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Wind Energy Conversion System
As introduced in the previous chapter, hybrid power systems can be a good solution for the integration of distributed renewable energies in a microgrid. Before presenting the proposed active wind generator (in Chap.V), a classical wind energy conversion system is firstly studied in this chapter, including the system modeling by EMR (Appendix E), the control design and the power balancing strategies. This energy conversion system enables to extract the maximum wind power by adjusting the wind turbine’s rotational speed. The obtained electrical power must be adapted (sinusoidal wave form at 50 Hz, phasing) before being sent to the grid. A “grid following” power balancing strategy must be used, while the wind generator works in Maximum Power Point Tracking (MPPT) strategy. Then the DC-bus voltage is regulated with the grid power.
Then, in order to reduce wind power variations, super-capacitors are used to build a first hybrid power system. The system modeling, the control design and the power balancing strategies are presented. The “grid following” strategy and the “power dispatching” strategy can both be used for the power balancing. In the “power dispatching” strategy, the DC-bus voltage is regulated with the powers from the wind generator and the super-capacitors. The performances of these two power balancing strategies are compared in the end of the chapter.
Study of a wind energy conversion system
Presentation
A classical wind energy conversion system consists of a 3-blade turbine, a gearbox, an electrical machine, a three-phase rectifier, a DC-bus capacitor, a three-phase inverter, line filters which are connected to the grid through a grid transformer (Fig.II-1).
When the wind energy conversion system works in MPPT strategy, the power, which is delivered to the grid, is very fluctuant. We are going to use a wind speed profile, which has been recorded in a wind farm in the north of the France (Fig.II-2) [Lec 04] [Bou 07].
Modeling of the wind energy conversion system
a) Average modeling of the electrical conversion chain
By using equivalent average models of power electronic converters (Appendix B), the average modeling of the electrical power conversion chain is obtained for the wind energy conversion system (Fig.II-3). The grid with transformer is considered as three-phase voltage sources and the electrical machine is considered as three-phase current sources. The two back-to-back voltage source converters introduce control inputs for the power control. As the DC bus has a relatively slow dynamic, it’s shown that we can have three different subsystems with their inner dynamic and control tasks: the wind generator, the grid connection system and the DC bus.
Hierarchical control structure
The wind energy conversion system is designed to transfer powers from the wind generator to the electrical grid. Two power converters are used to regulate the power exchange. A hierarchical control structure is used to implement the control system (Appendix C). Two Switching Control Units and two Automatic Control Units are used seperately in the control system for the two power converters. A common Power Control Unit and a common Mode Control Unit are used for the power balancing and the energy management of the entire power system (Fig.II-9).
In the SCU of each converter, the IGBT drivers and PWM techniques are used to control the commutation circuits. These control units are not the main concern of this study, so they will not be detailed here. However, the control algorithms in the ACU should be presented in order to highlight the physical quantities, which can be used for the power flow control among the different energy sources.
The ACU is designed from the EMR of the system modeling according to inversion rules (Appendix E). The use of an average modeling of power electronic converters gives three different subsystems, whose ACUs are now respectively detailed.
Automatic control unit
a) Control of the wind generator
The electrical power vs. speed curves of a typical wind turbine is given in Fig.II-10. For example if the wind velocity is v1 the output power can be raised to the maximum value at point A by setting the mechanical speed to Ω1. If the wind speed changes to v2 the power output jumps to point B. For this wind velocity the maximum power can be extracted by setting the speed to Ω2 at point C. This shows that, as the wind speed changes, the generator speed should track these changes in order to extract the maximum power. This strategy is called Maximum Power Point Tracking (MPPT) strategy.
The EMR of the wind energy conversion system modeling (Fig.II-8) shows that the speed (Ωtur) can be controlled by acting on two inputs: the aerodynamic torque (Ttur) and the torque of the generator (Tgear). As here we consider a normal operation with a constant pitch angle, the aerodynamic torque must be considered as a perturbation input (linked to the wind speed) for the system. So the turbine speed can be controlled by acting on the gearbox torque (Tgear) via the control input (mrec) of the power electronic converter.
From the EMR of the wind energy conversion system modeling, an action chain appears from the control inputs (mrec) of the rectifier to the gear’s mechanical torque (Tgear) (Fig.II-8). The control scheme of the wind energy generation system is obtained by inverting this action chain (Fig.II-11). It consists to calculate the reference of the rectifier’s duty ratios (mrec_ref) according to a torque references (Tgear_ref). It is composed of a torque control, a field oriented control and a rectifier control.
Table of contents :
Introduction
Chapter I Context and Methodologies
I.1 Renewable energy sources and distributed generation
I.1.1 Renewable energy sources
I.1.2 Distributed generation
I.1.3 Smart grid
I.1.4 Microgrid
I.1.5 Hybrid power system
I.2 Hydrogen as alternative energy carrier to electricity
I.2.1 Hydrogen market
I.2.2 Hydrogen-electric economy
I.2.3 Hydrogen as storage for electricity
I.3 Integration of renewable energy based generators into a microgrid
I.3.1 General framework of the microgrid operation
I.3.2 Problems of renewable energy sources
I.3.3 Concept of active generator
I.4 Presentation of the studied active generator
I.4.1 Technologies of wind generators
I.4.2 Classification of energy storage systems
1.4.3 Long-term storage system through hydrogen technologies
1.4.4 Fast-dynamic storage unit by super-capacitors
I.4.5 Structure of the studied hybrid power system
I.5 Objectives and methodologies of the PhD thesis
I.5.1 Objectives
I.5.2 Tools
I.5.3 Methods
I.5.4 Thesis layout
Chapter II Wind Energy Conversion System
II.1 Study of a wind energy conversion system
II.1.1 Presentation
II.1.2 Modeling of the wind energy conversion system
II.1.3 Hierarchical control structure
II.1.4 Automatic control unit
II.1.5 Power control unit
II.1.6 Mode control unit
II.2 Experimental test of the grid connection control
II.2.1 Wind power emulator
II.2.2 Experimental implementation
II.2.3 Simulation and experimental results
II.2.6 Discussion
II.3 Study of a wind/super-capacitor hybrid power generator
II.3.1 Presentation
II.3.2 Modeling of the super-capacitor storage system
II.3.3 Modeling of the hybrid power system
II.3.4 Hierarchical control of the hybrid power system
II.3.5 Power balancing strategies of the wind/super-capacitors hybrid power system
II.4 Experimental test of the wind/super-capacitor hybrid power generator
II.4.1 Experimental implementation
II.4.2 Test of the grid following strategy
II.4.3 Test of the power dispatching strategy
II.4.4 Discussion
II.5 Conclusion
Chapter III Fuel Cell for Energy Backup from Hydrogen
III.1 Overview of fuel cells
III.1.1 Technologies
III.1.2 Operating principles
III.1.3 Fuel cell system
III.1.4 Technical challenges
III.1.5 Modeling methods
III.2 Studied fuel cell system
III.2.1 Introduction
III.2.2 System operation
III.3 Modeling of the fuel cell stack
III.3.1 Open-circuit voltage
III.3.2 Operating voltage
III.3.3 Stack modeling
III.3.4 Graphical representation
III.4 Modeling and control of the auxiliary systems
III.4.1 Modeling and control of the power conditioning system
III.4.1 Modeling of the fuel processing system
III.4.2 Modeling and control of the oxidant processing system
III.4.3 Modeling and control of the thermal management system
III.4.5 Overall control and supervision system
III.5 Modeling simplification and identification
III.5.1 Simplification of the modeling
III.5.2 Experimental characterization of the fuel cell behavior
III.5.3 Identification of the modeling parameters
III.5.4 Dynamic limitations in transient states
III.6 Real-time fuel cell emulator
III.6.1 Structure of the fuel cell Emulator
III.6.2 Modeling and control of the fuel cell emulator
III.6.3 Implementation of the fuel cell emulator
III.6.4 Experiment results
III.7 Conclusion
Chapter IV Electrolyzer for Energy Storage into Hydrogen
IV.1 Overview of electrolyzers
IV.1.1 Technologies
IV.1.2 Operating principles
IV.1.3 System performance
IV.1.4 Commercialized products
IV.2 Studied electrolyzer system
IV.2.1 Introduction
IV.2.2 System operation
IV.2.3 Experimental tests
IV.3 Modeling of the electrolyzer stack
IV.3.1 Open-circuit voltage
IV.3.2 Operation voltage
IV.3.3 Stack modeling
IV.3.4 Graphical representation
IV.4 Modeling and control of the auxiliary systems
IV.4.1 Power conversion system
IV.4.2 Hydrogen handling system
IV.4.3 Oxygen handling system
IV.4.4 Water and thermal management system
IV.4.5 Macroscopic representation of the electrolyzer system
IV.5 Modeling simplification and identification
IV.5.1 Simplification of the modeling
IV.5.2 Experimental characterization of the electrolyzer behavior
IV.5.3 Identification of the modeling parameters
IV.5.4 Dynamic limitations in transient states
IV.6 Real-time electrolyzer emulator
IV.6.1 Structure of the electrolyzer emulator
IV.6.2 Modeling and control of the electrolyzer emulator
IV.6.3 Implementation of the electrolyzer emulator
IV.6.4 Experimental results
IV.7 Conclusion
Chapter V Active Wind Generator
V.1 Modeling of the active wind generator
V.1.1 Presentation
V.1.2 Equivalent average modeling
V.1.3 DC-bus modeling
V.1.4 Energetic macroscopic representation
V.2 Control of the active wind generator
V.2.1 Hierarchical control structure
V.2.2 Automatic control unit
V.2.3 Power control unit
V.3 Power balancing strategies for the active wind generator
V.3.1 Role of the power balancing
V.3.2 Power flow modeling
V.3.3 Grid following strategy
V.3.4 Power dispatching strategy
V.4 Experimental tests
V.4.1 Experimental implementation
V.4.2 Test of the grid following strategy
V.4.3 Test of the power dispatching strategy
V.4.4 Comparison and discussion
V.5 Energy management of the active wind generator
V.5.1 Studied microgrid
V.5.2 Energy management
V.5.3 Mode control unit
V.5.4 Normal operating mode
V.5.5 Short-term recovering modes
V.5.6 Long-term recovering modes
V.5.7 Entire recovering modes
V.6 Performance tests of the energy management strategies
V.6.1 Presentation
V.6.2 Normal operating mode
V.6.3 Short-term recovering modes
V.6.4 Long-term recovering modes
V.6.5 Discussion
V.6.6 Efficiency analysis
V.6.7 Cost evaluation
V.7 Conclusion
Conclusion
Appendix
Appendix A: The ongoing research & development on Distributed Generation
Appendix B: Equivalent Continuous Modeling of Power Converters
Appendix C: Control Structure of Power Systems through Power Converters
Appendix D: Causal Ordering Graph (COG)
Appendix E: Energetic Macroscopic Representation (EMR)
Appendix F: Multi-Level Representation (MLR)
Appendix G: Hardware In-the-Loop (HIL) Simulation
Appendix H: Ancillary Services in the Context of Microgrid
Appendix I: Technical Data of the Used Super-Capacitors
Bibliography:
Curriculum Vitae (english version)
Curriculum Vitae (version française)
Résumé Etendu en Français
