UNIVERSITY OF CALGARY. Fuzzy Logic Controller for a Hydro Pumped Storage Plant to Provide Frequency Regulation in. an Isolated Hybrid Micro-Grid

Transcription

1 UNIVERSITY OF CALGARY Fuzzy Logic Controller for a Hydro Pumped Storage Plant to Provide Frequency Regulation in an Isolated Hybrid Micro-Grid by Alberto Jose Imperato A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE GRADUATE PROGRAM IN ELECTRICAL ENGINEERING CALGARY, ALBERTA JULY, 2016 Alberto Jose Imperato 2016

2 Abstract The operation of isolated micro-grids requires that such systems must be able to regulate the system frequency also in addition to voltage. The combined operation of a reversible hydro pumped storage unit, a wind generator, a diesel generator and a dump load is analyzed in this study. The main balancing mechanism of the micro-grid is the hydro pumped storage facility. A fuzzy inference system is used to control this primary component to provide frequency regulation. Also, a supervisory controller is implemented to select the operation of other balancing mechanisms in order to support the operation of the hydro pumped storage. Model of the proposed isolated hybrid micro-grid is developed and the designed controllers are tested in this thesis. By monitoring the system frequency and active power of various components, it is concluded that the proposed frequency control scheme provides a reliable frequency regulation while reducing the frequency deviation during disturbances. ii

3 Acknowledgements I express my greatest gratitude and acknowledgement to my supervisor Dr. Om Malik. His diligent support and guidance throughout the different stages of this study have been key to achieving the completion of this thesis. I would also like to thank the members of the examination committee for taking the time to review this thesis. My deepest thanks to my wife, for her unconditional love and for always believing in me no matter how difficult the situation. Her tireless support helped me to accomplish this achievement. I would also like to thank my uncle and aunt for being a great support throughout these years and for allowing me and my wife to have a place in their life. Especial thanks to my father, mother, sister, and to the memory of my two grandfathers which besides the distance were a source of inspiration for the achievement of this goal. Lastly, I would like to thank God for giving me the opportunity of presenting this study. iii

4 Table of Contents Abstract... ii Acknowledgements... iii Table of Contents... iv List of Tables... vii List of Figures and Illustrations... ix List of Symbols, Abbreviations and Nomenclature... xii CHAPTER 1: INTRODUCTION Operation Overview of a Hybrid Micro-grid Dispatch Strategies Operational Requirements Hydro-Pumped Storage facility Operation Modes Diesel Generator Operation Requirements Power System Control Balancing Mechanisms Frequency Regulation Control Hydro-Pumped Storage Control Diesel Engine Control Dump Load Control Scope and Objectives Thesis Outline...15 CHAPTER 2: MODELLING OF THE HYBRID MICRO-GRID Hybrid Power System Configuration Hydro-Pumped Storage (HPS) Facility Model Tunnel and Penstock Water Dynamics Water Reservoir Model Pump-Turbine Dynamics in Turbine Mode Pump-Turbine Dynamics in Pumping Mode Hydraulic Speed Governor Excitation System Synchronous Machine Diesel Generator (DG) Model Diesel Engine and Speed Governor Excitation System Synchronous Machine Clutch Model Wind Generator (WG) Model Wind Turbine Asynchronous Generator Dump Load (DL) Model Chapter Summary...46 CHAPTER 3: DESIGN OF LOCAL CONTROLLERS FOR THE OPERATION OF THE HYBRID MICRO-GRID...48 iv

5 3.1. Outline of the Local Controllers of the Hybrid Miro-Grid HPS Facility in Turbine Mode Frequency Controller PID Frequency Controller Fuzzy Logic Frequency Controller Design Frequency Controllers Comparison HPS Facility in Pumping Mode Frequency Controller Fuzzy Logic Frequency Controller Design Frequency Controller Simulations Diesel Engine Frequency Controller Diesel Engine Isochronous Operation Mode Diesel Engine Commissioning Anticipation Anticipation of Low Water Level in HPS Upper Reservoir Anticipation of Maximum Generation Limit of HPS Facility Diesel Engine Controller Simulations Dump Load Frequency Controller Dump Load Frequency Controller Design Dump Load Frequency Controller Simulations Chapter Summary...98 CHAPTER 4: SUPERVISORY CONTROL STRATEGY FOR THE OPERATION OF THE HYBRID MICRO-GRID Dispatch Strategy Control Strategy Hierarchical Controller Logic Supervisory Controller First Control Layer Supervisory Controller Inputs Supervisory Controller Outputs Supervisor Controller Logic of Operation HPS Operation From Turbine Mode to Pumping Mode HPS Operation From Pumping Mode to Turbine Mode Chapter Summary CHAPTER 5: STUDIES OF THE OPERATION MODES AND RESULTS OF THE HYBRID POWER SYSTEM HPS Facility Power Limits HPS Facility Pumping Mode Power Limits HPS Facility Pumping Mode Power Limits - DL Activation Scenario HPS Facility Pumping Mode Power Limits - DL Deactivation Scenario HPS Facility Turbine Mode Power Limits HPS Facility Turbine Mode Power Limits - DG Activation Scenario HPS Facility Turbine Mode Power Limits - DG Deactivation Scenario Water Reservoir Limits Water Reservoir - Lower Limit Water Reservoir - Upper Limit HPS Transition from Turbine Mode to Pumping Mode HPS Transition from Pumping Mode to Turbine Mode System Evaluation under Wind Disturbance v

6 5.6. Economic Analysis - DL and WG Pitch Angle Control Chapter Summary CHAPTER 6: CONCLUSIONS AND FUTURE WORK Conclusions Main Contributions Future Work REFERENCES APPENDIX A: MODEL PARAMETERS A.1. HPS Facility Tunnel and Penstock Parameters A.2. Upper Water Reservoir Parameters A.3. HPS Facility Turbine Mode Parameters A.4. HPS Facility Pumping Mode Parameters A.5. HPS Facility Hydraulic Speed Governor Parameters A.6. HPS Facility Excitation System Parameters A.7. HPS Facility Synchronous Machine Parameters A.8. Diesel Engine and Speed Governor A.9. DG Excitation System Parameters A.10. DG Synchronous Machine Parameters A.11. Clutch Model Parameters A.12. Wind Turbine Parameters A.13. WG Asynchronous Machine Parameters A.14. DL Model Parameters APPENDIX B: PID CONTROLLERS B.1. HPS Hydraulic Speed Governor Original PID Regulator B.2. HPS Hydraulic Speed Governor Tuned PID Regulator B.3. HPS Fuzzy Logic Controller Scaling Factors B.4. HPS Non-Linear Fuzzy Logic Controller Output Membership Function Values 177 B.5. DL Frequency Regulator Parameters vi

7 List of Tables Table Typical Start-Up Time of an HPS [7], [8]... 6 Table Variables of Synchronous Machine Model [4], [58] Table Subscripts of Synchronous Machine Variables [4], [58] Table Clutch States under different conditions [4], [12] Table Fuzzy Rules Set Table Fuzzy Rules Set Table Fuzzy Rules Set Table Fuzzy Rule Set Table Cost Comparison between DL and WG pitch angle controller Table A.1 - Tunnel and Penstock Parameters Table A.2 - Upper Water Reservoir Parameters Table A.3 - Turbine Parameters Table A.4 - Pump Parameters Table A.5 - Hydraulic Speed Governor Parameters Table A.6 - Excitation System Parameters Table A.7 - HPS Synchronous Machine Parameters Table A.8 - Diesel Engine and Speed Governor Parameters Table A.9 - DG Excitation System Parameters Table A.10 - DG Synchronous Machine Parameters Table A.11 - Clutch Model Parameters Table A.12 - Wind Turbine Parameters Table A.13 - WG Asynchronous Machine Parameters Table A.14 - DL Model Parameters Table B.1 - HPS Original PID Parameters vii

8 Table B.2 - HPS Tuned PID Parameters Table B.3 - HPS Fuzzy Logic Scaling Factors Table B.4 - Output Membership Function Constant Values Table B.5 - DL Frequency Regulator Parameters viii

9 List of Figures and Illustrations Figure Block diagram of Hybrid Micro-grid Power System Figure General Structure of a hydro-pumped storage plant [7] Figure Layout of Reversible Pump-Turbine unit in vertical position [7], [85] Figure Block diagram of tunnel and penstock water dynamics [7] Figure Block Diagram of an HPS facility in turbine mode [7] Figure Pump head-flow curve and tunnel-penstock system curve with throttling effect of gate closing [7] Figure Equilibrium operating points at different wicket gate positions [7] Figure Block diagram of an HPS facility in pumping mode with gate frictional effects [7] Figure Block diagram of Hydraulic Turbine Speed Governor [49] Figure Block diagram of Gate Servomotor model [49] Figure Block diagram of excitation system [56] Figure Equivalent circuits of synchronous machine Y connected stator windings Figure Block diagram of diesel engine and governor model [63] Figure Block diagram of Clutch Model [12] Figure Equivalent circuits of an asynchronous machine in d-q representation [75] Figure Output Power of Wind Generator Figure DL Simulink Model Figure HPS in Turbine Mode PID Controller Schematic [49] Figure PID Frequency Control +25kW Load Step Figure PID Frequency Control +50kW Load Step Figure Linear Fuzzy PID Controller Schematic [77] Figure Linear Fuzzy Logic PID Frequency Controller Membership Functions Figure Linear Fuzzy PID Frequency Controller Control Surface ix

10 Figure Non-Linear Fuzzy Logic PID Frequency Controller Membership Functions Figure Non-Linear Fuzzy PID Frequency Controller Control Surface Figure Fuzzy Controllers vs PID frequency comparison on +25 kw load step Figure Non-Linear Fuzzy vs Linear Fuzzy frequency comparison on +25 kw load step. 62 Figure Non-Linear Fuzzy vs PID frequency comparison on +50 kw load step Figure 3-12 Fuzzy Logic Frequency Controller Schematic Figure Fuzzy Logic Controller Membership Functions Figure Fuzzy Logic Controller Control Surface Figure Fuzzy Controller Frequency and Power Results under a +25 kw load step Figure Real Power Results According to Wind Speed Variations Figure Frequency under Wind Speed Variations Figure Diesel Engine Speed Reference Change Figure Diesel Engine Controller Logic of Operation based on tdis Figure Isolated Micro-Grid Load Profile [4], [80] Figure Fuzzy Logic Controller Membership Functions Figure Fuzzy Logic Control Surface Figure Diesel Engine Controller Block Diagram Figure Diesel Engine Controller Simulations for Low Water Level in Upper Reservoir.. 86 Figure Diesel Engine Controller Simulations for HPS at Maximum Generation Point Figure DL Frequency Controller Schematic [41] Figure DL Controller Frequency and Power Result for +25 kw load step Figure DL Controller Frequency and Power Result for +50 kw load step Figure DL Controller Frequency and Power Result for Wind Speed Variations Figure Second Layer Control Switch [4] Figure Block Diagram of the Hierarchical Logic between controllers [4] x

11 Figure Supervisory Control Logic for HPS transition from Turbine Mode to Pumping Mode Figure Supervisory Control Logic for HPS transition from Pumping Mode to Turbine Mode Figure HPS Pumping Mode at Maximum Capacity - DL Activation Scenario Figure DL Frequency Controller - Running Status Signal from Supervisory Controller Figure HPS Pumping Mode at Maximum Capacity - DL Deactivation Scenario Figure DL Frequency Controller - Running Status Signal from Supervisory Controller Figure HPS Turbine Mode at Maximum Capacity - DG Activation Scenario Figure 5-6 Diesel Engine Speed Controller Clutch Status Signal from Supervisory Controller Figure HPS Turbine Mode at Maximum Capacity - DG Activation Scenario Figure HPS Turbine Mode at Maximum Capacity - DG Deactivation Scenario Figure Diesel Engine and Synchronous Machine Speed Figure System Simulations for Low Water Level in Upper Reservoir Figure Diesel Engine and Synchronous Machine Speed Figure System Simulations for High Water Level in Upper Reservoir Figure 5-13 Overflow Valve Status Signal from Supervisory Controller Figure HPS Transition from Turbine Mode to Pumping Mode Scenario Figure Supervisory Controller Status Signals and Upper Reservoir Water Level Figure HPS Transition from Pumping Mode to Turbine Mode Scenario Figure Supervisory Controller Status Signals and Upper Reservoir Water Level Figure HPS Operation under Wind Gust Disturbance Scenario xi

12 List of Symbols, Abbreviations and Nomenclature General Notation Vrms Δ Frequency Deviation Nominal desired frequency Actual frequency of the system RMS Voltage Speed reference Hydro pumped storage unit speed deviation Discharge time of upper reservoir Tunnel and Penstock Parameters Dynamic head at the junction of the tunnel and penstock Dynamic flow at the junction of the tunnel and penstock Dynamic head established by pump-turbine unit Dynamic flow established by pump-turbine unit Total available static head Frictional coefficient of the penstock Frictional coefficient of the turbine Head loss in the system Water starting time of the pipe segment Length of the water tunnel Water velocity Gravity constant Dynamic water head Water flow at nominal operation Cross sectional area of the penstock Water Reservoir Parameters Volume of Upper Water Reservoir Flow of water entering or leaving the reservoir Height of the water level in the tank Area of the bottom of the tank Pump-Turbine in Turbine Mode Parameters Hydro Pumped Storage Active Power Opening of the wicket gates in per unit xii

13 No load water flow in per unit Mechanical output power from the turbine in per unit Dynamic head in per unit Dynamic flow in per unit Pump-Turbine in Pumping Mode Parameters Coefficient for pump curve fitting Coefficient for pump curve fitting Coefficient for pump curve fitting Equivalent frictional coefficient Gate position frictional coefficient Maximum opening of wicket gates in per unit Pump efficiency Hydraulic Speed Governor Parameters Servomotor gain Servomotor time constant Static gain of hydraulic speed governor Excitation System Parameters Exciter voltage Regulator s output Stator terminal voltage transducer time constant Main regulator gain Main regulator time constant Exciter gain Exciter time constant Lead-lag compensator time constant Lead-lag compensator time constant Derivative feedback gain Derivative feedback time constant Lower limit of voltage regulator output Upper limit of voltage regulator output xiii

14 Synchronous Machine Parameters Voltage Current Flux Resistance Inductance Angular speed Time constant Direct axis quantity Quadrature axis quantity Rotor quantity Stator quantity Leakage quantity Magnetizing quantity Field winding quantity Damper winding quantity Second damper winding in quadrature axis Third damper winding in quadrature axis Speed variation Inertia constant Mechanical torque Electromagnetic torque Damping factor Mechanical speed of the rotor Speed of operation Pole pairs Friction factor Diesel Engine and Clutch Parameters,,,,, Clutch torque Synchronous machine inertia constant Diesel engine inertia constant Synchronous machine torque Diesel engine torque Synchronous machine rotational speed Diesel engine rotational speed Diesel engine governor time constants Diesel engine governor gain Diesel engine time constant xiv

15 Wind Generator Parameters Wind turbine mechanical output power Wind turbine performance coefficient Air density Turbine blades swept area Wind speed Tip speed ratio Pitch angle of the blades Mechanical torque of wind turbine Asynchronous Machine Parameters Voltage Current Flux Resistance Inductance Direct axis quantity Quadrature axis quantity Rotor quantity Stator quantity Rotor angular speed Rotor angular position Electromagnetic torque Mechanical torque Inertia constant Friction factor Pole pairs Abbreviations Symbol HPS DG WG DL LFC N Z P LN SN SP Definition Hydro Pumped Storage Diesel Generator Wind Generator Dump Load Load-Frequency Controller Negative Zero Positive Large Negative Small Negative Small Positive xv

16 LP NL NM NS PS PM PL CF CM CS OS OM OF PLL Large Positive Negative Large Negative Medium Negative Small Positive Small Positive Medium Positive Large Close Fast Close Medium Close Slow Open Slow Open Medium Open Fast Phase Lock Loop xvi

17 CHAPTER 1: INTRODUCTION Modern environmental regulations, technology changes and economic incentives implemented in power generation are changing the future of conventional power systems and how energy generation and transmission is done. With the development of more renewable energy generating units and the improvement of energy storage systems new opportunities have opened up for the application of on-site power generation. The introduction of small power distributed generation has been presented as a viable and promising solution to meet the ever increasing demand of local power focusing on environmental impact, power reliability and quality. Small scale generators are usually located close to the load. Therefore, this configuration provides a minimization of the losses in the system. With the introduction of renewable power sources the cost of energy production and environmental impact of the power system operation can be reduced when compared with the performance and effects that the fossil fuel-based energy has on the environment. Furthermore, there is also a necessity of finding new sources of energy which can replace and overcome the limited availability nature of traditional fossil fuel-based energy sources, such as oil, natural gas and coal. Therefore, the growing need of reducing losses in the system and carbon emissions makes the concept of distributed generation and renewable energy sources more attractive [1], [2]. A concept which involves the implementation of such generation capabilities as on-site distributed generation with renewable energy applications, energy storage and load control can be attributed to a Micro-grid. A micro-grid is a regionally limited energy system that is comprised of several distributed small power generators, energy storage devices, load control and local loads. All elements of a micro-grid are usually located near the supplied load. Hence a micro-grid can be an industrial or commercial facility, a university campus, a hospital, an off-grid remote community, amongst other 1

18 facilities. Since renewable power sources can be widely used in micro-grids, and due to the different dynamic properties and electrical characteristics of each technology, this represents a challenge in the operation of such systems, especially when operating in an off-grid or islanded scenario [3]. When operating in such conditions the stability of the system is the most important variable to consider and it has to be maintained within the specified limits at all times in order to ensure a reliable operation of the system. When using renewable energy sources such as wind, solar, hydro, etc., it is necessary to account for the operation intermittency of these technologies and how this intermittence affects the stability of the system. For example, wind power is directly related to the wind speed and its variations. This means that changes in the wind speed will affect the power generated by the wind generator and as a result it will induce changes in the power balance of the system which affects the system s frequency balance. Therefore, wind power as a power source can be unreliable from a system s stability point of view. To resolve this issue Hybrid Micro-grid power systems have been introduced. A typical Hybrid Micro-grid combines the benefits of using renewable power sources with the reliability of using controllable fuel-based generators. With the use of fuel-based generators it is possible to supply power during power shortages scenarios and ensure a reliable operation and energy supply to the loads [4]. More elements are also introduced in order to provide reliable service to the supplied loads. These elements are called balancing mechanisms, as they are used to balance the system s generated power with the load consumption [5], [6]. Batteries, dump loads, pumped hydro storage facilities, fuel cells and even fuel-based generators can be used as balancing mechanisms. 2

19 Implementing more than one balancing mechanism in a power system requires the design of a control system that regulates the operation of all the balancing mechanisms in order to maintain suitable operation conditions. The main objective of this thesis is to propose a new control strategy that can provide frequency regulation to the system by coordinating the operation of three balancing mechanisms, under the islanded operation of a Hybrid Micro-grid system. The main objective of the system s controller is to provide frequency control under different operation scenarios of the system Operation Overview of a Hybrid Micro-grid A hybrid micro-grid power system is considered as a viable and environmentally friendly solution to provide power to remote communities where grid connection is not available. As previously mentioned, a hybrid micro-grid combines the use of renewable power sources with fuel-based energy sources and the implementation of other balancing mechanisms in order to increase the system s reliability. In this study, a Wind Generator (WG) is used as the main renewable power source of the system, and a Diesel Generator (DG), a Hydro-Pumped Storage (HPS) facility and a Dump Load (DL) are used as balancing mechanisms to maintain the frequency of the system stable. These balancing mechanisms are used to increase the use of the renewable power source, in this case the WG. The HPS facility is used as an energy storage system that can absorb excess power during high wind conditions and use the stored energy to generate power during low wind conditions, in order to reduce the use of fuel-based generation. In a similar way, a DL has been implemented to consume excess power present in the system when the HPS is at full capacity when operating in Pumping Mode and to account for the start-up time of the HPS from shutdown to normal pumping [7], [8]. Similarly, a DG has been included in the system to generate power when 3

20 the HPS is at full capacity when operating in Turbine Mode and to account for the start-up time of the HPS from shutdown to on-line generation [7], [8]. Operation of the balancing mechanisms must be coordinated. These balancing mechanisms are coordinated according to a dispatch strategy. Some dispatch strategies that can be applied are summarized in the next section Dispatch Strategies The operation of a hybrid micro-grid implements the dispatch strategy chosen by the operator to coordinate the balancing mechanisms available in the system. Some of the dispatch strategies are [4], [9]: Best Economical Operation: the dispatch objective is to minimize the energy production and maintenance costs. Fuel-based units should be operated under best efficiency conditions. Hence, this strategy may not use all the power available from the renewable power source. Highest Reliability: the main objective of this strategy is system reliability, usually by implementing back-up contingency generators and increasing the frequency of scheduled maintenance routines. Lowest Carbon Footprint: this strategy prioritizes the use of renewable power sources and minimizes the use of fuel-based generators. Service Delivery Optimization: this dispatch aims to optimize the service delivery by increasing the amount of generator runtime. This strategy suggests higher production and maintenance costs. 4

21 Component Lifecycle Optimization: this strategy is structured to maximize the system s components lifetime, specifically to ensure and optimize the battery lifecycle operation of the battery systems. Load Optimization: this dispatch objective is to improve the system s operation by using load side management by minimizing energy storage and operating generators at better efficiency levels. Best Quality of Supply: in this dispatch strategy electricity quality variables are prioritized, such as frequency, harmonic distortion, voltage range, etc. This may involve higher production costs and lower system efficiencies. In a real system, several dispatch strategies are implemented in order to ensure a reliable operation of the network. As well as dispatch strategies, it is necessary that the Hybrid Micro-grid system satisfies some operational requirements. The following section describes the operational requirements that a Micro-grid should comply with Operational Requirements Suitable operation of the Micro-grid is achieved when power is supplied to the load and the voltage and frequency standards are met. According to standard EN [10] for a low voltage level (LV) system, where LV implies that the voltage of the system is Vrms 1000V, the voltage variation should be maintained between ±10% for 95% of the week. In a similar way, according to standard EN [10] for a LV system, the frequency variation should be maintained between ±1% for 99.5% of the week, and -6%/+4% for 100% of the week. 5

22 Hydro-Pumped Storage facility Operation Modes HPS facilities are used, where appropriate, as a load balancing mechanism in a Micro-grid power system. The HPS stores energy by pumping water from a lower elevation reservoir to a higher elevation reservoir during high wind conditions, called Pumping Mode, and releases the water through the turbine generators during low wind conditions, called Turbine Mode. The overall efficiency of HPS facilities is around 70% to 85% [7], [8]. The amount of energy that an HPS facility can store or supply at a specific time is limited. The restrictions on the amount of power supply or storage that an HPS can deliver depends on the physical design of the facility and on the system dynamics and electrical characteristics. Similarly, the HPS has restrictions of typical time delays for its start-up that need to be respected in order to protect the HPS facility integrity [7], [8]. These time delays are presented in Table 1.1. Table Typical Start-Up Time of an HPS [7], [8] Mode Condition Response Time Generating Shutdown to on-line 60 to 90 seconds Generating On-line to full load generating 5 to 15 seconds Pumping Shutdown to normal pumping 6 minutes Pumping Spinning-in-air to normal pumping 60 seconds The time of operation of an HPS facility is restricted by the size of the water reservoirs, that defines the amount of water available to either pump or release in order to consume or generate energy. The HPS implemented in this study is based on the model proposed in [11], located in Ramea Island, in southern Newfoundland, Canada, where the lower reservoir is actually a body of water (e.g. river, coast, etc) and the upper reservoir is a man-made concrete-based tank. 6

23 Diesel Generator Operation Requirements The DG is only used in the system when the power of the HPS facility in turbine mode is at its full capacity or when the HPS facility is making the transition from pumping mode to turbine mode, in order to cover for the start-up time of 60 to 90 seconds presented in Table 1.1. Besides these conditions, when the DG is not operating its diesel engine should be tuned off in order to minimize the fuel consumption. Once one of the above conditions is present the DG operation is required and the diesel engine must be started and coupled to the electric generator. The system stability is maintained when the voltage and frequency is kept within the limits established by [10], and when the power supplied to the load is ensured by using the combined operation of the different balancing mechanisms implemented in the network Power System Control As stated previously, Hybrid Micro-grids are often used in islanded operation to supply power to remote communities. By operating in the off-grid mode the micro-grid needs to be able to self-regulate its voltage and frequency. Therefore, the voltage and frequency controllers need to be designed and tested to comply with the frequency and voltage standards defined by [10], in order to ensure a reliable operation of the micro-grid under different operation scenarios. This thesis focuses on the design of a frequency controller for a hybrid micro-grid. Even though voltage control is also provided, it is not considered in the scope of this study. Prior studies such as [12] [18] provided and maintained voltage control throughout the use of an excitation system of a synchronous machine. The excitation system and the synchronous machine are permanently connected to the system and therefore provide voltage regulation. The voltage regulation presented in this study is provided by implementing the same approach used by the previously mentioned studies. 7

24 In order to provide and maintain frequency regulation, within the defined limits, it is necessary that the various sources in the system are able to regulate the real power balance of the network. In a system with more than one balancing mechanism, the control system needs to combine the operation of the different balancing mechanisms available in order to ensure the frequency regulation of the power system. Several strategies have been proposed in different studies to provide frequency control during islanded operation of hybrid micro-grids. These strategies change according to the configuration and design of the power system components. In [19], [20], the frequency control of the system is only provided by the regulation of dump loads. In a similar way in [21] the frequency regulation is accomplished by solely using a wind pitch angle control. A Battery Energy Storage System (BESS), a WG and a HPS are combined in [22] to regulate the frequency of the system. Studies in [23], [24] provided frequency regulation by combining the operation of BESS and DL. In [25], [26] the frequency regulation was provided by using DG only. Similarly in [27], [28] the frequency regulation of the system is maintained by using an HPS facility. As stated previously, different strategies are implemented in each study and the frequency regulation of the network can be provided by using one or several balancing mechanisms in order to ensure the reliability and stability of the network in operation Balancing Mechanisms In order to provide frequency regulation it is required to combine the operation of the different balancing mechanisms available in the system. Several studies such as [18], [29] [35] implement the calculation of the power mismatch of the system. In this technique the mismatch 8

25 between the net generated power and the net load power consumption is computed. The calculated power mismatch is then distributed to the various balancing mechanisms in order to perform different operations, according to the system s requirements. This thesis follows the technique implemented in studies [18], [29] [35] to regulate the operation of the different balancing mechanisms. The supervisory controller calculates the power mismatch and commissions the required balancing mechanisms in the system. Therefore, the running status of the balancing mechanisms is regulated by the supervisory controller based on the system s conditions. As established in [4], it is necessary that each balancing mechanism must be able to provide frequency regulation in order to ensure the reliable frequency control of the micro-grid. Therefore, frequency controllers must be developed for each balancing mechanism Frequency Regulation Control Frequency regulation control in a power system is normally provided by an automatic Load-Frequency Controller (LFC) to regulate and maintain the system s frequency to its nominal value (i.e. 60 Hz). The reference signals to control the different units in the system are sent by the LFC in order to increase or decrease the power of the units to achieve frequency regulation [36]. In an islanded hybrid micro-grid operation, it is necessary that all the balancing mechanisms of the system can provide frequency regulation. For the current study, the HPS will supply power (Turbine Mode) during low wind conditions and it will consume power (Pumping Mode) during high wind situations. The DG will provide power during shortages of water in the HPS, when the HPS, in turbine mode, is operating at full capacity and to provide support during the HPS transition from pumping mode to turbine mode. The DL will consume excess power in 9

26 the system when the HPS, in pumping mode, is operating at full capacity and to provide support during the HPS transition from Turbine Mode to Pumping Mode. The DG and the HPS, in turbine mode, both have frequency regulators within their speed governors and these regulators have been thoroughly tested and implemented to perform frequency regulation. The HPS, in pumping mode, and the DL do not have built in frequency regulators and, therefore, these controllers must be developed to regulate the power output of each component according to the system s frequency requirements. Studies [12], [13], [15], [16], [27], [28] have implemented traditional PID or logic controllers to achieve active power regulation in hybrid micro-grid systems. Even though PID regulators are able to have good performance when they are optimally tuned, these types of controllers are only tuned for specific operating conditions. Hence, a tuned PID controller that is efficiently operating with a balancing mechanism may not regulate effectively when the conditions of operation or the balancing mechanisms have been modified. Fuzzy logic controllers have considerable advantages when compared with traditional PID controllers, especially when operating over a wide range of system conditions and disturbances [37]. Due to this, fuzzy logic controllers have also been implemented to provide frequency regulation in hybrid micro-grid power systems. By implementing the capabilities and characteristic of fuzzy logic controllers, it is possible to create fuzzy rules based on operator s experience to design a controller. A fuzzy logic controller can be designed to regulate the HPS, in pumping mode and turbine mode, and DL power consumption to match the system frequency deviation. Fuzzy logic controllers using frequency deviation as a reference to control active power have been implemented in studies [19], [20], [38]. 10

27 The frequency deviation is used to control the HPS wicket gates to regulate the active power of the turbine in [38]. Studies [19] and [20] use a fuzzy logic controller for frequency regulation to control the active power consumption of a DL. The fuzzy logic frequency controller developed in the present study follows the same approach of [4], where the fuzzy logic controller will be implemented in several balancing mechanisms in order to achieve an optimal system operation when combining the dynamics and characteristics of such mechanisms. The controller has been designed to improve the system operation and to ensure reliability and frequency stability under different system conditions and disturbances. Development of a fuzzy logic controller for the HPS facility, for both pumping mode and turbine mode, to provide frequency regulation by controlling the active power output of the turbine is described in this study Hydro-Pumped Storage Control As previously stated, HPS operation is limited by two constraints: the water reservoir dimensions, which defines the amount of time that the HPS facility can operate during turbine mode, and the time delay for start-up of an HPS plant. Studies [27], [28] implemented an HPS facility by having a single water tunnel, called penstock, for the turbine and a single penstock for the pump. This configuration allows more flexibility in the operation of the HPS facility but at the same time the capital cost and maintenance of the HPS plant will increase accordingly. Instead, study [7] implements an HPS facility using a single reversible machine and a single penstock, operating as both a pump and a turbine. In order to achieve the switch from generating mode to pumping mode the rotating direction of the 11

28 synchronous machine needs to be reversed. During the pumping operation mode the synchronous machine runs as a synchronous motor. Since only a single machine is being used for the pumping-turbine operation it is necessary to consider the start-up times established by [7], [8] in order to account for the operational limits and response times of the unit when switching from one operation mode to another. According to [7], the speed governor only operates during the turbine mode to control the wicket gates opening and, therefore, regulates the active power provided by the turbine. During the pumping mode the synchronous machine runs as a synchronous motor and usually the wicket gates opening is fixed at a specific operating point. In the present study, using the HPS model presented in [7], it is proposed to use a fuzzy logic controller to provide frequency regulation during both turbine and pumping mode. In this case, the fuzzy logic controller will regulate the wicket gate opening during the turbine mode to define the amount of active power supplied by the turbine when operating as a generator. Similarly, the fuzzy logic controller will regulate the opening of the wicket gates during the pumping mode to control the amount of active power consumption when running as a motor Diesel Engine Control As previously mentioned, when the DG is not operating its diesel engine should be turned off to save fuel. Once the DG is required in the system the diesel engine is turned on. When turning the diesel engine on it is necessary to account for the fact that the diesel engine does not start immediately and there is a delay that needs to be taken into consideration. As it is presented in [12] the diesel engine start delay is reflected in the system as frequency deviation. This delay can be avoided by preparing the diesel generator for coupling. 12

29 In this study, the control strategy applied in [4] is implemented, where a fuzzy logic controller was developed to anticipate the need of the DG and prepare the diesel engine for coupling before the system s frequency is affected by the engine s start delay. As described before, the proposed control strategy for the regulation of the balancing mechanisms implemented in this study is based on a fuzzy logic controller to ensure an optimal and reliable operation of the system under different operation scenarios and disturbances Dump Load Control Similar to the DG, when the DL is not in use its controller will turn off the DL in order to avoid wasting power that could be used for the HPS facility. Once the DL is required by the system conditions, the controller will turn on the DL. As presented in [39], design of the DL is based on the application of a switched resistor bank. Therefore, the DL does not have any delay when starting up by the switching of the resistors. The control system of the DL follows the design implemented in [40], [41] where a discrete controller was designed to regulate the switching of the resistors in order to match the load power consumption according to the frequency and frequency deviation of the system. As previously mentioned, the DL will only operate when the HPS facility is at its full power capacity, when operating in pumping mode, or when the HPS facility is making the transition from turbine mode to pumping mode so as to cover for the start-up time delay of the HPS facility Scope and Objectives Design of a control strategy to achieve reliable operation of a hybrid micro-grid power system operating in off-grid (islanded) conditions by measuring the frequency and frequency deviation of the system, ensuring that the system frequency does not exceed the limits established 13

30 by the standard EN [10], is proposed in this thesis. Additionally, individual fuzzy logic controllers are designed to improve the system s overall operation performance. The hybrid micro-grid power system under study involves the implementation of the following components: a wind generator (WG), a diesel generator (DG), a hydro-pumped storage (HPS) facility and a dump load (DL). The presented control strategy manages the operation of three balancing mechanisms to provide frequency regulation to the system. The control strategy must comply with the following rules: Maintain the system frequency within the operational limits of the system. The HPS facility operates according to its star-up times and time of operation limits. The DG starts its diesel engine to be ready for coupling when the HPS is at full capacity, in turbine mode, or when the HPS is making the transition from the pumping mode to the turbine mode. The DL starts consuming power when the HPS is at full capacity, in pumping mode, or when the HPS is making the transition from the turbine mode to the pumping mode. The main contributions from this thesis are the following; (i) development of a control strategy to regulate the operation of a hybrid micro-grid power system operating in islanded mode and managing the active power dispatch of three balancing mechanisms, (ii) design of a fuzzy logic controller to regulate the operation of a reversible single unit HPS facility during both turbine and pumping operation modes according to its operational limits, (iii) implementation of a control system to regulate the load consumption of a DL according to the frequency deviation of the system and finally (iv) the implementation of a fuzzy logic controller to control the commissioning of a diesel engine. 14

31 1.4.Thesis Outline This thesis is structured in six chapters as below. Modelling of the different components of the hybrid micro-grid power system is presented in chapter 2. In this chapter the characteristic and mathematical models of each component of the micro-grid are described. The design of the balancing mechanisms local controllers are described in chapter 3. Different frequency controllers of each balancing mechanism are presented and simulated in this chapter. The supervisory control strategy of the system is presented in chapter 4. The control strategy implemented and the operational guidelines that have to be followed to ensure the reliable operation of the system while maintaining the operational limits of frequency and frequency deviations are defined. The control strategy of this chapter is called Supervisory Control since it manages the overall operation of the system and the commissioning of the balancing mechanisms. In chapter 5, various operation scenarios and system s response under disturbances are simulated and presented. A variety of system scenarios are implemented to ensure that the control strategy is able to follow the operational guidelines while maintaining frequency operational limits. Also the overall performance of the micro-grid is tested under external disturbances, such as wind speed variations and main load changes. An economic analysis of the system configuration and its elements in order to evaluate the economic viability of the implementation of this type of a system is also presented. To finish, Chapter 6 concludes this thesis by presenting the findings of the present study and establishing the major contributions of the thesis. It also describes future work and further research concepts. 15

32 Parameters used in the mathematical models of the various components of the hybrid micro-grid are presented in Appendix A. The PID controllers used as a comparison and their parameters when evaluating the performance of the developed fuzzy logic controller are described in Appendix B. 16

33 CHAPTER 2: MODELLING OF THE HYBRID MICRO-GRID Mathematical models chosen to represent the different components of the hybrid microgrid power system are described in this chapter. All the system models and different test scenarios are implemented and tested on the simulation tool MATLAB SIMULINK to evaluate the frequency regulation of the system and its stability under disturbances. The hybrid micro-grid power system is composed of a wind generator (WG), a hydropumped storage (HPS) facility, a dump load (DL) and a diesel generator (DG). The model of each individual component is presented first and then all the models are combined to form the hybrid micro-grid power system model. Additionally, different local controllers for each component are also defined in this chapter. For instance, the DG active power is controlled by adjusting the speed reference set point of its speed governor. Similarly, the HPS facility and the DL follow a power set point to maintain the frequency regulation of the system. The WG is treated as a system disturbance. Therefore, the control of the WG, through the change of the blade pitch angle, is considered out of the scope of this study. Models of the different elements are described throughout this chapter and are presented as following. An overview of the hybrid micro-grid configuration is described in section 2.1. After that, the individual mathematical model of each component is presented in detail in sections 2.2 through 2.5. To conclude, a brief summary of the models defined in this chapter is given in section Hybrid Power System Configuration The configuration of the hybrid micro-grid power system used in this study is comprised of a WG, an HPS facility, a DL and a DG. The configuration of the system follows the structure 17

34 used in [11], and also implements models from [4] and [16]. Therefore, the size of the different components, voltage level and structure of the hybrid micro-grid is based on the literature and, thus, the effect of changing such characteristics is outside the scope of this study. Hybrid-microgrid power systems used in [4], [7], [11], [12], [27], [28], [42] share similarities with the system implemented in this thesis. A block diagram of the hybrid micro-grid is presented in Figure 2-1. Figure Block diagram of Hybrid Micro-grid Power System All components of the system operate at the same voltage level. Hence it is not necessary to include transformers in the system. The nominal voltage level of the micro-grid, Vrms, is 480V. The generated power is distributed amongst the system generators in the following way: the DG nominal power is 300kW, the WG nominal power is 275kW and the HPS facility nominal power, in turbine mode, is 150kW. Therefore, the maximum installed generation in the system is 725kW. 18

35 This total maximum generation capacity is only available when the wind conditions are optimum and the HPS facility has water in its upper reservoir. The controlled power consumption available in the system is comprised as following: the HPS facility nominal power, in pumping mode, is 150kW, and the DL nominal power is 200kW. Hence, the total controlled power consumption available in the system is 350kW. The power rating of the different components is based on the ratings implemented in studies [4], [11], [12], [41]. The dynamics and models of each component are presented in detail in the following sections. The HPS facility model is described in Section 2.2, the DG model is defined in Section 2.3, the WG and its dynamics are described in section 2.4, and the DL model is presented in section Hydro-Pumped Storage (HPS) Facility Model An HPS facility using a reversible pump-turbine unit is selected as an energy storage system with a power rating of 150kW and a nominal voltage Vrms of 480V. The overall HPS facility configuration implements the following models: hydraulic turbine dynamics, water reservoir, synchronous generator, turbine and pumping operation modes, speed governor and excitation system. A general structure of a hydro-pumped storage plant is presented in An HPS facility using a reversible pump-turbine unit is selected as an energy storage system with a power rating of 150kW and a nominal voltage Vrms of 480V. The overall HPS facility configuration implements the following models: hydraulic turbine dynamics, water reservoir, synchronous generator, turbine and pumping operation modes, speed governor and excitation system.. 19

36 Figure General Structure of a hydro-pumped storage plant [7] The HPS facility has two main operation modes, turbine mode and pumping mode. When operating in turbine mode the HPS will generate power. In order to produce power the HPS synchronous generator requires two inputs: mechanical power and field voltage. The mechanical power is provided from the water turbine, when releasing water from the upper reservoir to the lower reservoir. The field voltage is provided by the generator excitation system. When operating in pumping mode the HPS will consume power from the system. To perform such an operation the synchronous generator is required to switch its operation to that of a synchronous motor. In order to make this change of operation the rotating direction of the synchronous machine needs to be reversed and as a result the mechanical input to the synchronous machine model will be negative. The field voltage input is still provided by the excitation system to provide voltage regulation to the system. The mechanical power provided or consumed by the turbine is regulated by controlling the opening of the wicket gates of the turbine. These gates control the flow of water that enters the 20

37 turbine. During turbine mode the regulation of these gates is controlled by the speed governor of the turbine in order to generate the required power. In pumping mode the opening of the wicket gates can be fixed at a specific operation point or, as it is implemented in this study, it can be regulated by a controller to provide frequency regulation during high wind conditions. A detailed layout of a synchronous machine and a reversible pump-turbine is shown in Figure 2-3. Figure Layout of Reversible Pump-Turbine unit in vertical position [7], [85] The mathematical model of the tunnel and penstock water dynamics are presented in Section 2.2.1, the water reservoir model is defined in Section 2.2.2, the pump-turbine dynamics in turbine mode and pumping mode are described in Sections and 2.2.4, respectively, the speed governor is presented in Section 2.2.5, the excitation system model is described in Section and the synchronous machine model and characteristics are defined in Section

38 Tunnel and Penstock Water Dynamics The reversible hydraulic pump-turbine provides mechanical power to the synchronous machine when operating in turbine mode and consumes mechanical power when operating in pumping mode. Since there is only one penstock that is used for both operation modes, the tunnel and penstock water dynamics remains the same for either the pumping or turbine mode. This model defines the penstock dimensions, its effects on the water dynamics and the time delays related to the response of the HPS facility. The tunnel and penstock water dynamics mathematical model implemented in this thesis has been used in several studies such as [7], [11], [43], [44]. The block diagram that represents the mathematical model of the tunnel and penstock water dynamics is depicted in Figure Figure Block diagram of tunnel and penstock water dynamics [7] In Figure 2-4, is the total available static head, is the dynamic head established by the pump-turbine unit, and are frictional coefficients of the penstock and the tunnel respectively, is the loss in the system due to the frictional coefficients, is the dynamic flow of water established by the pump-turbine unit and, known as the water starting time of the pipe segment [7], [45], is defined by Equations 2.1 and 2.2. g (2.1) 22

39 (2.2) In Equation 2.1, represents the length of the water tunnel, is the water velocity, g is the gravity constant and is the hydraulic water head. In Equation 2.2, is the water flow at nominal operation of the turbine and is the cross-sectional area of the penstock. Therefore, represents the time required for a specific head of to accelerate the water in the penstock from standstill to the water velocity,. Usually, at full loads has a value that lies between 0.5 and 4.0 seconds [45]. All parameters of this model are listed in Appendix A.1 [11] Water Reservoir Model The HPS time of operation is defined by the size of the water reservoirs. The water reservoir models implemented in this study are based on the proposed HPS facility presented in [11], located in Ramea island, Newfoundland, Canada. The lower reservoir is a body of water, in this case the Coast of Ramea Island. Hence, the lower reservoir model is not limited to specific dimensions and does not imply operation time constrains to the HPS facility. Comparatively, the upper reservoir is a man-made concrete-based tank that is proposed to be located on a hill, at 63 meters in height, named Man of War. According to [11] the proposed size of the upper reservoir for a head, 63 m, will contain a volume of 3,932 m. The height of the reservoir walls is 2 meters. Based on the information presented in [11] the physical dimensions of the upper reservoir are, in Length-Width-Height, 50m(L)x39.32m(W)x2m(H). The calculated nominal flow of a 150kW HPS facility is presented in [11] with a value of m s. Thereby, the minimum operating time of the HPS facility, when operating at nominal capacity, is 3.14 hours. 23

40 The mathematical model of the tank is defined by the differential equation shown in Equation 2.2, based on [46]. (2.3) where represents the flow of water, also expressed as, entering or leaving the tank, is the area of the bottom of the tank and is the height of the water level in the tank. Equation 2.3 can be also expressed as Equation 2.4. (2.4) Equation 2.4 can be integrated on both sides to find the amount of water that the tank will have during the operation of the HPS facility based on the flow of water and the cross-sectional area of the upper reservoir. The level of water in the tank can be computed using Equation 2.5. (2.5) By implementing the mathematical model defined by Equation 2.5 it is possible to simulate how the water level in the tank will behave according to the change of flow in the HPS facility. When the HPS facility is operating in turbine mode water is released to provide power to the turbine, therefore, the water level of the upper reservoir will start to decrease. Instead, when the HPS facility operates in pumping mode water is pumped to the upper reservoir and, hence, the water level of the upper reservoir will start to rise. During the turbine mode of operation of the HPS facility, the upper reservoir model besides defining the minimum operation time of the HPS facility, also establishes the minimum level of water that can be reached to ensure a reliable operation of the unit. According to the dimensions of the tank and penstock cross sectional area defined by [11] the level of water can reach to a 24

41 minimum of 0.4 meters. Therefore, it must be considered in the logic of the controller the shutdown of the HPS facility and the startup and coupling of the Diesel Engine to the Diesel Generator when the level of water is approaching the lower limit level of 0.4 meters. During the pumping mode operation, of the HPS facility, the upper reservoir defines the maximum limit of water level and time of operation of the HPS facility. In order to increase the reliability of the micro-grid operation, an overflow valve in the model of the upper reservoir is considered. This valve will drain to the coast (i.e. lower reservoir) the excess water that is being pumped, allowing the HPS facility to continue its operation, in pumping mode, to provide frequency regulation to the system. Hence, due to the implementation of this valve the operation of the HPS facility, in pumping mode, is not limited by the size of the reservoir but to the nominal capacity of the unit. The upper reservoir parameters, taken from [11], are given in Appendix A Pump-Turbine Dynamics in Turbine Mode When the HPS facility runs in the turbine mode, its operation is equivalent to a regular hydro-turbine. Equation 2.6 defines the relationship between the turbine head, turbine flow and gate opening [7], [43], [45], [47], [48], [49]. (2.6) As previously is the dynamic head, is the dynamic flow of water, denotes the opening of the wicket gates controlled by the output of the speed governor and the bar above each variable indicates that the value is in per unit (pu). The per unit power output of the unit in the turbine mode is given by Equation 2.7. (2.7) 25

42 where, is the per unit no load flow and is the per unit mechanical output power from the turbine. This power will provide the mechanical power input to the synchronous machine model. Block diagram of the HPS facility operating in turbine mode is shown in Figure 2-5. Figure Block Diagram of an HPS facility in turbine mode [7] The block called Tunnel and Penstock Water Dynamics refers to the model presented in section 2.2.1, Figure 2-4. This system includes two types of losses, the frictional losses and, introduced in the Tunnel and Penstock Water Dynamics model, and, the no load flow loss. The described model is implemented when the HPS facility is generating power to the micro-grid in the turbine mode. Parameters of the HPS facility, in turbine mode, are presented in Appendix A Pump-Turbine Dynamics in Pumping Mode When the HPS facility operates in the pumping mode, the pump-turbine dynamics are governed by its head-flow characteristic curve at the rated operating speed. The head-flow curve defines the pump water flow at the rated operating speed to the dynamic head developed by the pump [7], [50] [52]. To incorporate speed variations during transient conditions, the pump affinity laws need to be implemented [50], [51].,, (2.8) 26

43 Equation 2.8 shows the pump affinity laws, where the subscripts 1 and 2 indicate operating points under two different speeds According to [52] the pump head-flow curve with speed variations defined in Equation 2.8 can be approximated by a quadratic polynomial equation presented in Equation 2.9. (2.9) Equation 2.9 defines the quadratic polynomial equation of the pump head-flow curve with speed variations, where is the speed of the pump and, are coefficients for curve fitting. According to [7] the effects of the changes of the wicket gates positioning are modelled as part of the penstock frictional head loss, which is similar to the throttling effects for a conventional centrifugal pump [50]. The system equilibrium operational point of the HPS facility when operating in pumping mode is defined by the intersection of the pump head-flow curve and the tunnel system curve [53]. Figure 2-6 shows the pump head flow curve and tunnel-penstock system curve with the throttling effect of gate closing. Figure Pump head-flow curve and tunnel-penstock system curve with throttling effect of gate closing [7] 27

44 In Figure 2-6 the system curve represents the total head (static head and frictional head) against which the water is to be pumped. As the wicket gates start to close, the frictional head increases and as a consequence the flow of water is reduced. After the wicket gate changes its position the tunnel-penstock system curve moves the equilibrium operating point by changing the total penstock equivalent friction [7]. When operating in pumping mode, the penstock frictional coefficient,, presented in Figure 2-4 is replaced by an equivalent frictional coefficient. (2.10) (2.11) Equations 2.10 and 2.11 defines the additional frictional coefficient caused by the change of position of the wicket gates, and where represents the maximum opening of the wicket gates in per unit. When the gates are fully opened, Equation 2.11 gives no additional friction to the system. On the contrary when the gates are almost closed Equation 2.11 gives infinite friction. A set of system curves with different wicket gate openings, by using Equation 2.11, the pump head-flow curve and the pump power output curve are shown in Figure

45 Head (pu), Hydraulic Power (pu) Penstock System Curves Pump Power Output Curve Pump Head-Flow Curve Pump Flow (pu) Figure Equilibrium operating points at different wicket gate positions [7] The block diagram of an HPS facility operating in pumping mode is presented in Figure 2-8, where is the pumping efficiency, which is around 90% at the rated operation point [7]. Figure Block diagram of an HPS facility in pumping mode with gate frictional effects [7] The block diagram presented in Figure 2-8 incorporates the pump head-flow curve defined by Equation 2.9 and the Tunnel and Penstock Water Dynamics model defined in Section 2.2.1, Figure 2-4, including the gate frictional effects. 29

46 The described model is implemented when the HPS facility is consuming power from the micro-grid in pumping mode. The parameters of the HPS facility, in pumping mode, are listed in Appendix A Hydraulic Speed Governor The hydraulic speed governor operates to control the wicket gates opening during the operation of the HPS facility, in turbine mode. The speed governor regulates the mechanical power provided by the turbine to the synchronous machine when operating as a generator. The model implemented for the speed governor is based on [43], [49], where the speed governor for the hydraulic turbine comprises the models of a PID governor system, and a servomotor connected to the HPS facility model, in turbine mode. The block diagram of the speed governor is depicted in Figure 2-9. Figure Block diagram of Hydraulic Turbine Speed Governor [49] The hydraulic turbine block implements the same model described in Section and depicted in Figure 2-5. The wicket gate servomotor is modeled by a second order system as shown in Figure

47 Figure Block diagram of Gate Servomotor model [49] The classic speed governor uses a PID controller to regulate the opening of the wicket gates through the operation of the servomotors. In this study it is proposed to replace the PID controller of the speed governor model with a Fuzzy Logic controller in order to improve the performance of the system. As previously mentioned, when the HPS facility operates in the pumping mode, opening of the wicket gates is no longer controlled by the speed governor and it is usually fixed at a specific operating point. In this study it is proposed to implement a Fuzzy Logic controller to regulate the opening of the wicket gates, during the pumping mode operation, to provide frequency regulation to the system. The mathematical model the speed governor implemented is available in the SIMULINK SimPower System Library. The hydraulic turbine speed governor parameters where extracted from [7], [11] and are presented in Appendix A.5. The PID parameters of this governor are shown in Appendix B Excitation System The synchronous machine requires field current to produce power and the excitation system is the component that provides and regulates such current. The excitation system is in charge of regulating the machine and system voltage [54]. The excitation system is connected to the synchronous machine during both synchronous generator and motor operations. 31

48 Even though the voltage regulation is not considered in the scope of this study, it is still necessary to control and maintain the system voltage level in order to ensure a reliable operation of the micro-grid. The voltage regulation is provided by the excitation systems of the HPS facility and DG synchronous machines. The mathematical model of excitation system is based on [55], [56]. The excitation system is defined by the transfer function shown in Equation (2.12) The selected excitation system model is the IEEE Standard DC1A Excitation System. A built in model is available in SIMULINK SimPower System Library [56]. The block diagram of the excitation system model is presented in Figure Figure Block diagram of excitation system [56] Since, the synchronous machine model is based in the d-q rotational frames, a d-q transformation is implemented at the input of the model to calculate the positive sequence terminal voltage of the synchronous machine. The voltage magnitude is calculated by using Equation (2.13) Parameters of the excitation system model were extracted from [7], [55], [56] and are listed in Appendix A.6. 32

49 Synchronous Machine The HPS facility synchronous machine uses a salient-pole rotor, and it can be operated as a synchronous generator or as a synchronous motor. The synchronous machine is rated 480V and 150kVA. The mathematical model of the synchronous machine is divided in two parts, electrical and mechanical and the implemented model is based on [57], [58]. The electrical section of the synchronous machine is modeled in the d-q frame by a sixthorder space-state model. The d-q circuits that create the electrical model of the machine are presented in Figure Figure Equivalent circuits of synchronous machine Y connected stator windings The definition of variables and the various subscripts implemented in the equivalent circuits depicted in Figure 2-12 are presented in Tables 2.1 and 2.2 respectively. Table Variables of Synchronous Machine Model [4], [58] Abbreviation Definition R Resistance L Inductance V Voltage i Current φ Flux ω Angular Speed 33

50 Table Subscripts of Synchronous Machine Variables [4], [58] Subscripts Definition d, q d and q axis quantity R, s Rotor and stator quantity l, m Leakage and magnetizing inductance f, k Field and damper winding quantity The mathematical model of the synchronous machine implemented in this study is comprised by Equations (2.14) (2.15) (2.16) (2.17) (2.18) (2.19) (2.20) (2.21) (2.22) (2.23) (2.24) (2.25) 34

51 As previously mentioned, the voltage regulation is outside the scope of this study. Therefore, the excitation system dynamics are modeled but not analyzed in detail. The mathematical model of the synchronous machine does not consider the magnetic saturation of the rotor and stator iron cores. Due to this, the magnetic characteristics of the machine present a linear behavior. The load angle and excitation system are the variables more influenced by the magnetic saturation of the cores [4], [59]. As presented in [60] the load angle calculation and its variation are very important when considering this variable for the design of a rotor position controller. Similarly, when performing system stability studies, the system s performance under extreme conditions, for example when a fault occurs in the system or when a sudden loss of one of the generating units happens, etc [45], [61], is evaluated and analyzed. Therefore, when performing such studies it is important to consider the generator load angle. This thesis is focused on evaluating the performance of the system under normal operation. Thus, system fault conditions are not considered and loss of stability is not anticipated. The mechanical section of the model is based on the mathematical model presented in [62] and is defined by Equations 2.26 and (2.26) 2 (2.27) where, is the speed variation with respect to the speed of operation, is the constant of inertia, is the mechanical torque, is the electromagnetic torque, is the damping factor representing the effect of damping windings, is the mechanical speed of the rotor and is the speed of operation. 35

52 The implemented synchronous machine model can be found in the SIMULINK SimPower System Library [58], [62]. All the parameters of the synchronous machine model are given in Appendix A Diesel Generator (DG) Model The diesel generator synchronous machine is rated 480V, 300kVA. The DG mathematical model is composed of the diesel engine, the synchronous machine, the excitation system and the clutch. Similar to the HPS model, in order to produce electrical power the DG requires mechanical power input, that is provided by the diesel engine, and the field current, produced by the excitation system. The generated power produced by the DG is defined by the diesel engine s governor and state of the clutch. Based on the speed reference the diesel engine s governor computes the estimated mechanical power output required by the DG. Additionally, the clutch performs the coupling of the diesel engine and the synchronous machine to transmit the mechanical power from one component to another. The diesel engine model as well as the engine s governor are described in Section 2.3.1, the synchronous machine, excitation system and clutch models are defined in sections 2.3.2, and respectively Diesel Engine and Speed Governor The mechanical power input to the synchronous machine is provided by the diesel engine. The model of the diesel engine is comprised of three parts, the engine, actuator and electronic control box. 36

53 The diesel engine mathematical model is based on [63], [64] and it has been widely implemented in several studies [4], [12] [15], [17], [39]. The mathematical model of the engine, actuator and electronic box are defined by separate transfer functions of high order, Figure 2-13 shows the block diagram of the diesel engine and governor model. Figure Block diagram of diesel engine and governor model [63] The electronic control box and actuator define the diesel engine s speed governor. When the DG operates in isochronous mode the governor provides frequency regulation by controlling the amount of fuel that is provided to the engine s fuel injectors, and thereby regulating the amount of fuel consumption according to the speed reference of the diesel engine [65], [66]. The engine block defines the engine dead time [63], [64] and it is modeled as a time delay. The mathematical model of the diesel engine and speed governor is available in SIMULINK SimPower System Library. The parameters for this model were taken from [63] and are listed in Appendix A Excitation System The excitation system model implemented for the DG is the same model described in the previous Section for the excitation system of the HPS facility, where an IEEE Standard DC1A Type excitation system model is defined. The excitation system model is available in 37

54 SIMULINK SimPower System Library. All the parameters of this model were extracted from [55], [56] and are listed in Appendix A Synchronous Machine The synchronous machine of the DG is rated 480V, 300kVA. Similar to the previous section, the mathematical model and equations implemented to simulate the dynamics and characteristics of the synchronous machine of the DG follow the same model presented in Section for the synchronous machine of the HPS facility, where the model of the synchronous machine is divided in an electrical and a mechanical section. The implemented model can be found in the SIMULINK SimPower System Library [58], [62]. The parameters of the synchronous machine of the DG model are given in Appendix A Clutch Model In order to transfer the mechanical power from the diesel engine to the synchronous machine the clutch must couple these two components. The coupling of the diesel engine depends on the state of the clutch, when the clutch is engaged the mechanical power is transferred to the synchronous machine from the diesel engine. Therefore, the DG is capable of producing real power to provide frequency regulation to the system. On the contrary, when the clutch is disengaged no mechanical power is transferred, since the diesel engine is mechanically disconnected from the synchronous machine, and thus the DG is not capable of producing real power. The clutch is implemented in this study to decouple the diesel engine when it is not required by the micro-grid system and, therefore, reduce its fuel consumption. The clutch transmits the torque from the diesel engine to the synchronous machine. This transmitted torque is estimated by the clutch model. Since the clutch is a connection element in 38

55 the DG model and it also defines the torque transmission as a consequence, it affects the diesel engine speed. The implemented clutch mathematical model is based on studies [4], [12], [17], [67]. As previously mentioned the clutch model modifies the mechanical power transmission to the synchronous machine and the diesel engine speed. Equations 2.28 and 2.29 define the mathematical model of the clutch. (2.28) 1 2 (2.29) where is the equivalent torque transmitted to the synchronous machine when the clutch is engaged, and are the inertia constants of the synchronous machine and diesel engine, respectively. Similarly, and are the torques of the synchronous machine and the diesel engine respectively, and is the diesel engine speed. The block diagram of the clutch mathematical model defined by Equations 2.28 and 2.29 is depicted in Figure /(H s +H de ) H de Figure Block diagram of Clutch Model [12] The control strategy implemented in this study follows the same principle established in [4], where the clutch is used as a control actuator. When the clutch is engaged the DG is commissioned for frequency regulation, and when the clutch is disengaged the DG is deactivated and no longer 39

56 provides frequency regulation. The operation of the clutch is controlled by logic signals 1 and 0, for ON and OFF, respectively. When the logic signal 1 or ON is activated the clutch couples the diesel engine to the synchronous machine and the torque is equal to Equation When the logic signal 0 or OFF is sent the clutch decouples the diesel engine from the synchronous machine and as a result the torque is equal to 0. As shown in Figure 2-14 the name of the control variable is defined as CLUTCH. The cranking dynamics are not modeled since the model of the diesel engine is not focused on the engine s first hand control. Nonetheless, as defined in [4] the effects of the engine cranking delay must be considered in the system. When the clutch receives the ON signal the diesel engines speed must be increased in order to match the synchronous machine speed. However, due to the cranking delay of the diesel engine its start-up is not immediate, and hence, the locking of the clutch is postponed. Since the DG can only start to provide frequency regulation when the clutch is engaged, the system frequency will be affected by the cranking delay. The cranking delay of a 300kVA DG unit is estimated to be about 0.5 seconds according to [4], [12]. This cranking delay is considered the time from the start of the diesel engine until it reaches an idle speed of 0.3 p.u. To take into account the effect of the cranking dynamics, an idle speed of 0.3 p.u. is considered as the rest speed, and a 0.5 seconds time delay is applied every time the diesel engine s speed is increased from 0.3 p.u. [4]. As previously stated, in order for the clutch to engage the speed of the diesel engine,, must match the speed of the synchronous machine,. As established in studies [4], [12], the relative speed difference between and must have a maximum value of 10-5 p.u. when the clutch is going to be engaged. Therefore, the clutch needs two signals in order to be engaged. 40

57 These are the CLUTCH signal ON and the relative speed difference calculation must be less than or equal to 10-5 p.u. The clutch states under different conditions are presented in Table 2.3 Table Clutch States under different conditions [4], [12] CLUTCH Signal Clutch State ON/ Engaged ON/1 > 10-5 Disengaged OFF/ Disengaged OFF/0 > 10-5 Disengaged To reduce the DG fuel consumption, the diesel engine should be turned off when the clutch is disengaged. In this case it implies that its reference speed will be set to the rest speed of 0.3 p.u until the diesel is required to be commissioned for frequency regulation again. All the clutch parameters are listed in Appendix A Wind Generator (WG) Model The wind generator is composed by a wind turbine and an asynchronous generator rated 480V, 275kVA. As previously mentioned, the WG is considered as a disturbance to the micro-grid system and therefore its control is outside the scope of this study Wind Turbine The wind turbine mathematical model is based on [68] [71]. The mechanical power output of the wind turbine is expressed by Equation 2.30., 2 (2.30) where is the mechanical output power of the wind turbine, is performance coefficient of the of the wind turbine, is the air density, is the turbine blades swept area, is the wind speed, is the tip speed ratio of the rotor blade tip speed to wind speed, is the pitch angle of the blades. The turbine swept area is established by the design of the blades of the wind turbine. 41

58 The air density at sea level is kg/m 3, according to [4], [72]. The performance coefficient is defined by Equation 2.31., (2.31) where,,,, and are fixed coefficients the wind turbine dynamics as defined in [69], and is given by Equation 2.33 below. Since the pitch angle control of the wind turbine is outside the scope of this study, is considered to be 0. The mathematical models that define the tip speed ratios and are defined in Equations 2.32 and 2.33 [4], [68], [70]. (2.32) (2.33) where is the angular speed of the turbine rotor and is the radius of the blades of the wind turbine. As is expressed in Equation 2.32 the value of depends on the turning blades angular speed and the wind speed. The wind turbine parameters are dependent on the implemented wind turbine design. In this study the wind turbine parameters are based on [4], where the wind turbine presents the following characteristics: the cut-in speed of 4.5 m/s, nominal speed of 14 m/s. These parameters are referenced to a commercial SIEMENS wind turbine [73], and based on the wind turbine power rating the blade radius selected is of 20 m [74]. The wind turbine parameters are presented in detail in Appendix A

59 Asynchronous Generator The asynchronous generator is connected to the wind turbine and is in charge of transforming the mechanical power from the wind turbine to electrical power generation. The asynchronous machine is rated 480V, 275kVA. To avoid the necessity of a gear box model the wind turbine output torque and the asynchronous machine model are defined in per unit. Similar to the synchronous machine model, the asynchronous machine mathematical model is defined with an electrical model and a mechanical model. The asynchronous machine both electrical and mechanical models are based on [57], [75]. As with the synchronous generator, the electrical section of the model is represented in the d-q rotating frame. Figure 2-15 shows the configuration of the equivalent electrical circuits of the model. Figure Equivalent circuits of an asynchronous machine in d-q representation [75] The equations that define the mathematical model of the asynchronous machine electrical section are shown by Equations The variables and subscripts of this model are almost the same as the synchronous machine variables, which are described in Tables 2.1 and 2.2. The only difference in this model is on the rotor subscript, which is changed from to. In a similar way, the variables implemented in the asynchronous machine model are rotor variables referred to the stator winding reference. 43

60 and (2.34) (2.35) (2.36) (2.37) 1.5 (2.38) (2.39) (2.40) (2.41) (2.42) (2.43) (2.44) The mechanical section of the asynchronous machine model is defined by Equations (2.45) (2.46) where represents the angular velocity of the rotor, is the rotor angular position, is the electromagnetic torque, is the mechanical torque, is known as the combined rotor and load inertia constant and is the combined rotor and load viscous friction coefficient. As defined in studies [4], [12], [14] [16], [19], [39], [41], a capacitor bank is connected to the WG in order to provide reactive power compensation. The power output, in p.u, of the WG 44

61 according to the wind speed, in m/s, is presented in Figure As seen from the figure, the cut in speed is 4.5 m/s and the maximum power output of the unit is reached at 14.1 m/s. WG Power Output (pu) Figure Output Power of Wind Generator Wind Speed (m/s) The mathematical model of the asynchronous machine is available in SIMULINK SimPower System Library [75]. The model parameters were extracted from [4], [39] and are given in Appendix A Dump Load (DL) Model The DL is rated 480V and has a nominal power consumption of 200kW. The DL is in charge of consuming excess power present in the system when the HPS facility, in pumping mode, has reached its maximum power or when it is necessary to cover for the time delay presented in Table 1.1, when the HPS facility is making the transition from the turbine mode to the pumping mode. The sizing and selection of the DL model follows the model implemented in [4], [39] [41]. The DL load model is composed of 8 three phase resistors performing discrete steps of 1kW each. The resistors are connected by implementing ideal switches, identified as S1-S8. The switches are commutated to consume the required power in order to maintain the frequency regulation of the system. To minimize disturbances in the micro-grid the switching is performed 45

62 at the zero crossing of voltage [41]. The ON and OFF signals to control the switches are regulated by a digital binary signal of 1 and 0 [4], [41]. The DL model was implemented in SIMULINK and is depicted in Figure Figure DL Simulink Model The DL power consumption is regulated by a discrete controller based on the design of studies [39] [41]. This controller establishes the amount of consumed power by sending a discrete binary signal to open and close the DL resistors switches. When the DL is not required in the system, the controller will turn off all the switches, and, therefore, the power consumed by the DL will be 0 in order to avoid consuming power when the DL is not required by the micro-grid. Parameters of the DL model and the DL controller are listed in Appendixes A.14 and B.5, respectively Chapter Summary The mathematical model of the hybrid micro-grid analyzed in this study is defined in this chapter, It is used to develop and test the proposed control strategy. 46

63 The hybrid micro-grid comprises the implementation of an HPS facility, operating in both turbine mode and pumping mode, a DG, a WG and a DL. Each component is modelled by a mathematical model of high order, so as to be able to test and evaluate the dynamic response of the micro-grid. Additionally, this chapter also describes the controllers of the HPS facility, DG, and DL. These controllers are implemented to provide frequency regulation of the system. The HPS facility is used as an energy storage system and its power output or consumption is regulated according to the power required by the system. The HPS controller establishes the operation of the HPS facility either as a synchronous generator or synchronous motor according to the requirements of the micro-gird. Similarly, the DL is used to consume the excess power that can be present in the microgrid and maintain frequency regulation under such conditions. The DG is commissioned when there is extra power consumption in the system. The DG is controlled by speed governor and the state of the clutch. By engaging the clutch, the speed governor starts running the DG in isochronous mode making this generator capable of providing frequency regulation to the system. On the contrary, when the clutch is disengaged the DG is no longer capable of providing frequency regulation and the generator operates as a synchronous condenser providing just voltage support to the micro-grid. All the mathematical models of the different elements that comprise the hybrid micro-grid power system have been implemented, tested and analyzed in the simulation software of MATLAB SIMULINK. 47

64 CHAPTER 3: DESIGN OF LOCAL CONTROLLERS FOR THE OPERATION OF THE HYBRID MICRO-GRID Local controllers for the individual components of the hybrid micro-grid, the HPS facility, both in turbine and pumping mode, the DG and DL, are described in this chapter. The local controllers are in charge of estimating the operational set points of the balancing mechanisms of the system, in order to generate or consume the required power to maintain the frequency regulation of the system. The local controllers models and simulations are presented throughout this chapter as follows. Section 3.1 presents an outline of the local controllers of each balancing mechanism. After that the design, analysis and simulation of the HPS facility, in turbine and pumping mode, DG and DL are shown through Sections 3.2 to 3.5, respectively. To conclude, a summary of the local controllers of the system is given in Section Outline of the Local Controllers of the Hybrid Miro-Grid The micro-grid local controllers are the following individual controllers of the balancing mechanisms: the HPS facility in turbine mode, frequency controller, the HPS facility in pumping mode, frequency controller, the diesel engine controller and the DL frequency controller. In order to maintain and provide the frequency regulation of the micro-grid the operational set point of the balancing mechanisms is regulated by the above mentioned controllers. The HPS facility, when operating in turbine mode, is controlled by the set point,. This set point defines the speed reference for the speed governor of the turbine. Therefore, during this operation mode the speed governor regulates the opening of the wicket gates to provide frequency regulation according to the set point,. However, if the water level in the upper reservoir is reaching its minimum level the set point for the speed governor will be set to 0. 48

65 Similarly, when the HPS facility operates in pumping mode the pump operation is controlled by the set point,. This set point establishes the reference frequency that is required to be maintained in the network. Since the speed governor only operates during the turbine mode, a fuzzy logic controller was developed to regulate the opening of the wicket gates, during the pumping mode, according to the set point,. The DG is controlled by the set point,. This set point defines the speed reference for the speed governor of the diesel engine. In a similar way to the HPS facility, in pumping mode, the DL operation is regulated by the set point,. This set point defines the reference frequency that the DL frequency controller will follow in order to maintain the frequency stability of the system. The local controller s detailed design is described in the following sections. The frequency controller of the HPS facility, in turbine mode, is described in Section 3.2. The HPS facility, in pumping mode, is defined in Section 3.3. The DG and DL controllers are presented in sections 3.4 and 3.5, respectively HPS Facility in Turbine Mode Frequency Controller This section describes the HPS facility, in turbine mode, frequency controller. The HPS facility, in turbine mode is controlled by the set point,. This set point is used by the speed governor to define the amount of power that needs to be generated by the turbine in order to regulate the frequency of the system. PID controllers have been implemented in previous studies to regulate the operation in turbine mode of an HPS facility [43], [49]. The PID controller in [49] has been analyzed and simulated in Section

66 In contrast, study [38] presents the design of a fuzzy logic controller for an HPS facility to provide frequency regulation. Similarly, the design and tuning procedure involved when changing from a properly tuned PID controller to a fuzzy logic controller is defined in [76], [77]. The procedure implemented in [76], [77] is followed to design the fuzzy logic controller in order to obtain an improved response of the controller. The comparison between the PID frequency controller and the fuzzy logic frequency controller is presented in Section PID Frequency Controller A PID controller to regulate the operation of an HPS facility, in turbine mode, is proposed in [43], [49]. The PID controller has the input set point of and it regulates the opening of the wicket gates to control the power produced by the HPS facility turbine,. The PID controller implemented in [43], [49] is depicted in Figures 2.9 and 3.1. Figure HPS in Turbine Mode PID Controller Schematic [49] The PID gains based on [43], [49] are listed in Appendix B.1. As presented in Figure 3-1 the controller uses a classical PID structure to regulate the operation of the HPS facility. The PID controller input, named error in Figure 3-1, is the speed deviation,, from the speed reference set point,, where is calculated as defined in Equation

67 (3.1) where is the speed deviation, is the actual rotational speed of the HPS unit and is the speed reference set point of the controller. The present study considers a maximum load step of ±50 kw, representing 33% of the HPS facility rating which is a considerable load step to test the HPS facility performance. The PID controller was manually tuned under the maximum load step scenario. The new PID gains are listed in Appendix B.2. The PID performance is tested under two scenarios. In the first scenario, the PID is tested with load step of 25 kw. The results are shown in Figure 3-2. Frequency of the system must be within the limits defined in standard EN [10], where for a LV system, the frequency variation should be maintained between ±1% for 99.5% of the week, and -6%/+4% for 100% of the week. Frequency (Hz) Simulation Time (s) Figure PID Frequency Control +25kW Load Step The results presented in Figure 3-2 show that the PID provides good frequency regulation under a load step of + 25kW, with a maximum frequency deviation,, of Hz. 51

68 Under the second scenario the PID control is tested with a load step of +50 kw, this is done to evaluate the response of the PID at maximum load step disturbance. Frequency (Hz) Simulation Time (s) Figure PID Frequency Control +50kW Load Step During the second scenario in Figure 3-3, with maximum load step, the PID still provides good frequency regulation. Even though the frequency deviation,, goes below -1% for 99.5% of the week limit, the amount of time, in percentage, that the frequency is under this limit just represents % of the week and the standard gives a 0.5% of the week that the frequency can reach those limits. Therefore, the performance of the PID controller is considered to be acceptable according to the limits defined in [10] Fuzzy Logic Frequency Controller Design A fuzzy logic controller to regulate the operation of an HPS facility, in turbine mode, is proposed in [38]. Similarly, studies in [76], [77] define the design and tuning procedure carried out for tuning the rules from the PID domain over to a fuzzy logic controller. This technique starts with a classical tuned PID controller, as the one presented in Section 3.2.1, then the PID controller 52

69 is replaced by an equivalent linear fuzzy logic PID controller and finally the fuzzy logic controller is adjusted and fine-tuned to achieve non-linear control surface. This study follows the procedure defined in [76], [77]. Therefore, the first step taken to design of the fuzzy logic frequency regulator for the operation of the HPS facility, in turbine mode, is the design of the equivalent linear fuzzy logic PID controller. The schematic of the linear fuzzy PID controller is depicted in Figure 3-4. Figure Linear Fuzzy PID Controller Schematic [77] Similar to the classical PID controller, the input to the linear fuzzy PID controller is the error from the speed reference, in this case named e in Figure 3-4. GE, GCE, GCU and GU are scaling factors determined from the Kp, Ki, Kd gains used by the conventional PID controller. The output of the controller u is the new operation point for the wicket gates servo motors. According to [76], [77] the expressions of the traditional PID and the linear fuzzy PID controller are related as defined by Equations 3.2, 3.3 and 3.4. These equations show the relationship between the classical PID and the equivalent linear Fuzzy PID. (3.2) (3.3) (3.4) 53

70 By assuming the maximum reference step as 1, thereby the maximum error e is 1. Since the input range of the fuzzy logic E is [-20 20] the scaling factor GE is defined as 20. By defining the previous scaling factor, GCE, GCU and GU can be solved from Equations 3.2, 3.3 and 3.4. The calculation of the scaling factors GCE, GCU and GU are defined in Equations [77]. The values of the scaling factors of the linear fuzzy PID controller are given in appendix B (3.5) 4 (3.6) 2 (3.7) (3.8) The linear fuzzy input membership functions were designed to be an equivalent of the PID controller [77]. The input control variables, after the scaling factors, E and CE are divided into 3 fuzzy membership functions: Negative (N), Zero (Z) and Positive (P). The input E and CE membership functions are depicted in Figure 3.5a and 3.5b. Similar to the inputs, the controller output u was designed according to the PID controller response. The fuzzy output variable u has been designed with 5 membership functions: Large Negative (LN), Small Negative (SN), Zero (Z), Small Positive (SP), Large Positive (LP). The output membership functions SP and LP increase the power operation set point of the turbine, while SN and LN decrease the power operation set point of the turbine. The input and output membership functions are shown in Figure

71 a) E input membership functions Degree of membership Degree of membership Degree of membership b) CE input membership functions c) u output membership functions Figure Linear Fuzzy Logic PID Frequency Controller Membership Functions 55

72 The designed fuzzy rules are defined as established in [77] and are listed in Table 3.1. The intersection of the row and column for the considered precedents defines the rule implication for a specific combination of precedents. For example: if E is P and CE is P then u is LP. CE Table Fuzzy Rules Set E N Z P N LN SN Z Z SN Z SP P Z SP LP The fuzzy rules are implemented using a Mamdani style inference system with an algebraic product for AND connective. The defuzzification method implemented for this system is the center of gravity method (COG). Relationship between the input and output for this fuzzy inference system is defined by Equation 3.9 [78].,, (3.9) where is the consequent of rule, and is characterized by the membership functions of and. The, is the area under the membership functions and, and is the number of fuzzy rules. The expression of Equation 3.9 is a weighted average of the s, 1,, [78]. 56

73 The control surface for the linear fuzzy PID controller, depicted in Figure 3.6, gives an overview of the controller response to variations of E and CE. The output u magnitude is increased when E and CE are both positive. When E and CE are both negative, the output u is decreased. In a similar way if E and CE have opposite signs the output u remains unchanged. As it can be seen from Figure 3.6 the control surface is linear and it is intended to operate as an equivalent of the classical PID controller u CE E Figure Linear Fuzzy PID Frequency Controller Control Surface After designing the linear fuzzy PID frequency controller, the last step was to adjust and fine-tune the linear controller to achieve a non-linear control surface. The values of the scaling factors are exactly the same as the one used for the linear fuzzy controller. To design the non-linear fuzzy controller the input membership functions were designed according to the procedure defined in [76], [77]. For the non-linear fuzzy controller the input control variables E and CE, after the scaling factors, are divided into 2 fuzzy membership 57

74 functions: Negative (N) and Positive (P). The input E and CE membership functions are depicted in Figures 3.7a and 3.7b. Similar to the inputs, the controller output u was adjusted to have a non-linear response. The fuzzy output variable u has been designed with 3 membership functions: Max, Zero and Min. a) E input membership functions b) CE input membership functions Figure Non-Linear Fuzzy Logic PID Frequency Controller Membership Functions 58

75 The output membership functions are not shown in Figure 3-7 since the non-linear fuzzy logic controller was designed using a Sugeno style inference system, in accordance with the procedure established in [76], [77]. This type of inference system is very similar to the Mamdani inference system. The fuzzification process of the input signals is exactly the same. The main difference between the Mamdani and Sugeno inference systems is that the Sugeno output membership function are either linear or constant [79]. For the implemented non-linear fuzzy controller the output membership functions are defined as constants, based on [77]. The constant values for the output membership functions are listed in Appendix B.4. The rules for the non-linear fuzzy PID controller are defined as established in [77] and are listed in Table 3.2. Similarly, as before the intersection of the row and column for the considered precedents defines the rule implication for a specific combination of precedents. For example: if E is P and CE is P then u is LP. CE Table Fuzzy Rules Set E N P N Min Zero P Zero Max The fuzzy rules are implemented using a Sugeno style inference system with an algebraic product of AND connective. The defuzzification method implemented for this system is the weighted average method. The relationship between input and output for this fuzzy inference system is defined by Equation 3.10 and 3.11[79]. (3.10), (3.11) 59

76 where and are the membership functions for the inputs E and CE, is the output level of each rule and is the rule weight. The control surface for the non-linear fuzzy PID controller, depicted in Figure 3-8, gives an overview of the controller response to variations of E and CE. This control surface has a higher gain near the center of the E and CE plane, when compared with the linear control surface, which helps to reduce the error in a faster way when the error is small. When the error is large the controller has a less aggressive response. In this way the control action is limited and it helps to minimize possible overshoots in the error signal [77]. Figure Non-Linear Fuzzy PID Frequency Controller Control Surface All the values of the constants, scaling factors, range of membership functions for both linear and non-linear fuzzy logic controllers are listed in Appendixes B.3 and B Frequency Controllers Comparison The three controllers designed, the PID, the linear fuzzy controller and the non-linear fuzzy controller are compared to evaluate their performance, and, therefore, define which controller 60

77 provides better frequency regulation to the system. The comparison is based on the frequency results of the simulations. In the first scenario, the PID controller is compared with the linear fuzzy logic controller and with the non-linear fuzzy logic controller under a load step of +25 kw. The simulation results are shown in Figure 3-9. Frequency (Hz) Simulation Time (s) a) Linear Fuzzy vs PID Frequency results Frequency (Hz) Simulation Time (s) b) Non-Linear Fuzzy vs PID Frequency results Figure Fuzzy Controllers vs PID frequency comparison on +25 kw load step 61

78 From Figure 3-9 it can be identified that both linear and non-linear fuzzy controllers have reduced settling times when compared with the PID controller. This scenario defines that both fuzzy logic controllers have a better performance when compared with the PID controller. In the second scenario, the load step is maintained at +25 kw but the linear and non-linear fuzzy logic controllers are compared. The simulation results are shown in Figure Frequency (Hz) Simulation Time (s) Figure Non-Linear Fuzzy vs Linear Fuzzy frequency comparison on +25 kw load step This scenario defines which of the fuzzy logic controllers provides better frequency regulation. As seen in Figure 3-10, the non-linear fuzzy logic controller provides a better frequency regulation when compared with the linear fuzzy controller since the non-linear fuzzy logic controller stabilizes the frequency back to the reference, i.e. 60 Hz, faster than the linear fuzzy controller. In the third scenario, the non-linear fuzzy logic and the PID controllers are tested under a maximum load step of +50 kw, in order to verify that the frequency regulation is maintained under 62

79 a maximum load consumption disturbance condition. The simulation results are depicted in Figure Frequency (Hz) Simulation Time (s) Figure Non-Linear Fuzzy vs PID frequency comparison on +50 kw load step Figure 3-11 shows that under maximum load step conditions the non-linear fuzzy logic controller provides a better frequency regulation when compared with the PID controller, since the overshoot and settling time of the frequency are reduced when compared with the frequency regulation provided by the PID controller. For a maximum load step of +50 kw the non-linear fuzzy logic controller frequency goes below the -1% for 99.5% of the week limit. Similar to the PID, the amount of time, in percentage, that the frequency is under this limit just represents % of the week and the standard allows 0.5% of the week that the frequency can reach those limits. Therefore, the performance of the nonlinear fuzzy logic controller is considered to be acceptable according to the limits defined in [10]. Based on the results of the previous simulations shown in Figures 3-9, 3-10 and 3-11, it can be summarized that the non-linear fuzzy logic controller gives the best performance. 63

80 Therefore, this last controller is the one selected to provide frequency regulation during the turbine operation mode of the HPS facility. By implementing a non-linear fuzzy logic controller to provide frequency regulation to the system it is possible to reduce the frequency deviation,, in the system and to reduce the overshoot and settling time of the frequency. Also, as previously mentioned, since the non-linear controller has a higher gain near the center it provides a faster error reduction when the error in the frequency signal is small. When is large the controller is able to regulate its action to reduce the overshoot and avoid possible saturation, as seen in the previous simulations results HPS Facility in Pumping Mode Frequency Controller This section describes the HPS facility, in pumping mode, frequency controller. During the pumping mode the synchronous machine runs as motor and usually the wicket gates opening is fixed at an operation point [7]. This thesis proposes the design of a fuzzy logic controller to regulate the opening of the wicket gates during the pumping mode, in order to provide frequency regulation. The HPS facility, in pumping mode, is controlled by the set point. This set point is used by the fuzzy logic controller to define the opening of the wicket gates in order to establish the amount of power that needs to be consumed by the pump in order to regulate the frequency of the system. Fuzzy logic controllers to regulate the frequency of the system using an HPS facility have been implemented previously [38]. In this studies, the fuzzy logic controller was implemented only during the turbine mode. Therefore, the performance of a fuzzy logic controller that regulates the frequency of the system during the pumping mode is simulated and analyzed in this section. 64

81 The fuzzy logic controller design for the pumping mode is described in the following sections. The fuzzy logic frequency controller design is described in Section The results of the simulations to evaluate the performance of the fuzzy controller are presented in Section Fuzzy Logic Frequency Controller Design Since during the pumping mode operation the HPS facility consumes power, the design of the fuzzy logic frequency controller is based on a fuzzy logic controller designed for a DL presented in [4]. The design has been adjusted to the system s dynamics and it is implemented to regulate the opening of the wicket gates. The fuzzy logic frequency controller inputs are the frequency deviation,, and the frequency deviation rate of change,. The frequency deviation is calculated as defined in Equation (3.12) where is actual frequency of the system and is the nominal desired frequency of 60 Hz. The frequency controller output is the gate opening,, which regulates the amount of power consumed by the HPS facility in pumping mode. The relationship between inputs and output is detailed later in Equation The fuzzy logic frequency controller is based on a Mamdani style inference system [78], and implements 49 fuzzy rules using the IF-THEN structure. Similar to the fuzzy logic controller design for the turbine mode operation, this controller has 2 inputs and 1 output. Therefore, the frequency regulator is a multiple input-single output controller. The schematic of the fuzzy logic controller is depicted in Figure

82 Figure 3-12 Fuzzy Logic Frequency Controller Schematic Figure 3-12 shows the schematic for the fuzzy logic frequency controller, where, and are the inputs to the controller and is the output variable that gives the required operation point for the wicket gates. The input membership functions for the fuzzy controller were designed to meet the requirements of the standard EN [10]. As stated in Section 1.1.2, the standard EN establishes that for an LV system, the frequency variation should be maintained between ±1% for 99.5% of the week, and -6%/+4% for 100% of the week [10]. The input control variables and are divided into 7 fuzzy membership functions: Negative Large (NL), Negative Medium (NM), Negative Small (NS), Zero (Z), Positive Small (PS), Positive Medium (PM), Positive Large (PL). The input variables and membership functions are depicted in Figure 3-13a and 3-13b. Similar to the inputs, the controller output was designed to meet the requirements of standard EN [10]. The fuzzy output variable has been designed with 7 membership functions: Close Fast (CF), Close Medium (CM), Close Slow (CS), Zero (Z), Open Slow (OS), Open Medium (OM), Open Fast (OF). The output membership functions OS, OM and OF increment the opening of the wicket gates, and, therefore, the power consumption of the pump is 66

83 increased, while the membership functions CS, CM and CF reduce the opening of the wicket gates, and, therefore, the power consumption of the pump is decreased. The input and output membership functions are shown in Figure NL NM NS Z PS PM PL Degree of membership deltaf a) input membership functions Degree of membership b) input membership functions Degree of membership c) output membership functions Figure Fuzzy Logic Controller Membership Functions 67

84 The designed fuzzy rules are based on [4] but were adjusted according to the system dynamics. The fuzzy rules are listed in Table 3.3. The intersection of the row and column for the considered precedents defines the rule implication for a specific combination of precedents. For example: if is PS and is NM then is OS. Table Fuzzy Rules Set NL NM NS Z PS PM PL NL OF OF OM OM OS OS Z NM OF OM OM OM OS Z CS NS OF OM OS OS Z CS CM Z OF OM OS Z CS CM CM PS OM OS Z CS CS CM CF PM OS Z CS CM CM CM CF PL Z CS CS CM CF CF CF The fuzzy rules are implemented using a Mamdani style inference system with an algebraic product for AND connective. The defuzzification method implemented for this system is the center of area method. The relationship between input and output for this fuzzy inference system is defined by Equation 3.13 [4], [78].,, (3.13) where is the area of the membership function, obtained by applying the min T-norm operator [4], [78], is the consequent of rule, that is the geometrical center of, and is the total number of fuzzy rules set. The control surface for fuzzy logic controller, depicted in Figure 3-14, gives an overview of the controller response to variations of and. The output magnitude is increased when and have both the same sign. When and have opposite sign, the 68

85 output is decreased. In a similar way, if magnitude and sign are able to counter the effect of, the output remains unchanged. Figure Fuzzy Logic Controller Control Surface Frequency Controller Simulations Similar to the frequency controller for the turbine mode, the fuzzy logic controller for the pumping mode is simulated and tested under different scenarios to evaluate its performance. The performance of the controller is evaluated by analyzing the frequency and power results. In the first scenario, the fuzzy controller is tested under a load step of +25 kw. The simulation results are shown in Figure

86 Real Power (kw) Frequency (Hz) Simulation Time (s) a) Frequency Results Real Power (kw) Simulation Time (s) b) Main Load real Power Simulation Time (s) c) HPS, in pumping mode, Real Power Figure Fuzzy Controller Frequency and Power Results under a +25 kw load step 70

87 From Figure3-15 it can be seen that the fuzzy logic controller provides a reliable frequency regulation to the system. Figures 3-15b and 3-15c show that once the load step of +25 kw occurs, at the simulation time of 2 seconds, the fuzzy logic controller closes the wicket gates in order to reduce the power consumed by the HPS facility, in pumping mode, from kw to kw, and therefore maintaining the frequency and power balance of the isolated micro-grid. In the second scenario, the fuzzy logic controller is tested under a wind speed variation with the main load kept at a constant value of 85 kw. This variation causes a change in the power generated by the WG and, therefore, it is seen as a disturbance to the system. The simulation results are shown in Figures 3-16 and Figure 3-16 shows the wind speed variation and the real power of the different elements in the system. As the wind speed changes, Figure 3-16a, the frequency and power balance is regulated by the fuzzy logic controller. As seen in Figure 3-16b, the HPS facility power consumption is increased when the wind speed increases. Similarly, when the wind speed is lower the HPS facility consumed power is reduced. 71

88 Wind Speed (m/s) Simulation Time (s) a) Wind Speed Variation Real Power (kw) Simulation Time (s) b) Real Power of HPS, Main Load and WG Figure Real Power Results According to Wind Speed Variations Frequency (Hz) Simulation Time (s) Figure Frequency under Wind Speed Variations 72

89 Figure 3-17 shows the frequency results for the wind speed variation in the system. From this figure it can be identified that the fuzzy logic controller provides an effective frequency regulation to the system, since the value of the frequency deviation during such conditions does not exceed the defined limits of standard EN [10]. The frequency deviation has a maximum value of Hz. This represents a maximum deviation of %, which is comparatively small with the limits defined by standard EN Based on the results of the simulations shown in Figures 3-15, 3-16 and 3-17, it can be concluded that the fuzzy logic controller designed to regulate opening of the wicket gates, during pumping operation mode of the HPS facility, is capable of providing reliable frequency regulation to the system. The implemented fuzzy logic controller was tested under main load consumption variations and wind changes, which are the main disturbance scenarios present in the modeled isolated micro-grid. Therefore, the fuzzy logic controller can be expected to regulate the power of the HPS facility according to such changes Diesel Engine Frequency Controller Similar to the HPS facility, in turbine mode, the DG provides frequency regulation to the system through the operation of its speed governor. The DG is able to provide frequency regulation to the system when operating in isochronous mode. In order for the DG to provide frequency regulation to the system, the synchronous machine and the diesel engine must be coupled by the clutch. The mathematical model of the clutch has been described in detail in Section The clutch has two states of operation: engaged and disengaged [4], [12], [16], [17]. During the engaged operation, the clutch couples the synchronous machine to the diesel engine, and, therefore, the DG is able to provide frequency regulation to the 73

90 system. On the contrary, during the disengaged operation, the clutch decouples the synchronous machine from the diesel engine, and as a result the DG is not able to provide frequency regulation. The state of the clutch is defined by the supervisory controller. When the ON signal is sent, the clutch will be engaged and when and OFF signal is sent the clutch will be disengaged. As previously mentioned in Section 2.3.4, when the clutch is disengaged the diesel engine will have an angular speed reference of 0.3 p.u. This resting speed is only used for simulation purposes. In reality when the clutch is in the OFF state, the diesel engine will be in its rest state [4]. Based on the operation principle presented in [4], [16], during normal operating conditions the diesel engine and the synchronous machine are decoupled. The synchronous machine remains connected to the network operating as a synchronous motor. During this operation mode the synchronous machine provides voltage support to the system and frequency regulation is not provided by the DG. When the DG is required to maintain the frequency regulation of the system, it is necessary that the clutch be engaged. In order to engage the clutch it is required that the synchronous machine and the diesel engine speed be equal. Since the synchronous machine is connected to the network, as a synchronous motor, the machine speed is always 1 p.u. Therefore, the speed of the diesel engine must be increased when it is required to provide frequency regulation. When the diesel engine speed reaches the synchronous machine speed, only then the clutch can be engaged. A speed reference deviation,, of 10-5 is defined as a speed difference limit to engage the clutch [4]. The diesel engine speed deviation with respect to the synchronous machine speed is defined by Equation (3.14) 74

91 where, is the diesel engine speed deviation, is the synchornpus machine speed and is the actual diesel engine speed. The effects of waiting for the diesel engine speed to reach the synchronous machine speed are observed in [12]. In this study, the system frequency decreased to Hz while waiting for the clutch to engage the diesel engine. This frequency deviation represents a of 3.78% which is larger than the defined by standard EN of ±1% during 99.5% of the week [10]. This study follows the design implemented in [4], where a speed controller is designed to regulate the diesel engine speed according to the system requirements. The controller prepares the diesel engine for coupling and prepares the DG for commissioning. The design of the diesel controller is presented in the following sections. The diesel engine isochronous operation mode is described in Section The diesel engine commissioning anticipation logic is defined in Section The results of the controller simulations are presented in Section Diesel Engine Isochronous Operation Mode The diesel engine operates in isochronous mode when the clutch is engaged and the DG is commissioned to perform frequency regulation. During the isochronous mode the diesel engine reference speed is 1 p.u. To achieve this, a 1 p.u. is defined as the speed reference of the diesel engine once the clutch signal is ON. Therefore, the isochronous operation mode depends directly on the clutch operation state. Once the speed difference between the diesel engine and the synchronous machine,, is equal to 10-5 the clutch will be locked, and as a result the speed reference of diesel engine will be 1 p.u. 75

92 During the isochronous mode the diesel engine regulates the frequency of the system, and it provides the power required by the system under load steps or wind speed variations. The results of the simulations of these scenarios are presented later in Section Diesel Engine Commissioning Anticipation As previously mentioned, the effect of waiting for the diesel engine speed to reach the synchronous machine speed is described in [12]. Due to this, the of the system reaches the limits defined by standard EN [10]. The present study follows the strategy implemented in [4], where the diesel engine commissioning anticipation is considered to improve the frequency regulation of the system. By anticipating the commissioning of the diesel engine it is possible to prepare the diesel engine for coupling with the synchronous machine, and therefore, reduce the amount of time it takes for coupling and reduce the frequency deviation of the system. The DG is commissioned to provide frequency regulation when the HPS facility, in turbine mode, is not able to provide frequency regulation to the system. The scenarios that can create such condition where the HPS facility, in turbine mode, is unable to provide frequency regulation are the following: 1) When the water level of the upper reservoir has reached its lower limit, 2) When the HPS facilty, in turbine mode, has reached its maximum generation limit. These two scenarios are considered in the design of the diesel engine controller Anticipation of Low Water Level in HPS Upper Reservoir The HPS facility, in turbine mode, is unable to provide frequency regulation once the water level in its upper reservoir has reached its lower limit of 0.4 meters ( ), since there is no more water that can be released to continue generating power. Under these conditions the diesel engine must be prepared for commissioning in order to ensure the frequency regulation of the system. 76

93 To prepare the diesel engine for frequency regulation during such conditions, the diesel engine controller has been designed to estimate the time left before the upper reservoir reaches its lower limit. This time is called discharge time ( ). To calculate, it is necessary to consider the following variables of the HPS facility: the actual volume of water in the upper reservoir, (m 3 ), and the flow of water, (m 3 /sec), at which the water is leaving the upper reservoir. The flow of water can be monitored from the HPS facility model measurements. However, the volume of water in the tank must be calculated. To find the actual volume of water in the upper reservoir it is necessary to first find the level of water in the tank, (m). The calculation of this value is defined by Equation 2.5 [46]. This equation shows that the level of water is related to the flow of water and the area of the bottom of the tank. Once these two variables are known, the calculation of is carried out by using Equation (3.15) where, is the actual level of water in the tank, is the area of the bottom of the tank, is the lower limit of the level of water in the tank and is the flow of water. The diesel engine behavior when the speed reference is changed form 0.3 p.u. (simulated rest state) to 1 p.u. (frequency regulation speed) is shown in Figure The diesel engine speed stabilizes at reference speed of 1 p.u. 2.5 seconds after has been changed. In addition to this time, it is also necessary to consider the cranking time of 0.5 sec [4], [12], the engine mathematical model delay of sec [4], [63]. Besides these times, it is also necessary 77

94 to account for the time it takes the HPS facility, in turbine mode, to be unloaded at different operation points. To identify the average time required by the HPS facility, in turbine mode, to be unloaded and reach its lower limit several tests were performed, one at 94% loading, another at 75 % loading and finally one at 53% loading. The results of these scenarios gave an average unloading time, to reach the lower limit of 0.4 meters, of sec. The figures and graphics of the results of these scenarios are presented in Section 3.4.3, where the diesel engine controller simulations are evaluated in detail. Diesel Engine Speed (p.u,) Simulation Time (s) Figure Diesel Engine Speed Reference Change Therefore, the controller must start the diesel engine 7.45 sec before the water level in the upper reservoir reaches its lower limit of 0.4 meters. Similar to [4], a 0.05 sec delay has been included to provide an extra time for the diesel engine to be ready. As a result, the diesel engine must be started 7.5 sec before the upper reservoir water level has reached its lower limit. The logic of operation of the diesel engine controller is defined by the flow chart presented in Figure

95 Calculate discharge time of reservoir ( ) 7.5sec No Diesel Engine OFF ( 0.3) Yes Diesel Engine ON ( 1) Calculate speed difference 10-5 Yes No Clutch OFF (Disengaged) Clutch ON (Engage) HPS Facility OFF ( 0) Figure Diesel Engine Controller Logic of Operation based on As shown in Figure 3-19 the logic of operation of the diesel engine controller is based on the calculation of the discharge time,. Once this time is calculated it is evaluated to define if it has reached the threshold of 7.5 sec. When this threshold is met the diesel engine speed reference is changed to 1 p.u. and the speed deviation is calculated. When is equal to or less 79

96 than 10-5 the clutch is engaged and the HPS facility, in turbine mode, is unloaded. After this, the DG is in charge of providing frequency regulation to the system Anticipation of Maximum Generation Limit of HPS Facility The maximum generation of the HPS facility depends on two variables that are unpredictable and that cannot be controlled: the load consumption of the micro-grid and, since the wind speed prediction is outside the scope of this study, the WG power generation is also considered as an uncontrolled variable of the system. However, following a similar technique as the one presented in [4], it is considered that when the generation unit of the HPS facility is operating close to its nominal capacity the probability of reaching its maximum generation limit is higher. Therefore, the diesel engine controller has been designed following the strategy presented in [4], where the diesel engine speed reference is regulated according to the power generated by the HPS facility. As previously mentioned, a maximum load step of ±50 kw is considered in the present study. Thus, the minimum generated power provided by the HPS facility at which the maximum generation could be reached is at 100 kw. From this point a load step of +50 kw or higher could bring the HPS power generation to its maximum capacity. One approach would be to change the diesel engine reference speed close to nominal when the power generated by the HPS facility surpasses 100 kw, therefore, the coupling of the diesel engine would be fast at any load step. However, this solution would increase the fuel consumption of the diesel engine when the power generation of the HPS facility is more than 100 kw. Following the design presented in [4], to avoid excessive fuel consumption a controller is designed to increase according to the power generated by the HPS facility. As a result, the diesel engine fuel consumption can be reduced. 80

97 The load consumption in real systems is not incremented in large load steps instantaneously. Typically, the load in a network increases gradually over a period of time. A load profile of an isolated micro-grid power system is depicted in Figure 3-20 [4], [80]. From this figure it can be seen that even though the load consumption in a real system has fast variation, such variations are not instantaneous. Figure Isolated Micro-Grid Load Profile [4], [80] Thereby, since large load steps normally do not occur in an instantaneous step, in a real system the power generated by the HPS facility should increase progressively. Hence, increasing with respect to the power generated by the HPS facility would permit regulation of in a way that is close to the nominal when the diesel engine is required to be commissioned because the HPS facility is about to reach its maximum generation limit. The engine controller has been designed for implementation as a fuzzy logic controller that increases the speed reference when the power generated by HPS facility increases. The input membership function evaluates the amount of power the HPS facility is generating. The output membership function defines the speed reference,. The input and output membership functions are depicted in Figure

98 1 Z PM PL Degree of membership Phydro 10 5 a) Phydro input membership functions Degree of membership wd The input control variable is divided into 3 fuzzy membership functions: Zero (Z), Positive Medium (PM) and Positive Large (PL). b) output membership functions Figure Fuzzy Logic Controller Membership Functions Similar to the input, the controller output has been designed with 3 membership functions: Z, PM and PL corresponding to 0.3, 0.64 and The output membership function regulates the speed reference of the diesel engine in order to increment it when increases. The output membership function brings the speed reference to the stand-by speed of 0.97 p.u. in 82

99 order to have a fast coupling of the diesel generator when the HPS facility is close to its maximum generation point. The designed fuzzy rules are based on [4] but were adjusted according to the HPS system dynamics. The fuzzy rules are listed in Table 3.4. The columns define the rule implication for a specific precedent. For example: if is PM then is Table Fuzzy Rule Set Z PM PL The fuzzy rules are implemented using a Mamdani style inference system with an algebraic product for AND connective. The defuzzification method implemented for this system is the center of center of area method. The relationship between input and output for this fuzzy inference system is defined by Equation (3.16) The control surface of the fuzzy logic controller, shown in Figure 3-22, gives an overview of the controller response to variations of. 83

100 Figure Fuzzy Logic Control Surface When the increases beyond 100 kw, the engine speed is increased. The maximum stand-by speed is 0.97 p.u. which defined when the power generation is at 146 kw or higher. By implementing this controller the diesel engine is capable of having a faster coupling with the synchronous machine, and therefore, the DG commissioning is faster and the frequency regulation of the system is maintained. The diesel engine controller overview of inputs and outputs is summarized in the block diagram shown in Figure The controller inputs are:,, and DG status. The last input is a signal sent by the supervisory controller when the HPS facility is making the transition from pumping mode to turbine mode. Details of this signal are presented in Chapter 4, which is related to the design of the supervisory control of the micro-grid. The controller output is. 84

101 DG Status DIESEL ENGINE CONTROLLER Figure Diesel Engine Controller Block Diagram Diesel Engine Controller Simulations The diesel engine frequency controller is simulated and tested under different scenarios to evaluate its performance. The performance of the controller is evaluated by analyzing the frequency and power results. The simulation analysis is centered in evaluating the frequency regulation of the system under different scenarios. In the first scenario, the water level of the upper reservoir is at meters and the HPS facility is generating kw, the WG is generating kw and the load of the system is 212kW. The initial at this conditions is sec. The simulation results are shown in Figure Water Level (m) Simulation Time (s) a) Water Level in Upper Reservoir 85

102 Angular Speed (p.u.) Simulation Time (s) b) Diesel Engine and Synchronous Machine Speed Frequency (Hz) Simulation Time (s) c) Frequency Real Power (kw) Simulation Time (s) d) Real Power of DG, HPS, WG and Main Load Figure Diesel Engine Controller Simulations for Low Water Level in Upper Reservoir 86

103 It can be seen from Figure 3-4 that the designed controller is able to provide frequency regulation when the upper reservoir is reaching its lower level of 0.4 meters. When the reaches 7.5 seconds the diesel engine is started and once the speed deviation is equal or less than 10-5 the clutch is engaged and the DG replaces the HPS facility to start generating power. From Figure 3-24c it can be seen that the frequency is maintained within the limits defined by standard EN [10] during the system transition. In the second scenario the HPS facility, in turbine mode, reaches its maximum generation capacity. The main load is gradually increased until the HPS facility reaches its nominal capacity. During this condition the diesel engine starts to increase its speed in order to maintain the frequency regulation of the system when the HPS facility is operating at maximum capacity. The results of this scenario are presented in Figure Real Power (kw) Simulation Time (s) a) Real Power of DG, HPS, WG and Main Load 87

104 Angular Speed (p.u.) Simulation Time (s) b) Diesel Engine and Synchronous Machine Speed Frequency (Hz) Simulation Time (s) c) Frequency Results Figure Diesel Engine Controller Simulations for HPS at Maximum Generation Point 88

105 Figure 3-25 shows these results when the HPS facility operates at maximum capacity. The power signals of the different elements of the system and how each generator behaves during the gradual increment of the system s load can be seen from Figure 3-25a. Figure 3-25b shows how the fuzzy logic controller regulates the speed of the diesel engine by increasing it every time the HPS facility power output is increased by each load step. When the HPS facility is reaching its maximum power output the speed reference of the diesel engine is set to 1 and the clutch is engaged, in order to maintain the frequency regulation of the system. As seen in Figure 3-25c, the frequency of the system is maintained within the limits defined by standard EN [10]. After the final load step at 50 seconds takes place, the frequency is regulated by the DG once the clutch is engaged. Based on the results of the previous simulations shown in Figures 3-24 and 3-25, it can be concluded that diesel engine controller designed to regulate the commissioning of the DG is capable of providing reliable frequency regulation to the system. As previously described and shown in the simulation results, the diesel engine controller has been designed to anticipate when the water level of the upper reservoir of the HPS facility is reaching its lower level limit of 0.4 meters, and therefore, the HPS facility is no longer able of providing frequency regulation to the system. Similarly, a fuzzy logic controller has been implemented to regulate the speed of the diesel engine once the HPS facility is generating close to its maximum capacity, in order to prepare the diesel engine to be engaged, by the clutch, to the synchronous machine. Once the HPS facility is generating at maximum capacity the DG is commissioned for frequency regulation in order to maintain the reliable operation of the isolated micro-grid. 89

106 3.5 Dump Load Frequency Controller The DL is controlled by measuring the frequency of the system and comparing it with the reference frequency of 60Hz. The dump load input is the frequency deviation,, which is defined by Equation The DL frequency controller is based on the design implemented in studies [39] [41]. The result of these studies show good performance of the frequency controller when implemented in isolated micro-grid power systems. In this section, the DL controller is incorporated to the existing isolated micro-grid and its performance is evaluated. The DL is required to provide frequency regulation to the system in the scenario where the HPS facility, in pumping mode, has reached its maximum capacity and it is still required to consume power from the system to maintain the frequency balance in the network. The DL is also used to provide frequency regulation during the HPS operation transition from turbine mode to pumping mode. These operation modes of the DL are defined by the supervisory control and it will be presented in detail in Chapter Dump Load Frequency Controller Design The design of the DL frequency controller follows the configuration proposed in studies [39] [41]. The frequency controller implemented in these studies uses the frequency deviation as an input to determine the amount of power that needs to be consumed by the resistor bank. The frequency deviation is integrated to obtain the phase error. After this, the phase error is then used by a Proportional-Differential (PD) controller. This controller produces an output signal which represents the required load power. The output signal of the controller is converted to an 8-bit digital signal. This signal regulates the switching of the eight three-phase resistors, which form the resistor bank of the DL. Besides providing frequency regulation to the system, the 90

107 controller switching is performed at the zero crossing of the voltage, in order to minimize the amount of voltage disturbance in the system [41]. Previous studies, such as [16], have implemented similar frequency controller to regulate the operation of the DL. The control strategy implemented in [16] uses as a control input the synchronous machine speed of the DG. Since the DG synchronous machine is always connected to the system, the synchronous machine speed varies according with the system s frequency. The regulation strategy proposed in [16] will require extra measurement equipment since it is to measure the synchronous machine speed of the DG. As previously mentioned, the frequency controller designed in this study uses the frequency deviation as an input to the controller, and therefore, avoids the requirement of measuring the DG synchronous machine speed. The DL frequency controller schematic is shown in Figure This schematic shows the general configuration of the frequency controller and how the eight switches are connected to the resistor bank. 91

108 a) DL Frequency Controller Schematic b) DL Frequency Controller Switching Schematic Figure DL Frequency Controller Schematic [41] 92