DE202023100612U1 - A system for integrated resource planning in distributed microgrids based on sustainable energy - Google Patents

Abstract

A system for integrated resource planning in distributed microgrids based on sustainable energy, the system comprising:
a combined memory-based virtual inertial unit (VIS) for supply-side management (SSM);
a Hybrid Electric Vehicle (HEV) charging station based Demand Response Support Unit (DRS) for Demand Side Management (DSM) as integrated resource planning (IRP) in distributed microgrids for optimal regulation of frequency and voltage;
a weighted absolute error integral square (ISWAE) control unit for tuning the controllers of distributed hµGs using quasi-oppositional chaotic self-herd optimization (QCSHO);
an analysis processing unit for evaluating a set of responses of the control unit with the VIS/DRS units taking into account recorded sun, wind and load data over the year; and
a central processor to study the optimal allocation of VIS/DRS units for economical operation of distributed hµGs.

Description

FIELD OF THE INVENTION

The present disclosure relates to a system for integrated resource planning in distributed microgrids based on sustainable energy using appropriate BEGS and EE.

BACKGROUND OF THE INVENTION

Global electricity consumption is increasing as conventional fossil resources are depleted, putting pressure on researchers to find alternative environmentally friendly and sustainable energy sources. However, the most promising sustainable resources such as solar and wind power are intermittent and weather dependent by nature, and the amount of electricity generated from a single source with current technology is insufficient to meet demand. Therefore, local hybrid microgrids (hµGs) are operated with sustainable generators from multiple sources to improve reliability through coordinated generation. Future power demand could be met by interconnecting such hµGs comprising locally available bioenergy generation systems (BEGS) based on waste and renewable energy (RES) based on solar and wind power with the dual objective of waste and energy management. The on-site collected, separated and treated waste for power generation is powered by suitable BEGS such as Biodiesel Engine Generator (BDEG), Biogas Turbine Generator (BGTG), Wastewater Powered Micro Hydro Turbine Generator (MHTG) and Biomass Combined Heat and Power (BCHP). ) combined to support the RES units such as Solar Photovoltaic Panels (SPV), Solar Thermal Power Plants (STP) and Wind Turbine Generators (WTG). Energy management in multi-microgrids is discussed when they are interconnected for distributed generation (DG). Conversely, these hµGs have low inertia because the generators have low capacity to handle higher renewable energy shares and load fluctuations. Today's pattern of demand fluctuations has also been influenced by changing consumer lifestyles with advanced electronic devices and equipment. As a result, providing quality power with adequate inertial support for hµGs incorporating intermittent renewable resources poses a major challenge. Virtual inertial concepts are recommended in the literature to address similar challenges. The virtual inertial control units based on tunable and derivative techniques are considered with supercapacitors and supermagnetic energy storage devices (SMES) for frequency control of microgrids (µGs). A detailed overview of virtual synchronous generators (VSG) integrated in µGs and power grids is discussed with their applications. A fast-switching inverter and a storage-based VSG are developed to dampen the oscillations of a DG. An advanced VSG is proposed for load frequency control (LFC) of microgrids. The inertia of an AC renewable energy microgrid is estimated by frequency response measurement using the curve fitting method. In most similar publications, VSGs were designed with an energy storage system (ESS) and a fast bidirectional converter to improve the inertia and damping effect of µGs.

Similarly, the disruption of EE units in hµGs could be handled by temporarily shifting their demand in combination with some non-essential loads such as hybrid electric vehicles (HEV), heat pumps, water heaters and freezers contributing to demand response (DR) with a focus on the Demand management (DSM) contribute. The efficiency of the renewable energy based hµGs could be improved by an appropriate DR strategy. The applications of different DR strategies in isolated µGs are compared. Energy management using an optimal DR strategy for grid-connected µGs with load and EE uncertainty is discussed, while the incentive-based DR strategy with such uncertainty is reported. Frequency regulation of multi-area systems with DR strategies is discussed. A DR emergency program based on real-time voltage control is reported. Frequency regulation in µG with minimal manipulated load is described, proposing an extensive core DR technique. DR strategies considering HEV charging stations are successfully applied in isolated and interconnected hµGs. A Demand Response Support (DRS) system with appropriate DR strategy for DSM-based IRP in distributed hµGs. The power quality of hµGs could be improved by simultaneously regulating the system voltage and frequency using an automatic voltage regulator (AVR) together with an automatic load frequency control (ALFC) in isolated hµGs and in interconnected µGs. RE plants such as WTG, SPV and STP plants with linear Fresnel reflector (LFR) are combined in this hµG with waste-based BEGS plants such as BDEG, BGTG, MHTG and BCHP systems integrated. Battery storage (BES) is used for the ALFC of isolated and interconnected hµGs.

The combined memory-based power management of isolated µG is reported. The dynamic power allocation of a combined battery-supercapacitor ESS in an SPV-based isolated µG is discussed in detail. Most work deals with ESS-based VIS for SSM of hµGs involving Capacitive Energy Storage (CES) and SMES. In contrast, HEV-based DRS are included in hµGs for DSM in isolated and interconnected modes. Therefore, there is an opportunity to develop and allocate a suitable VIS/DRS system for the optimal regulation of voltage and frequency in distributed hµGs.

The ALFC of hµGs asserts the effectiveness of classic proportional-integral-derivative (PID) controllers over traditional integral (I), proportional-integral (PI), proportional-derivative (PD), and integral-derivative (ID) controllers. Some modern controllers, such as Model Predictive Controllers, Fractional Order Controllers, and their mixtures are also used in similar work, but have been combined with PID controllers. The effective tuning of these controllers in the ALFC of these complex hµGs is accomplished by applying some basic optimization techniques like Particle Swarm Optimization (PSO), Locust Optimization Technique (GOA), Salp Swarm Technique (SSA), Selfish Herd Optimization (SHO) or their hybrids like Quasi-oppositional selfish-herd optimization (QSHO), chaotic selfish-herd optimization (CSHO) achieved. Likewise, the quasi-oppositional chaotic antlion optimization technique is projected through the hybridization of quasi-opposition-based learning (QOBL) and chaotic linear search (CLS) with antlion optimization. Inspirationally, a novel technique called Quasi-Oppositional Chaotic Herd Optimization (QCSHO) is proposed here for tuning the PID controllers by combining QOBL and CLS techniques with the basic SHO technique [32] to solve the chaotic behavior of the selfish herds to replicate.

Considering the previous discussion, it becomes clear that a system for integrated resource planning in sustainable energy-based distributed microgrids using appropriate BEGS and EE is needed.

SUMMARY OF THE INVENTION

The present disclosure aims to provide a system for integrated resource planning in sustainable energy-based distributed microgrids. The system is dedicated to evaluating the optimal allocation of appropriate integrated resources for clean, sustainable energy-based hybrid microgrids with distributed generation. The problems arising from the disruption of renewable resources and the low inertia of these microgrids are mitigated by coordinating appropriate Demand Response Support (DRS) and Virtual Inertia Support (VIS) systems. Primarily, 3 distributed, unequal microgrids with locally available solar/wind/bioenergy resources are planned, including combined storage-based VIS and EV charging station-based DRS units for supply and demand-side management, respectively. The planned system is modeled in MATLAB, taking into account real-time recorded solar/wind data with 12-month loads and simulated in different scenarios of source and load fluctuations. The controllers are tuned using a new quasi-oppositional chaotic selfish-herd (QCSHO) optimization for optimal frequency and voltage regulation in the distributed microgrids after confirming their superiority over other current techniques by comparing the system responses. Then the coordination of frequency, voltage and grid load for the projected microgrids is examined without retuning the controllers in 5 extreme scenarios of source and load fluctuations. Finally, the answers are evaluated for 22 different possible assignments of DRS and VIS units in different micro-grids. Also, the most appropriate allocation of built-in resources is recorded after comparing their overall responses.

In one embodiment, a system for integrated resource planning in distributed microgrids based on sustainable energy is disclosed. The system includes a combined memory-based Virtual Inertia Support (VIS) unit for supply-side management (SSM). The system further includes a Hybrid Electric Vehicle (HEV) charging station based Demand Reduction Support (DRS) unit for Demand Side Management (DSM) as Integrated Resource Scheduling (IRP) in distributed microgrids for optimal regulation of frequency and voltage. The system further includes a controller for introducing the integral square of the weighted absolute error (ISWAE) for tuning the controllers of distributed hµGs using a quasi-oppositional chaotic self-herd optimization technique (QCSHO). The system further comprises an analysis processing unit for evaluating a series of responses of the control unit with the VIS/DRS units taking into account the recorded sun, wind and load data throughout the year. The system also includes a central processor to study the optimal allocation of VIS/DRS units for economical operation of distributed hµGs.

The objective of this disclosure is to keep the grid voltage and frequency (440V-50Hz) within acceptable limits regardless of renewable energy penetration and load differentials.

Another objective of the present disclosure is to estimate the net apparent power deviation (which is a linear combination of real and reactive power).

Another object of the present invention is to provide a fast and cost-effective system for integrated resource planning in sustainable energy-based distributed microgrids.

In order to further clarify the advantages and features of the present disclosure, a more detailed description of the invention will be given by reference to specific embodiments that are illustrated in the accompanying drawings. It is understood that these drawings represent only typical embodiments of the invention and therefore should not be considered as limiting the scope of the invention. The invention will be described and illustrated with additional specificity and detail with the accompanying drawings.

character list

• 1 zeigt ein Blockdiagramm eines Systems für die integrierte Ressourcenplanung in verteilten Mikronetzen auf der Grundlage nachhaltiger Energien in Übereinstimmung mit einer Ausführungsform der vorliegenden Offenbarung;
• 2 zeigt Simulink-Modelle von (a) verteilten hµGs, (b) VIS-Einheit und (c) DRS-Einheit gemäß einer Ausführungsform der vorliegenden Offenbarung;
• 3 zeigt ein Flussdiagramm der QCSHO-Technik;
• 4 zeigt Tabelle 1 mit Solar-/Wind- und Belastungsdaten gemäß einer Ausführungsform der vorliegenden Offenbarung;
• 5 veranschaulicht den in Tabelle 2 dargestellten Vergleich von Zielfunktionen mit PID-Reglern gemäß einer Ausführungsform der vorliegenden Offenbarung;
• 6 veranschaulicht Tabelle 3 zeigt den Vergleich des Reglers mit dem auf QCSHO abgestimmten ISWAE gemäß einer Ausführungsform der vorliegenden Offenbarung;
• 7 veranschaulicht Tabelle 4 zeigt die Gewinne des PID-Reglers mit QCSHO-abgestimmter ISWAE gemäß einer Ausführungsform der vorliegenden Offenbarung; und
• 8 veranschaulicht die in Tabelle 5 dargestellte Verteilung von DRS und VIS in hybriden Microgrids gemäß einer Ausführungsform der vorliegenden Offenbarung.

These and other features, aspects and advantages of the present disclosure will be better understood when the following detailed description is read with reference to the accompanying drawings, in which like characters represent like parts throughout the drawings, wherein:

• 1 Figure 12 shows a block diagram of a system for integrated resource planning in distributed microgrids based on sustainable energy, in accordance with an embodiment of the present disclosure;
• 2 12 shows Simulink models of (a) distributed hµGs, (b) VIS entity, and (c) DRS entity, according to an embodiment of the present disclosure;
• 3 shows a flow chart of the QCSHO technique;
• 4 Table 1 shows solar/wind and load data according to an embodiment of the present disclosure;
• 5 illustrates the comparison of objective functions with PID controllers presented in Table 2, according to an embodiment of the present disclosure;
• 6 Table 3 shows the comparison of the controller to the QCSHO tuned ISWAE according to an embodiment of the present disclosure;
• 7 Table 4 shows the gains of the PID controller with QCSHO tuned ISWAE according to an embodiment of the present disclosure; and
• 8th FIG. 5 illustrates the distribution of DRS and VIS in hybrid microgrids presented in Table 5, according to an embodiment of the present disclosure.

Those skilled in the art will understand that the elements in the drawings are presented for simplicity and are not necessarily drawn to scale. For example, the flow charts illustrate the method of key steps to enhance understanding of aspects of the present disclosure. Furthermore, one or more components of the device may be represented in the drawings by conventional symbols, and the drawings only show the specific details relevant to an understanding of the embodiments of the present disclosure, not to encircle the drawings with details to overload, which are easily recognizable to those skilled in the art familiar with the present description.

DETAILED DESCRIPTION:

For the purposes of promoting an understanding of the invention, reference will now be made to the embodiment illustrated in the drawings and specific words will be used to describe the same. It should be understood, however, that no limitation on the scope of the invention is intended, and such alterations and further modifications to the illustrated system and such further applications of the principles of the invention set forth therein are contemplated as would occur to those skilled in the art invention would normally come to mind.

Those skilled in the art will understand that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting.

When this specification refers to "an aspect," "another aspect," or the like, it means that a particular feature, structure, or characteristic described in connection with the embodiment is present in at least one embodiment included in the present disclosure. Therefore, the phrases "in one embodiment," "in another embodiment," and similar phrases throughout this specification may or may not all refer to the same embodiment.

The terms "comprises," "including," or other variations thereof are intended to cover non-exclusive inclusion, such that a method or method that includes a list of steps includes not only those steps, but may also include other steps that are not expressly stated or pertaining to any such process or method. Likewise, any device or subsystem or element or structure or component preceded by "comprises...a" does not, without further limitation, exclude the existence of other devices or other subsystem or other element or other structure or other component or additional device or additional subsystems or additional elements or additional structures or additional components.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this invention pertains. The system, methods, and examples provided herein are for purposes of illustration only and are not intended to be limiting.

Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

In 1 Illustrated is a block diagram of a system for integrated resource planning in distributed microgrids based on sustainable energy, according to an embodiment of the present disclosure. The system 100 includes a combined memory-based virtual inertial support unit 102 for supply-side management (SSM).

In one embodiment, a hybrid electric vehicle (HEV) charging station-based Demand Response Support (DRS) unit 104 is integrated with a combined storage-based Virtual Inertia Support (VIS) unit 102 for Demand Side Management (DSM) as resource scheduling (IRP) connected in distributed microgrids for optimal regulation of frequency and voltage.

In one embodiment, a controller 106 is coupled to a hybrid electric vehicle (HEV) charging station-based demand response support unit (DRS) 104 to calculate the integral squared of the weighted absolute error (ISWAE) for tuning the controllers of distributed hµGs using a quasi-oppositional chaotic selfish herd optimization technique (QCSHO).

In one embodiment, an analysis processing unit 108 is coupled to the controller 106 to evaluate a series of controller 106 responses to the VIS/DRS units given the recorded solar, wind, and load data throughout the year.

In one embodiment, a central processor 110 is coupled to analysis unit 108 to examine the optimal allocation of VIS/DRS units for economical operation of distributed hµGs.

In another embodiment, the combined storage comprises a battery energy storage (BES) and a capacitive energy storage (CES).

In another embodiment, quasi-oppositional chaotic selfish herd optimization (QCSHO) is developed by hybridizing chaotic linear search (CLS) and quasi-opposition-based learning (QOBL) with basic selfish herd optimization (SHO) techniques.

In another embodiment, the three interconnected sustainable energy based dissimilar hµGs comprise 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 third microgrid (hµG3) with combined SSM/DSM based IRP.

In another embodiment, the control unit maintains the voltage frequency (440v-50Hz) of the system within an accessible limit, independent of EE penetration and load differentials, with the frequency of each hµG controlled by an automatic load frequency control (ALFC) unit , which is a combination of primary (Droop) and secondary (PID) control.

In another embodiment, the inertial support for the hµGs is practically provided with appropriate ESS and bi-directional power converters considering the SSM-based IRP to prevent system frequency instability in autonomous hµGs due to low inertia.

In another embodiment, the step responses of the distributed hµGs are determined by separately tuning integral (I), proportional-integral (PI), proportional-derivative (PD), integral-derivative (ID), and PID controllers using QCSHO-optimized ISWAE estimated, confirming the superiority of PID, favoring the QCSHO technique with ISWAE function (J) for tuning the gains of PID controllers.

2 12 shows Simulink models of (a) distributed hµGs, (b) VIS entity, and (c) DRS entity, in accordance with an embodiment of the present disclosure. The system is studied by proposing distributed hµGs with available sustainable local resources for energy production using appropriate BEGS and RES. However, not all of these resources may be available in the same place, so DG is encouraged. Therefore, three interconnected sustainable energy based unequal hµGs are proposed here, consisting of STP/BCHP/BDEG1 type LFC units in the first microgrid (hµG1), WEA/BGTG/BDEG2 units in the second microgrid (hµG2) and SPV/ MHTG/BDEG3 entities in the third microgrid (hµG3) with combined SSM/DSM based IRP. The linearized system model of the entire proposed hµG is in shown. The main goal of the system is to keep the system voltage and frequency (440V-50Hz) within the allowable limits regardless of EE penetration and load differences. The component-wise linear model of this hµG is presented concisely, taking into account the design values of the parameters.

Frequency control system

$$\mathrm{\Delta }{P}_{tii}={\displaystyle {\sum }_{\underset{\left(j\ne i\right)}{j,i=1}}^3\mathrm{\Delta }{P}_{tij}}={\displaystyle {\sum }_{\underset{\left(j\ne i\right)}{j,i=1}}^3\left(\mathrm{\Delta }{f}_i-\mathrm{\Delta }{f}_i\right)\frac{2\pi T_{ij}}{s}}$$

The grid frequency of each hµG is controlled by an ALFC unit consisting of a combination of primary (droop) and secondary (PID) control. The effect of the ALFC with PID controllers (ΔX _i1 ) is expressed in (1) for the i ^te hµG and the effective power exchange am i ^ten hµG (ΔPt _ii ) considering the frequency distortion (B _i ) is given in (2) (for i = 1, 2, 3). $$\mathrm{<figure-callout\; id="1"\; label="\Delta \; X\; i"\; filenames="DE202023100612U1\_0031.png,DE202023100612U1\_0032.png"\; state="\{\{state\}\}">\Delta </figure-callout>}{X}_i{1}_{}=\left(-B_i\mathrm{\Delta }{f}_i-\mathrm{\Delta }{P}_{tii}\right)\left({\mathrm{<figure-callout\; id="1"\; label="K\; p\; i"\; filenames="DE202023100612U1\_0031.png,DE202023100612U1\_0032.png"\; state="\{\{state\}\}">K</figure-callout>}}_{pi}+\frac{K_{pi1}}{s}+s{K}_{pi1}\right)-\frac{\mathrm{\Delta }{f}_i}{K_i}$$

$$\mathrm{\Delta }{P}_{tii}={\displaystyle {\sum }_{\underset{\left(j\ne i\right)}{j,i=1}}^3\mathrm{\Delta }{P}_{tij}}={\displaystyle {\sum }_{\underset{\left(j\ne i\right)}{j,i=1}}^3\left(\mathrm{\Delta }{f}_i-\mathrm{\Delta }{f}_i\right)\frac{2\pi T_{ij}}{s}}$$

Voltage regulation system

$$\mathrm{\Delta }{p}_{EQi}=\left(p_s\mathrm{\Delta }{\delta }_i+K_1\mathrm{\Delta }{E}_{f_i}\right)$$

$$\mathrm{\Delta }{u}_i=K_2\mathrm{\Delta }{\delta }_i+K_3\mathrm{\Delta }{E}_{f_i}$$

The mains voltage of the i ^ten hµG is regulated by special AVR units. Detailed modeling of the AVR units is discussed and successfully operated in both isolated and interconnected hµGs. Separate PID controllers were used for each AVR unit with the control measures (ΔX _i2 ) evaluated in (3). The power exchange (ΔPEQ) for the AVR and the voltage change (Δui) for the i ^te hµGs are given in (4)-(5). $$\mathrm{<figure-callout\; id="2"\; label="\Delta \; X\; i"\; filenames="DE202023100612U1\_0032.png"\; state="\{\{state\}\}">\Delta </figure-callout>}{X}_i{2}_{}=\left({\mathrm{<figure-callout\; id="2"\; label="K\; p\; i"\; filenames="DE202023100612U1\_0032.png"\; state="\{\{state\}\}">K</figure-callout>}}_{pi}+\frac{K_{Ii2}}{s}+s{K}_{Di2}\right)\Delta E_{V_i}$$

$$\mathrm{\Delta }{p}_{EQi}=\left(p_s\mathrm{\Delta }{\delta }_i+{\mathrm{<figure-callout\; id="1"\; label="K"\; filenames="DE202023100612U1\_0031.png,DE202023100612U1\_0032.png"\; state="\{\{state\}\}">K</figure-callout>}}_1\mathrm{\Delta }{E}_{f_i}\right)$$

$$\mathrm{\Delta }{\mathrm{and}}_i={\mathrm{<figure-callout\; id="2"\; label="K"\; filenames="DE202023100612U1\_0032.png"\; state="\{\{state\}\}">K</figure-callout>}}_2\mathrm{\Delta }{\delta }_i+{\mathrm{<figure-callout\; id="3"\; label="K"\; filenames="DE202023100612U1\_0031.png"\; state="\{\{state\}\}">K</figure-callout>}}_3\mathrm{\Delta }{E}_{f_i}$$

Virtual inertial support system

Larger load fluctuations or a higher proportion of renewable energy are mostly responsible for the instability of the system frequency in autonomous hµGs due to the low inertia. Therefore, it is crucial to provide these hµGs with inertial support, practically with appropriate ESS and bi-directional power converters considering the SSM-based IRP. The inertial power (H) of the hµG can be estimated by (6) considering the apparent power s^, the mechanical (T _m , P _m ) and electrical (T _e , P _e ) torques/powers when measuring the system frequency (ω = 2nf). become. $$\frac{\mathrm{i.e}\omega }{\mathrm{i.e}t}={\omega }^2\left(\frac{T_m-T_e}{2H\widehat{S}}\right)=\frac{\omega \left(p_m-p_e\right)}{2H\widehat{S}}$$

Therefore, most of the techniques applied are derivative-based virtual inertial control according to (6) by estimating an inertial gain times derivative of the measured frequency error (Δf), emulating a derivative controller. However, the derivative controllers used alone amplify the stationary error and the noise signals and cause saturation effects. To overcome these problems, a PI controller is used in parallel with the derivative controller (combined to emulate the PID controller) to control Δf as in shown to process.

$$\Delta P_{VIis}=\left(\frac{K_{BE}{K}_{BES}}{1+s{T}_{BES}}+\frac{K_{CE}{K}_{CES}}{1+s{T}_{CES}}\right)\left(\frac{1+s{T}_1}{1+s{T}_2}\right)\left(\frac{1+s{T}_3}{1+s{T}_4}\right)\Delta X_{i3}$$

In the VIS, supercapacitors and SMES have already been used successfully as ESS. However, a higher share of renewable energy from WT/SPV should be absorbed to offset their negative impact on system frequency by using some storage units like BES. Therefore it features a new VIS unit with a fast switching bidirectional power converter (see ), which combines CES and BES units based on their state of charge (SOC) (see ). The BES unit could store more power for longer periods depending on the stack size/capacity of the battery units and support the CES unit in case of higher EE penetration or load fluctuations. Separate PID controllers with control functions (ΔXi3) according to (7) are used to control each VIS unit of the i-th hµG. The regulated power exchange (ΔPVISi) in each VIS unit can be estimated using the linearized model (8) for the ith hµGs considering healthy battery conditions, ie 20% < SOC < 90%. $$\mathrm{<figure-callout\; id="3"\; label="\Delta \; X\; i"\; filenames="DE202023100612U1\_0031.png"\; state="\{\{state\}\}">\Delta </figure-callout>}{X}_i{3}_{}=\left(-B_i\Delta f_i-\Delta P_{tii}\right)\left({\mathrm{<figure-callout\; id="3"\; label="K\; p\; i"\; filenames="DE202023100612U1\_0031.png"\; state="\{\{state\}\}">K</figure-callout>}}_{pi}+\frac{K_{pi3}}{s}+s{K}_{pi3}\right)-\frac{\Delta f_i}{R_i})$$

$$\Delta P_{VIis}=\left(\frac{K_{BE}{K}_{\mathrm{<figure-callout\; id="1"\; label="B\; E\; S"\; filenames="DE202023100612U1\_0031.png,DE202023100612U1\_0032.png"\; state="\{\{state\}\}">B</figure-callout>}ES}}{1+s{T}_{BES}}+\frac{K_{CE}{K}_{\mathrm{<figure-callout\; id="1"\; label="C\; E\; S"\; filenames="DE202023100612U1\_0031.png,DE202023100612U1\_0032.png"\; state="\{\{state\}\}">C</figure-callout>}ES}}{1+s{T}_{CES}}\right)\left(\frac{1+s{\mathrm{<figure-callout\; id="1"\; label="T"\; filenames="DE202023100612U1\_0031.png,DE202023100612U1\_0032.png"\; state="\{\{state\}\}">T</figure-callout>}}_1}{1+s{T}_2}\right)\left(\frac{1+s{\mathrm{<figure-callout\; id="3"\; label="T"\; filenames="DE202023100612U1\_0031.png"\; state="\{\{state\}\}">T</figure-callout>}}_3}{1+s{T}_4}\right)\mathrm{<figure-callout\; id="3"\; label="\Delta \; X\; i"\; filenames="DE202023100612U1\_0031.png"\; state="\{\{state\}\}">\Delta </figure-callout>}{X}_i{3}_{}$$

System to support demand management

$$\Delta P_{DRMi}={\displaystyle {\sum }_{k=1}^{n}SO{C}_i^K\frac{K_{HEV}}{1+s{T}_{HEV}}}$$

The combined demand response could allow for a smooth frequency response characteristic by appropriately controlling the frequency threshold of all DRS devices connected to the hµGs. The HEV charging station of the place is considered as a DRS unit DSM as in modeled. The Net power (ΔP _DRMi ) to be activated by each DRS unit of the i ^th hµG at any time can be evaluated by the strategy (9) considering the change of the maximum available DRS (ΔP _DRMi ) in the i ^th hµGs, which is determined by (10) is estimated based on the DRS contract with the HEV charging station. The range of maximum frequency regulation is determined by the utility, Δf _m = 0.05 Hz. The grid loading coefficients (L _DRi ) for DRS are proportional controller gains and are tuned using optimization techniques. $$\Delta P_{DRi}=\{\begin{array}{l}-\frac{\Delta f_i}{\Delta f_m}\Delta P_{DRMi}-L_{DRMi}\Delta P_{tii},-\Delta f_m\le \Delta f_i\le \Delta f_m\\ \frac{-\Delta f_i}{\left|\Delta f_i\right|}\Delta P_{DRMi},\text{otherwise}\end{array}$$

$$\Delta P_{DRMi}={\displaystyle {\sum }_{k=1}^{n}SO{C}_i^K\frac{K_{\mathrm{<figure-callout\; id="1"\; label="H\; E\; V"\; filenames="DE202023100612U1\_0031.png,DE202023100612U1\_0032.png"\; state="\{\{state\}\}">H</figure-callout>}EV}}{1+s{T}_{HEV}}}$$

System of renewable energy

$$\Delta P_{STV}=\eta S\frac{\left\{1-0.005\left(25+T_a\right)\right\}}{1+s{T}_{PV}}\Delta \varphi$$

$$\Delta P_{WTG}=\frac{0.5\rho C_p{A}_r{V}_w^2}{1+s{T}_{wt}}\Delta V_W$$

$$100C_P=\left(44-1.67\beta \right)\text{sin}\left(\frac{\left(\lambda -3\right)\pi }{15-0.3\beta }\right)-1.84\left(\lambda -3\right)\beta$$

$$\begin{array}{l}\Delta P_{RES1}=\Delta P_{STP},\\ \Delta P_{RES2}=\Delta P_{WTG},\\ \Delta P_{RES3}=\Delta P_{SPV},\end{array}$$

The LFC system is preferred to the parabolic trough collector system because of the smaller area requirement, whose power exchange with changes in solar radiation (ΔΦ) is expressed as follows (11). The power exchange of the SPV unit due to a change in ΔΦ is given in (12) with the efficiency (η = 10%), the temperature (T _a = 27.2 °C) and the solar area (S = 4084 m ² ). The power exchange of the WTG unit due to the change in wind speed (ΔVW) is given in (13) with the air density (ρ = 1.25 kg/m ³ ) and the swept area (A _r = 1735 m ² ), whose power coefficient (C _p ) is determined by (14). The net RES penetration of each hµG is estimated as follows (15). $$\Delta P_{STP}=\left(\frac{K_{\mathrm{<figure-callout\; id="1"\; label="L\; f\; R"\; filenames="DE202023100612U1\_0031.png,DE202023100612U1\_0032.png"\; state="\{\{state\}\}">L</figure-callout>}fR}}{1+s{T}_{LfR}}\right)\left(\frac{K_{HX}{\mathrm{<figure-callout\; id="1"\; label="T\; H\; X"\; filenames="DE202023100612U1\_0031.png,DE202023100612U1\_0032.png"\; state="\{\{state\}\}">T</figure-callout>}}_{HX}}{1+s{T}_{HX}}\right)\left(\frac{1}{1+s{T}_{ST}}\right)\Delta \varphi$$

$$\Delta P_{STV}=nS\frac{\left\{1-0.005\left(25+T_a\right)\right\}}{1+s{T}_{PV}}\Delta \varphi$$

$$\Delta P_{WTG}=\frac{0.5\rho C_p{A}_{\mathrm{<figure-callout\; id="2"\; label="right\; V\; w"\; filenames="DE202023100612U1\_0032.png"\; state="\{\{state\}\}">right</figure-callout>}}{V}_w^{}{}_2^{}}{1+s{T}_{wt}}\Delta V_W$$

$$100C_P=\left(44-1.67\beta \right)\text{sin}\left(\frac{\left(\lambda -3\right)\pi }{15-0.3\beta }\right)-1.84\left(\lambda -3\right)\beta$$

$$\begin{array}{l}\Delta P_{RES1}=\Delta P_{STP},\\ \Delta P_{RES2}=\Delta P_{WTG},\\ \Delta P_{RES3}=\Delta P_{SPV},\end{array}$$

Bioenergy production system

$$\Delta P_{BC}=\left(\frac{1+s{X}_C}{\left(1+s{X}_C\right)\left(1+s{b}_B\right)}\right)\left(\frac{1+s{T}_{CR}}{1+s{T}_{BG}}\right)\left(\frac{K_{BG}}{1+s{T}_{BT}}\right)\Delta X_{21}$$

$$\Delta P_{MH}=\left(\frac{K_{MH}}{1+s{T}_{HG}}\right)\left(\frac{1+s{T}_{RS}}{1+s{T}_{RH}}\right)\left(\frac{1-s{T}_{HT}}{1+0.5s{T}_{HT}}\right)\Delta X_{31}$$

$$\Delta P_{BDi}=K_{BDi}\left(\frac{1}{1+s{T}_{VA}}\right)\left(\frac{1}{1+s{T}_{BE}}\right)\Delta X_{i1}$$

$$\begin{array}{l}\Delta P_{BEG1}=\Delta P_{Bc}+\Delta P_{BD1},\\ \Delta P_{BEG2}=\Delta P_{BG}+\Delta P_{BD1},\\ \Delta P_{BEG3}=\Delta P_{MH}+\Delta P_{BD3},\end{array}$$

The municipal waste (solid/liquid) is collected separately, separated and treated to be ready for power generation in the relevant BEGS plants (ie BCHP, BDEG, BGTG and MHTG) supporting the incineration based hµGs. The detailed modeling of these blocks is discussed, but the performance provided by blocks BCHP, BGTG, MHTG and BDEGi is given in (16)-(19). In addition, the net contribution of each BEGS unit with ^its hµG is estimated as follows (20) (for i=1,2,3). $$\Delta P_{BC}=\left(\frac{{\mathrm{<figure-callout\; id="1"\; label="K\; B\; C"\; filenames="DE202023100612U1\_0031.png,DE202023100612U1\_0032.png"\; state="\{\{state\}\}">K</figure-callout>}}_{BC}}{1+s{T}_{BSG}}\right)\left(\frac{1+s{K}_{\mathrm{<figure-callout\; id="1"\; label="R\; T\; R"\; filenames="DE202023100612U1\_0031.png,DE202023100612U1\_0032.png"\; state="\{\{state\}\}">R</figure-callout>}}{T}_R{}_{}}{1+s{T}_R}\right)\left(\frac{1}{1+s{T}_{BCT}}\right)\Delta X_{11}$$

$$\Delta P_{BC}=\left(\frac{1+s{X}_C}{\left(1+s{X}_C\right)\left(1+s{b}_B\right)}\right)\left(\frac{1+s{\mathrm{<figure-callout\; id="1"\; label="T\; C\; R"\; filenames="DE202023100612U1\_0031.png,DE202023100612U1\_0032.png"\; state="\{\{state\}\}">T</figure-callout>}}_{CR}}{1+s{T}_{BG}}\right)\left(\frac{{\mathrm{<figure-callout\; id="1"\; label="K\; B\; G"\; filenames="DE202023100612U1\_0031.png,DE202023100612U1\_0032.png"\; state="\{\{state\}\}">K</figure-callout>}}_{BG}}{1+s{T}_{BT}}\right)\Delta X_{21}$$

$$\Delta P_{MH}=\left(\frac{{\mathrm{<figure-callout\; id="1"\; label="K\; M\; H"\; filenames="DE202023100612U1\_0031.png,DE202023100612U1\_0032.png"\; state="\{\{state\}\}">K</figure-callout>}}_{MH}}{1+s{T}_{HG}}\right)\left(\frac{1+s{\mathrm{<figure-callout\; id="1"\; label="T\; R\; S"\; filenames="DE202023100612U1\_0031.png,DE202023100612U1\_0032.png"\; state="\{\{state\}\}">T</figure-callout>}}_{RS}}{1+s{T}_{RH}}\right)\left(\frac{1-s{\mathrm{<figure-callout\; id="1"\; label="T\; H\; T"\; filenames="DE202023100612U1\_0031.png,DE202023100612U1\_0032.png"\; state="\{\{state\}\}">T</figure-callout>}}_{HT}}{1+0.5s{T}_{HT}}\right)\Delta X_{31}$$

$$\Delta P_{BDi}=K_{BDi}\left(\frac{1}{1+s{T}_{VA}}\right)\left(\frac{1}{1+s{T}_{BE}}\right)\mathrm{<figure-callout\; id="1"\; label="\Delta \; X\; i"\; filenames="DE202023100612U1\_0031.png,DE202023100612U1\_0032.png"\; state="\{\{state\}\}">\Delta </figure-callout>}{X}_i{1}_{}$$

$$\begin{array}{l}\Delta P_{BEG1}=\Delta P_{Bc}+\Delta P_{BD1},\\ \Delta P_{BEG2}=\Delta P_{BG}+\Delta P_{BD1},\\ \Delta P_{BEG3}=\Delta P_{MH}+\Delta P_{BD3},\end{array}$$

Dynamic load generator system

$$\Delta P_{L_i}=\Delta P_{L{L}_i}+\Delta P_{NL}$$

Monthly electricity consumption data from 3 consumers at different locations were used and normalized to fit the simulation time (t _sim ) of the proposed hµGs as listed in the last 4 columns of Table 1. In this way, the effective linear load deviation (Δ _PLLi ) of the i ^th hµG for the analysis of system performance throughout the year could be replicated using this real data. However, the practical load pattern consists of non-linearities introduced jointly by multiple non-linear devices and power converters. Therefore, an effective non-linear loading configuration (NL) (Δ _PNL ) as (21) was considered for the design of the net loading (Δ _PLi ) as (22), which corresponds to the actual demand pattern of the i ^th hµG. $$\Delta P_{NL}=\left(3\text{sin}\left(\mathrm{36,942}t\right)+5\text{sin}\left(4.417t\right)-10\text{sin}\left(5t\right)\right)\times {10}^{-3}$$

$$\Delta P_{L_i}=\Delta P_{L{L}_i}+\Delta P_{NL}$$

$$\Delta P_{E_i}=\Delta P_{RESi}+\Delta P_{BEGi}+\Delta P_{DRi}+\Delta P_{VISi}-\Delta P_{QEi}-\Delta P_{Li}-\Delta P_{tii}$$

The effective load-generator dynamics for the i ^te hµGs can be estimated as follows (23), considering a single generator-load model for low inertia (H = 0.1 s) with 50 Hz nominal frequency. Here the instantaneous net power error (Δ _PEi ) of the i ^th hµG is estimated as (24) considering all sources and loads with VIS/DRS. $$\Delta f_i=\frac{1}{D_i+s{M}_i}\Delta P_{E_i}$$

$$\Delta P_{E_i}=\Delta P_{RESi}+\Delta P_{BEGi}+\Delta P_{DRi}+\Delta P_{VISi}-\Delta P_{QEi}-\Delta P_{Li}-\Delta P_{tii}$$

formulation of the goal

The objective function for the proposed system is given by the net apparent power deviation (which is a linear combination of active and reactive power, ie ΔS = ΔP + jΔQ or ΔS ² = ΔP ² + ΔQ ² ) considering the appropriate weights (tolerance factor) for Δf (∝ΔP) and Δu (∝ΔQ) estimated as wf and wv. This could be implemented as an objective function (J) called the integral squared of the weighted absolute error (ISWAE), which must be minimized for a simultaneous reduction in line voltage, frequency and interconnection stress. The precise target for the proposed composite system could be expressed as (25) where frequency control (wf = 1 to replicate the zero tolerance for Δf) takes precedence over voltage with a 10% attenuation (w _v = 0.9 to replicate the ± 10% tolerance for Δu) and allowing the network loading to be reduced by 5% (w _t = 0.95 to replicate the ± 5% tolerance for Δu). $$\begin{array}{ll}\text{minutes}\text{.}\hfill & j={\displaystyle {\int }_0^{t_{sim}}\left\{{\displaystyle {\sum }_{i=1}^3\left[{\left(w_f\left|\Delta f_i\right|\right)}^2+{\left(w_v\left|\Delta {\mathrm{and}}_i\right|\right)}^2\right]+{\left(w_t\left|\Delta P_{tii}\right|\right)}^2}\right\}\mathrm{i.e}t}\hfill \\ \text{s}\text{.t:}\hfill & \text{pounds}\le K_{cij}\le \mathrm{and}b\hfill \end{array}$$

Here, K _Cij denotes the PID gains (K _Pij , K _Iij and K _Dij ) for the j ^th controller in the i ^th hµGs, taking into account the limits of Ib and ub (lower and upper limit). The fundamental error figures (FOD), such as peak overshoot, peak undershoot and settling times for voltage, frequency and grid loading of the proposed hµGs are evaluated with a bandwidth of 0.1%. the lei The development index based on FOD (JFOD) of the distributed hµGs is estimated to be (26) to examine the overall system responses. $$J_{fOD}={\displaystyle {\sum }_{i=1}^3\left(u_{\mathrm{<figure-callout\; id="2"\; label="S\; f\; i"\; filenames="DE202023100612U1\_0032.png"\; state="\{\{state\}\}">S</figure-callout>}fi}^2+O_{\mathrm{<figure-callout\; id="2"\; label="S\; f\; i"\; filenames="DE202023100612U1\_0032.png"\; state="\{\{state\}\}">S</figure-callout>}fi}^2+T_{\mathrm{<figure-callout\; id="2"\; label="S\; f\; i"\; filenames="DE202023100612U1\_0032.png"\; state="\{\{state\}\}">S</figure-callout>}fi}^2+u_{\mathrm{<figure-callout\; id="2"\; label="S\; V\; i"\; filenames="DE202023100612U1\_0032.png"\; state="\{\{state\}\}">S</figure-callout>}Vi}^2+O_{\mathrm{<figure-callout\; id="2"\; label="S\; V\; i"\; filenames="DE202023100612U1\_0032.png"\; state="\{\{state\}\}">S</figure-callout>}Vi}^2+T_{\mathrm{<figure-callout\; id="2"\; label="S\; V\; i"\; filenames="DE202023100612U1\_0032.png"\; state="\{\{state\}\}">S</figure-callout>}Vi}^2+u_{STii}^2+O_{STii}^2+T_{STii}^2\right)}$$

Each hµG is equipped with 3 PID controllers to regulate the frequency (ΔX _i1 ), voltage (ΔX _i2 ) and inertia (ΔX _i3 ) of the proposed interconnected hµGs as in shown. In each hµG, 10 controller gains (3 × 3PID gains + 1 proportional gain for DRS) must be set. Thus, for smooth coordination of the proposed distributed hµGs, 30 (10 × 3) gain parameters need to be tuned, making conventional tuning of controllers in such a complex network difficult. Metaheuristic engineering plays an important role in overcoming such challenges by precisely tuning controller gains. The superior performance of the hybrid technique in relation to the basic technique in such scenarios. Therefore, a new QCSHO technique was developed that hybridizes the concepts of QOBL and CLS in the basic SHO to tune all controller gains by minimizing the target (J) as well as the JFOD, which is widely discussed.

3 shows a flow chart of the QCSHO technique.

Quasi-oppositional chaotic selfish stove optimization

The selfish behavior of animals forming herds to survive when threatened by predators is known as the SHO technique. The movement of these selfish animals to gain central position in intraspecific competitions during predation to increase their chances of survival is called SHO.

wobei, Ch_i ∈ (0,1) and Ch_i ≠ 0.25, 0.5, 0.75In most realistic scenarios, chaos reigns in the herd during a predator attack, resulting in chaotic movement of the selfish herd towards the center in order to survive. The expected competition between the selfish herds is very high in the Chaos Zone, as in shown, forcing a crowded movement near the center. However, the threat to life of the animals facing the predators (H _xi , H _xy , H _xj ) is greater than that of the conspecifics facing them (O _xi , O _xy , O _xj ). Again, an animal with less competition (H*xi, H*xy, H*xj) may be available near opposite points (called quasi-opposite) compared to the exact opposite points (O _xi , O _xy , O _xj ) in relation to the center of the herd, as in shown. This idea inspired to modify SHO such that the chaotic higher risk animals (H*xi, H*xy, H*xj) are replaced with the lower survival risk individuals (Hxi, Hxy, Hxj) during predation in order to to get a better optimal solution. A new QCSHO procedure is developed by combining the concepts of QOBL and CLS with the basic SHO procedure to mimic this typical activity of selfish herds during predation, considering a logistic map for CLS in (27), to get a faster solution. $$C{H}_{i+1}=\mu \left(1-C{H}_i\right)C{H}_i$$

where, Ch _i ∈ (0,1) and Ch _i ≠ 0.25, 0.5, 0.75

$$H_{xy}^{\ast }=\{\begin{array}{l}{M}_{xy}+\left(O_{xy}-M_{xy}\right)R_j,if{H}_{xy}<M_{xy}\\ O_{xy}+\left(M_{xy}-O_{xy}\right)R_j,otherwise\end{array}$$

The central (M _xy ) and opposite (O _xy ) animal for a selected (H _xy ) individual in the herd can be expressed by (28) considering the maximum (ub) and minimum (Ib) 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 ) to the hop rate (J _r ). $$\begin{array}{ccc}{M}_{xy}=\raisebox{1ex}{$\left(\mathrm{and}{b}_y-l{b}_y\right)$}\!\left/ \!\raisebox{-1ex}{$2$}\right.,& \text{and}& O_{xy}=\mathrm{and}{b}_y+l{b}_y-H_{xy}\end{array}$$

$$H_{xy}^{\ast }=\{\begin{array}{l}{M}_{xy}+\left(O_{xy}-M_{xy}\right)R_j,if{H}_{xy}<M_{xy}\\ O_{xy}+\left(M_{xy}-O_{xy}\right)R_j,OtHe\mathrm{right}wise\end{array}$$

The performance of SHO is expected to be improved by the modified QCSHO method with the advantages of CLS and QOBL, which has a faster search process than the normal stochastic one search supported. The individual steps of the proposed QCSHO procedure are in presented as a flowchart containing both CLS and QOBL steps. The new QCSHO method calls the Simulink models in to assess/update herd survival values in each iteration, as in explained by passing on the tuned controller gains and finding the optimal solution. The detailed simulation results and performance comparisons are discussed.

Analysis of the simulated results

This in The presented linear transfer function model of the projected hµGs is developed, while all optimization procedures are encoded in script files. The performances of the distributed hµGs are examined for the simultaneous voltage-frequency regulation by simulations on a desktop computer (4 GB RAM, Intel 3.4 GHz i7-4770 CPU) in the following approaches.

Step-response analysis for method selection

It is of crucial importance to choose suitable methods such as optimization methods, objective functions and controllers in order to study the system responses in an appropriate way. The step responses of the proposed distributed hµGs are analyzed to fit these methods considering a step change in ΔP _L (decreases from 0.75 pu to 0.725), ΔΦ (increases from 0.615 kW/m2 to 0.645), ΔV _w (increases from 6.3 m/s to 6.96), for t _sim = 20s to be compared with a normal daily scenario. Then the superior methods for simultaneous voltage-frequency control in distributed hµGs using both VIS and DRS are compiled.

technology range

The proposed distributed hµGs in is simulated with PSO, GOA, SSA, SHO, QSHO, CSHO and the projected QCSHO to minimize the objective functions J in (25). The convergence curves of all 7 step response optimization methods are compared considering PID controllers with the proposed ISWAE as (25) for initial 20 s of the loads. The FOD-based index of performance (JFOD) of the system, expressed in (26), is estimated for all methods considering the ISWAE and compared in the first column of Table 3.

Objective assortment

The optimized step responses of the distributed hµGs are estimated for the same scenario using all 5 objective functions Integral Absolute Error (IAE), Integral Squared Error (ISE), Integral Time-Weighted Absolute Error (ITAE) and Integral Time-Weighted Squared Error (ITSE) along with ISWAE and compared for QCSHO-tuned PID controllers, confirming the superiority of ISWAE over others.

Controller range

The step responses of the distributed hµGs are obtained by separately tuning integral (I), proportional-integral (PI), proportional-derivative (PD), integral-derivative (ID), and PID controllers using QCSHO-optimized ISWAE estimated and compared in Table 3 under Estimation of JFOD, confirming the superiority of PGD. Finally, the proposed QCSHO method using the ISWAE function (J) is preferred for tuning the gains of PID controllers, as this combination performs better than the others, as shown in FIG and Table 4 (better values are highlighted in bold). The QCSHO tuned PID controller gains for interconnected hµGs are listed in Table 4 considering ISWAE and are used without retuning the PID controllers to study the stability of the system.

Responses from distributed hµGs

To study the system responses throughout the year under different climatic conditions, the real-time recorded monthly averages of sun and wind and the normalized load data with a duration of 10s for each month are arranged as indicated in Table 1, where t _sim = 120s is taken into account. The proposed distributed hµGs, as in 2(a) modeled, the EE- Penetration simulated with LL and a load change scenario including NL using the tuned PID gains from Table 4. The responses for all five cases are plotted and discussed individually below, comparing system responses to both VIS and DRS.

In this case, a normal daily scenario is assumed, where the available RES generations (SPV, STP and WEA) are coordinated with all BEGS entities (BCHP, MHTG, BGTG and BDEGi), including the support of VISi and DRSi Units to provide a specific LL (ΔPL = ΔPLL). This confirms the improvement in power quality due to faster support by VIS/DRS units to smooth Δf _i and Δu _i at minimum ΔP _tij . The mains voltage, the mains frequency and the mains load are restored to their nominal values within 3s, 6s or 1s in the event of any source/load fault.

Unavailability of solar supports

In this case, a normal night or cloudy sky scenario is assumed (or during maintenance/failure of SPV and STP units), with the WT generations only feeding the same LL. The waste incineration based BEGS units will be coordinated to meet excess demand in the absence of solar support. These also confirm the improvement in power quality with faster settling of Δf _i , Δu _i and ΔP _tij to their nominal values within 2s, 5s and 1s, respectively, for any source/load disturbance in the absence of solar support.

Unavailability of wind turbines

This case is assumed assuming a normal daily scenario with unfavorable wind speeds (or during maintenance/failure of a WTG) with only solar-assisted renewables (ie STP and SPV) supplying the LL. The waste incineration based BEGS units are coordinated to meet the excess demand in the absence of wind support. The frequency, voltage and load profiles of the distributed hµGs that are supported in this scenario with VIS/DRS are recorded. These again confirm the improvement in power quality with faster transient response of Δf _i , Δu _i and ΔP _tij to their nominal values within 2.5s, 6s and 1s, respectively, for any source/load disturbance in the absence of wind support.

Unavailability of wind and solar power support

In this case, a stormy day/night climate scenario is assumed, where there is no RES production (or maintenance/failure of all RES assets) to deliver this LL. All waste incineration based BEGS units are coordinated to meet the total demand in these extreme climatic conditions. The frequency, voltage and network load profiles of the distributed hµGs that are supported in this scenario with VIS/DRS are recorded. These also confirm the power quality improvement with faster transient response of Δf _i , Δu _i and ΔP _tij to their nominal values within 3s, 5s and 1s, respectively, for any disturbance in source/load in the absence of EE support.

Responses of distributed hµGs in normal climate with NL

In this case, a generalized scenario is assumed, considering the same climatic conditions as scenario 1, where all available RES generations together provide LL and NL (ΔP _L = ΔP _LL + ΔP _NL ). All BEGS plants are coordinated to cover the excess demand. The frequency, voltage and network load profiles of the projected distributed hµGs that are supported with VIS/DRS in this general load scenario are recorded. These further confirm the improvement in power quality with faster settling of Δf _i , Δu _i and ΔP _tij to their nominal values within 3s, 6s and 1s respectively for any source/load disturbance.

Responses in all five cases above have confirmed the year-round reliability of the planned VIS and DRS units with novel QCSHO tuned PID controllers in distributed hµGs. It will be further expanded to investigate the optimal allocation of VIS and DRS units for the economical operation of distributed hµGs.

Optimal assignment of VIS/DRS units

The planned distributed hµG were successfully tested in various extreme climate/load scenarios, with each hµG being supported by both VIS and DRS units. However, arranging both VIS and DRS units for each hµG with appropriate control strategies adds complexity and volume to the system, which may not be economical (due to additional costs associated with both support systems). Also, the availability of suitable DR devices and ESS for all of these support systems may not always be possible. Therefore, it is important to study the most appropriate arrangements of these VIS and DRS units based on the IRP for economical operation of the distributed hµGs. All 22 possible arrangements of these VIS and DRS units in the distributed hµGs are simulated with ISWAE considering QCSHO matched PID as listed in Table 5.

The FOD-based index of performance (JFOD) was estimated to examine the optimal allocation of VIS/DRS units for all of these 22 cases and compared in Table 5. As shown in Table 5, optimal system performance could be achieved if each hµG were assigned both VIS and DRS units, but this would be less economical. The experimental result in Table 5 could also be qualitatively justified for the optimal allocation of support systems in the proposed distributed microgrids, i.e. H. Connection of two DRS units in hµG1 and hpG3, while only one VIS unit is connected in hµG2. Since the proposed system of distributed microgrids includes solar-based RE systems in hµG1 (STP unit) and hµG3 (SPV unit), which can generate excess energy during higher solar irradiance or off-peak periods. The power imbalance disturbance in this scenario could be managed by absorbing the excess power with connected DRS units. On the other hand, the wind-power based renewable energy source (WEA unit) is included in hµG2, whose power output falls to zero immediately beyond the cut-off/cut-on speed, which together with sudden load changes leads to an inertia imbalance. Therefore, a VIS unit should be installed in hµG2 to provide proper inertial support in such scenarios. It is also economical to allocate a smaller number of VIS units than the DRS units, since installing VIS units in microgrids requires the purchase of suitable ESS and bidirectional converters while the DRS units are owned by private/public companies that are not utilities. Table 5 therefore confirms that installing a VIS unit in hµG2 and a DRS unit in hµG1 and hµG3 is the most economical IRP for the proposed distributed hµGs, respectively.

To study the responses to optimal assignment, the hµG2 is challenged by a four-step inertial variation with ±40% and ±20% change (in M ₂ ) that spans one VIS unit. The other two microgrids are only connected to DRS units and the source and loads are simulated according to the generalized case 5 scenario including NL. The dynamic responses of hµG2 in terms of frequency (Δf) and voltage (Δu) are recorded. Comparing the responses of all 4 inertial variants confirms the virtual inertial support as all responses follow the nominal values (at ΔM = 0%). It has demonstrated the effectiveness and adaptability of the proposed QCSHO technique with faster coordination by VIS/DRS systems throughout the year despite climatic differences.

Overall, the results have confirmed the improvement in system responses that Virtual Inertia (VIS) / Demand Response (DRS) allocation based integrated resource planning in the planned microgrids with the novel QCSHO-tuned PID controllers considering ISWAE for optimal voltage , frequency and connection network loads within accessible operating limits. Responses were also adaptive throughout the year, with 5 extreme climate and load variations in the distributed microgrids with DRS and VIS units (without readjustment of the controllers). Another 22 possible distributions of VIS/DRS units were studied by evaluating and comparing the FOD-based performance index for the optimal allocation of IRP in the proposed distributed microgrids. Therefore, the allocation of a VIS unit in the 2nd hµG along with a DRS unit in each of the 1st and 3rd hµGs is suggested as the most appropriate and economical IRP for the proposed system, as the replies confirm. It could be further expanded with suitable optimization methods in order to tune the classic or modern controllers such as fractional controllers, neural controllers, fuzzy controllers, model predictive controllers and their mixtures in the future.

4 Table 1 shows solar/wind and load data according to an embodiment of the present disclosure.

5 illustrates the comparison of target functions with PID controllers presented in Table 2 according to an embodiment of the present disclosure.

6 Table 3 shows the comparison of the controller with the QCSHO tuned ISWAE according to an embodiment of the present disclosure.

7 illustrates the gains of the PID controller with QCSHO tuned ISWAE presented in Table 4 according to an embodiment of the present disclosure.

8th illustrates the distribution of DRS and VIS in hybrid micro-grids presented in Table 5, in accordance with an embodiment of the present disclosure.

The drawings and the foregoing description give examples of embodiments. Those skilled in the art will understand that one or more of the elements described may well be combined into a single functional element. Alternatively, certain elements can be broken down into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of the processes described herein may be changed and is not limited to the manner described herein. Additionally, the actions of a flowchart need not be performed in the order shown; Also, not all actions have to be carried out. Also, the actions that are not dependent on other actions can be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples. Numerous variations are possible, regardless of whether they are explicitly mentioned in the description or not, e.g. B. Differences in structure, dimensions and use of materials. The scope of the embodiments is at least as broad as indicated in the following claims.

Advantages, other benefits, and solutions to problems have been described above with respect to particular embodiments. However, the benefits, advantages, problem solutions, and components that can cause an advantage, benefit, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all claims.

Reference List

100

A system for integrated resource planning in sustainable energy and based distributed microgrids.

102

Combined storage to support Virtual Inertia (VIS)

104

Hybrid Electric Vehicle (HEV) charging station based Demand Response Support (DRS) unit

106

control unit

108

analysis processing unit

110

CPU

202

control unit 11

204

load1

206

control unit 22

208

load2

210

control unit 33

212

load3

214

Controller i3

216

Bidirectional converter

220

condition of the load

302

begin

304

Read the values for N,ub,lb,dim,Maxltr

306

Initialize the population of animals: A=lb+(ub-lb).rand(N.din)

308

Divide A into 2 groups H (prey) and P (predator): NH=Floor(N.rand(0.7,0.9));NP=N-NHi Ch1=rand()

310

Initializing the global memory & evaluating survival values for each member in H&P

312

Apply herd shift operations for SVh<Chi & exit for SVh<Ch,in H for the ith iteration

314

Apply predator movement operations to get all members in P for the ith iteration

316

Is i<Max1tr ?

318

Show the best solution

320

Stop

322

Reading jump rate for quasi-opposition=Jr & Rj=rand (0.1)

324

Update each member's survival stats in H&P for ith iteration

326

Perform the appropriate steal and restore phase for the ith iteration

Claims (7)

A system for integrated resource planning in distributed microgrids based on sustainable energy, the system comprising: a combined memory-based virtual inertial unit (VIS) for supply-side management (SSM); a Hybrid Electric Vehicle (HEV) charging station based Demand Response Support Unit (DRS) for Demand Side Management (DSM) as integrated resource planning (IRP) in distributed microgrids for optimal regulation of frequency and voltage; a weighted absolute error integral square (ISWAE) control unit for tuning the controllers of distributed hµGs using quasi-oppositional chaotic self-herd optimization (QCSHO); an analysis processing unit for evaluating a set of responses of the control unit with the VIS/DRS units taking into account recorded sun, wind and load data over the year; and a central processor to study the optimal allocation of VIS/DRS units for economical operation of distributed hµGs. system after claim 1 , wherein the combined storage comprises a battery energy storage (BES) and a capacitive energy storage (CES). system after claim 1 , where quasi-oppositional chaotic selfish-herd optimization (QCSHO) is developed by hybridizing chaotic linear search (CLS) and quasi-opposition-based learning (QOBL) with basic selfish-herd optimization (SHO) techniques. system after claim 1 , where the three interconnected sustainable energy based dissimilar hµGs are the LFR STP/BCHP/BDEG1 type units in the first microgrid (hµG1), WTG/BGTG/BDEG2 type units in the second microgrid (hµG2) and SPV type units /MHTG/BDEG3 in the third micro-grid (hµG3), with a combined SSM/DSM-based IRP. system after claim 1 , where the control unit maintains the system voltage frequency (440v-50Hz) within an accessible limit, independent of EE penetration and load differentials, with the frequency of each hµG being controlled by an ALFC unit using a combination of primary ( droop) control and secondary (PID) control. system after claim 1 , where the inertial support to the hµGs is practically provided with appropriate ESS and bi-directional power converters considering the SSM-based IRP to prevent low-inertia system frequency instability in autonomous hµGs. system after claim 1 , where the step responses of the distributed hµGs are optimized by separately tuning integral (I), proportional-integral (PI), proportional-derivative (PD), integral-derivative (ID), and PID controllers using QCSHO ISWAE can be estimated, confirming the superiority of PID, favoring the QCSHO technique with the ISWAE function (J) for tuning the gains of PID controllers.

Images