Modeling and Control Dynamics in Microgrid Systems with Renewable Energy Resources

Modelling and Control Dynamics in Microgrid Systems with Renewable Energy Resources looks at complete microgrid systems integrated with renewable energy resources (RERs) such as solar, wind, biomass or fuel cells that facilitate remote applications and allow access to pollution-free energy. Designed and dedicated to providing a complete package on microgrid systems modelling and control dynamics, this book elaborates several aspects of control systems from classical approach to advanced techniques based on artificial intelligence. It captures the typical modes of operation of microgrid systems with distributed energy storage applications like battery, flywheel, electrical vehicles infrastructures that are integrated within microgrids with desired targets. More importantly, the techno-economics of these microgrid systems are well addressed to accelerate the process of achieving the SDG7 i.e., affordable and clean energy for all (E4ALL). This reference presents the latest developments including step by step modelling processes, data security and standards protocol for commissioning of microgrid projects, making this a useful tool for researchers, engineers and industrialists wanting a comprehensive reference on energy systems models.

• Includes simulations with case studies and real-world applications of energy system models
• Detailed systematic modeling with mathematical analysis is covered
• Features possible operating scenarios with solutions to the encountered issues

• Language: English
• Publisher: Academic Press
• Release date: Nov 23, 2023
• ISBN: 9780323909907

Book preview

Front Cover for Modeling and Control Dynamics in Microgrid Systems with Renewable Energy Resources - 1st edition - by Ramesh C. Bansal, Jackson J. Justo, Francis A. Mwasilu

Modeling and Control Dynamics in Microgrid Systems with Renewable Energy Resources

Edited by

Ramesh C. Bansal

Department of Electrical Engineering, University of Sharjah, Sharjah, United Arab Emirates

Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria, South Africa

Jackson J. Justo

Department of Electrical Engineering, College of Engineering and Technology, University of Dar es Salaam, Dar es Salaam, Tanzania

Francis A. Mwasilu

Department of Electrical Engineering, College of Engineering and Technology, University of Dar es Salaam, Dar es Salaam, Tanzania

Table of Contents

Cover image

Title page

Copyright

List of contributors

Section I: Fundamentals of microgrids

Chapter One. Overview of renewable energy power system dynamics

Abstract

1.1 Introduction

1.2 Components of renewable energy and dynamics

1.3 Modeling of solar PV

1.4 Modeling of wind energy conversion systems

1.5 Geothermal energy

1.6 Modeling of hydropower plant

1.7 Modeling of DC–DC converters

1.8 Causes of low inertia and effects to grid

1.9 Power system inertia and frequency stability

1.10 Use of DSTATCOM for RE high penetration grids

1.11 Conclusion

References

Chapter Two. Conceptual framework of microgrid and virtual power plants with renewable energy resources

Abstract

2.1 Introduction

2.2 Introducing the POET framework

2.3 Technology perspective

2.4 Equipment perspective

2.5 Operation perspective

2.6 Performance perspective

2.7 Conclusion and recommendation

References

Chapter Three. Overview of optimal operations of renewable energy power systems in microgrid and virtual power plants

Abstract

3.1 Introduction

3.2 Microgrid and virtual power plants

3.3 Virtual power plant

3.4 Scheduling problem of grid rich with renewables

3.5 Optimization criterion for hybrid distributed energy resources

3.6 Virtual inertia inverter

3.7 Stand-alone applications

3.8 Stability issues

3.9 Conclusion and recommendation

References

Chapter Four. Hybrid microgrids: architecture, modeling, limitations, and solutions

Abstract

4.1 Introduction of microgrids

4.2 Types of microgrids

4.3 Architecture and operation of hybrid AC/DC microgrid

4.4 Modeling of hybrid AC/DC microgrid

4.5 Implementation challenges and solutions of hybrid AC/DC microgrid

4.6 Technical challenges

4.7 Conclusions

References

Chapter Five. Techno-economic analysis of renewable integrated power system for enhanced resilience

Abstract

5.1 Introduction

5.2 Optimal placement framework

5.3 Solution methodology

5.4 Case study results

5.5 Conclusion

Appendix

References

Chapter Six. Techno-economic analysis of renewable power systems

Abstract

Nomenclature

Indices

Abbreviation

6.1 Introduction

6.2 Cost, environmental, and operational aspects of renewable power systems

6.3 Metrics used for techno-economic analysis of renewable power systems

6.4 Internal rate of return of investment on renewable capacity

6.5 Payback period of investment in renewable capacity

6.6 Techno-economic assessment of renewable systems in the literature

6.7 Conclusion

References

Section II: Modeling and control of microgrids

Chapter Seven. Comprehensive discussions on energy storage devices: modeling, control, stability analysis with renewable energy resources in microgrid and virtual power plants

Abstract

7.1 Introduction to microgrids

7.2 Energy storage technologies

7.3 Classification of energy storage systems

7.4 Electrostatic and magnetic energy storage

7.5 Supermagnetic energy storage

7.6 Modeling of SMESS

7.7 Electrochemical energy storage

7.8 Comparison among the energy storage systems

7.9 Applications of energy storage in microgrids

7.10 Application of BESS in the enhancement of frequency and voltage stability of a microgrid

7.11 Industrial energy storage solutions—case study

7.12 Conclusion

References

Chapter Eight. Direct current microgrid systems with classical control techniques

Abstract

8.1 Introduction

8.2 Primary controller

8.3 Secondary controller

8.4 Conclusion

References

Chapter Nine. Modeling and control dynamics of microgrid with renewable energy systems

Abstract

9.1 Introduction

9.2 Dynamic modeling of doubly fed induction generator–based wind turbine for microgrid application

9.3 Control design of stand-alone doubly fed induction generator–based wind power system

9.4 DFIG's system control strategy

References

Chapter Ten. Modeling of an isolated microgrid supplying continuous power at stable voltage using a novel pumped hydro storage system with solar energy

Abstract

10.1 Introduction

10.2 Energy storage devices

10.3 PHS with novel operational methodology

10.4 System modeling

10.5 Technological aspects

10.6 Design and components selection

10.7 Case study

10.8 Results and discussion

10.9 Economical analysis

10.10 Applications of present microgrid

10.11 Conclusions

References

Chapter Eleven. Fault ride through/low voltage ride through capability of doubly fed induction generator–based wind energy conversion system: a comprehensive review

Abstract

11.1 Introduction

11.2 Secondary/auxilary hardware to enhance FRT

11.3 Control approaches to enhance FRT

11.4 Conclusion

References

Chapter Twelve. Fault ride through techniques based on hardware circuits for DFIG based wind turbines

Abstract

12.1 Introduction

12.2 Modeling and control of doubly fed induction generator

12.3 Simulation results and discussion

12.4 Example of parameters optimization for hardware-based low-voltage ride-through scheme

12.5 Determination of fitness function

12.6 Synthesis of genetic algorithm optimization

12.7 Optimization using a genetic algorithm approach

12.8 Systematic determination of a genetic algorithm solution

12.9 Performance validation and discussions

Appendix A

References

Chapter Thirteen. Microgrid system design, modeling, and simulation

Abstract

13.1 Introduction

13.2 Microgrid grid system

13.3 Distributed energy resources

13.4 Microgrid system

13.5 Microgrid software simulation and implementation

13.6 Solved and unsolved problems on protective relaying system

13.7 Conclusion

Acknowledgments

References

Chapter Fourteen. Data security and privacy, cyber-security enhancement, and systems recovery approaches for microgrid networks

Abstract

14.1 Introduction

14.2 Microgrid overview

14.3 Microgrids as cyber-physical systems

14.4 Role of data security and privacy in microgrids

14.5 Cyber-attack risks and vulnerabilities in microgrids

14.6 Desirable microgrid data qualities for enhanced cyber-security

14.7 Approaches to enhancing microgrid cyber-security

14.8 Microgrid recovery systems

14.9 Conclusion

References

Index

Copyright

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List of contributors

Ramesh C. Bansal

Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria, South Africa

Department of Electrical Engineering, University of Sharjah, Sharjah, United Arab Emirates

Kalyan Chatterjee,     Electrical Engineering Department, Indian Institute of Technology (ISM), Dhanbad, Jharkhand, India

Ehsan Haghi,     Musqueam Indian Band, Vancouver, BC, Canada

Jackson J. Justo,     Department of Electrical Engineering, College of Engineering and Technology, University of Dar es Salaam, Dar es Salaam, Tanzania

Neeraj Kanwar,     Manipal University Jaipur, Jaipur, Rajasthan, India

Senthil Krishnamurthy,     Cape Peninsula University of Technology, Center for Substation Automation and Energy Management Systems (CSAEMS), Department of Electrical, Electronics and Computer Engineering, Bellville, South Africa

Sujit Kumar,     Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, India

Halleluyah A. Kupolati,     Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria, South Africa

Prashant Mani Tripathi,     Electrical Engineering Department, Indian Institute of Technology (ISM), Dhanbad, Jharkhand, India

G. Manikanta,     Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, India

Irene H. Masenge,     Department of Electrical Engineering, College of Engineering and Technology, University of Dar es Salaam, Dar es Salaam, Tanzania

Anirban Mishra,     Electrical Engineering Department, Indian Institute of Technology (ISM), Dhanbad, Jharkhand, India

Godwin Elinazi Mnkeni

Department of Electrical Engineering, College of Engineering and Technology, University of Dar es Salaam, Dar es Salaam, Tanzania

Department of Electrical Engineering, Mbeya University of Science and Technology, Mbeya, Tanzania

Aviti Thadei Mushi,     Department of Electrical Engineering, College of Engineering and Technology, University of Dar es Salaam, Dar es Salaam, Tanzania

Francis A. Mwasilu,     Department of Electrical Engineering, College of Engineering and Technology, University of Dar es Salaam, Dar es Salaam, Tanzania

Bakari Mohamedi Mfaume Mwinyiwiwa,     Department of Electrical Engineering, College of Engineering and Technology, University of Dar es Salaam, Dar es Salaam, Tanzania

Raj M. Naidoo,     Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria, South Africa

Nishkar R. Naraindath,     Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria, South Africa

Emmanuel Idowu Ogunwole,     Cape Peninsula University of Technology, Center for Substation Automation and Energy Management Systems (CSAEMS), Department of Electrical, Electronics and Computer Engineering, Bellville, South Africa

Bahadur Singh Pali,     Department of Electrical and Electronics Engineering, Bhagwan Parshuram Institute of Technology, GGS IP University, Delhi, India

Rishi Ratan Sinha,     Manipal University Jaipur, Jaipur, Rajasthan, India

Owdean Suwi

Department of Electrical Engineering, College of Engineering and Technology, University of Dar es Salaam, Dar es Salaam, Tanzania

Department of Electronic and Electrical Engineering, Mbeya University of Science and Technology, Mbeya, Tanzania

Shelly Vadhera,     Department of Electrical Engineering, National Institute of Technology, Kurukshetra, Haryana, India

Balaji V. Venkatasubramanian,     School of Technology, Woxsen University, Hyderabad, Telangana, India

H.K. Yashaswini,     Department of Electrical and Electronics Engineering, Jain (Deemed-to-be) University, Bengaluru, Karnataka, India

Section I

Fundamentals of microgrids

Outline

Chapter One Overview of renewable energy power system dynamics

Chapter Two Conceptual framework of microgrid and virtual power plants with renewable energy resources

Chapter Three Overview of optimal operations of renewable energy power systems in microgrid and virtual power plants

Chapter Four Hybrid microgrids: architecture, modeling, limitations, and solutions

Chapter Five Techno-economic analysis of renewable integrated power system for enhanced resilience

Chapter Six Techno-economic analysis of renewable power systems

Chapter One

Overview of renewable energy power system dynamics

Aviti Thadei Mushi¹, Owdean Suwi¹,² and Jackson J. Justo¹,    ¹Department of Electrical Engineering, College of Engineering and Technology, University of Dar es Salaam, Dar es Salaam, Tanzania,    ²Department of Electronic and Electrical Engineering, Mbeya University of Science and Technology, Mbeya, Tanzania

Abstract

Contemporary proliferation of renewable power generation is causing an overhaul in the topology, composition, and dynamics of electrical grids. Over recent decades, the penetration of renewable energy sources (RESs), especially photovoltaic, micro hydro, and wind power plants, has been promoted in most countries. However, as these both alternative sources have power electronics at the grid interface (inverters), they are electrically decoupled from the grid. Subsequently, the stability and reliability of power systems are compromised. Inertia in power systems has been traditionally determined by considering all the rotating masses directly connected to the grid. However, as the penetration of grid-connected RES increases, the inertia of the power system decreases due to the reduction of the directly connected rotating machines. Consequently, modern power systems require a new set of strategies to include RESs. In fact, hidden inertia, synthetic inertia, and virtual inertia are terms currently used to represent artificial inertia created by inverter-dominating generating units like RESs. This chapter provides an overview of RESs and highlights the inertia concept and methods to estimate rotational inertia in different parts of the world. In addition, an extensive discussion on wind and photovoltaic power plants and their contribution to inertia and power system stability is also presented.

Keywords

Frequency control; grid stability; inertia; power systems; inverter-interfaced renewable energy sources

1.1 Introduction

Depletion of fossil fuel, global warming, and environmental pollution clarify the importance of renewable energy sources (RESs). Renewable energy is derived from the Earth’s natural resources that are not finite or exhaustible, especially during human lifetime. Renewable resources include biomass energy (such as ethanol), microbial fuel cell [1], hydropower, geothermal power, wind energy, and solar energy [2]. Offline solar photovoltaic (PV) can power rural villages [3–5], or airports [6] at an equitable levelized cost of energy (LCOE) if the wind is not enough to give economies of scale [7–9]. However, high penetration of RES decreases power systems inertia, and hence, the system becomes more sensitive to disturbances. This results in problems with frequency control because it increases the rate of change of frequency and may lead to unplanned load shedding or tripping of generating units.

Traditionally, the imbalances between generation and consumption cause frequency variations in a power system [10]. To maintain frequency in its nominal value, power systems rely on synchronous machines connected to the grid, which store kinetic energy and this is automatically extracted in response to a sudden power imbalance [11]. However, due to the new environmental policies and the limited fossil fuel reserves, conventional generators are being replaced by RES-based generators [12,13]. Among the different RES available, the most promising for electrical power generation are PV and wind power installations, which are inverter-interfaced RES (II-RES) [14]. Massive installation and penetration of II-RES into the grid can cause several issues that should be taken into account [15] for the stable operation of the grid. First, as they depend on weather conditions, these sources are intermittent and uncertain, placing stress on power system operation [16]. Moreover, as they are connected to the grid through inverters that electrically decouple them from the grid, the effective inertia of the power system is reduced [17,18]. This inertia reduction affects the system's reliability, compromising the frequency stability [18]. The rotational inertia is related to both nadir (i.e., minimum frequency) and rate of change of frequency (ROCOF) [19]. In fact, larger nadirs and faster ROCOFs are obtained in low rotational inertia power systems, subsequently making them more sensitive to frequency deviations [13,20]. As a result, over the last decade, several frequency control techniques have been proposed to facilitate the massive penetration of wind power and PV resources into the grid [21]. In addition, several recent researches have investigated the use of smart inverters with voltage and frequency support to enhance grid stability with large penetration of RES in microgrid (MG) configuration [22]. Such solutions are commonly referred to as hidden, synthetic, or virtual inertia [23]. This chapter focuses on the overview of RES and discusses the current and future inertia concept for power systems. Additionally, the possibilities of wind and PV power plants to contribute to inertia and participate in frequency control are also presented. The overall objective is to introduce a comprehensive survey of the effects of the increase in RES on power system stability in terms of inertia and frequency. Different models of wind-driven and photovoltaic systems used for frequency control studies have been introduced. The up-to-date effective frequency regulation methods that can be used with highly RES-penetrated power systems have been revised and compared. These methods include virtual inertia-based methods depending on energy storage devices, deloading of RESs, various inertial, response techniques, and demand response at the load section including under frequency load shedding. Extensive comparisons among these methods have been carried out to guide power system designers, operators, researchers, and grid code task forces in the proper incorporation of RES for frequency regulation of power systems [24].

Microgrids are classified as grid-connected (i.e., utility-interactive systems) and stand-alone systems (i.e., off-grid microgrid). Depending on the nature of interconnected energy resources, they can be designed to provide either DC or AC power service, operate interconnected or independent of the utility grid, and can comprise several other energy sources including energy storage systems such as batteries, supercapacitors, or flywheels. For stand-alone PV-battery systems, they operate independently of the electric utility grid and are generally designed and sized to supply certain DC and/or AC electrical loads. Fig. 1.1 shows a typical system configuration of the (A) grid-connected PV-battery hybrid system and (B) off-grid PV-battery system.

Figure 1.1 Typical system configuration of the (A) grid-connected PV-battery hybrid system and (B) off-grid PV-battery system.

Details of each system of Fig. 1.1 comprise the following

1. Grid-connected PV-battery hybrid system: solar PV system connected to a load via DC–DC converter along with the battery energy storage system (BESS). Also, an inverter is used to interface this hybrid system with AC load and grid as shown in Fig. 1.1. The system operates by supplying power to the load using either one of the sources or two or three components depending on the load demand, operating time, and available resources. This system is also known as grid-tied MG. The operation can be summarized in Table 1.1, by considering MG power as PG and grid power as PD.

2. Off-grid PV-battery hybrid system: the architectural concept of the off-grid solar PV-system configuration works in such a way the two sources complement each other. In such a case, the BESS is designed in such a way that it can supply power for a certain period of time even in the absence of a grid or minimum solar insolation.

Table 1.1

1.2 Components of renewable energy and dynamics

Typical examples of RESs are solar PV, wind energy conversion system (WECS), geothermal, hydro, and biogas. These systems are independently modeled in the following subsections. The transient stability performance of the power system deteriorates with increasing the participation level of the PV system [25]. It is possible to design energy generation policies if proper simulations are done, as was developed in 2015 in China [26]. These RESs can be made up of microgrids, such as AC- or DC-based microgrids to supply different applications and loads. Therefore, there are DC–DC and AC–DC converters that are used for power conditions. In addition, BESSs are needed to provide backup, stabilization, and autonomy of the RES.

Moreover, the increase of the special purpose vehicle (SPV) distribution generations (DG) in the grid system leads to a decrease in the system inertia. This is because the SPV has no rotating part to compensate for the inertia in the grid network as traditionally done. Therefore, SPV systems converters have to be modeled in such a way to provide inertia to the system artificially. In this context, as no rotating part in SPV, the inverter that connects the grid is modeled to provide the inertia to the system virtually [27].

In this research, RE represents the existing power systems with different levels. However, because of the intermittent nature of these sources, it is necessary to analyze systems’ reliability with different RE penetration levels. This work presented a simulation method for the reliability evaluation of renewable penetrated power systems. Some reliability indices were proposed for the case of power systems with renewable power plants. The adopted approach used the historical data of renewable energy resources, mainly wind and solar to estimate the power that can be generated and compared with the demand to find the power mismatch. Therefore this approach can be utilized to determine the penetration level that renewable energy can be shared, and it also helps the system operators in deciding the percentage of the generation that RE power plants can provide [27].

1.3 Modeling of solar PV

Solar PV cell is modeled using the single-diode model shown in Fig. 1.2. The cells are arranged in series and parallel to form a solar PV module. Several modules are strung in parallel and series to form a solar array. Solar cells are found to be efficient energy generators even in cases of partial shading [28].

Figure 1.2 Single-diode model of solar PV.

Key: Equation =light current (A)

Equation current lost due to shunt resistance (A)

Equation =diode reverse saturation current (A)

Equation =series resistance (Ω)

Equation =shunt resistance

Mathematically, the operation of the solar cell is described by the following equations for the output cell current and voltage.

1.4 Modeling of wind energy conversion systems

WECS consists of a wind turbine (WT) with its blades and shaft coupled to the generator, which can either be an induction generator or a permanent magnet generator (PMSG). Fig. 1.3 shows a typical PMSG WT configuration, which is connected to the grid through AC–DC–AC converters (AC–DC is the rectifier, while DC–AC is an inverter). In this case, the WT controller takes the role to regulate the load and input power through the two power converters.

Figure 1.3 Typical configuration of PMSG-based wind energy conversion system.

1.5 Geothermal energy

The geothermal energy conversion is modeled as follows (Fig. 1.4).

Figure 1.4 Model of geothermal energy generation.

1.6 Modeling of hydropower plant

Hydropower plants are ubiquitous and provide a large share of existing generation in the grid network. The micro hydro is made up of the following [29].

1. Water diversion (intake)

2. Pipeline (penstock)

The power generated by the water flow is calculated by the following:

Equation (1.1)

where P=power generated in kW, Qm=discharge (quantity of water cubic meter), g=acceleration due to gravity in meter per second squared, H=net available head in meters (gross head – losses) (Fig. 1.5).

Figure 1.5 Model of a micro hydro plant.

The micro hydro plant when operated in a feedback loop can be modeled as in Fig. 1.6.

Figure 1.6 Micro hydro plant in feedback operation.

1.7 Modeling of DC–DC converters

Some of the DC–DC converters used are boost converters, buck converters, buck-boost converters, and cuk converters of some other similar configuration with higher performance compared to previously mentioned types. These converters are placed at different locations of the RES for power conditioning. The converters’ components are the source, inductor, capacitor, diode, and high-power switching devices such as metal-oxide-semiconductor field-effect transistor, insulated-gate bipolar transistor, or gate turn off. Any change to the connected converters load is modeled as a disturbance current Equation as shown by the following references (Figs. 1.7–1.9) [30–32].

Figure 1.7 Boost-type converter equivalent circuit.

Figure 1.8 Buck-type converter equivalent circuit.

Figure 1.9 Buck-boost-type converter equivalent circuit.

The dynamic model for each converter is derived in the following expression:

Equation (1.2)

The steady-state situation of the converters is captured by the following general expression (Fig. 1.10).

Equation (1.3)

where

EquationEquation

Figure 1.10 Typical topology of boost DC to DC Converter with voltage controller and load observer.

1.8 Causes of low inertia and effects to grid

Inertia in power systems refers to the energy stored in large rotating generators and some industrial motors, which gives them the tendency to remain rotating. This stored energy can be particularly valuable when a large power plant fails, as it can temporarily make up for the power lost from the failed generator. The small generation schemes which employ distributed generations can be grid-connected using inverters and other power electronics devices, lead to grid instability. This decrease results in a high ROCOF and frequency deviations under power imbalance that substantially affect the frequency stability of the system [33,34].

1.9 Power system inertia and frequency stability

Frequency stability of power systems is known as the behavior of power systems against any disturbances which tend to reduce the frequency of power systems below their nominal value. Inertia is known as the time duration of a generator in which the generator provides its rated power from its stored kinetic energy (KE) to the power system during disturbances as given by Eq. (1.4) [35]. Now, solar PV systems do not have rotating masses, hence no stored KE, while wind generators have rotating masses but are decoupled from power systems through power electronic devices with MPP techniques [36,37]. So, the more penetration of the RES into the power systems network the more the rated MVA of power systems with constant KE and the less the inertia of power systems as described by Eq. (1.5). Therefore increasing the share of RES, especially solar PV and wind energy in the grid network, has a negative effect on power system inertia.

Equation (1.4)

Equation (1.5)

Equation (1.6)

where H is the generator inertia, J is the generator moment of inertia, n is the generator-rated speed, Sn is the rated MVA of the generator, Hsys is the power system inertia, Ssys is the rated MVA of the power system, ΣKE is the summation of stored KE (in MWs) in all synchronous generators, ΔPm is the change in generator mechanical power, ΔPl is the change in electrical frequency independent demand and D is the load damping factor (Fig. 1.11).

Figure 1.11 Algorithm of determining the acceptable level of RES for achieving frequency stability.

1.10 Use of DSTATCOM for RE high penetration grids

Sometimes, the grid may suffer from low voltage at the receiving ends due to long distances. However, these can be alleviated using the FACTS devices such as dynamic voltage restorer (DVR) for voltage regulation, DSTATCOM, or STATCOM technologies [38]. Increased penetration of RES in a radial distribution network leads to voltage rise due to reverse power flow and lack of reactive power injection. Mitigation measures could involve installing reactive power compensation (such as capacitor/reactor, static var compensator (SVS), and STATCOM) and energy storage devices (battery bank, flywheel, supercapacitor) or active power curtailment [39,40]. As RES output varies due to the change in solar radiation or wind speed profile meaning that output fluctuates within the short term and voltage rises or drops. Hence, SVS is selected in such study as a reactive power compensator that suits short-term voltage stability problems [41]. RES plants can also inject reactive power into the grid to maintain voltage within the desired range, but reactive power prioritization without limit can lead to voltage collapse as a result of a considerable reduction of real power demand [40]. Technically, the sizing of reactive shunt compensation depends on many parameters, including RES penetration level, network loading, and voltage strength at the point of common coupling (PCC) [40]. The most common issue that can happen in a remote weak grid network is when the voltage magnitude at the receiving end of the line is higher than the magnitude at the sending end. Such operating conditions of voltage rise can happen in high-voltage lines that have no or only a light load connected to the receiving end; this is due to line capacitance, and is the so-called Ferranti effect [42]. Shunt reactors are used to reduce the voltage rise caused by the Ferranti effect. To combat such effect in the grid network, authors of [43] used a shunt reactor to compensate reactive power (inductive) to reduce voltage rise due to the Ferranti effect on the open end of extra-high-voltage cable lines. Similarly, in Ref. [44], the shunt reactors managed to suppress overvoltages at the end of the cables.

1.11 Conclusion

This chapter has reviewed some dynamics of RE and their effects on grid operation. The analysis is based on the fact that, by selectively replacing synchronous generators with inertia-less wind plants, the grid inertia is inherently affected. As illustrated in the chapter discussion, the increasing penetration level of inertia-less generators affects the angular stability margin and primary frequency response of the grid system. Moreover, as the proportion of inertia-less generators increased, the rotor angle deviation can be observed, following a transient event, which also increased indicating a lower angular stability margin. Similarly, the available spinning reserve decreased as fewer generators were available to contribute to the primary frequency response. This calls for a need for fast frequency response (FFR) generators, which can also include synthetic inertia and battery storage, that can quickly contribute to the system frequency recovery following a generation loss. More importantly, the impact of reduced inertia on the system modes is not observed and changes in these modes were more likely caused by the change in system topology with fewer generators available to participate in system oscillations. This chapter also demonstrated that only reducing the inertia of synchronous generators does not provide an accurate analysis of the challenges associated with the increasing level of renewable energy integration. As a part of future work, the capability of wind and solar PV plants to provide FFR has to be more explored to provide measures to mitigate the impacts of a higher level of renewable energy integration in the grid.

References

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