A Unified Index for Power Quality Evaluation in ...

A Unified Index for Power Quality Evaluation in Distributed Generation Systems Ghada S. Elbasuony,a Shady H.E. Abdel Aleem,b* Ahmed M. Ibrahim,c Adel M. Sharaf d a

Electrical Power and Machines Department, Faculty of Engineering, Cairo University, Giza, Egypt

(email: [email protected]) b

15th of May Higher Institute of Engineering, Mathematical and Physical Sciences, Helwan, Cairo,

Egypt (email: [email protected]) c

Electrical Power and Machines Department, Faculty of Engineering, Cairo University, Giza, Egypt

(email: [email protected]) d

Sharaf Energy Systems, Inc., Fredericton-NB, E3C2P2, Canada (email: [email protected])

* Corresponding author: Tel.: +201227567489 | E-mail address: [email protected]

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Abstract— Excessive penetration of distributed generation units with their electronic power interfaces into distribution systems has introduced different power quality problems into these systems. To date, there is no agreed upon approach to quantify the overall power quality performance of a system in the presence of distributed generation units. In this paper, a unified index for power quality assessment in different distributed generation systems is proposed using the analytic hierarchy process. The unified index is determined in terms of six power quality performance parameters, namely voltage total harmonic distortion, current total harmonic distortion, frequency deviation, voltage sags score, voltage flicker, and power factor at different interface buses of three grid-connected distributed generation systems: distributed wind, wind/photovoltaic, and hybrid wind/photovoltaic/fuel cell energy systems. Simulations are carried out in the Matlab-Simulink environment. The results show that the proposed index can facilitate overall power quality evaluation of different systems under different operating conditions. It makes the assessment simple and effective as one number only is used for each comparison. Furthermore, based on the values of the index in the different systems, it was found that the hybrid energy system shows better power quality performance compared to the other systems.

Keywords— Analytic hierarchy process; decision making; distributed generation; hybrid energy systems; power quality.

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NOMENCLATURE CI CR FDR fm fr Fp h ht i IFL Ih j RF λmax n Nf PF Plt PQIG PQIL PQIPV PQIw Pst SS THDI THDV Vh wp XC XL Zh

Consistency index Consistency ratio Frequency deviation ratio The measured frequency at the fundamental harmonic order Rated frequency at the fundamental harmonic order Measured value of the factor p Harmonic order Harmonic tuning order Number of the DG systems Instantaneous flicker level The hth harmonic line current Number of buses Fundamental resistance of the STPF Maximum eigenvalue of a judgment matrix The judgment matrix dimension Number of the selected factors that affect the PQ performance Power factor Long-term flicker severity value Power quality index at the grid bus Power quality index at the load bus Power quality index at the PV bus Power quality index at the wind bus Short-term flicker severity value Sag score Current total harmonic distortion Voltage total harmonic distortion The hth harmonic load voltage Weight of the factor p Fundamental capacitive reactance of the STPF Fundamental inductive reactance of the STPF The hth harmonic impedance of the STPF

LIST OF ABBREVIATIONS AHP D-FACTS DG FC FD HC PCC PEM PQ PQI PV STPF THD UPQI

Analytic hierarchy process Distributed flexible AC transmission systems Distributed generation Fuel cell Frequency deviation Hosting capacity Point of common coupling Polymer electrolyte membrane Power quality Power quality index Photovoltaic Single-tuned passive filter Total harmonic distortion Unified power quality index -3-

1. Introduction Fossil fuel resources depletion, global warming rise due to increased greenhouse gas emissions, and energy demand growth are driving heavy reliance on renewable energy resources and energy conservation measures worldwide. Currently, renewable energy-based power generation is rapidly developing across the world in response to technical, economic, and environmental developments as well as political and social initiatives. These developments have led to the emergence of new energy planning strategies and updated electric utility energy policies to address future energy mix scenarios with renewables such as wind, photovoltaic, solar thermal, tidal wave, fuel cell, and others and with low-carbon energy production technologies such as natural gas [1]. Electrical power generation topology has changed dramatically across the world. In the near past, electric power systems were structured in a centralized generation scheme where electricity is locally produced. Nowadays, the increasing rate of distributed generation (DG) penetration into the distribution systems has transformed the electricity sector into a smart decentralized one that is more driven by a mix of technologies and decentralized operators [2]. Unfortunately, many challenges have also arisen along with the DG systems. Excessive penetration of DG units with their electronic power interfaces into distribution systems has introduced different reliability and power quality (PQ) issues and implications such as increased power losses, thermal overloading of transformers and feeders, over and under voltages, protection failure, and increased fault and harmonic distortion levels [3] as well as system security and reliability issues [4]. In addition to the stochastic nature of renewables (predominantly solar and wind energy systems), which cause high fluctuations in power generation and have negative impacts on power grids, many of the problems are due to the bi-directional power flows in distribution systems with DG units. A lot of studies have investigated the impacts of various DG types on PQ performance of distribution systems. For grid-connected photovoltaic (PV) systems, different PQ problems may occur such as overvoltage, flickers, harmonic distortion of current and voltage, and power factor (PF) reduction [5]. Harmonic distortion is usually due to the used power conditioning interface units in such systems, and it can be problematic with regard to the allowable level of harmonic distortion, particularly in low-voltage buses [6] or at light load conditions [7]. This is why a harmonic compensation scheme is integrated with the DG units or employed within their power control loops [8]. For grid-connected wind energy systems, PQ problems include harmonics and inter-harmonics, voltage sags and swells, interruptions, flickers, and imbalance [9]. However, impacts of wind energy systems on the PQ performance of systems mainly depend on the type of the wind system [10]. To sum up, effects of the DG systems on PQ of a distribution system depend on many factors such as type, location, connection, voltage level, interface, control strategy, and size of the units [11]. These

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problems usually occur when the penetration level exceeds the capacity of a system to host more DG units, which is called hosting capacity (HC) in the literature [12]. Simply stated, HC corresponds to the evaluation of distribution networks’ readiness for increasing amounts of DG units. The determination of HC enables the stakeholder to quantify the impact of DG units on the performance of the power system by using a set of assessment indices [13]. The selection of these indices depends on factors of interest such as imbalance, voltage profile, harmonic distortion levels, and others. If any exceeds its performance limit, this penetration level is then determined as the system’s HC. In practical terms, this can be manipulated either in a deterministic or stochastic manner. Despite the benefits recently introduced by HC studies in distribution networks [14], a set of performance indices may be needed that will result in more than one HC value. This necessitates working on how to bring together the various indices in a unified global index in order to assure consistent trade-off between pros and cons of the practice of renewable energy resources [15]. Power quality is the capability of the grid to compensate or reject the disturbances. Thus, evaluation of PQ performance can be a key enabler for such a unified index. In the literature, many standard indices measure each of the PQ disturbances, but no single measure can indicate the overall PQ of a distribution system, particularly in the presence of DG units. This is mainly because PQ has different meanings for people in various electric groups. IEEE Standard 1159 defines PQ as the concept of powering and grounding sensitive equipment [16]. Network operators usually use PQ as a form of terminology to express the quality of voltage of buses, customers use it to express voltage and current deviations [17], some utilities may use it to define the service quality or reliability, and energy markets use it to represent the product quality. It is logical that everyone describes PQ from his perspective. Conclusively, in this work, PQ will be defined as a set of technical electrical parameters that may allow any equipment to operate in its scheduled manner without substantial loss of performance [18]. Introducing one index to evaluate overall power quality performance of a system will inspire new facilities in planning, monitoring, and assessment of integrated hybrid smart grid-based renewable energy systems. Consequently, in this paper, a unified index for power quality assessment in different distributed generation systems is proposed using the analytic hierarchy process (AHP). The unified power quality index (UPQI) is determined in terms of six PQ and energy efficiency performance parameters, namely voltage total harmonic distortion, current total harmonic distortion, frequency deviation, voltage sags score, voltage flicker, and power factor at different interface buses of three gridconnected distributed generation systems: distributed wind energy system, combined wind/PV system, and hybrid wind/PV/fuel cell (FC) system.

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The selected indices represent the common PQ problems. They are selected based on the PQ indices that are stated in different international standards such as IEEE 519 for harmonic limits [19], IEEE Standard 1547 for interconnecting distributed resources with electric power systems [20], and IEEE 1159 for monitoring electric power quality [16]. Also, different practice codes, drafts and available studies in the literature are taken into account while selecting these indices [21]. Hybrid linear, nonlinear and induction motor loads are considered. A single-tuned passive filter (STPF) is designed and connected in parallel with these loads for reactive power compensation and harmonic distortion mitigation [22]. Simulations are carried out in the Matlab-Simulink environment. A comparative study of the results of the simulations, with and without compensation, is provided to show the advantages of adopting the filter design for improving the UPQI. Furthermore, a comparative study of the simulation results under normal operating conditions as well as under a three-phase fault condition is presented to show their impacts on the determined values of the UPQI in such cases. In what follows, a literature survey on related works that used the AHP in the quantification of PQ is presented and discussed. Further, contributions of this work are summarized. 1.1. Literature review Defining a standard algorithm for overall power quality evaluation of a distribution network with and without DG systems still poses a difficulty, although few trials have been reported in the literature. In Abdelrahman et al. [23], two global PQ indices (average and aggregated) based on AHP are presented to reduce a large amount of PQ data for comparing PQ performance at different sites. The considered PQ phenomena are total harmonic distortion (THD), long-term flicker, and voltage unbalance. The global index was calculated with equal weights at each site but with different weights for the load bus types (commercial, residential, and mixed). However, the study did not consider the other types of buses, the effect of using a compensation scheme on the presented indices, and the spread presence of DG systems in distribution networks. Furthermore, the study assumed weights for the different PQ phenomena regarding a standard bus without taking into consideration the variation of the importance of PQ phenomena based on bus types. In Lee et al. [24], an UPQI for electric distribution network is presented using two-level AHP to compare PQ performance in three cases: ideal, possible, and real cases. The system under study was a local distribution system without DG systems. The authors used reliability measures for the average interruption frequency and duration measures, in addition to PQ indices that were represented by an average system root-mean-square (rms) variation index for evaluation of voltage sags and THD index for harmonic distortion analysis. However, the study mainly focused on load buses without paying attention to other bus types, DG systems, and compensation schemes. In Gosbell et al. [25], historical data to represent PQ disturbances by one average number is introduced using AHP. The considered PQ measures represented the voltage levels and unbalance, -6-

flickers, and harmonics. The PQ indices were then averaged to give an index to each site and then one index to the feeder. Further, the feeder indices were assumed to be aggregated together to get an index for the substation and then for the district. Despite the well-presented method, the study was mainly concerned with the customers' maximum loads. Moreover, all the indices were weighted equally for all the considered sites. Rather than AHP, in Salarvand et al. [26], a global PQ index using an intelligent method based on artificial neural network (ANN) and fuzzy logic to obtain a quantitative global index for PQ evaluation is introduced. Different standard indices calculated from stored PQ historical data of various sites are considered, taking into account costs of PQ and the impact of poor PQ on the calculated cost. The study mainly focused on continuous disturbances such as harmonics, voltage unbalance, and flicker as well as discrete disturbances such as voltage swells and sags. However, the study did not take into consideration DG systems and compensation schemes as well as their impacts on the PQ costs. Moreover, the same importance was assumed for each PQ aspect at all points of measurement. 1.2. Contribution The contribution of this work is twofold. First, we introduce one index to evaluate overall power quality performance of different hybrid smart grid-connected DG systems, taking into account the type of interface buses and in turn their priorities. This may help to investigate impacts of the DG units on the PQ performance of a system in a specific, measurable, and practical manner. Simply, a bus that has low PQ index can be identified easily, and therefore overall PQ of the system can be evaluated depending on the aggregation of the buses’ indices. The second contribution in this study is the investigation of the impacts of reactive power compensation, harmonic mitigation, and faults on the proposed unified index. Finally, putting a global index into practice can help to sustain power quality within acceptable limits in the long-term, particularly given the nature of rapidly changing power systems. The rest of the paper is organized as follows: Section 2 presents the problem statement and the UPQI formulation, Section 3 introduces the results and their discussion, and finally, Section 4 presents the conclusions, recommendations, and limitations of our study in addition to a preview of future studies. 2. Problem Statement Nowadays, there is a need to propose a single index for a comprehensive evaluation of PQ in order to achieve a number of benefits such as fast PQ monitoring and assessment, reduction of storage of big data on PQ measures, and efficient planning and pricing policies in deregulated electricity markets. Accordingly, this work presents a quantitative unified index for PQ evaluation in distribution systems. The unified index is determined in terms of six power quality performance parameters, namely voltage -7-

total harmonic distortion (THDV), current total harmonic distortion (THDI), frequency deviation (FD), voltage sags score (SS), instantaneous flicker level (IFL), and power factor (PF). The ideal AHP as a structured procedure for organizing different attributes is employed to provide a comprehensive framework for quantifying importance (weights) of the considered PQ criteria in different scenarios. The proposed algorithm is implemented at different interface buses of three gridconnected DG systems. The evaluation procedure is illustrated in Fig. 1, where the DG systems are firstly modeled by Matlab-Simulink. The simulation results give the current and voltage at the desired buses, which are further used to calculate the different PQ indices at each bus. Further, the AHP is adjusted to calculate weights of each PQ index at the considered buses of the DG systems. This was performed using Matlab-Excel integration tools. In what follows, modeling of the DG systems, components, hybrid loads, STPF, and PQ indices are provided. Hence, formulation of the proposed UPQI using AHP is presented. 2.1. DG systems modeling The studied grid-connected distributed generation systems are distributed wind, wind/PV, and hybrid wind/PV/FC energy systems. Fig. 2 presents the models of the studied systems. Fig. 2a shows the first grid-connected wind energy system with two distributed wind systems (11 kV, 3.6 MVA each) connected to a 138 kV (infinite bus) grid through transformers and feeders, a set of buses, and a hybrid AC load that consists of linear loads with a lumped rating of 1.8 MW, 0.43 Mvar, nonlinear loads with a lumped rating of 0.9 MW, and 0.43 Mvar, and 0.2 MW, four poles induction motor loads. The hybrid load is connected to the point of common coupling (PCC) through a step-down transformer at the voltage level 4.16 kV. The generation voltage of the wind system is 11 kV and is stepped up to 25 kV through a step-up transformer. The wind farms are connected to the hybrid load through two 12 km parallel feeders and two 25 kV/4.16 kV, 5 MVA transformers [27]. The second grid-connected wind/PV system includes a wind system with a rating of 11 kV, 3.6 MVA and a PV system of 1.6 kV, 1 MW connected to the hybrid loads through feeders and transformers [28]. To achieve an effective energy management system, in the third grid-connected wind/PV/FC energy system, the PV is integrated with powered polymer electrolyte membrane fuel cells (PEMFCs) and a smart grid AC-DC interface as shown in Fig. 2b [29]. 2.1.1. Modeling of the wind farm Wind turbines convert the kinetic energy of the air into electricity. The mechanical power captured by the wind turbines is given by Eq. (1).

Pm  0.5 ACP   ,  VW3

(1)

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where ρ is the air density, A is the swept area of the rotor, VW is the wind speed, and CP (λ, β) is the power coefficient as a function of the tip-speed ratio (λ) and the pitch angle (β). In the DG systems under study, a fixed speed wind turbine with squirrel cage induction generator is used as shown in Fig. 3a. 2.1.2. Modeling of the PV cells The single-diode equivalent circuit, shown in Fig. 3b, is used for the PV modeling. The output voltage of a cell is given by Eq. (2). Vc 

AkTc  I ph  I o  I c ln  e Io 

   Rs I c 

(2)

where e is the electron charge, k is Boltzmann constant, Ic is the cell’s output current, Iph is the photocurrent, Io is the reverse saturation current of the diode, Rs is the series resistance of the cell, Tc is the cell operating temperature in Kelvin, and Vc is the cell’s output voltage. 2.1.3. Modeling of the fuel cells A fuel cell converts the chemical energy from fuel into electricity. In this study, typical PEMFCs are connected in series-parallel combination to obtain the desired rating. The equivalent circuit of the FC is shown in Fig. 3c. It should be mentioned that there are three major loss factors for PEMFCs, called kinetic loss (in the low load region), ohmic loss (with the increase of load), and mass transfer loss (at high current density). While the kinetic and mass transfer losses continue taking place, the ohmic losses region (the linear region of the V-I characteristic of the FC) is a suitable measure to study the impact of using the FC on the PQ of a system. This is why the simple voltage-source with internal ohmic resistance was considered to model the PEMFCs. A boost chopper is used for regulating the necessary DC voltage across the capacitor [28]. 2.2. Modeling of the single-tuned passive filter In industrial power systems, shunt capacitors and tuned filters are commonly used for voltage support, reactive power compensation, power factor correction, and harmonic distortion mitigation [3]. Compared to the distributed flexible AC transmission systems (D-FACTS) and active power filters, passive filters are still the most used filters because they are simple, reliable, easily configured and maintained, and inexpensive [30]. Fig. 4 shows the single-phase equivalent circuit and the frequency response of the STPF. The hth harmonic equivalent impedance (Zh) of the STPF is expressed in Eq. (3), and the fundamental values of the inductive (XL) and capacitive (XC) reactances are given in Eq. (4) in terms of the harmonic tuning order (ht) of the STPF. X   Z h  RF  j  hX L  C  h  

(3)

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XL 

XC ht2

(4)

RF is the fundamental value of the filter’s resistance. It is determined in terms of quality factor (q), ht, and XL, as expressed in Eq. (5). q

ht X L RF

(5)

In this work, the STPF is tuned at the 5th harmonic order, where the capacitance (C) equals 316 μF, and q = 35. The STPF is connected to the load bus to improve the load bus’s power factor to be in a desired range (>90%) as well as to reduce the harmonic distortion. 2.3. Formulation of the PQ indices The following indices are calculated to evaluate impacts of the DG units on the PQ performance of the studied systems. 2.3.1. Harmonic distortion Harmonic distortion is caused by various types of harmonic generating equipment such as power converters, rotating machines, arc furnaces, fluorescent lamps, and others. DG units can rarely present harmonic distortion, but in most cases, they increase harmonic distortion in the distribution system when connected to nonlinear loads [12]. In general, harmonics due to DG systems mainly depend on the interface between the DG units and the distribution system but also on the interaction between the loads and the DG units [31]. Harmonics can cause many problems such as parallel and series resonance, thermal overloading of lines and cables, overheating of transformers, false operation of protection devices, and many problems which in turn decrease reliability and increase losses of power systems [32]. According to IEEE 519 [19], THDI and THDV, calculated at the PCC up to the 40th harmonic order, are regarded as the PQ indices to represent harmonic distortion in the systems under study. They are formulated as follows: h  40

THDI 

I h 1

2 h

(6)

I1 h  40

THDV 

V h 1

2

h

V1

(7)

where Ih and Vh are the hth harmonic current and voltage measured at the PCC, respectively, and I1 and V1 are their fundamental values. For the systems under study, and according to IEEE 519, the maximum allowable THDV and THDI values are 5%.

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2.3.2. Voltage sag According to the IEEE Std. 1159 [16], voltage sag is defined as ―a decrease of the rms voltage from 0.1 to 0.9 per unit (pu) for a duration of 0.5 cycles to 1 minute.‖ Voltage sags can be produced due to heavy load switching, faults, or transformer energizing [33]. Also, voltage fluctuation due to DG units can cause voltage sags. Statistical indices of voltage sag can be classified into three categories, namely single-event, single-site, and system index for sags in more than one site [34]. In this work, Detroit Edison's sag-score method is used to calculate the ―sag score‖ from the voltage magnitudes of the three phases (VA, VB, VC). ―The larger the sag score (SS), the more severe the event is considered to be‖ [35]. The SS is defined as follows:  V  VB  VC  SS  1   A  3  

(8)

According to EN 50160, limits of voltage sag for rapid changes in the supply voltage equal 4% in the normal case and 6% infrequently [36]. 2.3.3. Power frequency deviation FD is defined as ―an increase or decrease in the power frequency. The duration of FD can be from several cycles to several hours‖ [37]. In intermittent DG systems, a FD problem may occur. According to EN 50160, the frequency range should be within ±1% of the rated frequency (50 Hz) during 95% of the week, and -6%/+4% during 100% of the week [36]. A frequency deviation ratio (FDR) can be defined as follows: FDR 

fm  fr 100 fr

(9)

where fm is the measured fundamental frequency (which varies with time) and fr is the rated system frequency. 2.3.4. Voltage flickers Flicker is defined as ―an impression of unsteadiness of visual sensation induced by a light luminance or spectral distribution and fluctuates with time‖ [38]. It is usually presented under timevarying conditions. UIE-IEC flicker meter is the reference methodology for measuring flicker according to IEC 1000-3-3. An estimated flicker severity over a short-period (usually 10 minutes) is known as Pst, and that evaluated through a long-period (two hours) is known as Plt. A block diagram of a flickermeter is explained in detail in [39]. It consists of five main blocks. Block 1 scales the input voltage to a reference voltage level while blocks 2, 3, and 4 simulate the lampeye-brain system’s response to rms voltage variations. The output of block 4 is the IFL. Block 5 performs a statistical analysis of the IFL by establishing its cumulative probability function, and then estimates Pst by a multi-point method, as follows: Pst 

 0.0314  P0.1    0.0525  P1s    0.0657  P3s    0.28  P10 s    0.08  P50 s 

(10) - 11 -

where P0.1, P1s, P3s, P10s, and P50s are the exceeded flicker levels during 0.1, 1, 3, 10, and 50% of the observation period. As the short-term flicker severity value is based on a 10-minute period as stated in IEC standards [40] and the simulation time was less than 10 minutes in this work, thus, an average IFL (also called instantaneous sensation) is used to compare flicker values among the different DG system configurations. 2.3.5. Power factor Power factor is the ratio of active power to the apparent power. Low PF results from inductive loads such as induction motors, transformers, and high-intensity discharge lighting. The true power factor measured at the PCC should be maintained in an acceptable range (> 90%) to increase the energy-transfer efficiency of the system [3]. Capacitor banks are the widely used method to improve the PF. 2.4. Problem formulation The analytic hierarchy process is employed to provide a comprehensive framework for quantifying importance (weights) of the considered PQ indices (criteria) for multiple interface buses in the DG systems under study in different scenarios. In what follows, a brief overview of the AHP is presented. Further, the problem formulation of the UPQI using AHP is presented. 2.4.1. Analytic hierarchy process With the increase in the multiplicity of engineering problems, the single objective analysis is presently no longer a good choice because of the conflict of objectives, i.e., improving a design objective value may degrade the performance of other objectives, particularly when there are a large number of alternatives [41]. In most cases, applying a prioritizing scheme is the key enabler to get the most feasible solution. Among the various multiple-criteria and multiple-attributes techniques, AHP is one of the most preferred decision-making tools that can be used to compare and evaluate different alternatives because of its several advantages such as simplicity, adaptability, and transparency. However, it has also some disadvantages such as interdependency between objectives and alternatives, and the need of assigning weights (pairwise comparison) on the basis of data availability or designer’s experience to judge which choice is to be preferred [42]. Simply, AHP is based on pairwise comparison between attributes to form a judgment matrix, and then the maximum eigenvalue of this matrix is used to evaluate the weight of each attribute [43]. Recently, AHP has been used in different power system applications. Choudhary and Shankar [44] have used it for energy planning aspects to choose the optimal locations of thermal power plants, and Dehghanian et al. [45] presented a fuzzy-based AHP for reliability and management aspects in maintenance scheduling of power system components and determining components that should have priority. In Abdel Aleem et al. [46], selection between different energy credits to achieve optimal - 12 -

energy credits for a system is proposed. In Bracale et al. [47], AHP is used in sizing and sitting of DG units to obtain the most significant improvement of PQ levels of a distribution system. The ideal AHP procedure is outlined as follows [42]: 1- Construct the hierarchical model. 2- Set up the judgment matrix. 3- Estimate the maximum eigenvalue (λmax) and the corresponding eigenvector of the judgment matrix. The eigenvector elements represent the relative importance (weights) of the corresponding factor, the so-called ―hierarchy ranking‖. 4- Check the consistency of the results by calculating the consistency index (CI), as follows:

CI 

max  N f N f 1

(11)

Further, calculate the consistency ratio (CR), as follows:

CR 

CI RI

(12)

where Nf is the dimension of the judgment matrix, and RI is the average stochastic consistency index that depends on the judgment matrix’s dimension. The pairwise comparison judgments are based on a fundamental scale from 1 to 9, expressed as follows: Scale 1 indicates that both criteria are equally important. Scale 3 indicates that one criterion is slightly more important than the other. Scale 5 indicates that one criterion is more important than the other. Scale 7 indicates that one criterion is far more important than the other. Scale 9 indicates that one criterion is extremely more important than the other. A flowchart for the procedure of implementation of the ideal AHP is shown in Fig. 5. Readers can refer to Saaty [42] for more details on the analytic hierarchy process. 2.4.2. Formulation of the proposed index In the systems under study, for each bus type, six PQ criteria are considered: THDV, THDI, SS, FD, IFL, and 1/PF. Minimization of these criteria corresponds to a better PQ of a system, and this is why the inverse of the PF was considered in the PQ criteria. A set of three different scenarios are taken into account for each bus type to signify its PQ priorities. Further, the judgment matrices are formed for all bus types, namely wind, hybrid PV, grid, and load buses, as follows:

Wind bus: In the first scenario, voltage flicker is assumed to be the most important criterion, THDI is assumed to be the most important criterion in the second scenario, and then FD is assumed to be the - 13 -

most important criterion in the third scenario. The judgment matrices of these scenarios are given in Table 1. As is obvious from the table, evaluations are consistent for all of them. Combining results of the three scenarios with equal aggregation will result in the average weights vector [0.1097 0.2200 0.0720 0.1887 0.1787 0.2303] T. PV/FC bus: In the first scenario, THDI is assumed to be the most important criterion, THDV is assumed to be the most important criterion in the second scenario, and then FD is assumed to be the most important criterion in the third scenario. The judgment matrices of these scenarios are given in Table 2; evaluations are consistent for all of them. Combining results of the three scenarios with equal aggregation will result in the average weights vector [0.1890 0.2273 0.0577 0.1037 0.1997 0.2227] T.

Grid bus: In the first scenario, THDV is assumed to be the most important criterion, FD is assumed to be the most important criterion in the second scenario, and then SS is assumed to be the most important criterion in the third scenario. The judgment matrices of these scenarios are given in Table 3, and evaluations are consistent for all of them. Combining results of the three scenarios with equal aggregation will result in the average weights vector [0.295 0.04933 0.2317 0.093 0.2647 0.066] T. It should be indicated that considering different scenarios will result in a considerable change in the weights vector. However, if they are aggregated equally, comparable average weights vectors will result.

Load bus: In the first scenario, THDI is assumed to be the most important criterion, SS is assumed to be the most important criterion in the second scenario, and then PF is assumed to be the most important criterion in the third scenario. The judgment matrices of these scenarios are given in Table 4; evaluations are consistent for all of them. Combining results of the three scenarios with equal aggregation will result in the average weights vector [0.095 0.29233 0.262 0.06833 0.051 0.23033] T.

Power quality index: Using the average weights vector for each bus, PQI is expressed as follows: Nf

PQI   wp Fp , p 1

Nf

w

p

1

(13)

p 1

where Nf is the number of the selected factors that affect the PQ performance, Fp is the measured value of the factor p, and wp is its weight. Thus:

PQI  w1 THDV   w2 THDI   w3  SS   w4  FD   w5  IFL   w6 1/ PF 

(14)

Accordingly, applying an average aggregation for all quality of power indices at all bus types, the UPQI of the system can be expressed as follows:

 PQIW  PQI PV  PQI G  PQI L  UPQI    4  

(15) - 14 -

3. Results and discussion In this section, simulation results of each DG system, with and without compensation, are presented and discussed under normal operating conditions as well as under a three-phase fault condition. 3.1. The UPQI results under normal operation conditions 3.1.1. Hybrid wind/PV/FC system The measured voltage and current of the different buses are used to estimate the PQ performance. The simulation results of this system, with and without compensation, are presented in Table 5. For the uncompensated system, THDV is almost negligible on the grid side. However, it exceeds the standard limit at the PV/FC bus. For all buses except the wind bus, THDI values do not comply with the IEEE 519 limits. Conversely, the wind bus presents high SS and IFL values compared to the other buses. Furthermore, both load and grid buses have low PF values. Estimating the PQ indices for these buses using Eq. (14) will result in: PQIw = 1.2217, PQIPV = 0.6943, PQIL = 1.4294 and PQIG = 0.2935. Recalling that the lower the value of the index, the higher is the system’s power quality performance; thus, we can say that the grid bus provides the best PQ measures, followed by the PV/FC bus, then the wind bus, and finally the load bus provides the worst PQ measures. Hence, using Eq. (15), the UPQI for this system before compensation equals 0.9097. On the other hand, for the compensated system, we noticed that the STPF effectively decreases the THDV at the wind bus by 33.4% and by 31% at the load bus. At the PV/FC bus, the THDV value is slightly decreased. For current harmonics, the THDI values are significantly reduced by 46.3% at the wind bus, 6% at the PV/FC bus, 49% at the load bus, and by 40% at the grid bus. In addition, the SS values are decreased by 12.6% at the wind bus, 33% at the PV/FC bus, 34% at the load bus, and by 66% at the grid bus. However, the IFL values did not change at all buses. The PF at the load bus is adequately improved to be 0.9171 lagging and is also increased at the grid bus to be 0.823 lagging. At the same time, the FD values reduced by 41.8% at the wind bus, 62.8% at the PV/FC bus, 51.5% at the load bus, and 33.4% at the grid bus. These improvements are reflected in the PQ indices with PQIw = 1.0867, PQIPV = 0.5825, PQIL = 1.0113, and PQIG = 0.2706, resulting in an UPQI that equals 0.7378. This indicates an improvement by 18.90% for the overall PQ performance of the system. To clarify the impacts of reactive power compensation on the considered PQ measures, Fig. 6 shows variations of the wind speed and solar irradiance during the simulation time, and Figs. 7 and 8 show variation of the active and reactive powers, SS, FD, and PF measured at the load and grid buses, respectively, in response to these variations with the DGs connected. We note that variation of the wind speed considerably affects the PQ performance of all the buses, whereas the solar irradiance variation has a little effect on the PQ performance of the buses. - 15 -

3.1.2. Combined wind/PV system The simulation results of this system, with and without compensation, are presented in Table 6. Before compensation, the PQ indices for the buses are PQIw = 1.5032, PQIPV = 0.5715, PQIL = 1.4113, and PQIG = 0.3266, resulting in an UPQI that equals 0.9531. After compensation, the PQ indices for the buses are PQIw =1.3823, PQIPV = 0.5562, PQIL = 1.1204, and PQIG =0.3046, resulting in an UPQI that equals 0.8409. This indicates an improvement by 11.77% for the overall PQ performance of the system. Compared to the hybrid wind/PV/FC system, it is noted that the wind and grid buses have lower PQ measures in the combined wind/PV system due to the increased SS, IFL, and FD values and the decreased PF value, which were accurately reflected in their PQ indices. On the contrary, the PV and load buses have better PQ indices in the wind/PV system compared to the hybrid wind/PV/FC system. Despite the variation of the PQ indices of the buses in the two systems, we can easily conclude, using the UPQI values, that the overall wind/PV system results provide lower PQ performance than the hybrid wind/PV/FC system. This validates the idea that hybridization of renewable energy systems is an essential way for enhancing PQ levels in the distribution systems. Besides, the compensation scheme in the wind/PV system was not at the same degree of effectiveness as that achieved in the hybrid wind/PV/FC system. 3.1.3. Distributed wind system The simulation results of the buses (wind bus 1, wind bus 2, load bus, and grid bus) of this system, with and without compensation, are presented in Table 7. Before compensation, the PQ indices for the buses are PQIw1 = 1.6797, PQIw2 = 1.6685, PQIL = 1.8990, and PQIG = 0.3977, resulting in a UPQI that equals 1.4112. After compensation, the PQ indices for the buses are PQIw1 =1.5379, PQIw2 = 1.5324, PQIL = 1.4027, and PQIG =0.3417, resulting in an UPQI that equals 1.2037. This indicates an improvement by 14.70% for the overall PQ performance of the system. Based on the UPQI results after compensation, and considering the hybrid wind/PV/FC system as a reference system with ideal quality of power, we can easily say that the combined wind/PV system has a lower overall PQ performance by 14%, and the distributed wind system has the lowest overall PQ performance by 63% compared to the reference system. 3.1.4. The UPQI results under three-phase fault condition The same DG systems are tested again when a three-phase fault occurs at the load bus in time period ranges from 2 to 4 seconds. This test aims to investigate the impact of such faults on the calculated UPQI values. Following the procedure explained for the systems’ normal operation conditions, the UPQI results are calculated and presented in Table 8.

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Comparison of the three DG systems under the three-phase fault condition indicates that the combined wind/PV system provides better PQ performance, while the distributed wind system suffers from the lowest PQ performance. In addition, it is evident that values of the indices are amplified considerably, which indicate the severity of faults on power systems operation and the need to use advanced compensators to avoid such shortcomings. Furthermore, the increased values of the indices show the need to investigate the risks that could face the protection schemes and PQ performance in the presence of DG systems and the required modifications that may be necessary to account for the DG presence. 4. Conclusions If not properly assessed, DG systems can cause different PQ problems to the distribution systems. Hence, it is necessary to evaluate these issues and move forward towards putting a global PQ index into practice to sustain power quality of smart grid-connected DG systems within acceptable limits in the long-term, particularly in the planning of new DG installations. In this paper, using AHP, a framework to assess the overall PQ performance of different hybrid smart grid-connected DG systems, is proposed taking into account different interface-bus types and PQ criteria in various scenarios under normal operating conditions as well as under a three-phase fault condition. Hybrid loads and passive filters for load power factor improvement and harmonic mitigation are explored. The proposed UPQI is evaluated by average aggregation of PQ indices of the buses. Under the different operating conditions, a comparative analysis of the results, with and without compensation, is presented and discussed to show their impacts on the UPQI. Furthermore, the usefulness of the UPQI in verifying the impacts of DG systems on PQ levels is discussed. Based on the achieved UPQI results, we concluded that the hybrid wind/PV/FC energy system shows the best PQ measures, followed by wind/PV system, and then the wind energy system, during normal operation and continuous load conditions. This validates the idea that hybridization of renewable energy systems is an essential way for increasing utilization and enhancing PQ levels in the distribution systems. Also, under the same test conditions, for the compensated systems using the STPFs, better values of the UPQI are obtained in the different systems but with different degrees of effectiveness. This indicates the need to use dynamic (active) or hybrid compensation schemes to surmount the fixed nature of reactive power compensation of passive filters to achieve satisfactory performance. However, under the three-phase fault condition, values of the indices increased considerably, which indicate the severity of faults on power systems operation and the need to use advanced compensators such as D-FACTS to avoid such shortcomings. As we have shown, the problem of the definition of an adequate assessment of PQ levels in the presence of DGs using one index to represent the overall PQ performance of a system has been taken into account, and it was found that the comparative analysis of buses and systems using a single index - 17 -

for a comprehensive PQ evaluation makes the general assessment simple and effective as one number only is used for each comparison. If a planner has multiple objectives affecting the choice among different planning alternatives, he can benefit from such unified indices, or they can be used in the sizing and sitting of DG units to obtain the most significant improvement of PQ levels. Our study was limited to the instantaneous penetration of renewables and its direct impacts on the power quality performance of balanced systems. Another factor that was beyond the framework of the study, and will be included in future studies, is normalization of the proposed index to enable its ease of use in both continuous and discrete disturbances in non-sinusoidal and unbalanced distribution networks. Since AHP has some disadvantages such as interdependency between objectives and alternatives in addition to the need to weight the criteria which complicates application of the PQ indices, therefore large quantities of historical PQ data are still crucial to set suitable weights. Accordingly, a fuzzy-based AHP framework can be used for the reduction of needed data on PQ measures. Finally, future studies will also investigate an automated online UPQI estimator for large DG systems with different sizing, siting, and hybrid configurations for many interface buses in DC and AC sub-grids including linear and nonlinear loads and D-FACTS as well as considering increased reliability and the evaluation of PQ indices in a probabilistic manner. Acknowledgements The authors would like to thank the respective editor and anonymous reviewers for their constructive comments and suggestions. References 1. Cerdeira Bento JP, Moutinho V. CO2 emissions, non-renewable and renewable electricity production, economic growth, and international trade in Italy. Renew Sustain Energy Rev 2016;55:142–55. doi:10.1016/j.rser.2015.10.151. 2. Atems B, Hotaling C. The effect of renewable and nonrenewable electricity generation on economic growth. Energy Policy 2018;112:111–8. doi:10.1016/j.enpol.2017.10.015. 3. Sakar S, Balci ME, Abdel Aleem SHE, Zobaa AF. Integration of large- scale PV plants in nonsinusoidal environments: Considerations on hosting capacity and harmonic distortion limits. Renew Sustain Energy Rev 2018;82:176–86. doi:10.1016/j.rser.2017.09.028. 4. Picciariello A, Alvehag K, Söder L. State-of-art review on regulation for distributed generation integration in distribution systems. 9th Int. Conf. Eur. Energy Mark. EEM 12, 2012. doi:10.1109/EEM.2012.6254769. 5. González P, Romero-Cadaval E, González E, Guerrero MA. Impact of Grid Connected Photovoltaic System in the Power Quality of a Distribution Network. In: Camarinha-Matos LM, editor. Technol. Innov. Sustain. Second IFIP WG 5.5/SOCOLNET Dr. Conf. Comput. Electr. Ind. Syst. DoCEIS 2011, Costa Caparica, Port. Febr. 21-23, 2011. Proc., Berlin, Heidelberg: Springer Berlin Heidelberg; 2011, p. 466–73. doi:10.1007/978-3-642-19170-1_51. - 18 -

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List of Tables Table 1 Judgment matrices and the resultant weights vectors of the three scenarios: Wind bus Scenario 1 Criteria THDV THDI SS IFL FD THDV 1 2 2 1/3 2 1/2 THDI 1 2 1/4 2 1/2 SS 1/2 1 1/5 2 IFL 3 4 5 1 6 FD 1/2 1/2 1/2 1/6 1 1/PF 4 2 3 1/2 4 λmax = 6.2441, RI =1.25, and CR= 3.99% < 10% (Pass) The weights vector (normalized eigenvector) = [0.12 0.103 0.075 0.403 0.059 0.239] T Scenario 2 Criteria THDV THDI SS IFL FD THDV 1 1/4 2 2 1/2 THDI 4 1 5 5 3 SS 1/2 1/5 1 1/2 1/2 IFL 1/2 1/5 2 1 1/3 FD 2 1/3 2 3 1 1/PF 2 1/2 2 3 3 λmax = 6.3152, RI = 1.25, and CR= 4.09 % < 10% (Pass) The weights vector (normalized eigenvector) = [0.103 0.395 0.076 0.074 0.131 0.221] T Scenario 3 Criteria THDV THDI SS IFL FD 1 1/2 3 2 1/3 THDV 2 1 4 2 1/2 THDI 1/3 1/4 1 1/3 1/5 SS 1/2 1/2 3 1 1/3 IFL 3 2 5 3 1 FD 3 2 2 4 1/2 1/PF λmax = 6. 3888, RI =1.25, and CR= 6.00% < 10% (Pass) The weights vector (normalized eigenvector) = [0.106 0.162 0.065 0.089 0.346 0.231] T

1/PF 1/4 1/2 1/3 2 1/4 1

1/PF 1/2 2 1/2 1/3 1/3 1

1/PF 1/3 1/2 1/2 1/4 2 1

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Table 2 Judgment matrices and the resultant weights vectors of the three scenarios: PV/FC bus Scenario 1 Criteria THDV THDI SS IFL FD 1 1/5 2 1/3 1/2 THDV 5 1 6 3 4 THDI 1/2 1/6 1 1/5 1/2 SS 3 1/3 5 1 2 IFL 2 1/4 2 1/2 1 FD 3 1/2 3 2 3 1/PF λmax = 6.2195, RI =1.25, and CR= 3.00% < 10% (Pass) The weights vector (normalized eigenvector) = [0.072 0.405 0.058 0.148 0.093 0.224] T Scenario 2 Criteria THDV THDI SS IFL FD THDV 1 3 6 5 3 THDI 1/3 1 3 2 1/2 SS 1/6 1/3 1 1/2 1/6 IFL 1/5 1/2 2 1 1/5 FD 1/3 2 6 5 1 1/PF 1/2 2 4 2 2 λmax = 6.3392, RI = 1.25, and CR= 5.00% < 10% (Pass) The weights vector (normalized eigenvector) = [0.389 0.115 0.050 0.074 0.160 0.213] T Scenario 3 Criteria THDV THDI SS IFL FD 1 1/2 3 2 1/3 THDV 2 1 4 2 1/2 THDI 1/3 1/4 1 1/3 1/5 SS 1/2 1/2 3 1 1/3 IFL 3 2 5 3 1 FD 3 2 2 4 1/2 1/PF λmax = 6.3888, RI =1.25, and CR= 6.00% < 10% (Pass) The weights vector (normalized eigenvector) = [0.106 0.162 0.065 0.089 0.346 0.231] T

1/PF 1/3 2 1/3 1/2 1/3 1

1/PF 2 1/2 1/4 1/2 1/2 1

1/PF 1/3 1/2 1/2 1/4 2 1

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Table 3 Judgment matrices and the resultant weights vectors of the three scenarios: Grid bus Scenario 1 Criteria THDV THDI SS IFL FD 1 6 3 4 2 THDV 1/6 1 1/4 1/3 1/5 THDI 1/3 4 1 2 1/2 SS 1/4 3 1/2 1 1/3 IFL 1/2 5 2 3 1 FD 1/5 2 1/3 1/2 1/4 1/PF λmax = 6.1689, RI =1.25, and CR= 3.00% < 10% (Pass) The weights vector (normalized eigenvector) = [0.408 0.051 0.144 0.095 0.234 0.067] T Scenario 2 Criteria THDV THDI SS IFL FD 1 5 2 3 1/2 THDV 1/5 1 1/4 1/3 1/7 THDI 1/2 4 1 2 1/3 SS 1/3 3 1/2 1 1/4 IFL 2 7 3 4 1 FD 1/4 2 1/3 1/2 1/5 1/PF λmax = 6.1426, RI = 1.25, and CR= 2.00% < 10% (Pass) The weights vector (normalized eigenvector) = [0.235 0.048 0.143 0.095 0.412 0.067] T Scenario 3 Criteria THDV THDI SS IFL FD 1 6 1/2 3 2 THDV 1/6 1 1/6 1/3 1/4 THDI 2 6 1 4 3 SS 1/3 3 1/4 1 1/3 IFL 1/2 4 1/3 3 1 FD 1/6 3 1/5 1/2 1/3 1/PF λmax = 6.32527, RI =1.25, and CR= 3.00% < 10% (Pass) The weights vector (normalized eigenvector) = [0.242 0.049 0.408 0.089 0.148 0.064] T

1/PF 5 1/2 3 2 4 1

1/PF 4 1/2 3 2 5 1

1/PF 6 1/3 5 2 3 1

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Table 4 Judgment matrices and the resultant weights vectors of the three scenarios: Load bus Scenario 1 Criteria THDV THDI SS IFL FD THDV 1 1/4 1/3 2 3 THDI 4 1 2 5 6 SS 3 1/2 1 4 5 IFL 1/2 1/5 1/4 1 2 FD 1/3 1/6 1/5 1/2 1 1/PF 2 1/3 1/2 3 4 λmax = 6.1689, RI =1.25, and CR= 3.00% < 10% (Pass) The weights vector (normalized eigenvector) = [0.095 0.408 0.234 0.0670 0.051 0.144] T Scenario 2 Criteria THDV THDI SS IFL FD THDV 1 1/3 1/4 2 3 THDI 3 1 1/2 4 5 SS 4 2 1 5 6 IFL 1/2 1/4 1/5 1 2 FD 1/3 1/5 1/6 1/2 1 1/PF 2 1/2 1/3 2 4 λmax = 6.1539, RI = 1.25, and CR= 3.00% < 10% (Pass) The weights vector (normalized eigenvector) = [0.095 0.235 0.408 0.071 0.051 0.139] T Scenario 3 Criteria THDV THDI SS IFL FD THDV 1 1/3 1/2 2 3 THDI 3 1 2 4 5 SS 2 1/2 1 3 4 IFL 1/2 1/4 1/3 1 2 FD 1/3 1/5 1/4 1/2 1 1/PF 4 2 3 5 6 λmax =6.1689 , RI =1.25, and CR= 3.00% < 10% (Pass) The weights vector (normalized eigenvector) = [0.095 0.234 0.144 0.067 0.051 0.408] T

1/PF 1/2 3 2 1/3 1/4 1

1/PF 1/2 2 3 1/2 1/4 1

1/PF 1/4 1/2 1/3 1/5 1/6 1

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Table 5 Power quality measures of the hybrid wind/PV/FC system with and without compensation Without compensation Wind PV/FC Load Parameters/buses bus bus bus THDV 0.0419 0.0580 0.0509

Grid bus 0.0062

Wind bus 0.0279

With compensation PV/FC Load bus bus 0.0573 0.0350

Grid bus 0.0062

THDI

0.0324

0.2061

0.123

0.1183

0.0174

0.1931

0.0629

0.0616

SS

11.2745

3.5901

4.0259

0.0125

9.8564

2.3798

2.657

0.0042

IFL

0.6612

0.6491

0.6436

0.7138

0.6159

0.6404

0.6393

0.6899

FD

0.2617

0.6188

0.2939

0.0317

0.1523

0.2299

0.1424

0.0211

PF

0.9958

0.9447

0.8200

0.725

0.9956

0.9320

0.9171

0.823

Table 6 Power quality measures of the combined wind/PV system with and without compensation Without compensation Wind PV Load Parameters/buses bus bus bus 0.0438 0.0428 0.0518 THDV

With compensation PV Load bus bus 0.0255 0.0358

Grid bus 0.0062

Wind bus 0.0288

THDI

0.0248

0.1335

0.1235

0.1331

0.0147

0.0918

0.0652

0.0711

SS

13.8804

2.2216

3.9305

0.0132

12.5508

1.9838

2.9995

0.0074

IFL

0.9043

0.7414

0.7124

0.7826

0.8513

0.7462

0.7390

0.7996

FD

0.5319

0.4218

0.5102

0.05582

0.4701

0.4287

0.3382

0.03832

PF

0.9945

0.9999

0.8196

0.6418

0.9952

0.9999

0.9167

0.8014

Grid bus 0.0062

Table 7 Power quality measures of the distributed wind system with and without compensation Without compensation Wind Wind Load Parameters/buses bus 1 bus 2 bus 0.0379 0.0379 0.0564 THDV

Grid bus 0.0056

Wind bus 1 0.0277

With compensation Wind Load bus 2 bus 0.0277 0.0488

Grid bus 0.0056

THDI

0.0232

0.0232

0.1273

0.1434

0.0150

0.0150

0.0754

0.0985

SS

15.6298

15.6298

5.7212

0.0382

13.5246

13.5244

3.9782

0.0277

IFL

1.0247

1.0247

0.6996

0.7736

0.9571

0.9571

0.7364

0.8028

FD

0.6864

0.627

0.5934

0.06813

0.818

0.789

0.701

0.0996

PF

0.9907

0.9907

0.8189

0.3904

0.9907

0.9907

0.9276

0.6169

- 26 -

Table 8 UPQI results under three-phase fault condition DG system Hybrid wind/PV/FC Combined wind/PV Distributed wind Compensation Without With Without With Without With UPQI 5.3213 4.8434 4.9286 4.4239 5.5708 5.4740

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List of Figures

Fig. 1. Procedure for determining the unified power quality index using AHP (Double column: full width)

- 28 -

(a)

- 29 -

(b) Fig. 2. Systems under study: (a) Wind energy system, and (b) Hybrid wind/PV/FC system. (Double column: full width) - 30 -

(a)

(b)

(c) Fig. 3. Models of the DG systems: (a) Fixed speed wind generation system, (b) Single-diode model of the PV cell, and (c) Equivalent circuit of the fuel cell. (Double column: full width)

- 31 -

(a)

(b)

Fig. 4. Modeling of the STPF: (a) Single-phase equivalent circuit, and (b) Impedance-frequency characteristic. (Single column: Half width)

- 32 -

Fig. 5. Flowchart for procedure of implementation of the ideal AHP. (1.5 column)

- 33 -

(a)

(b) Fig. 6. Wind speed and solar irradiance variations: (a) Wind speed, and (b) Solar irradiance. (Double column: full width)

- 34 -

(a)

(b)

(c)

- 35 -

(d)

(e) Fig. 7. Load bus PQ measures with and without compensation: (a) Active power, (b) Reactive power, (c) Sag score, (d) Frequency deviation, and (e) Power factor. (Double column: full width)

- 36 -

(a)

(b)

(c)

- 37 -

(d)

(e) Fig. 8. Grid bus PQ measures with and without compensation: (a) Active power, (b) Reactive power, (c) Sag score, (d) Frequency deviation, and (e) Power factor. (Double column: full width)

- 38 -