Integration of Large Scale PV-Based Generation into Power Systems: A Survey

energies Review Integration of Large Scale PV-Based Generation into Power Systems: A Survey Elyas Rakhshani 1, Kumars Rouzbehi 2, Adolfo J. Sánchez 2, Ana Cabrera Tobar 3and Edris Pouresmaeil 4,* 1Department of Electrical Sustainable Energy, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands; [email protected] 2Departamento de Ingeniería de Sistemas y Automática, Universidad de Sevilla, Camino de los Descubrimientos s/n, 41092 Sevilla, Spain; [email protected] (K.R.); [email protected] (A.J.S.) 3Department of Electrical Engineering, Universidad Técnica del Norte, Av. 17 de julio s/n, 100150 Ibarra, Ecuador; [email protected] 4 Department of Electrical Engineering and Automation, Aalto University, Maarintie 8, 02150 Espoo, Finland *Correspondence: [email protected]; Tel.: +35-850-598-4479 Received: 11 March 2019; Accepted: 8 April 2019; Published: 13 April 2019   Abstract: This paper reports a general overview of current research on analysis and control of the power grid with grid scale PV-based power generations as well as of various consequences of grid scale integration of PV generation units into the power systems. Moreover, the history of PV renewable growth, deregulation of power system and issues related to grid-connected PV systems considering its contribution to various responsibilities like frequency control, virtual inertia capabilities and voltage regulation are discussed. Moreover, various outcomes of the high-penetrated grid with PV power plants such as power quality, active and reactive power control, protection, balancing and reliability under various loading conditions are reviewed and discussed. Keywords: integration of PV plant to grid; large-scale PV power plant; modern power systems 1. Introduction Energy and the matter of renewable resources are critical issues in future power grids. During previous years, energy demand has increased drastically [ 1 ] and due to global warming, renewable energy development is crucial in order to reduce conventional fossil power plant harmful emissions [ 2 , 3 ]. Currently, renewable energies being harvested are solar, wind power and hydraulic energy. Facing problems such as climate change and environmental awareness in parallel with huge deregulations in conventional power systems have forced governments to think more deeply about the alternative sources of energy to substitute the traditional sources of energy. These matters have changed the face of the conventional grids and it is expected a rapid deregulation and revolution due to the massive integration of the renewable based generations with stochastic behavior like photovoltaic (PV) systems and wind power. Solar power is becoming more attractive. Solar energy has a huge harvesting potential and based on European Photovoltaic Industry Association (EPIA) reports, the European cumulative PV power was around 29,777 MW in 2010, while as shown in Figure 1, just in 2014, this value for the entire European Union was more than 88,636 MW [ 4 ]. It predicts that in 2019 the capacities can be between 121,087 MW to 158,156 MW, which suggests a strong year for the PV industry and the rate of installations will continue to increase through the next 5 years [5]. Energies 2019,12, 1425; doi:10.3390/en12081425 www.mdpi.com/journal/energies Energies 2019,12, 1425 2 of 19 Energies 2019, 12, x FOR PEER REVIEW 2 of 19 Figure 1. European cumulative solar PV market scenarios until 2019 [5]. PV is a popular source of energy both on the utility’s side and for residential home use. PV uses semi-conductor technologies for converting the energy from the sunlight into the electricity. PV, thus, only operates when there is enough energy from the Sun during a day, and it must be mixed with other type of power generation systems to guarantee a continuous supply of the electricity. The PV market is very promising and photovoltaic energy plays a key role on the energy map of various countries. For example, more than about 4% of the electricity demand in Spain was provided by PV during the last summer. Currently, the largest PV plant is located in the Tengger Desert Solar Park in China, with a total capacity of 1500 MW, followed by the Datong Solar Power Top Runner Base, also in China, and Kurnool Ultra Mega Solar Park in India, both with a capacity of 1000 MW [6]. These ranges of generation will be continuing to increase at least in the next following years and simultaneously make the power grid more complex. One of the most important aspects to take into account in renewable energy is its stochastic nature and its consequences and impact on power grids. Managing this new scenario of power grid full of high-penetrated renewable generations will be complicated and it will be necessary to perform deep research in several aspects. The impact of large-scale PV based generation units are the focus of many strategic researches on the integration of renewable energy [7,8]. A PV-based power generation unit usually works in the grid connected mode. Nevertheless, unlike conventional generation units, high-capacity PV units, like in the range of multi MW, will have a huge effect on the dynamic performance of the interconnected power system and also will have an important impact on the reliable and stable operation of interconnected systems [9,10]. Thus, in the view of the assessment of the grid-connected operation characteristics of the grid scale PV systems, researchers have carried out a vast number of modeling and control studies on dynamic impact and PV system and their contribution on grid support and ancillary services [11,12]. This review paper presents a discussion of large-scale PV power plants and their massive potential to become an important player of the modern future power system. Therefore, benefits, issues, various effects of high penetration of PV and other requirements to support the correct functioning of PV plants are discussed and reviewed. In Section 2, grid scale PV-based generation unit structures, modelling and control strategies are presented. Modern power systems with the grid scale PV-based generation reviewed and discussed in Section 3. In Section 4, contributions of large scale PV power plants to the grid services such as frequency, reactive, voltage control, balancing and reliability under various loading condition are reviewed. Finally, in Section 5, the paper ends with some concluding remarks. 2. Large Scale PV Power Plant In recent years, the most auspicious usage of PV-based generations has been their integration into the interconnected power grid [13–21]. In [13], a review of existing grid integrated PV-based topologies was presented. Readers should refer to [8,9] for more comprehensive explanations for various PV structures and their technologies. In the category of distributed systems, PV may be broadly classified into four types: (1) very large scale; (2) large-scale; (3) medium Scale, and (4) small scale PV systems. In the small scale PV system, the range of capacities is up to 250 kW. For medium scale, it is around 250 to Figure 1. European cumulative solar PV market scenarios until 2019 [5]. PV is a popular source of energy both on the utility’s side and for residential home use. PV uses semi-conductor technologies for converting the energy from the sunlight into the electricity. PV, thus, only operates when there is enough energy from the Sun during a day, and it must be mixed with other type of power generation systems to guarantee a continuous supply of the electricity. The PV market is very promising and photovoltaic energy plays a key role on the energy map of various countries. For example, more than about 4% of the electricity demand in Spain was provided by PV during the last summer. Currently, the largest PV plant is located in the Tengger Desert Solar Park in China, with a total capacity of 1500 MW, followed by the Datong Solar Power Top Runner Base, also in China, and Kurnool Ultra Mega Solar Park in India, both with a capacity of 1000 MW [6]. These ranges of generation will be continuing to increase at least in the next following years and simultaneously make the power grid more complex. One of the most important aspects to take into account in renewable energy is its stochastic nature and its consequences and impact on power grids. Managing this new scenario of power grid full of high-penetrated renewable generations will be complicated and it will be necessary to perform deep research in several aspects. The impact of large-scale PV based generation units are the focus of many strategic researches on the integration of renewable energy [ 7 , 8 ]. A PV-based power generation unit usually works in the grid connected mode. Nevertheless, unlike conventional generation units, high-capacity PV units, like in the range of multi MW, will have a huge effect on the dynamic performance of the interconnected power system and also will have an important impact on the reliable and stable operation of interconnected systems [ 9 , 10 ]. Thus, in the view of the assessment of the grid-connected operation characteristics of the grid scale PV systems, researchers have carried out a vast number of modeling and control studies on dynamic impact and PV system and their contribution on grid support and ancillary services [ 11 , 12 ]. This review paper presents a discussion of large-scale PV power plants and their massive potential to become an important player of the modern future power system. Therefore, benefits, issues, various effects of high penetration of PV and other requirements to support the correct functioning of PV plants are discussed and reviewed. In Section 2, grid scale PV-based generation unit structures, modelling and control strategies are presented. Modern power systems with the grid scale PV-based generation reviewed and discussed in Section 3. In Section 4, contributions of large scale PV power plants to the grid services such as frequency, reactive, voltage control, balancing and reliability under various loading condition are reviewed. Finally, in Section 5, the paper ends with some concluding remarks. 2. Large Scale PV Power Plant In recent years, the most auspicious usage of PV-based generations has been their integration into the interconnected power grid [ 13 – 21 ]. In [ 13 ], a review of existing grid integrated PV-based topologies was presented. Readers should refer to [ 8 , 9 ] for more comprehensive explanations for various PV structures and their technologies. In the category of distributed systems, PV may be broadly classified into four types: (1) very large scale; (2) large-scale; (3) medium Scale, and (4) small scale PV systems. In the small scale PV system, the range of capacities is up to 250 kW. For medium scale, it is around Energies 2019,12, 1425 3 of 19 250 to 1000 kW. Large scale, it is considered around 1 to 100 MW, and for very large scale, the power capacity is higher than 100 MW [14]. 2.1. Structures The typical layout of a Utility-Scale PV based system requires several transformers, PV inverters and PV arrays (Figure 2). The connection among these elements depends on the topology used by the PV inverter. Generally, two topologies are used for connection of the PV systems to internal grid of the power plant: central and multistring inverter (Figure 3). In the first structure, only one inverter is used to connect a PV array with the transformer. Commonly, this has a single stage of conversion (DC-AC). Meanwhile, the multistring inverter, has two conversion stages (DC-DC and DC-AC). Commonly, the last topology interconnects one string of PV panels to the internal grid AC grid of the power plant [ 14 ]. According to an in depth comparison developed by Cabrera et al. [ 14 ], the central inverter is the most used topology in large scale power plants. The main advantages of this topology over the second one are: (i) competitive cost, (ii) robustness, (iii) low maintenance, and (iv) a reduced number of inverters in the field. However, the multistring inverter is used commonly to enhance the control of the maximum power point. This could be necessary when the power plant is located in irregular surfaces. Moreover, this topology is used for each PV string, so the number of inverters used increases in comparison with a power plant that uses only central inverter. For instance, the Long Island PVPP with a rate of 37 MW uses 50 central inverters. Meanwhile, the Veprek PVPP with a similar capacity uses 3069 multistring inverters According to Massi Pavan et al. [ 15 ], and a central inverter configuration has 1.5% less efficiency than a multistring one. However, the cost for the installation and maintenance of the second one increases by 60%. Energies 2019, 12, x FOR PEER REVIEW 3 of 19 1000 kW. Large scale, it is considered around 1 to 100 MW, and for very large scale, the power capacity is higher than 100 MW [14]. 2.1. Structures The typical layout of a Utility-Scale PV based system requires several transformers, PV inverters and PV arrays (Figure 2). The connection among these elements depends on the topology used by the PV inverter. Generally, two topologies are used for connection of the PV systems to internal grid of the power plant: central and multistring inverter (Figure 3). In the first structure, only one inverter is used to connect a PV array with the transformer. Commonly, this has a single stage of conversion (DC-AC). Meanwhile, the multistring inverter, has two conversion stages (DC-DC and DC-AC). Commonly, the last topology interconnects one string of PV panels to the internal grid AC grid of the power plant [14] According to an in depth comparison developed by Cabrera et al. [14], the central inverter is the most used topology in large scale power plants. The main advantages of this topology over the second one are: (i) competitive cost, (ii) robustness, (iii) low maintenance, and (iv) a reduced number of inverters in the field. However, the multistring inverter is used commonly to enhance the control of the maximum power point. This could be necessary when the power plant is located in irregular surfaces. Moreover, this topology is used for each PV string, so the number of inverters used increases in comparison with a power plant that uses only central inverter. For instance, the Long Island PVPP with a rate of 37 MW uses 50 central inverters. Meanwhile, the Veprek PVPP with a similar capacity uses 3069 multistring inverters According to Massi Pavan et al. [15], and a central inverter configuration has 1.5% less efficiency than a multistring one. However, the cost for the installation and maintenance of the second one increases by 60%. DC/AC Inverter DC/AC Inverter DC/AC Inverter DC/AC Inverter DC/AC Inverter DC/AC Inverter 1 MW PCS PV Set 1 PV Set 2 PV Set N Figure 2. A grid scale PV-based generation unit. Figure 2. A grid scale PV-based generation unit. Energies 2019,12, 1425 4 of 19 Energies 2019, 12, x FOR PEER REVIEW 4 of 19 Figure 3. PV inverter topologies: (a) Central PV inverter; (b) Multistring PV inverter. 2.2. Characteristics of Grid-Connected PV Systems Based on various works [21–28], the most important characteristics of PV systems and related grid codes are: • The PV-based generators together with inverters will be connected in a parallel structure to the power grid, and loads will be supplied properly when the power grid is accessible. As reported in [2,6,29], the produced power by the PV generation will decrease the apparent load, and then the excess energy flows into the power grid. • Most of the current PV-based inverters don’t have any capabilities for providing any type of reactive power/voltage support. PV systems, usually must work at unity power factor and the utility is responsible for VAR requirements. • Based on regular standards like IEEE 1547, upon the deviations of frequency/voltage grid from their standard ranges, the inverter has to be automatically isolated from the power grid until normal conditions return. • Geographical factors, location of PVs and factors related to the environment are important characteristics to be taken into account for PV systems. All of these factors can be divided into two periods: (1) day-time and (2) night-time. During the day-time period, PV systems will have to deal with weather disturbances such as clouds and effects of the temperature on the efficiency of the system. Cloudy weather may considerably decrease the net radiation and may cause a fast variation in the output of a PV system. During night-time, the PV system is not collecting energy, which means the output power from the PV systems will be zero. However, the PV system may have a battery energy storage system (BESS), which can provide, for a while, energy to the grid. The Transmission System Operator (TSO) will be informed beforehand if the PV system has or not BESS as well as the possible night-time energy the PV system can deliver or not to the grid. • It is quite hard to have a good capability to dispatch ancillary services of PV systems without considering additional energy storage devices. • There is a lack of coordination between PV resources and other conventional plants. Management of reactive power for related feeders is not properly designed for very high PV production. Figure 3. PV inverter topologies: (a) Central PV inverter; (b) Multistring PV inverter. 2.2. Characteristics of Grid-Connected PV Systems Based on various works [ 21 – 28 ], the most important characteristics of PV systems and related grid codes are: • The PV-based generators together with inverters will be connected in a parallel structure to the power grid, and loads will be supplied properly when the power grid is accessible. As reported in [ 2 , 6 , 29 ], the produced power by the PV generation will decrease the apparent load, and then the excess energy flows into the power grid. • Most of the current PV-based inverters don’t have any capabilities for providing any type of reactive power/voltage support. PV systems, usually must work at unity power factor and the utility is responsible for VAR requirements. • Based on regular standards like IEEE 1547, upon the deviations of frequency/voltage grid from their standard ranges, the inverter has to be automatically isolated from the power grid until normal conditions return. • Geographical factors, location of PVs and factors related to the environment are important characteristics to be taken into account for PV systems. All of these factors can be divided into two periods: (1) day-time and (2) night-time. During the day-time period, PV systems will have to deal with weather disturbances such as clouds and effects of the temperature on the efficiency of the system. Cloudy weather may considerably decrease the net radiation and may cause a fast variation in the output of a PV system. During night-time, the PV system is not collecting energy, which means the output power from the PV systems will be zero. However, the PV system may have a battery energy storage system (BESS), which can provide, for a while, energy to the grid. The Transmission System Operator (TSO) will be informed beforehand if the PV system has or not BESS as well as the possible night-time energy the PV system can deliver or not to the grid. • It is quite hard to have a good capability to dispatch ancillary services of PV systems without considering additional energy storage devices. • There is a lack of coordination between PV resources and other conventional plants. Management of reactive power for related feeders is not properly designed for very high PV production. Energies 2019,12, 1425 5 of 19 2.3. Modeling and Control of Large-Scale PV Plant Dynamic behavior of PV generators, especially with high grid penetration, is different from the conventional generation unit responses. Therefore, it is crucial to find an appropriate model for analyzing the effects of grid scale PV-based power generation on the dynamic stability and oscillations of a large power grid. Generally, grid-connected PV systems are based on core device modeling, which will reflect the characteristics of the PV modules and inverter control [ 30 ]. One of the most used corebased models applied for power system analysis is presented in [ 31 ]. The schematic design of this model is shown in Figure 4., where I PV is the control variable and is calculated at each time step as the sum of I PV at the previous step, ∆IMPPT PV reflects the actions of the MPPT, ∆IIr PV represents the impacts of variations in irradiance and ∆IVac PV represents the influence of any sudden variations in AC grid voltage. Energies 2019, 12, x FOR PEER REVIEW 5 of 19 2.3. Modeling and Control of Large-Scale PV Plant Dynamic behavior of PV generators, especially with high grid penetration, is different from the conventional generation unit responses. Therefore, it is crucial to find an appropriate model for analyzing the effects of grid scale PV-based power generation on the dynamic stability and oscillations of a large power grid. Generally, grid-connected PV systems are based on core device modeling, which will reflect the characteristics of the PV modules and inverter control [30]. One of the most used corebased models applied for power system analysis is presented in [31]. The schematic design of this model is shown in Figure 4., where IPV is the control variable and is calculated at each time step as the sum of IPV at the previous step, MPPT PV I reflects the actions of the MPPT, Ir PV I represents the impacts of variations in irradiance and Vac PV I represents the influence of any sudden variations in AC grid voltage. MPPT pv I MPPT pv I r I ac V pv I Sudden change in voltage ac V  Sudden change in irradiance PV Array Characteristic pv V r I r I pv I Figure 4. A generic method for PV generation. In some of the works, solar PV power plants are modeled as static generators [32–34], neglecting the variations of the power output from the PV system. The analysis of possible impacts of output power fluctuation of PV systems with a detailed model is addressed in [32]. For simulation of a grid scale interconnected system, usually, a simplified model or models based on transfer functions are applied [10,33]. Moreover, it should be noted that when a large-scale PV system acts as a renewable power plant, it is very important to find a criterion for selecting the usual patterns for PV output power during each specific season for using the analysis related to system power flow to show carefully the influences of variations on the power grid [35]. The simulation of large system dynamics using a single converter equivalent is adequate for most studies. Regarding the control of the PV system, several MPPT approaches have been reported in the literature. Numerous MPPT algorithms are explained and discussed in [16–19] focusing on duty cycle control in DC/DC inverters. The main methodologies used for MPPT are: the open circuit voltage approach, constant voltage (CV), incremental conductance (IC) method, ripple-based method and the perturb and observe (called P&O) technique [20–23] and [25,26]. These MPPT techniques differ from each other in several aspects such as simplicity, required measurements, convergence speed, effectiveness, cost and parameterization. P&O and IC techniques are the most widely used. The P&O algorithm works by perturbing the array terminal voltage. It is based on the observation of the sign of the equation 𝑑𝑃/𝑑𝑉𝑃𝑉 . The perturbation is done periodically and the output power of the PV system is compared with the obtained values form the previous cycles [23,25,26,31]. If the power increases 𝑑𝑃/𝑑𝑉𝑃𝑉 > 0 , the control system changes the operating point of the system in that direction. If 𝑑𝑃/𝑑𝑉𝑃𝑉 < 0 , the operating point has changed in the opposite direction. This algorithm continues until it reaches the MPP. When the system is close to its MPP, then the output power oscillates around the maximum value. This issue might bring some power losses in the PV system. The model schematic shown in Figure 4 uses P&O, but in this case IPv is adjusted for the MPPT. The incremental conductance (IC) method is another approach which due to its high tracking accuracy at the steady state is widely used for different applications. In this approach there is a good flexibility to deal with rapidly changing atmospheric conditions [23]. This algorithm works according to the observation of the equation 𝑑𝐼𝑃𝑉/𝑑𝑉𝑃𝑉 +𝐼𝑃𝑉 𝑉𝑃𝑉 < 0 which has to be held at the MPP value, while 𝐼𝑃𝑉 Figure 4. A generic method for PV generation. In some of the works, solar PV power plants are modeled as static generators [ 32 – 34 ], neglecting the variations of the power output from the PV system. The analysis of possible impacts of output power fluctuation of PV systems with a detailed model is addressed in [ 32 ]. For simulation of a grid scale interconnected system, usually, a simplified model or models based on transfer functions are applied [ 10 , 33 ]. Moreover, it should be noted that when a large-scale PV system acts as a renewable power plant, it is very important to find a criterion for selecting the usual patterns for PV output power during each specific season for using the analysis related to system power flow to show carefully the influences of variations on the power grid [ 35 ]. The simulation of large system dynamics using a single converter equivalent is adequate for most studies. Regarding the control of the PV system, several MPPT approaches have been reported in the literature. Numerous MPPT algorithms are explained and discussed in [ 16 – 19 ] focusing on duty cycle control in DC/DC inverters. The main methodologies used for MPPT are: the open circuit voltage approach, constant voltage (CV), incremental conductance (IC) method, ripple-based method and the perturb and observe (called P&O) technique [ 20 – 23 ] and [ 25 , 26 ]. These MPPT techniques differ from each other in several aspects such as simplicity, required measurements, convergence speed, effectiveness, cost and parameterization. P&O and IC techniques are the most widely used. The P&O algorithm works by perturbing the array terminal voltage. It is based on the observation of the sign of the equation dP/dVPV . The perturbation is done periodically and the output power of the PV system is compared with the obtained values form the previous cycles [ 23 , 25 , 26 , 31 ]. If the power increases dP/dVPV > 0, the control system changes the operating point of the system in that direction. If dP/dVPV < 0, the operating point has changed in the opposite direction. This algorithm continues until it reaches the MPP. When the system is close to its MPP, then the output power oscillates around the maximum value. This issue might bring some power losses in the PV system. The model schematic shown in Figure 4uses P&O, but in this case IPv is adjusted for the MPPT. The incremental conductance (IC) method is another approach which due to its high tracking accuracy at the steady state is widely used for different applications. In this approach there is a good flexibility to deal with rapidly changing atmospheric conditions [ 23 ]. This algorithm works according Energies 2019,12, 1425 6 of 19 to the observation of the equation dIPV/dVPV +IPV VPV < 0 which has to be held at the MPP value, while IPV and VPV will be the current and voltages of the PV array. Therefore, the MPP can be easily tracked with comparing the instantaneous value of the conductance IPV/VPV to the incremental conductance dIPV/dVPV . Thus, as soon as the MPP has been reached, the operation of the PV system will be maintained at this point and the perturbation procedure will be stopped except if another change happens in dIPV. Inside the DC/AC converter, a PWM-based control technique is used for regulating the amount of exchange of the active power and the reactive power between the PV-based generation and the rest of the grid [ 20 ]. The DC generated power coming from a PV array can be reflected as real power injected into the power grid. MPPT control will be managed by DC/DC part and it will depend on irradiation and environment conditions. By means of PWM control in the DC/AC part power quality issues and grid synchronization will be done. Active and reactive powers will be calculated as shown in Figure 5. In order to utilize overall control, two main procedures such as voltage control and the active power control considering DC voltage values in DC bus will be used [34]. Energies 2019, 12, x FOR PEER REVIEW 6 of 19 and 𝑉𝑃𝑉 will be the current and voltages of the PV array. Therefore, the MPP can be easily tracked with comparing the instantaneous value of the conductance 𝐼𝑃𝑉/𝑉𝑃𝑉 to the incremental conductance 𝑑𝐼𝑃𝑉/𝑑𝑉𝑃𝑉 . Thus, as soon as the MPP has been reached, the operation of the PV system will be maintained at this point and the perturbation procedure will be stopped except if another change happens in 𝑑𝐼𝑃𝑉. Inside the DC/AC converter, a PWM-based control technique is used for regulating the amount of exchange of the active power and the reactive power between the PV-based generation and the rest of the grid [20]. The DC generated power coming from a PV array can be reflected as real power injected into the power grid. MPPT control will be managed by DC/DC part and it will depend on irradiation and environment conditions. By means of PWM control in the DC/AC part power quality issues and grid synchronization will be done. Active and reactive powers will be calculated as shown in Figure 5. In order to utilize overall control, two main procedures such as voltage control and the active power control considering DC voltage values in DC bus will be used [34]. Modified P&O MPPT method P&Q control signal Figure 5. Schematic diagram of a two-stage grid connected PV system. 3. Modern Power System with PV Systems Advances in power processing and control systems, widespread deregulation of power markets with strategic importance dispensed to power supply securities, especially in the last decades, is leading to a new restructuring of electrical power systems. This scenario is moving from a centralized approach to different flexible distributed generation patterns, which has resulted in what are broadly known as modern distributed power systems (DPS) [36]. 3.1. Conventional Power System In conventional power system, the utilities were mostly owned by the government. In the other words, the traditional power system was a kind of vertically integrated utility (VIU) structure which holds and operates the main part of the physical assets, including most of the generating units and transmission lines. The utility has control over all generators and by using optimal power flow can redispatch to respond to any changes. This monopolistic situation is not fair and with a huge increment in the number of distributed generators (DG), a new restructure and deregulation was necessary for power systems [37,38]. 3.2. Power System Deregulation In a competitive electrical grid environment, the VIU does not exist anymore and therefore, the new competitive scheme will be full of generation companies (GENCOs), distribution companies (DISCOs) transmission companies and independent system operators (ISOs) [37] and [39–41]. In this new competitive environment, offering the highest quality electricity and ancillary services is very important. Services such as operating reserves, reactive or voltage regulation and active power/frequency regulation will be extremely essential for supporting renewable-based power integration. This is especially critical with the integration of intermittent resources of energies [42–46]. 3.3. Distributed Power System Distributed power systems (DPS) are devoted to customer load supply, which are geographically distributed in an inherent manner, by using distributed generators and energy storage systems spread Figure 5. Schematic diagram of a two-stage grid connected PV system. 3. Modern Power System with PV Systems Advances in power processing and control systems, widespread deregulation of power markets with strategic importance dispensed to power supply securities, especially in the last decades, is leading to a new restructuring of electrical power systems. This scenario is moving from a centralized approach to different flexible distributed generation patterns, which has resulted in what are broadly known as modern distributed power systems (DPS) [36]. 3.1. Conventional Power System In conventional power system, the utilities were mostly owned by the government. In the other words, the traditional power system was a kind of vertically integrated utility (VIU) structure which holds and operates the main part of the physical assets, including most of the generating units and transmission lines. The utility has control over all generators and by using optimal power flow can re-dispatch to respond to any changes. This monopolistic situation is not fair and with a huge increment in the number of distributed generators (DG), a new restructure and deregulation was necessary for power systems [37,38]. 3.2. Power System Deregulation In a competitive electrical grid environment, the VIU does not exist anymore and therefore, the new competitive scheme will be full of generation companies (GENCOs), distribution companies (DISCOs) transmission companies and independent system operators (ISOs) [ 37 ] and [ 39 – 41 ]. In this new competitive environment, offering the highest quality electricity and ancillary services is very important. Services such as operating reserves, reactive or voltage regulation and active power/frequency regulation will be extremely essential for supporting renewable-based power integration. This is especially critical with the integration of intermittent resources of energies [42–46]. Energies 2019,12, 1425 7 of 19 3.3. Distributed Power System Distributed power systems (DPS) are devoted to customer load supply, which are geographically distributed in an inherent manner, by using distributed generators and energy storage systems spread among distribution networks. Power processing capabilities offered by power electronics have opened new possibilities in the control of DG, which allows implementing ancillary services oriented to compensate the drawbacks of conventional generation units and to improve the dynamic performance of the electrical grid. Among the benefits endorsing DPS [ 47 ], it can be highlighted in their capability to: •Decrease the weaknesses of the electrical power grid. •Support sufficient standby generation for improving the system reliability. •Enhance the regulation mechanism and also for stability of the conventional power grid. •Decrease the environmental impact of power generation. •Offset the costs of properties of new transmission system schemes. It is also worth mentioning here that DPS reduce transmission power losses, as well as the length and total number of transmission lines which have to be built. From the point of view of controllability, DPS allow drawing more flexible and adaptable power systems. 3.4. Operation and Regulation of DPS Despite of their unquestionable potential, distributed power systems (DPS) still face important challenges in several areas before becoming a widespread reality. The growing use of distributed power units based on renewable sources of energy will gradually move toward a highly stochastic scenario that eventually might have a huge effect on the online balance in power exchanges, paving the way for the extensive usage of distributed energy storage systems [48–51]. Today, applying energy storage systems with photovoltaic systems is promising and a lot of attention and various studies are being performed in this field. Therefore, an encouraging research concept which opened new windows in the field of hybrid RE/ESS generation system is the concept of virtual inertia (VI) and virtual synchronous generator (VSG) [ 52 – 58 ]. In the VSG concept, a short term energy storage system should be added next to converters of renewable-based power generation systems. A suitable coordination mechanism and a proper control system between the converter and storage device is essential [ 53 ]. As shown in Figure 6, VSG presents a solution in which the inverter can be controlled in a way that it will behave as a normal synchronous generator. Energies 2019, 12, x FOR PEER REVIEW 7 of 19 among distribution networks. Power processing capabilities offered by power electronics have opened new possibilities in the control of DG, which allows implementing ancillary services oriented to compensate the drawbacks of conventional generation units and to improve the dynamic performance of the electrical grid. Among the benefits endorsing DPS [47], it can be highlighted in their capability to: • Decrease the weaknesses of the electrical power grid. • Support sufficient standby generation for improving the system reliability. • Enhance the regulation mechanism and also for stability of the conventional power grid. • Decrease the environmental impact of power generation. • Offset the costs of properties of new transmission system schemes. It is also worth mentioning here that DPS reduce transmission power losses, as well as the length and total number of transmission lines which have to be built. From the point of view of controllability, DPS allow drawing more flexible and adaptable power systems. 3.4. Operation and Regulation of DPS Despite of their unquestionable potential, distributed power systems (DPS) still face important challenges in several areas before becoming a widespread reality. The growing use of distributed power units based on renewable sources of energy will gradually move toward a highly stochastic scenario that eventually might have a huge effect on the online balance in power exchanges, paving the way for the extensive usage of distributed energy storage systems [48–51]. Today, applying energy storage systems with photovoltaic systems is promising and a lot of attention and various studies are being performed in this field. Therefore, an encouraging research concept which opened new windows in the field of hybrid RE/ESS generation system is the concept of virtual inertia (VI) and virtual synchronous generator (VSG) [52–58]. In the VSG concept, a short term energy storage system should be added next to converters of renewable-based power generation systems. A suitable coordination mechanism and a proper control system between the converter and storage device is essential [53]. As shown in Figure 6, VSG presents a solution in which the inverter can be controlled in a way that it will behave as a normal synchronous generator. Distributed generator Short term energy storage Bi-directional inverter Power grid Control signal Control signal VSG control grid voltage and frequency Figure 6. Principle of the VSG. In modern power system, application of advanced power electronics, FACTS equipment and HVDC systems are becoming more important [59–62]. Moreover, the regular use of different DC line applications may be estimated in the future to fulfil the needs of a deregulated modern power grid [61]. In addition to the exigencies of large-scale integration of DGs, power systems are required to operate under the intensive and competitive conditions set by the deregulated electricity market. This results in a more and more complex operation scheme that forces DPSs to work very close to their limit stability. Thus, it is necessary to make an intensive research effort in this field to apply advanced control methods and new concepts, like active management of distribution networks and smart grids, to enable an efficient and reliable operation of DPS [63–68]. 4. Contribution of Large PV Power Plants to Ancillary Services Figure 6. Principle of the VSG. In modern power system, application of advanced power electronics, FACTS equipment and HVDC systems are becoming more important [ 59 – 62 ]. Moreover, the regular use of different DC line applications may be estimated in the future to fulfil the needs of a deregulated modern power grid [ 61 ]. In addition to the exigencies of large-scale integration of DGs, power systems are required to operate under the intensive and competitive conditions set by the deregulated electricity market. This results in a more and more complex operation scheme that forces DPSs to work very close to their limit stability. Thus, it is necessary to make an intensive research effort in this field to apply advanced Energies 2019,12, 1425 8 of 19 control methods and new concepts, like active management of distribution networks and smart grids, to enable an efficient and reliable operation of DPS [63–68]. 4. Contribution of Large PV Power Plants to Ancillary Services Large PV power plants have vast potential to become an important player in the future power system and studies in this field are quite meritorious. In this part of the paper, the typical effects of contributed grid scale PV plants in the grid will be investigated and reviewed. 4.1. Active Power and Frequency PV-based power generation systems have essentially a different nature compared to conventional synchronous generators. PV-based units have stochastic behaviors under various conditions without inertia and dynamic behavior of this kind of generators is subjected by the features and control methodologies used for their power converter parts [ 69 ]. Under normal conditions, PV generation has a positive effect on power system behavior. However, in some conditions like cloudy environments and fault contingencies, if the total size of a PV system is large enough, frequency problems may arise at the interconnected power systems [ 70 ]. Until now, FACTS controller or PSS, static VAR Compensators and STATCOMs are proposed to improve the power system behavior which may not be always economic [ 71 , 72 ]. It is worth mentioning that the location, degree of penetration and the manner of dispatching the existing conventional generators have a considerable effect and based on these factors the PV plants may have beneficial or detrimental effects on the system. In some cases with adverse effect of PVs, it is recommendable to keep the critical synchronous generators in service, as well as the SVCs for adequate damping of low frequency oscillations [ 69 ]. Alternative power generation sources like P, have a great potential for damping the oscillations in a large power system. Even a single PV-based generating system located at the weakest bus of the grid can still enhance the system loading margins [73]. The damping can be introduced through the high power converters that used as interfaces to the network [ 73 – 75 ]. Essentially, the AC generation units which are coupled with gas turbines must operate the electric power at a synchronous frequency. Moreover, in the distributed generators, the power electronic interfaces must act as a frequency changer as well, that change the DC power (for example, in PV) to the utility standard of 60 or 50 Hz. Bidirectional control and unidirectional control are known as two general methods for the PV contribution on active power/frequency services (Figure 7). Energies 2019, 12, x FOR PEER REVIEW 8 of 19 Large PV power plants have vast potential to become an important player in the future power system and studies in this field are quite meritorious. In this part of the paper, the typical effects of contributed grid scale PV plants in the grid will be investigated and reviewed. 4.1. Active Power and Frequency PV-based power generation systems have essentially a different nature compared to conventional synchronous generators. PV-based units have stochastic behaviors under various conditions without inertia and dynamic behavior of this kind of generators is subjected by the features and control methodologies used for their power converter parts [69]. Under normal conditions, PV generation has a positive effect on power system behavior. However, in some conditions like cloudy environments and fault contingencies, if the total size of a PV system is large enough, frequency problems may arise at the interconnected power systems [70]. Until now, FACTS controller or PSS, static VAR Compensators and STATCOMs are proposed to improve the power system behavior which may not be always economic [71,72]. It is worth mentioning that the location, degree of penetration and the manner of dispatching the existing conventional generators have a considerable effect and based on these factors the PV plants may have beneficial or detrimental effects on the system. In some cases with adverse effect of PVs, it is recommendable to keep the critical synchronous generators in service, as well as the SVCs for adequate damping of low frequency oscillations [69]. Alternative power generation sources like P, have a great potential for damping the oscillations in a large power system. Even a single PV-based generating system located at the weakest bus of the grid can still enhance the system loading margins [73]. The damping can be introduced through the high power converters that used as interfaces to the network [73–75]. Essentially, the AC generation units which are coupled with gas turbines must operate the electric power at a synchronous frequency. Moreover, in the distributed generators, the power electronic interfaces must act as a frequency changer as well, that change the DC power (for example, in PV) to the utility standard of 60 or 50 Hz. Bidirectional control and unidirectional control are known as two general methods for the PV contribution on active power/frequency services (Figure 7). Figure 7. Power control for DGs: (a) Bidirectional; (b) Unidirectional [75]. In the bidirectional method, plant installations and various energy storage technologies will be required to act as a reserve most of the time. However, in unidirectional method, the DG will be acting only in under or over frequency conditions. For example, in the unidirectional method with DGs that just works in its maximum power output, they can contribute in over-frequency conditions by reducing their output power [75]. Applying a coordinated advanced control for PV system in automatic generation control (AGC) will lead to an interesting research field in load-frequency control services [76,77]. AGC or load frequency control (LFC) is essential in power system stability and control analysis. The main goal of the AGC is to eliminate any mismatches between generators and load demand and its concepts are well-known [38–42]. With the increasing trend in displacing the conventional power plants with renewable plants, effects in frequency control will come into sight. Applying various intelligent control methods like fuzzy, neural network and observer methods will be useful for more flexibility [78,79]. Figure 7. Power control for DGs: (a) Bidirectional; (b) Unidirectional [75]. In the bidirectional method, plant installations and various energy storage technologies will be required to act as a reserve most of the time. However, in unidirectional method, the DG will be acting only in under or over frequency conditions. For example, in the unidirectional method with DGs that just works in its maximum power output, they can contribute in over-frequency conditions by reducing their output power [ 75 ]. Applying a coordinated advanced control for PV system in automatic generation control (AGC) will lead to an interesting research field in load-frequency control services [ 76 , 77 ]. AGC or load frequency control (LFC) is essential in power system stability and Energies 2019,12, 1425 9 of 19 control analysis. The main goal of the AGC is to eliminate any mismatches between generators and load demand and its concepts are well-known [ 38 – 42 ]. With the increasing trend in displacing the conventional power plants with renewable plants, effects in frequency control will come into sight. Applying various intelligent control methods like fuzzy, neural network and observer methods will be useful for more flexibility [78,79]. Generally, when a photovoltaic plant stops producing, a conventional power plant will try to replace its place in the electricity grid. This situation will occur in case of a sudden change in irradiation, if the rate of irradiation drops is within certain limits such that there is enough time for governors to respond to it and otherwise in a worst case scenario where a UFLS relay may discard some loads. The frequency relay will activate when the frequency reaches a critical value around 59.7 Hz [ 80 ]. During a fault condition, the response of the PV-based generation units will not show oscillations. This is due to the fact that they do have any mechanical parts and therefore they settle down much faster than conventional generators [80]. 4.2. Rotor Angle Stability It should be noted that the oscillatory stability is related to the categories which is called rotor angle stability. Generally, oscillatory instability in low frequency ranges will be caused due to the lack of enough damping torque. This damping in conventional generation is primarily given by the damper winding of machines [ 81 ]. Generally, there are two types of oscillatory instability, local and global. Local instabilities will involve a minor area, and typically are due to rotor angle oscillations of each generator against the other part of the system. This oscillations are usually called local plant mode oscillations [ 27 , 38 ]. Global oscillations are usually caused by interactions between major groups of generators that are usually expanded in a very large interconnected area. Global oscillations will have widespread effects and it may lead to some partial or full black out in the system. In such scenario, a group of generation units in one area will swing against another set of generation units in the neighbor area. These issues are known as inter area oscillations. Application of HVDC links in parallel with AC links can improve these oscillations [62]. Based on eigenvalue analysis, a system with PV generators will improve dynamic responses by shifting the critical modes to the left half plan and can enhance the dynamic stability of the interconnected power grid by adding more damping over critical modes [ 27 ]. In several reports the local and global oscillations can be improved using different technique for PV plant control. As reported in in [ 82 ], the POD at PV is designed by using wide-area signal. The damping ratios of the local modes are slightly increased by the integration of POD at PV, while on [ 83 , 84 ] the global modes are improved for systems with high penetration of PV power plants. In case of small signal stability analysis for PV effects, there is a limit on the operation of the PV based power generation, as far as the system oscillation stability is of concern [ 85 ]. The influence of the PV-based penetration on the grid oscillations and its dynamic stability will vary according to the changes in the system operating’s condition, which is due to the effects of damping torque impact from the PV system which can be positive or negative. The most serious operating condition for the PV-based power generation unit will arise when the sign of the damping torque’s contribution of the PV system is changing [83–85]. 4.3. Reactive Power and Voltage The existing interconnected grid is not completely designed for large scale support of interconnected PV and any kind of change in voltage limits during the high solar irradiation is possible. A possible solution for reducing the voltage rise in the feeder, is to operate PV-based generation units with the ability of providing reactive power [ 86 ]. In addition, in the case of voltage collapse the inverter has to be able to support sufficient reactive current and stabilize the grid within some time frames defined by grid codes. It should be noted that, the total power generated by the PV generation units follows carefully the pattern of irradiance (due to the MPPT control). Considering the Energies 2019,12, 1425 16 of 19 35. Dahal, S.; Mithulananthan, N.; Saha, T. An approach to control a photovoltaic generator to damp low frequency oscillations in an emerging distribution system. In Proceedings of the IEEE Power and Energy Society General Meeting, Detroit, MI, USA, 24–28 July 2011; pp. 1–8. 36. Lee, F.C.; Barbosa, P.; Xu, P.; Zhang, J.; Yang, B.; Canales, F. Topologies and design considerations for distributed power system applications. Proc. IEEE 2001,89, 939–950. [CrossRef] 37. Saez-de-Ibarra, A.; Milo, A.; Gaztañaga, H.; Debusschere, V.; Bacha, S. Co-Optimization of Storage System Sizing and Control Strategy for Intelligent Photovoltaic Power Plants Market Integration. IEEE Trans. Sustain. Energy 2016,7, 1749–1761. [CrossRef] 38. Lai, L.L. Power System Restructuring and Deregulation, Trading, Performance and Information Technology; John Wiley & Sons, Ltd.: London, UK, 2001. 39. Kundur, P. Power System Stability and Control; McGraw-Hill: New York, NY, USA, 1994. 40. Rakhshani, E. Intelligent linear-quadratic optimal output feedback regulator for a deregulated automatic generation control system. Electr. Power Compon. Syst. 2012,40, 513–533. [CrossRef] 41. Rakhshani, E.; Sadeh, J. Practical viewpoints on load frequency control problem in a deregulated power system. Energy Convers. Manag. 2010,51, 1148–1156. [CrossRef] 42. Rakhshani, E.; Rodriguez, P. Active Power and Frequency Control Considering Large Scale RES. In Large Scale Renewable Power Generation; Springer-Verlag: Berlin/Heidelberg, Germany, 2014; pp. 233–271. 43. Chamana, M.; Chowdhury, B.H.; Jahanbakhsh, F. Distributed Control of Voltage Regulating Devices in the Presence of High PV Penetration to Mitigate Ramp-Rate Issues. IEEE Trans. Smart Grid 2018 ,9, 1086–1095. [CrossRef] 44. UCTE Operation Handbook, v 2.5E; UCTE, July 2004. 45. Wang, G.; Ciobotaru, M.; Agelidis, V.G. Power Management for Improved Dispatch of Utility-Scale PV Plants. IEEE Trans. Power Syst. 2016,31, 2297–2306. [CrossRef] 46. Rebours, Y.G.; Kirschen, D.S.; Trotignon, M.; Rossignol, S. Survey of Frequency and Voltage Control Ancillary Services—Part I: Technical Features. IEEE Trans. Power Syst. 2007,22. [CrossRef] 47. Corsi, S.; Marannino, P.; Losignore, N.; Moreschini, G.; Piccini, G. Coordination between the reactive power scheduling function and the hierarchical voltage control of the EHV ENEL system. IEEE Trans. Power Syst. 1995,10, 686–694. [CrossRef] 48. An Agreement Report of Brookings and Hoover Institutions: Assessing the Role of Distributed Power Systems in the U.S. Power Sector., The Brookings Institution Energy Security Initiative, USA. 2011. Available online: http://www.recycled-energy.com/images/uploads/Brookings_Hoover_DPS_study.pdf (accessed on 30 August 2018). 49. Papaefthymiou, S.V.; Karamanou, E.G.; Papathanassiou, S.A.; Papadopoulos, M.P. A Wind-Hydro-Pumped Storage Station Leading to High RES Penetration in the Autonomous Island System of Ikaria. IEEE Trans. Sustain. Energy 2010,1, 163–172. [CrossRef] 50. Taylor, J.; Halnes, A. Analysis Of compressed air energy storage. In Proceedings of the PCIC Europe, Oslo, Norway, 15–17 June 2010; pp. 1–5. 51. Bhatt, R.; Chowdhury, B. Grid frequency and voltage support using PV systems with energy storage. In Proceedings of the North American Power Symposium (NAPS), Boston, MA, USA, 4–6 August 2011; pp. 1–6. 52. Kang, B.-K.; Kim, S.-T.; Sung, B.-C.; Park, J.-W. A Study on Optimal Sizing of Superconducting Magnetic Energy Storage in Distribution Power System. IEEE Trans. Appl. Supercond. 2012,22, 5701004. 53. Mehrasa, M.; Pouresmaeil, E.; Sepehr, A.; Pournazarian, B.; Marzband, M.; Catal ã o, J.P.S. Control technique for the operation of grid-tied converters with high penetration of renewable energy resources. Electr. Power Syst. Res. 2019,166, 18–28. [CrossRef] 54. Mehrasa, M.; Pouresmaeil, E.; Soltani, H.; Blaabjerg, F.; Calado, M.R.A.; Catal ã o, J.P.S. Virtual Inertia and Mechanical Power-Based Control Strategy to Provide Stable Grid Operation under High Renewables Penetration. Appl. Sci. 2019,9, 1043–1058. [CrossRef] 55. Wang, L.; Vo, Q.; Prokhorov, A.V. Stability Improvement of a Multimachine Power System Connected with a Large-Scale Hybrid Wind-Photovoltaic Farm Using a Supercapacitor. IEEE Trans. Ind. Appl. 2018 ,54, 50–60. [CrossRef] 56. Chen, S.; Zhang, T.; Gooi, H.B.; Masiello, R.D.; Katzenstein, W. Penetration Rate and Effectiveness Studies of Aggregated BESS for Frequency Regulation. IEEE Trans. Smart Grid 2016,7, 167–177. [CrossRef] Energies 2019,12, 1425 17 of 19 57. Bi, D.; Wang, S.; Ge, B.; Yang, X. Control strategy of grid-connected photovoltaic system with energy storage. In Proceedings of the International Conference on Electrical Machines and Systems, Beijing, China, 20–23 August 2011; pp. 1–4. 58. Delille, G.; François, B.; Malarange, G. Dynamic frequency control support: A virtual inertia provided by distributed energy storage to isolated power systems. In Proceedings of the IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe), Gothenburg, Sweden, 11–13 October 2010; pp. 1–8. 59. Wang, D.; Meng, K.; Gao, X.; Qiu, J.; Lai, L.L.; Dong, Z.Y. Coordinated Dispatch of Virtual Energy Storage Systems in LV Grids for Voltage Regulation. IEEE Trans. Ind. Inform. 2018,14, 2452–2462. [CrossRef] 60. Hulpin, Y.; Ernst, D. Ancillary services and operation of multi-terminal HVDC grids. In Proceedings of the International Workshop on Transmission Networks for Offshore Wind Power Plants, Aarhus, Denmark, 25–26 October 2011; pp. 1–6. 61. Zhu, J.; Guerrero, J.M.; Hung, W.; Booth, C.D.; Adam, G.P. Generic inertia emulation controller for multi-terminal voltage-source-converter high voltage direct current systems. IET Renew. Power Gener. 2014 , 8, 740–748. [CrossRef] 62. Povh, D. Use of HVDC and FACTS. Proc. IEEE 2000,88, 235–245. [CrossRef] 63. Dai, J.; Phulpin, Y.; Sarlette, A.; Ernst, D. Coordinated primary frequency control among non-synchronous systems connected by a multi-terminal high-voltage direct current grid Generation. IET Transm. Distrib. 2012,6, 99–108. [CrossRef] 64. Conti, S.; Nicolosi, R.; Rizzo, S.A. Generalized Systematic Approach to Assess Distribution System Reliability with Renewable Distributed Generators and Microgrids. IEEE Trans. Power Deliv. 2012 ,27, 261–270. [CrossRef] 65. Stimoniaris, D.; Tsiamitros, D.; Dialynas, E. Improved Energy Storage Management and PV-Active Power Control Infrastructure and Strategies for Microgrids. IEEE Trans. Power Syst. 2016,31, 813–820. [CrossRef] 66. Podmore, R.; Robinson, M.R. The Role of Simulators for Smart Grid Development. IEEE Trans. Smart Grid 2010,1, 205–212. [CrossRef] 67. Ipakchi, A.; Albuyeh, F. Grid of the future. IEEE Power Energy. Mag. 2009,7, 52–62. [CrossRef] 68. Kanchev, H.; Lu, D.; Colas, F.; Lazarov, V.; Francois, B. Energy Management and Operational Planning of a Microgrid with a PV-Based Active Generator for Smart Grid Applications. IEEE Trans. Ind. Electron. 2011 ,58, 4583–4592. [CrossRef] 69. Nguyen, C.P.; Flueck, A.J. Agent Based Restoration with Distributed Energy Storage Support in Smart Grids. IEEE Trans. Smart Grid 2012,3, 1029–1038. [CrossRef] 70. Liu, H.; Jin, L.; Le, D.; Chowdhury, A.A. Impact of high penetration of solar photovoltaic generation on power system small signal stability. In Proceedings of the International Conference on Power System Technology (POWERCON), Hangzhou, China, 24–28 October 2010; pp. 1–7. 71. Shah, R.; Mithulananthan, N.; Sode-Yome, A.; Lee, K.Y. Impact of large-scale PV penetration on power system oscillatory stability. In Proceedings of the IEEE Power and Energy Society General Meeting, Minneapolis, MN, USA, 25–29 July 2010; pp. 1–7. 72. Noroozian, N.; Andersson, G. Damping of Power System Oscillations by Use of Controllable Components. IEEE Trans. Power Deliv. 1994,9, 2046–2054. [CrossRef] 73. Galrmos, G.D.; Hatziadoniu, C.; Cheng, X.J.; Maratukuron, D. Advanced Static Compensation for Flexible AC Transmission. IEEE Trans. Power Syst. 1993,8, 113–121. 74. Shah, R.; Mithulananthan, N. Interconnection of PV based generator and its impact on Bangladesh power system stability. In Proceedings of the International Conference on the Developments in Renewable Energy Technology (ICDRET), Dhaka, Bangladesh, 17–19 December 2009; pp. 1–6. 75. Yu, S.; Zhang, L.; Iu, H.H.; Fernando, T.; Wong, K.P. A DSE-Based Power System Frequency Restoration Strategy for PV-Integrated Power Systems Considering Solar Irradiance Variations. IEEE Trans. Ind. Inform. 2017,13, 2511–2518. [CrossRef] 76. De Leon, F.; Boon-Teck, O. Damping power system oscillations by unidirectional control of alternative power generation plants. In Proceedings of the IEEE Power Engineering Society Winter Meeting 2001, Columbus, OH, USA, 28 January–1 February 2010; pp. 747–752. 77. Keyhani, A.; Chatterjee, A. Automatic Generation Control Structure for Smart Power Grids. IEEE Trans. Smart Grid 2012,3, 1310–1316. [CrossRef] 78. Bevrani, H.; Ghosh, A.; Ledwich, G. Renewable energy sources and frequency regulation: Survey and new perspectives. IET Renew. Power Gener. 2010,4, 438–457. [CrossRef] Energies 2019,12, 1425 18 of 19 79. Datta, M.; Senjyu, T.; Yona, A.; Funabashi, T. Minimal-order observer-based coordinated control method for isolated power utility connected multiple photovoltaic systems to reduce frequency deviations. IET Renew. Power Gener. 2010,4, 153–164. [CrossRef] 80. Yu, Y.N. Electric Power System Dynamics; Academic Press: London, UK, 1983. 81. Du, W.; Wang, H.; Xiao, L.-Y. Power system small-signal stability as affected by grid-connected photovoltaic generation. Eur. Trans. Electr. Power 2011,22, 688–703. [CrossRef] 82. Shah, R.; Mithulananthan, N.; Lee, K.Y. Large-Scale PV Plant with a Robust Controller Considering Power Oscillation Damping. IEEE Trans. Energy Convers. 2013,28, 106–116. [CrossRef] 83. Zhou, L.; Yu, X.; Li, B.; Zheng, C.; Liu, J.; Liu, Q.; Guo, K. Damping Inter-Area Oscillations with Large-Scale PV Plant by Modified Multiple-Model Adaptive Control Strategy. IEEE Trans. Sustain. Energy 2017 ,8, 1629–1636. [CrossRef] 84. You, S.; Kou, G.; Liu, Y.; Zhang, X.; Cui, Y.; Till, M.J.; Yao, W.; Liu, Y. Impact of High PV Penetration on the Inter-Area Oscillations in the U.S. Eastern Interconnection. IEEE Access 2017,5, 4361–4369. [CrossRef] 85. Datta, M.; Senjyu, T.; Yona, A.; Funabashi, T.; Kim, C.-H. A frequency-control approach by photovoltaic generator in a PV-diesel hybrid power system. IEEE Trans. Energy Convers. 2011,26, 559–571. [CrossRef] 86. Alquthami, T.; Ravindra, H.; Faruque, M.O.; Steurer, M.; Baldwin, T. Study of photovoltaic integration impact on system stability using custom model of PV arrays integrated with PSS/E. In Proceedings of the North American Power Symposium (NAPS), Arlington, TX, USA, 26–28 September 2010; pp. 1–8. 87. Brown, M.; Stetz, T.; Reimann, T.; Valov, B.; Arnold, G. Optimal reactive power supply in distribution networks-Technological and economical assessment for PV-Systems. In Proceedings of the 24th European Photovoltaic Solar Energy Conference and Exhibition, Hamburg, Germany, 21–25 September 2009. 88. Tan, Y.T.; Kirschen, D.S. Impact on the Power System of a Large Penetration of Photovoltaic Generation. In Proceedings of the IEEE Power Engineering Society General Meeting, Galgary, AB, Canada, 24–28 June 2007; pp. 1–8. 89. Katiraei, F.; Mauch, K.; Dignard-Bailey, L. Integration of photovoltaic power system in high penetration cluster for distribution networks and mini-grids. Int. J. Distrib. Energy Resour. 2007,3, 207–223. 90. Teleke, S.; Jahanbakhsh, F.; Katiraei, F.; Aguero, J.R. Analysis of interconnection of photovoltaic distributed generation. In Proceedings of the IEEE Industry Applications Society Annual Meeting (IAS), Orlando, FL, USA, 9–13 October 2011; pp. 1–6. 91. Canova, A.; Giaccone, L.; Spertino, F.; Tartaglia, M. Electrical Impact of Photovoltaic Plant in Distributed Network. IEEE Trans. Ind. Appl. 2009,45, 341–347. [CrossRef] 92. Refaat, S.S.; Abu-Rub, H.; Sanfilippo, A.P.; Mohamed, A. Impact of grid-tied large-scale photovoltaic system on dynamic voltage stability of electric power grids. IET Renew. Power Gener. 2018,12, 157–164. [CrossRef] 93. Brown, R.E.; Freeman, L.A. Analyzing the Reliability Impact of Distributed Generation. In Proceedings of the 2001 Power Engineering Society Summer Meeting, Vancouver, BC, Canada, 15–19 July 2001; pp. 1013–1018. 94. Chowdhury, B.H. Effect of central station photovoltaic plant on power system security. In Proceedings of the 21st IEEE Photovoltaic Specialist Conference, Kissimmee, FL, USA, 21–25 May 1990. 95. Kongtonpisan, S.; Chaitusaney, S. Impact of Grid-Connected Photovoltaic System on total system losses with consideration of capacitor bank setting using Genetic algorithm. In Proceedings of the 8th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Khon Kaen, Thailand, 17–20 May 2011; pp. 885–888. 96. Aziz, T.; Dahal, S.; Mithulananthan, N.; Saha, T.K. Impact of widespread penetrations of renewable generation on distribution system stability. In Proceedings of the International Conference on Electrical and Computer Engineering (ICECE), Dhaka, Bangladesh, 8–10 September 2010; pp. 338–341. 97. Baghzouz, Y. Voltage Regulation and Overcurrent Protection Issues in Distribution Feeders with Distributed Generation—A Case Study. In Proceedings of the 38th Hawaii International Conference on System Sciences, Big Island, HI, USA, 3–6 January 2005. 98. Shang, Y. Resilient Multiscale Coordination Control against Adversarial Nodes. Energies 2018 ,11, 1844. [CrossRef] 99. Lim, S.H.; Kim, J.S.; Kim, M.H.; Kim, J.C. Improvement of Protection Coordination of Protective Devices Through Application of a SFCL in a Power Distribution System with a Dispersed Generation. IEEE Trans. Appl. Supercond. 2012,22. Energies 2019,12, 1425 19 of 19 100. Fusco, F.; Nolan, G.; Ringwood, J.V. Variability reduction through optimal combination of wind/wave resources—An Irish case study. Energy 2010,35, 314–325. [CrossRef] 101. Rafique, S.F.; Zhang, J. Energy management system, generation and demand predictors: A review. IET Gener. Transm. Distrib. 2018,12, 519–530. [CrossRef] 102. Dokopoulos, P.S.; Saramourtsis, A.C.; Bakirtzis, A.G. Prediction and evaluation of the performance of wind-diesel energy systems. IEEE Trans. Energy Convers. 1996,11, 385–393. [CrossRef] 103. Byun, J.; Hong, I.; Kang, B.; Park, S. A Smart Energy Distribution and Management System for Renewable Energy Distribution and Context-aware Services based on User Patterns and Load Forecasting. IEEE Trans. Consum. Electron. 2011,57. [CrossRef] 104. Boehme, T.; Wallace, A.R.; Harrison, G.P. Applying Time Series to Power Flow Analysis in Networks with High Wind Penetration. IEEE Trans. Power Syst. 2007,22, 951–957. [CrossRef] 105. Kramer, O.; Gieseke, F. Analysis of wind energy time series with kernel methods and neural networks. In Proceedings of the Seventh International Conference on Natural Computation (ICNC), Shanghai, China, 26–28 July 2011; Volume 4, pp. 2381–2385. 106. Nejabatkhah, F.; Danyali, S.; Hosseini, S.H.; Sabahi, M.; Niapour, S.M. Modeling and Control of a New Three-Input DC–DC Boost Converter for Hybrid PV/FC/Battery Power System. IEEE Trans. Power Electron. 2012,27, 2309–2324. [CrossRef] 107. Pelacchi, P.; Poli, D. The influence of wind generation on power system reliability and the possible use of hydrogen storages. Electr. Power Syst. Res. 2010,80, 249–255. [CrossRef] 108. Ilic, M.; Black, J.W.; Prica, M. Distributed electric power systems of the future: Institutional and technological drivers for near-optimal performance. Electr. Power Syst. Res. 2007,77, 1160–1177. [CrossRef] 109. Kato, T.; Inoue, T.; Suzuoki, Y. Impact of large-scale penetration of photovoltaic power generation systems on fluctuation property of electricity load. In Proceedings of the IEEE PES Transmission and Distribution Conference and Exposition, São Paulo, Brazil, 8–10 November 2010; pp. 1–6. 110. Morjaria, M.; Anichkov, D.; Chadliev, V.; Soni, S. A Grid-Friendly Plant: The Role of Utility-Scale Photovoltaic Plants in Grid Stability and Reliability. IEEE Power Energy Mag. 2014,12, 87–95. [CrossRef] 111. Shahidirad, N.; Niroomand, M.; Hooshmand, R.A. Investigation of PV Power Plant Structures Based on Monte Carlo Reliability and Economic Analysis. IEEE J. Photovolt. 2018,8, 825–833. [CrossRef] 112. Eriksson, R.; Modig, N.; Elkington, K. Synthetic inertia versus fast frequency response: A definition. IET Renew. Power Gener. 2018,12, 507–514. [CrossRef] 113. Beck, H.P.; Hesse, R. Virtual synchronous machine. In Proceedings of the EPQU, Barcelona, Spain, 9–11 October 2007; pp. 1–6. 114. Hesse, R.; Beck, H.; Turschner, D. Conditioning Device for Energy Supply Networks. US8510090B2. 2007. 115. Gao, F.; Iravani, M.R. A control strategy for a distributed generation unit in grid-connected and autonomous modes of operation. IEEE Trans. Power Deliv. 2008,23, 850–859. 116. Van Wesenbeeck, M.P.N.; de Haan, S.W.H.; Varela, P.; Visscher, K. Grid tied converter with virtual kinetic storage. In Proceedings of the IEEE PowerTech, Bucharest, Romania, 28 June–2 July 2009; pp. 1–7. 117. Vrana, T.K.; Hille, C. A novel control method for dispersed converters providing dynamic frequency response. Electr. Eng. 2011,93, 217–226. [CrossRef] 118. Rodriguez, P.; Candela, J.I.; Rocabert, J.; Teodorescu, R. Synchronous Power Controller for a Generating System Based on Static Power Converters. International Patent WO 2012/117 131 A1, 7 September 2012. 119. D’Arco, S.; Suul, J.A.; Fosso, O.B. Control system tuning and stability analysis of Virtual Synchronous Machines. In Proceedings of the 2013 IEEE Energy Conversion Congress and Exposition (ECCE 2013), Denver, Colorado, 15–19 September 2013; pp. 2664–2671. 120. Zhong, Q.C.; Weiss, G. Synchronverters: Inverters that mimic synchronous generators. IEEE Trans. Ind. Electron. 2011,58, 1259–1267. [CrossRef] 121. Sakimoto, K.; Miura, Y.; Ise, T. Stabilization of a power system with a distributed generator by a Virtual Synchronous Generator function. In Proceedings of the ICPE 2011-ECCE Asia, Jeju Island, Korea, 30 May–3 June 2011; pp. 1498–1505. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).