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E-raamat: Model Predictive Control for Microgrids: From power electronic converters to energy management

(Federation University Australia, School of Engineering, Information Technology and Physical Sciences, Australia), (Aalborg University, Department of Energy Technology, Denmark), (Federation University Australia, School of Engineering, )
  • Formaat: EPUB+DRM
  • Sari: Energy Engineering
  • Ilmumisaeg: 22-Oct-2021
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781839533983
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Model Predictive Control for Microgrids: From power electronic converters to energy managementSuurem pilt
  • Formaat: EPUB+DRM
  • Sari: Energy Engineering
  • Ilmumisaeg: 22-Oct-2021
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781839533983
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Microgrids have emerged as a promising solution for accommodating the integration of renewable energy resources. But the intermittency of renewable generation is posing challenges such as voltage/frequency fluctuations, and grid stability issues in grid-connected modes. Model predictive control (MPC) is a method for controlling a process while satisfying a set of constraints. It has been in use for chemical plants and in oil refineries since the 1980s, but in recent years has been deployed for power systems and electronics as well.

This concise work for researchers, engineers and graduate students focuses on the use of MPC for distributed renewable power generation in microgrids. Fluctuating outputs from renewable energy sources and variable load demands are covered, as are control design concepts. The authors provide examples and case studies to validate the theory with both simulation and experimental results and review the shortcomings and future developments.

Chapters treat power electronic converters and control; modelling and hierarchical control of microgrids; use of MPC for PV and wind power; voltage support; parallel PV-ESS microgrids; secondary restoration capability; and tertiary power flow optimization.



Model predictive control (MPC) is a method for controlling a process while satisfying a set of constraints. The use of MPC for controlling power systems has been gaining traction in recent years. This work presents the use of MPC for distributed renewable power generation in microgrids.

List of figures
xi
List of tables
xvii
About the authors xix
Abbreviations xxi
1 Introduction
1(20)
1.1 Microgrid fundamentals
1(8)
1.2 Operation considerations
9(3)
1.2.1 Power sharing
9(1)
1.2.2 Power balancing
10(1)
1.2.3 Power quality
10(1)
1.2.4 Seamless mode transition
11(1)
1.2.5 System stability
11(1)
1.3 Key technologies and challenges
12(9)
1.3.1 New semiconductor devices
12(1)
1.3.2 Power electronic converters and control
12(1)
1.3.3 Renewable intermittency
13(1)
1.3.4 Lack of systematic approaches
13(1)
1.3.5 Large-scale grid integration and its impact on the main grid
14(1)
1.3.6 Energy storage
14(1)
1.3.7 Smart sensors
15(1)
1.3.8 Information and communication technology
15(1)
References
16(5)
2 Power electronic converters and control
21(40)
2.1 Power electronic converters in energy conversion
21(3)
2.1.1 DC--DC converters
21(1)
2.1.2 DC--AC converters (inverters)
22(1)
2.1.2.1 Grid-forming inverters
23(1)
2.1.2.2 Grid-feeding inverters
24(1)
2.1.2.3 Grid-supporting inverter
24(1)
2.2 Control of a single converter
24(21)
2.2.1 Voltage-oriented control
25(2)
2.2.2 Direct control
27(1)
2.2.3 Fuzzy logic control
27(2)
2.2.4 Sliding mode control
29(1)
2.2.5 Predictive control
30(1)
2.2.5.1 Deadbeat-based predictive control
31(1)
2.2.5.2 VPC
32(7)
2.2.5.3 MPC
39(6)
2.3 Control of parallel inverters
45(16)
2.3.1 Centralized control
45(1)
2.3.2 Circular chain control
46(1)
2.3.3 Master-slave control
46(2)
2.3.4 Average load sharing
48(2)
2.3.5 Droop control
50(2)
References
52(4)
Further reading
56(5)
3 Distributed renewable power generation
61(36)
3.1 Distributed generation
61(2)
3.2 Wind power generation
63(11)
3.2.1 Wind turbine characteristics
64(1)
3.2.2 Constant speed constant frequency system
65(1)
3.2.3 VSCF system
66(1)
3.2.3.1 Wound field synchronous generator
67(1)
3.2.3.2 Permanent-magnet synchronous generator
67(1)
3.2.3.3 Doubly fed induction generator
68(1)
3.2.3.4 Squirrel cage induction generator
69(1)
3.2.4 Recent advances in wind power generation
70(4)
3.3 Solar PVs generation
74(23)
3.3.1 Principle and configuration of PV systems
74(3)
3.3.2 Power converters and recent advance of MPC for PV systems
77(1)
3.3.2.1 Single-phase single-stage
77(1)
3.3.2.2 Single-phase multiple-stage
78(1)
3.3.2.3 Three-phase single-stage
79(1)
3.3.2.4 MPPT control of PV system
80(3)
3.3.2.5 Grid-side inverter control of PV system
83(1)
References
84(5)
Further reading
89(8)
4 Modeling and hierarchical control of microgrids
97(28)
4.1 Modeling of MGs
97(1)
4.2 Hierarchical control architecture of MGs
98(27)
4.2.1 Primary control
99(7)
4.2.2 Secondary control
106(1)
4.2.2.1 Centralized secondary control
107(1)
4.2.2.2 Distributed secondary control
107(4)
4.2.2.3 Decentralized secondary control
111(1)
4.2.3 Tertiary control
111(3)
References
114(6)
Further reading
120(5)
5 MPC of PV-wind-storage microgrids
125(28)
5.1 Introduction
125(5)
5.2 Modeling of PV system and its control structure
130(2)
5.3 Modeling of wind turbine system and its control structure
132(1)
5.4 Modeling of ESS and its control structure
133(2)
5.5 Modeling of the AC subgrid and its control structure
135(2)
5.6 System level control
137(3)
5.6.1 Mode 1 operation
139(1)
5.6.2 Mode 2 operation
139(1)
5.6.2.1 Low wind speed low solar irradiation, and heavy load
139(1)
5.6.2.2 High wind speed high solar irradiation, and light load
140(1)
5.6.3 Mode 3 operation
140(1)
5.7 Case studies
140(5)
5.7.1 Fluctuation output from renewable energy
141(1)
5.7.2 Grid-connected operation
141(1)
5.7.3 Islanded operation
142(1)
5.7.4 Grid-synchronization and connection
143(2)
5.8 Conclusion
145(8)
References
145(3)
Further reading
148(5)
6 MPC of PV-ESS MGs with voltage support
153(24)
6.1 Introduction
153(5)
6.2 Model predictive power control scheme
158(6)
6.3 Voltage support
164(2)
6.4 Verification
166(3)
6.4.1 Flexible power injection from PV-ESS
167(1)
6.4.2 Grid voltage support by PV-ESS
168(1)
6.5 Conclusion
169(8)
References
169(5)
Further reading
174(3)
7 MPC of parallel PV-ESS microgrids
177(28)
7.1 Introduction
177(3)
7.2 MPCC for solar PVs
180(3)
7.3 MPPCofBESS DC--DC converters
183(5)
7.4 MPVC of parallel inverters
188(2)
7.5 Verification
190(4)
7.5.1 MPPT of PV system
190(2)
7.5.2 Charging and discharging processes of BESS
192(1)
7.5.3 Power sharing between parallel inverters
193(1)
7.6 Conclusion
194(11)
References
197(2)
Further reading
199(6)
8 MPC of MGs with secondary restoration capability
205(26)
8.1 Background and system configuration
205(3)
8.2 Washout filter-based power-sharing method
208(4)
8.3 Improved model predictive voltage control scheme
212(4)
8.4 Results
216(7)
8.5 Conclusion
223(8)
References
223(3)
Further reading
226(5)
9 MPC of MGs with tertiary power flow optimization
231(22)
9.1 Tertiary control of MGs and MPC
231(4)
9.2 MPC for economic dispatch and optimal power flow in MGs
235(7)
9.3 MPC for networked MGs
242(2)
9.4 Future trend
244(1)
9.4.1 New mathematical formulation
244(1)
9.4.2 Holistic and intelligent MPC approaches
244(1)
9.4.3 MPC in DC MGs
245(1)
9.4.4 Distributed and decentralized control
245(1)
9.5 Conclusion
245(8)
References
246(3)
Further readings
249(4)
Index 253
Jiefeng Hu is an associate professor in power electronics and smart microgrids, and the program coordinator (electrical engineering) of the School of Engineering, Information Technology and Physical Sciences at Federation University Australia. Previously, he was an assistant professor at The Hong Kong Polytechnic University, where he led an international team to develop renewable energy technologies for smart cities. He has published more than 100 research papers, and serves as editor/ associate editor on prestigious IET and IEEE journals.



Josep Guerrero is a professor with the Department of Energy Technology, Aalborg University, Denmark. He is responsible for the Microgrid Research Program, and the founder and director of the Centre for Research on Microgrids (CROM). Prof. Guerrero's research interests focus on different microgrid aspects, including power electronics, distributed energy-storage systems, control, energy management, metering and the use of the IoT. From 2014 to 2018 he was awarded a Highly Cited Researcher by Thomson Reuters.



Syed Islam is a professor and the dean for the School of Engineering, Information Technology and Physical Sciences at Federation University Australia. His awards include the Curtin University inaugural award for Research Development and two Sir John Madsen medals. He has published over 270 technical papers on condition monitoring of transformers, wind energy and smart power systems, and serves on prestigious committees and boards, and in editorial capacities of key journals.
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