using some storage units such as BES. Hence, this work proposes a new VIS unit including fast-switching bidirectional power converter as in Figure 3c, combining CES and BES units-based on their state-of-charge (SOC) conditions as shown in Figure 3c. The BES unit could store more power for longer durations depending on the stack size/capacity of the battery units and support the CES unit during higher RES penetration or load variations.
Separate PID controllers are dedicated with control actions (∆X i3 ) expressed as (7) for regulating each VIS unit of ith microgrids. The regulated power exchange (∆P VISi ) in every VIS units could be estimated by its linearized model as (8) for ith microgrids, considering healthy battery conditions, i.e., 20% < SOC < 90% [26].

Demand Response Support System
The combined demand response could afford smooth characteristics of frequency response by suitably managing the frequency threshold of every DRS devices associated to the microgrids. The HEV charging station of locality is considered as the DRS unit in this work for contract-based DSM as modelled in Figure 3d. Net power to be activated by each DRS unit of ith microgrid at any instance (∆P DRi ) could be assessed by the strategy (9) [12,13] considering the change in maximum available DRS (∆P DRMi ) in ith microgrids  (10) based on DRS contract with the charging station of HEV [17,18]. The range of maximum frequency regulation is decided by the utility, and this work considered, ∆f m = 0.05 Hz, where the tie-line loading coefficients (L DRi ) for DRS are proportional controller gains and tuned with optimization techniques [17,18].

Renewable Energy System
The LFR type STP unit is preferred over parabolic trough collector type STP for lesser land requisition [17], whose power exchange is expressed as (11) with change in solar irradiance (∆Φ) [1,17]. The power exchange of SPV unit due to change in ∆Φ is stated in (12) with efficiency (η = 10%), temperature (Ta = 27.2 • C), and solar surface area (S = 4084 m 2 ) as testified in [30]. The power exchange of WTG unit owing to change in wind speed (∆V W ) is articulated in (13) with air density (ρ = 1.25 kg/m 3 ) and blade-swept area (A r = 1735 m 2 ), whose power coefficient (C p ) is assessed by (14) [30]. The net RES penetration of each microgrids is estimated as (15).
The monthly average wind/solar data of Bhubaneswar city (India), were collected from "NASA Surface meteorology and Solar Energy-Available Tables" [31] and considered in this work to study the system performance round the year as mentioned in Figure 4.

Bioenergy Generator System
The community wastes (solid/liquid) are separately collected, segregated, and preprocessed to be ready for power generation in appropriate BEGS units (i.e., BCHP, BDEG, BGTG, and MHTG) and support the waste-to-energy-based microgrids [1]. The detail

Load-Generator Dynamic System
This work has used the monthly power consumption data of 3 consumers from different locations of Bhubaneswar in 2017 and normalized those to fit within the simulation time (t sim ) of the proposed microgrids as illustrated in Figures 5 and 6. So, the effective linear load (LL) deviation (∆P LLi ) of ith microgrids in Figure 5 could be replicated for analyzing the system performances round the year, from this real data. However, the practical load pattern consists of nonlinearities injected collectively by several non-linear devices and power converters. Hence, this work has included an effective non-linear loading (NL) configuration (∆P NL ) as (21) [1] for designing the net loading (∆P Li ) as (22), equivalent to actual demand pattern of ith microgrids, as illustrated in Figure 6.
The effective load-generator dynamics for ith microgrids could be estimated as (23), considering single generator-load model [29] for low-inertia (H = 0.1 s) with 50 Hz nominal frequency. Here, the net instantaneous power error (∆P Li ) of ith microgrids is estimated as (24) considering all source and loadings with VIS/DRS.

Objective Function Formulation
The system objective is estimated by the net deviation in apparent power considering corresponding weights for ∆u and ∆f as w v and w f . This could be implemented as the objective function (J), named "integral-square of weighted absolute error" (ISWAE) [1], which needs to be minimized for simultaneous reduction in system voltage, frequency, and tie-line loading. The exact objective for the proposed interconnected system could be expressed as (25), prioritizing frequency regulation (w f = 1) than the voltage with a 10% slackening (w v = 0.9) and relaxing tie-line loading 5% (w t = 0.95).
Here, K Cij denotes the PID gains (K Pij , K Iij , and K Dij ) for jth controller in ith microgrids, considering limits of lb and ub (lower and upper bounds).
The basic figures of demerits (FOD) [1], such as peak-overshoots, peak-undershoots, and settling-times for voltage, frequency, and tie-line loading of the proposed microgrids are assessed with a 0.1% band. The performance index based on FOD (JFOD) of the distributed microgrids is estimated by (26) to investigate the overall system responses.
This work proposed a new QCSHO algorithm discussed in the next section for tuning all controller gains for minimizing the objective (J).

Quasi Oppositional Chaotic Selfish Herd Optimization
The selfish conduct of animals scavenging in herds, for survival during threat from predators was reported as SHO algorithm by Fausto et al. [28]. The movement of these selfish animals to acquire the central position with intraspecific competitions during predation for increasing their survival chance is articulated as SHO.
SHO algorithm is inspired from the predator/preys models, during predators' attack, which may influence a chaotic selfish-herd movement towards the center for survival. The expected competitions among selfish herds are very high in the chaos zone as illustrated in Figure 7a Figure 7a. This idea inspired to modify SHO so as to replace the higher-risk chaotic animals (H* xi , H* xy , and H* xj ) with the lower survival-risk individuals (H xi , H xy , and H xj ) during predation, for a better optimal solution. A new QCSHO algorithm is developed by hybridizing both the concepts of QOBL and CLS into the basic SHO algorithm to imitate this typical activity of selfish-herds during predation, considering a logistic map for CLS in (27), for a faster solution [27].
where, Ch i ∈ (0, 1) and Ch i = 0.25, 0.5, 0.75 (27) The central (M xy ) and opposite (O xy ) animal for a selected (H xy ) individual in the herd could be expressed by (28) considering maximum/minimum limit of the parameters. According to QOBL, the quasi-opposite animal in the herd is selected based on (29), comparing a random number (R j ) with the jumping rate.
M xy = ub y + lb y /2, and O xy = ub y + lb y − H xy The performance of SHO is expected to be improved by the modified QCSHO with the advantages of both CLS and QOBL that supports faster search process than normal stochastic search [27]. The detail steps of proposed QCSHO algorithm are illustrated as flowchart in Figure 7b with inclusions of both CLS and QOBL steps. The new QCSHO algorithm invokes the Simulink models of the system to evaluate/update the survival values of herds in each iteration as explained in Figure 7b, by passing the tuned controller gains and finding the optimal solution. The detailed simulation results and performance comparisons are discussed in the following section.

Simulation Studies and Analysis of Results
The linear transfer-function model of the projected microgrids, illustrated in Figure 3a, is developed by Simulink ® of MATLAB ® R2015a software, whereas all the optimization algorithms are coded in script files. The performances of distributed microgrids were investigated for simultaneous voltage-frequency regulation by simulating with a desktop (4 GB RAM, Intel-3.4 GHz i7-4770 CPU) in following approaches.

Step Response Analysis for Method Selection
It is crucial to choose suitable methods such as optimization technique, objective function, and controllers for investigating the system responses commendably. The step responses of the proposed distributed microgrids are analyzed to compare these methods considering a step change in ∆P L (drops to 0.725 from 0.75 p.u.), ∆Φ (rises to 0.645 from 0.615 kW/m 2 ), and ∆V w (rises to 6.96 from 6.3 m/s), for t sim = 20 s with normal day scenario, in this subsection. Then, the superior methods are assorted to study the simultaneous voltage-frequency regulation in distributed microgrids with both VIS and DRS.
The proposed distributed microgrids in Figure 3a is simulated with PSO, GOA, SSA, SHO, QSHO, and CSHO and the projected QCSHO to minimize the objective functions J in (25). The important parameters for corresponding algorithms are recorded in Table 2. Table 2. Key parameters for corresponding algorithms. The convergence curves of all 7 optimization algorithms with step responses are compared in Figure 8, considering PID controllers with proposed ISWAE as (25) for initial 20 s of loadings. The FOD-based performance index (J FOD ) of the system expressed in (26) are estimated for all algorithms considering ISWAE and compared in the first column of Table 3. It is observed from Figure 8 and Table 3 that the proposed QCSHO performs superior over other algorithms. The optimized step responses of the distributed microgrids are estimated at the same scenario using all 4 objective functions: integral absolute error (IAE), integral square error (ISE), integral time weighted absolute error (ITAE), and integral time weighted square error (ITSE), along with ISWAE and compared in Table 4, for QCSHO tuned PID controllers, which confirmed the superiority of ISWAE over others. The step responses of the distributed microgrids are estimated by separately tuning I, PI, PD, ID, and PID controllers using QCSHO optimized ISWAE and compared in Table  5 estimating JFOD, which confirmed the superiority of PID.   The step responses of the distributed microgrids are estimated by separately tuning I, PI, PD, ID, and PID controllers using QCSHO optimized ISWAE and compared in Table 5 estimating J FOD , which confirmed the superiority of PID. Finally, the proposed QCSHO algorithm with ISWAE function (J) are preferred for tuning the gains of PID controllers in rest part of the study as these combinations outperform over others, interpreted from Figure 8, Tables 3 and 4 (the superior values are highlighted in bold fonts). The QCSHO tuned PID controller gains for interconnected microgrids are listed in Table 6 considering ISWAE and used in rest of the study without re-tuning PID to investigate the sturdiness of the system.

Response of Distributed Microgrids
In order to study the system responses round the year in different Indian climatic conditions, the real-time monthly average recorded solar-wind data for Bhubaneswar region [31] and the normalized loading data are arranged with 10 s durations for each month as in Figures 4-6, considering t sim = 120 s. The proposed distributed microgrids, as modelled in Figure 3a, are simulated for simultaneous voltage-frequency regulation in 4 different scenarios of RES penetration with LL as in Figure 5 and one scenario of load variation including NL as in Figure 6 using tuned PID gains from Table 5. The responses for all five cases are plotted and discussed below individually comparing the system responses with both VIS and DRS.

Case 1: Normal Day with Both Solar and Wind Support
This instance is presumed considering a normal day climatic scenario with available RES (SPV, STP, and WTG) generations coordinated with all waste-to-energy-based BEGS (BCHP, MHTG, BGTG, and BDEG i ) units including supports from both VIS i and DRS i units to supply specified LL (∆P L = ∆P LL ). The frequency and voltage responses of the projected distributed microgrids in this scenario are plotted in Figure 9a,b, respectively, along with the tie-line loading in Figure 9c. These confirmed the power quality improvement due to faster support by VIS/DRS units to smoothen ∆f i and ∆u i with minimal ∆P tij . The system voltage, frequency, tie-line loadings are settled to their nominal values within 3, 6, and 1 s, respectively, for every perturbation in source/load as shown in Figure 9a-c. The variation of solar/wind data [31] used for simulation explicitly express the intermittency and the regulated voltage-frequency responses of the system in Case-I scenario confirms the adequacy of control and operation management.

Case 2: Unavailability of Solar Support
This instance is presumed considering a normal night climatic scenario or a cloudy day scenario (or during the maintenance/failure of both SPV and STP units) with WTG generations only to supply the same LL. The waste-to-energy-based BEGS units were coordinated to meet the excess demand in absence of solar support. The frequency, voltage, and tie-line loading responses of the distributed microgrids supported with VIS/DRS in this scenario are plotted in Figure 10a-c, respectively. These also confirmed the power quality improvement with faster settling of ∆f i , ∆u i , and ∆P tij to their nominal values within 2, 5, and 1 s, respectively, for every perturbation in source/load during absence of solar support as shown in Figure 10a-c. due to faster support by VIS/DRS units to smoothen ∆fi and ∆ui with minimal ∆Ptij. The system voltage, frequency, tie-line loadings are settled to their nominal values within 3, 6, and 1 s, respectively, for every perturbation in source/load as shown in Figure 9a-c. The variation of solar/wind data [31] used for simulation explicitly express the intermittency and the regulated voltage-frequency responses of the system in Case-I scenario confirms the adequacy of control and operation management. The frequency, voltage, and tie-line loading responses of the distributed microgrids supported with VIS/DRS in this scenario are plotted in Figure 11a-c, respectively. These confirmed again the power quality improvement with faster settling of ∆fi, ∆ui, and ∆Ptij to their nominal values within 2.5, 6, and 1 s, respectively, for every perturbation in source/load during absence of wind support as shown in Figure 11a-c.

Case 3: Unavailability of Wind Support
This instance is presumed considering a normal day climatic scenario with unfavorable wind speed (or during the maintenance/failure of WTG unit) with only solar-based RES (i.e., STP and SPV) support to supply that LL. The waste-to-energy-based BEGS units were coordinated to meet the excess demand in absence of wind support.
The frequency, voltage, and tie-line loading responses of the distributed microgrids supported with VIS/DRS in this scenario are plotted in Figure 11a-c, respectively. These confirmed again the power quality improvement with faster settling of ∆f i , ∆u i , and ∆P tij to their nominal values within 2.5, 6, and 1 s, respectively, for every perturbation in source/load during absence of wind support as shown in Figure 11a-c.

Case 4: Unavailability of Both Wind and Solar Support
This case is assumed considering a stormy day/night climatic scenario without any RES generations (or maintenance/failure of all RES units) to supply that LL. All the wasteto-energy-based BEGS units were coordinated to meet the total demand in these extreme climatic conditions. The frequency, voltage, and tie-line loading responses of the distributed microgrids supported with VIS/DRS in this scenario are plotted in Figure 12a-c, respectively. These also confirmed the power quality improvement with quicker settling of ∆fi, ∆ui, and ∆Ptij to their nominal values within 3, 5, and 1 s, respectively, for every perturbation in source/load during absence of RES support as shown in Figure 12a

Case 4: Unavailability of Both Wind and Solar Support
This case is assumed considering a stormy day/night climatic scenario without any RES generations (or maintenance/failure of all RES units) to supply that LL. All the waste-to-energy-based BEGS units were coordinated to meet the total demand in these extreme climatic conditions. The frequency, voltage, and tie-line loading responses of the distributed microgrids supported with VIS/DRS in this scenario are plotted in Figure 12a-c, respectively. These also confirmed the power quality improvement with quicker settling of ∆f i , ∆u i , and ∆P tij to their nominal values within 3, 5, and 1 s, respectively, for every perturbation in source/load during absence of RES support as shown in Figure 12a

Case 4: Unavailability of Both Wind and Solar Support
This case is assumed considering a stormy day/night climatic scenario without any RES generations (or maintenance/failure of all RES units) to supply that LL. All the wasteto-energy-based BEGS units were coordinated to meet the total demand in these extreme climatic conditions. The frequency, voltage, and tie-line loading responses of the distributed microgrids supported with VIS/DRS in this scenario are plotted in Figure 12a-c, respectively. These also confirmed the power quality improvement with quicker settling of ∆fi, ∆ui, and ∆Ptij to their nominal values within 3, 5, and 1 s, respectively, for every perturbation in source/load during absence of RES support as shown in Figure 12a-c.

Case 5: Responses of Distributed Microgrids in Normal Climate with NL
This instance is presumed considering a generalized scenario considering same climatic condition as scenario 1, with all available RES generations supplying combined LL and NL (ΔPL = ΔPLL + ΔPNL). All the waste-to-energy-based BEGS units were coordinated to meet the excess demand. The frequency, voltage, and tie-line loading responses of the projected distributed microgrids supported with VIS/DRS in this generalized loading scenario are plotted in Figure 13a-c, respectively. These are still confirming the power quality improvement with quicker settling of ∆fi, ∆ui, and ∆Ptij to their nominal values within 3, 6, and 1 s, respectively, for every perturbation in source/load as shown in Figure 13a-c.
The responses in all the aforesaid five instances have confirmed robustness of the QCSHO tuned PID controllers and the reliability of projected VIS and DRS units throughout the year, with novel control strategy in distributed microgrids. The work is extended further to investigate the optimal allocation of VIS and DRS units for economic operation of distributed microgrids in next sub-section.

Case 5: Responses of Distributed Microgrids in Normal Climate with NL
This instance is presumed considering a generalized scenario considering same climatic condition as scenario 1, with all available RES generations supplying combined LL and NL (∆P L = ∆P LL + ∆P NL ). All the waste-to-energy-based BEGS units were coordinated to meet the excess demand. The frequency, voltage, and tie-line loading responses of the projected distributed microgrids supported with VIS/DRS in this generalized loading scenario are plotted in Figure 13a-c, respectively. These are still confirming the power quality improvement with quicker settling of ∆f i , ∆u i , and ∆P tij to their nominal values within 3, 6, and 1 s, respectively, for every perturbation in source/load as shown in Figure 13a-c.  The responses in all the aforesaid five instances have confirmed robustness of the QCSHO tuned PID controllers and the reliability of projected VIS and DRS units throughout the year, with novel control strategy in distributed microgrids. The work is extended further to investigate the optimal allocation of VIS and DRS units for economic operation of distributed microgrids in next sub-section.

Optimal Allocation of VIS/DRS Units
The projected distributed microgrids were tested successfully in different extreme climatic/loading scenarios, supported by both VIS and DRS units in each microgrid. However, the arrangement of both VIS and DRS units for each microgrid with suitable control strategies make the system more complex and bulky, which may not be economic (due to additional costs associated with both the support systems). Again, the availability of suitable DR devices and ESS for all these support systems may not be always feasible.
Hence, it is essential to investigate the most suitable arrangements of these VIS and DRS units-based IRP for economic operation of the distributed microgrids. All the 10 possible arrangements of these VIS and DRS units in the distributed microgrids are simulated considering QCSHO tuned PID with ISWAE as listed in Table 6.
The FOD-based performance index (J FOD ) was estimated to investigate the optimal allocation of VIS/DRS units for all these 10 cases. The values are compared in Table 6. The optimal system performances could be achieved by allocating both VIS and DRS units to each microgrid, but less economic. Therefore, it is confirmed that installing one VIS unit to hµG2, whereas two DRS units to hµG1 and hµG3 each provide the most economic IRP for the projected distributed microgrids.
All these case studies have confirmed the efficacy and adaptiveness of the proposed QCSHO algorithm for both the modes of operation with quicker coordination by VIS/DRS systems in every climatic variation round the year. Furthermore, suitable monitoring system needs to be implemented in the microgrids for managing the quality of power supply [32]. The acronyms used in this work are listed in Table 7 with their expansions for swift references.

Conclusions
The results obtained in this study have collectively confirmed the performance improvement of distributed microgrids, incorporating the proposed VIS/DRS system. The system performances using new QCSHO tuned PID controllers considering ISWAE were found superior to expedite optimal voltage, frequency, and tie-line loadings within accessible operating limits. Again, the overall system responses supported with both VIS and DRS units have witnessed adaptive throughout the year, with 5 different scenarios of extreme climatic and load variations. The smooth regulation and faster settling of voltage and frequency in all these five scenarios (without re-tuning the controller gains) confirmed the sturdiness of the proposed system. The study is extended further for optimal allocation of these VIS/DRS units in the projected microgrids estimating FOD-based performance index in 10 possible arrangements. Finally, the system responses have confirmed the economic IRP by incorporating the proposed VIS in the second microgrid only and 2 DRS units in other two microgrids for optimal allocation. This work could be extended further with modern controllers such as model-predictive, fractional-order, fuzzy, their blends along with available standard technique for tuning the control parameters, and comparative performance study.