Model Predictive Control of Microgrids:
The book shows how the operation of renewable-energy microgrids can be facilitated by the use of model predictive control (MPC). It gives readers a wide overview of control methods for microgrid operation at all levels, ranging from quality of service, to integration in the electricity market. MPC-b...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer
2020
|
| Ausgabe: | 1st ed. 2020 |
| Schriftenreihe: | Advances in Industrial Control
|
| Schlagworte: | |
| Zusammenfassung: | The book shows how the operation of renewable-energy microgrids can be facilitated by the use of model predictive control (MPC). It gives readers a wide overview of control methods for microgrid operation at all levels, ranging from quality of service, to integration in the electricity market. MPC-based solutions are provided for the main control issues related to energy management and optimal operation of microgrids.The authors present MPC techniques for case studies that include different renewable sources – mainly photovoltaic and wind – as well as hybrid storage using batteries, hydrogen and supercapacitors. Experimental results for a pilot-scale microgrid are also presented, as well as simulations of scheduling in the electricity market and integration of electric and hybrid vehicles into the microgrid. in order to replicate the examples provided in the book and to develop and validate control algorithms on existing or projected microgrids.Model Predictive Control of Microgrids will interest researchers and practitioners, enabling them to keep abreast of a rapidly developing field. The text will also help to guide graduate students through processes from the conception and initial design of a microgrid through its implementation to the optimization of microgrid management.Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control |
| Beschreibung: | The book shows how the operation of renewable-energy microgrids can be facilitated by the use of model predictive control (MPC). It gives readers a wide overview of control methods for microgrid operation at all levels, ranging from quality of service, to integration in the electricity market. MPC-based solutions are provided for the main control issues related to energy management and optimal operation of microgrids.The authors present MPC techniques for case studies that include different renewable sources – mainly photovoltaic and wind – as well as hybrid storage using batteries, hydrogen and supercapacitors. Experimental results for a pilot-scale microgrid are also presented, as well as simulations of scheduling in the electricity market and integration of electric and hybrid vehicles into the microgrid. The authors also provide a modular simulator to be run in MATLAB/Simulink, for readers to create their own microgrids using the blocks supplied, in order to replicate the examples provided in the book and to develop and validate control algorithms on existing or projected microgrids.Model Predictive Control of Microgrids will interest researchers and practitioners, enabling them to keep abreast of a rapidly developing field. The text will also help to guide graduate students through processes from the conception and initial design of a microgrid through its implementation to the optimization of microgrid management.Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control 1: Microgrid Control Issues.- 1.1: Microgrid as a New Paradigm for the Electrical System.- 1.1.1: Microgrids and Storage.- 1.1.2: Microgrids Around the World.- 1.2: Control of Microgrids.- 1.2.1: Control Goals and Challenges.- 1.2.2: Control Techniques.- 1.2.3: Introduction to Model Predictive Control.- 1.3: Microgrids and the Electrical System.- 1.3.1: Microgrids and Electric Vehicles.- 1.3.2: Networks of Microgrids.- 1.4: Outline of the Chapters.- 2: Overview of Control Methods Applied to Microgrids.- 3: Dynamic Models of Microgrids and Components.- 4: Basic Energy and Power Management Systems in Microgrids.- 5: Hybrid MPC Applied to Economical Dispatch of Microgrids.- 5.1: Management Electricity Markets.- 5.1.1: Day-Ahead Market.- 5.1.2: Intraday Market.- 5.1.3: Ancilliary Services.- 5.2: Energy Storage Systems in the Electrical Energy Market.- 5.3: Tertiary Model Predictive Control-Schedule.- 5.3.1: Mixed Logic Dynamic Systems.- 5.3.2: Model of the Plant.- 5.3.3: Day-Ahead - Market MPC.- 5.3.4: Intraday Market MPC.- 5.3.5: Regulation Service Market MPC.- 5.3.6: System Constraints.- 5.4: Tertiary Model Predictive Control-Load.- 5.4.1: Objective Function.- 5.4.2: System Constraints.- 5.5: Experimental Results.- 5.5.1: Day-Ahead Market.- 5.5.2: Intraday Market.- 5.5.3: Regulation Service Market.- 5.5.4: Load Sharing.- 5.6: Comparison with other Control Methods.- 6: Enhancement of Power Quality using Finite-State MPC.- 6.1: Power Quality in the Smart Grid.- 6.1.1: Analysis of Power Quality Issues in the Components.- 6.1.2: Distributed Storage Interface.- 6.1.3: Microgrid Operation Modes.- 6.2: MPC Methods for Power Converters.- 6.2.1: Direct or Finite Control Set MPC.- 6.2.2: MPC with Continuous Control Set.- 6.3: Primary Model Predictive Control Design.- 6.3.1: VSI Current MPC Based on Fourier Transform.- 6.4: Secondary Model Predictive Control Design.- 6.4.1: VSI Voltage MPC Based on Fourier Transform.- 6.4.2: VSI Current MPC Based on Fourier Transform.- - 6.5: Simulation Results.- 6.5.1: VSI Current MPC Based on Fourier Transform.- 6.5.2: VSI Voltage MPC Based on Fourier Transform.- 7: Integration of Electric Vehicles in Microgrids.- 8: Stochastic MPC and Failure Management.- 9: Distributed MPC for Networks of Microgrids.- 10: Glossary.- 11: Index |
| Beschreibung: | 266 p 528 grams |
| ISBN: | 9783030245726 |
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| 500 | |a The book shows how the operation of renewable-energy microgrids can be facilitated by the use of model predictive control (MPC). It gives readers a wide overview of control methods for microgrid operation at all levels, ranging from quality of service, to integration in the electricity market. MPC-based solutions are provided for the main control issues related to energy management and optimal operation of microgrids.The authors present MPC techniques for case studies that include different renewable sources – mainly photovoltaic and wind – as well as hybrid storage using batteries, hydrogen and supercapacitors. Experimental results for a pilot-scale microgrid are also presented, as well as simulations of scheduling in the electricity market and integration of electric and hybrid vehicles into the microgrid. The authors also provide a modular simulator to be run in MATLAB/Simulink, for readers to create their own microgrids using the blocks supplied, in order to replicate the examples provided in the book and to develop and validate control algorithms on existing or projected microgrids.Model Predictive Control of Microgrids will interest researchers and practitioners, enabling them to keep abreast of a rapidly developing field. The text will also help to guide graduate students through processes from the conception and initial design of a microgrid through its implementation to the optimization of microgrid management.Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control | ||
| 500 | |a 1: Microgrid Control Issues.- 1.1: Microgrid as a New Paradigm for the Electrical System.- 1.1.1: Microgrids and Storage.- 1.1.2: Microgrids Around the World.- 1.2: Control of Microgrids.- 1.2.1: Control Goals and Challenges.- 1.2.2: Control Techniques.- 1.2.3: Introduction to Model Predictive Control.- 1.3: Microgrids and the Electrical System.- 1.3.1: Microgrids and Electric Vehicles.- 1.3.2: Networks of Microgrids.- 1.4: Outline of the Chapters.- 2: Overview of Control Methods Applied to Microgrids.- 3: Dynamic Models of Microgrids and Components.- 4: Basic Energy and Power Management Systems in Microgrids.- 5: Hybrid MPC Applied to Economical Dispatch of Microgrids.- 5.1: Management Electricity Markets.- 5.1.1: Day-Ahead Market.- 5.1.2: Intraday Market.- 5.1.3: Ancilliary Services.- 5.2: Energy Storage Systems in the Electrical Energy Market.- 5.3: Tertiary Model Predictive Control-Schedule.- 5.3.1: Mixed Logic Dynamic Systems.- 5.3.2: Model of the Plant.- 5.3.3: Day-Ahead | ||
| 500 | |a - Market MPC.- 5.3.4: Intraday Market MPC.- 5.3.5: Regulation Service Market MPC.- 5.3.6: System Constraints.- 5.4: Tertiary Model Predictive Control-Load.- 5.4.1: Objective Function.- 5.4.2: System Constraints.- 5.5: Experimental Results.- 5.5.1: Day-Ahead Market.- 5.5.2: Intraday Market.- 5.5.3: Regulation Service Market.- 5.5.4: Load Sharing.- 5.6: Comparison with other Control Methods.- 6: Enhancement of Power Quality using Finite-State MPC.- 6.1: Power Quality in the Smart Grid.- 6.1.1: Analysis of Power Quality Issues in the Components.- 6.1.2: Distributed Storage Interface.- 6.1.3: Microgrid Operation Modes.- 6.2: MPC Methods for Power Converters.- 6.2.1: Direct or Finite Control Set MPC.- 6.2.2: MPC with Continuous Control Set.- 6.3: Primary Model Predictive Control Design.- 6.3.1: VSI Current MPC Based on Fourier Transform.- 6.4: Secondary Model Predictive Control Design.- 6.4.1: VSI Voltage MPC Based on Fourier Transform.- 6.4.2: VSI Current MPC Based on Fourier Transform.- | ||
| 500 | |a - 6.5: Simulation Results.- 6.5.1: VSI Current MPC Based on Fourier Transform.- 6.5.2: VSI Voltage MPC Based on Fourier Transform.- 7: Integration of Electric Vehicles in Microgrids.- 8: Stochastic MPC and Failure Management.- 9: Distributed MPC for Networks of Microgrids.- 10: Glossary.- 11: Index | ||
| 520 | |a The book shows how the operation of renewable-energy microgrids can be facilitated by the use of model predictive control (MPC). It gives readers a wide overview of control methods for microgrid operation at all levels, ranging from quality of service, to integration in the electricity market. MPC-based solutions are provided for the main control issues related to energy management and optimal operation of microgrids.The authors present MPC techniques for case studies that include different renewable sources – mainly photovoltaic and wind – as well as hybrid storage using batteries, hydrogen and supercapacitors. Experimental results for a pilot-scale microgrid are also presented, as well as simulations of scheduling in the electricity market and integration of electric and hybrid vehicles into the microgrid. in order to replicate the examples provided in the book and to develop and validate control algorithms on existing or projected microgrids.Model Predictive Control of Microgrids will interest researchers and practitioners, enabling them to keep abreast of a rapidly developing field. The text will also help to guide graduate students through processes from the conception and initial design of a microgrid through its implementation to the optimization of microgrid management.Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control | ||
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Datensatz im Suchindex
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| author | Bordons, Carlos |
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| building | Verbundindex |
| bvnumber | BV046925707 |
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| edition | 1st ed. 2020 |
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| id | DE-604.BV046925707 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T15:32:47Z |
| indexdate | 2024-07-10T08:57:40Z |
| institution | BVB |
| isbn | 9783030245726 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032334820 |
| open_access_boolean | |
| owner | DE-29T |
| owner_facet | DE-29T |
| physical | 266 p 528 grams |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Springer |
| record_format | marc |
| series2 | Advances in Industrial Control |
| spelling | Bordons, Carlos Verfasser aut Model Predictive Control of Microgrids 1st ed. 2020 Cham Springer 2020 266 p 528 grams txt rdacontent n rdamedia nc rdacarrier Advances in Industrial Control The book shows how the operation of renewable-energy microgrids can be facilitated by the use of model predictive control (MPC). It gives readers a wide overview of control methods for microgrid operation at all levels, ranging from quality of service, to integration in the electricity market. MPC-based solutions are provided for the main control issues related to energy management and optimal operation of microgrids.The authors present MPC techniques for case studies that include different renewable sources – mainly photovoltaic and wind – as well as hybrid storage using batteries, hydrogen and supercapacitors. Experimental results for a pilot-scale microgrid are also presented, as well as simulations of scheduling in the electricity market and integration of electric and hybrid vehicles into the microgrid. The authors also provide a modular simulator to be run in MATLAB/Simulink, for readers to create their own microgrids using the blocks supplied, in order to replicate the examples provided in the book and to develop and validate control algorithms on existing or projected microgrids.Model Predictive Control of Microgrids will interest researchers and practitioners, enabling them to keep abreast of a rapidly developing field. The text will also help to guide graduate students through processes from the conception and initial design of a microgrid through its implementation to the optimization of microgrid management.Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control 1: Microgrid Control Issues.- 1.1: Microgrid as a New Paradigm for the Electrical System.- 1.1.1: Microgrids and Storage.- 1.1.2: Microgrids Around the World.- 1.2: Control of Microgrids.- 1.2.1: Control Goals and Challenges.- 1.2.2: Control Techniques.- 1.2.3: Introduction to Model Predictive Control.- 1.3: Microgrids and the Electrical System.- 1.3.1: Microgrids and Electric Vehicles.- 1.3.2: Networks of Microgrids.- 1.4: Outline of the Chapters.- 2: Overview of Control Methods Applied to Microgrids.- 3: Dynamic Models of Microgrids and Components.- 4: Basic Energy and Power Management Systems in Microgrids.- 5: Hybrid MPC Applied to Economical Dispatch of Microgrids.- 5.1: Management Electricity Markets.- 5.1.1: Day-Ahead Market.- 5.1.2: Intraday Market.- 5.1.3: Ancilliary Services.- 5.2: Energy Storage Systems in the Electrical Energy Market.- 5.3: Tertiary Model Predictive Control-Schedule.- 5.3.1: Mixed Logic Dynamic Systems.- 5.3.2: Model of the Plant.- 5.3.3: Day-Ahead - Market MPC.- 5.3.4: Intraday Market MPC.- 5.3.5: Regulation Service Market MPC.- 5.3.6: System Constraints.- 5.4: Tertiary Model Predictive Control-Load.- 5.4.1: Objective Function.- 5.4.2: System Constraints.- 5.5: Experimental Results.- 5.5.1: Day-Ahead Market.- 5.5.2: Intraday Market.- 5.5.3: Regulation Service Market.- 5.5.4: Load Sharing.- 5.6: Comparison with other Control Methods.- 6: Enhancement of Power Quality using Finite-State MPC.- 6.1: Power Quality in the Smart Grid.- 6.1.1: Analysis of Power Quality Issues in the Components.- 6.1.2: Distributed Storage Interface.- 6.1.3: Microgrid Operation Modes.- 6.2: MPC Methods for Power Converters.- 6.2.1: Direct or Finite Control Set MPC.- 6.2.2: MPC with Continuous Control Set.- 6.3: Primary Model Predictive Control Design.- 6.3.1: VSI Current MPC Based on Fourier Transform.- 6.4: Secondary Model Predictive Control Design.- 6.4.1: VSI Voltage MPC Based on Fourier Transform.- 6.4.2: VSI Current MPC Based on Fourier Transform.- - 6.5: Simulation Results.- 6.5.1: VSI Current MPC Based on Fourier Transform.- 6.5.2: VSI Voltage MPC Based on Fourier Transform.- 7: Integration of Electric Vehicles in Microgrids.- 8: Stochastic MPC and Failure Management.- 9: Distributed MPC for Networks of Microgrids.- 10: Glossary.- 11: Index The book shows how the operation of renewable-energy microgrids can be facilitated by the use of model predictive control (MPC). It gives readers a wide overview of control methods for microgrid operation at all levels, ranging from quality of service, to integration in the electricity market. MPC-based solutions are provided for the main control issues related to energy management and optimal operation of microgrids.The authors present MPC techniques for case studies that include different renewable sources – mainly photovoltaic and wind – as well as hybrid storage using batteries, hydrogen and supercapacitors. Experimental results for a pilot-scale microgrid are also presented, as well as simulations of scheduling in the electricity market and integration of electric and hybrid vehicles into the microgrid. in order to replicate the examples provided in the book and to develop and validate control algorithms on existing or projected microgrids.Model Predictive Control of Microgrids will interest researchers and practitioners, enabling them to keep abreast of a rapidly developing field. The text will also help to guide graduate students through processes from the conception and initial design of a microgrid through its implementation to the optimization of microgrid management.Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control bicssc bisacsh Energy systems Control engineering Power electronics Environmental engineering Biotechnology Energy Microgrid Energietechnik (DE-588)1121110479 gnd rswk-swf Modellprädiktive Regelung (DE-588)1135937567 gnd rswk-swf Hardcover, Softcover / Technik/Wärmetechnik, Energietechnik, Kraftwerktechnik Microgrid Energietechnik (DE-588)1121110479 s Modellprädiktive Regelung (DE-588)1135937567 s DE-604 Garcia-Torres, Félix Sonstige oth Ridao, Miguel A. Sonstige oth |
| spellingShingle | Bordons, Carlos Model Predictive Control of Microgrids bicssc bisacsh Energy systems Control engineering Power electronics Environmental engineering Biotechnology Energy Microgrid Energietechnik (DE-588)1121110479 gnd Modellprädiktive Regelung (DE-588)1135937567 gnd |
| subject_GND | (DE-588)1121110479 (DE-588)1135937567 |
| title | Model Predictive Control of Microgrids |
| title_auth | Model Predictive Control of Microgrids |
| title_exact_search | Model Predictive Control of Microgrids |
| title_exact_search_txtP | Model Predictive Control of Microgrids |
| title_full | Model Predictive Control of Microgrids |
| title_fullStr | Model Predictive Control of Microgrids |
| title_full_unstemmed | Model Predictive Control of Microgrids |
| title_short | Model Predictive Control of Microgrids |
| title_sort | model predictive control of microgrids |
| topic | bicssc bisacsh Energy systems Control engineering Power electronics Environmental engineering Biotechnology Energy Microgrid Energietechnik (DE-588)1121110479 gnd Modellprädiktive Regelung (DE-588)1135937567 gnd |
| topic_facet | bicssc bisacsh Energy systems Control engineering Power electronics Environmental engineering Biotechnology Energy Microgrid Energietechnik Modellprädiktive Regelung |
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