Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming:
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Kassel
Kassel University Press
[2018]
|
| Schriftenreihe: | Energy management and power system operation
Vol. 3 |
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis Inhaltsverzeichnis |
| Beschreibung: | xii, 169 Seiten Diagramme 21 cm, 250 g |
| ISBN: | 9783737604642 3737604649 |
Internformat
MARC
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| 100 | 1 | |a Nemati, Mohsen Shiralizadeh |e Verfasser |0 (DE-588)1161478809 |4 aut | |
| 245 | 1 | 0 | |a Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming |c Mohsen Shiralizadeh Nemati |
| 264 | 1 | |a Kassel |b Kassel University Press |c [2018] | |
| 300 | |a xii, 169 Seiten |b Diagramme |c 21 cm, 250 g | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Energy management and power system operation |v Vol. 3 | |
| 502 | |b Dissertation |c Universität Stuttgart |d 2017 | ||
| 650 | 0 | 7 | |a Genetischer Algorithmus |0 (DE-588)4265092-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Gemischt-ganzzahlige Optimierung |0 (DE-588)4156566-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Microgrid |g Energietechnik |0 (DE-588)1121110479 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Blindleistungsregelung |0 (DE-588)4313844-5 |2 gnd |9 rswk-swf |
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| 650 | 0 | 7 | |a Energiemanagement |0 (DE-588)4124833-8 |2 gnd |9 rswk-swf |
| 653 | |a Economic dispatch | ||
| 653 | |a Genetic algorithm | ||
| 653 | |a Microgrids | ||
| 653 | |a Mixed integer linear programming | ||
| 653 | |a Unit commitment | ||
| 655 | 7 | |0 (DE-588)4113937-9 |a Hochschulschrift |2 gnd-content | |
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| 776 | 0 | 8 | |i Erscheint auch als |z 978-3-7376-0465-9 |n Online-Ausgabe |a Nemati, Mohsen Shiralizadeh |t Optimization of Unit Commitment and Economic Dispatch in Microgrids Based on Genetic Algorithm and Mixed Integer Linear Programming |d Kassel : Kassel University Press, 2018 |h Online-Ressource |
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Datensatz im Suchindex
| _version_ | 1804181521756585984 |
|---|---|
| adam_text | CONTENT
ACKNOWLEDGEMENTS............................................................................................................
I
ABSTRACT................................................................................................................................II
KURZFASSUNG......................................................................................................................
IV
CONTENT...............................................................................................................................
VI
LIST OF
ABBREVIATIONS.........................................................................................................
X
1 INTRODUCTION.................................................... 1
1.1 BACKGROUND AND MOTIVATION
......................................................................................
1
1.2 OBJECTIVES AND CONTRIBUTION OF THE
THESIS...............................................................3
1.3 OUTLINE OF THE
WORK....................................................................................................4
2 ENERGY MANAGEMENT SYSTEM OF
MICROGRIDS....................................................... 6
2.1
INTRODUCTION...............................................................................................................
6
2.1.1 TRADITIONAL CONCEPT OF ENERGY SYSTEMS
............................................................
6
2.1.2 DRIVERS OF DISTRIBUTED GENERATION DEVELOPMENT
...............................................
6
2.2 MICROGRID
CONCEPT....................................................................................................7
2.3 MICROGRID
BENEFITS.....................................................................................................9
2.4 ANCILLARY SERVICES IN / FROM THE MICROGRIDS
...........................................................
10
2.5 CHALLENGES AND BARRIERS OF
MICROGRIDS..................................................................11
2.6 MARKET MODELS FOR
MICROGRIDS................................................................................11
2.6.1 MICROGRIDS INTERNAL MARKET
MODELS...................................................................12
2.6.2 MICROGRIDS EXTERNAL
MARKET...............................................................................14
2.7 ENERGY MANAGEMENT SYSTEM
FRAMEWORK.............................................................17
3 MODELS AND OPERATION POLICIES IN MICROGRID*S EMS
.......................................
18
3.1 COMPONENT MODELS AND ASSUMPTIONS IN
EMS..................................................... 18
3.1.1 SEMI-CONTROLLABLE INTERMITTENT
RES.................................................................18
3.1.2 DISTRIBUTED DISPATCHABLE GENERATORS
(DDG)...................................................21
3.1.3 ENERGY STORAGE
SYSTEM....................................................................................26
3.2 MICROGRID OPERATION
POLICIES..................................................................................31
3.2.1 COST-EFFECTIVE OPERATION (MOP
1)...................................................................32
3.2.2 GRID SUPPORTING MODE (MOP 2
).......................................................................32
3.2.3 MAXIMUM ISLANDING DEGREE (MOP 3)
..............................................................
33
3.2.4 ECO-FRIENDLY OPERATION (MOP 4 )
.....................................................................33
3.2.5 MULTIFUNCTIONAL POLICY (MOP
5).........................................................................34
3.2.6 REFERENCE MODE
(REF)...................................................................................34
4 OPTIMIZATION OF THE MICROGRIDS* UC AND ED WITH GENETIC
ALGORITHM..........35
4.1 BACKGROUND AND
BASICS.........................................................................................
36
4.2 MAIN FEATURES OF THE GA RELATED TO THE W
ORK.....................................................37
4.3 BINARY CODING VS. REAL
ENCODING.........................................................................
38
4.4 GENETIC ALGORITHM PROCESS FOR UC &
ED...............................................................39
4.5 COMPONENT MODELS AND
INPUTS.............................................................................
41
4.6 DATA TRANSFORMATION AND ENCODING IN P.U. VALUES
..............................................
41
4.7 REFERENCE ANALYSIS
CASE......................................................................................43
4.8 FIRST POPULATION INITIALIZATION
.................................................................................
43
4.8.1 RANDOM
INITIALIZATION........................................................................................44
4.8.2 INITIALIZATION BASED ON PRIORITY LIST
METHOD.....................................................44
4.8.3 EVALUATION OF DIFFERENT INITIALIZATIONS METHODS
...............................................
46
4.9 RANKING OF THE
INDIVIDUALS.....................................................................................47
4.10 SELECTION OF THE BEST
SOLUTIONS.............................................................................
48
4.10.1 TOURNAMENT
SELECTION......................................................................................46
4.10.2 ELITISM
SELECTION...............................................................................................
48
4.10.3 THE INFLUENCE OF ELITISM SELECTION ON THE OPTIMIZATION
RESULTS....................49
4.11 CROSSOVER
METHODS...............................................................................................
49
4.11.1 SINGLE POINT CROSSOVER
(SPX).........................................................................50
4.11.2 TWO POINT CROSSOVER (TPX) AND MULTI POINT CROSSOVER (MPX)
.....................
50
4.11.3 SIMULATED BINARY CROSSOVER (SBX)
.................................................................
50
4.11.4 DYNAMIC-TYPE CROSSOVER
(DTX):....................................................................52
4.11.5 COMPARISON OF DIFFERENT CROSSOVER
METHODS..................................................52
4.12 MUTATION
PROCESS....................................................................................................53
4.12.1 RANDOM MUTATION METHOD (RM
)......................................................................53
4.12.2 SEMI-PROBABILISTIC MUTATION METHOD
(SPM)....................................................54
4.12.3 ADOPTIVE SEMI PROBABILISTIC MUTATION (ASPM)
...............................................
56
4.12.4 PERFORMANCE OF DIFFERENT MUTATION
METHODS...................................................56
4.13 NETWORK ANALYSIS
TYPES.........................................................................................57
4.14 CONSTRAINTS HANDLING AND PENALTY
FUNCTION.........................................................58
4.14.1 PENALTY
FUNCTION..............................................................................................
58
4.14.2 INFLUENCE OF DIFFERENT PENALTY ASSIGNMENT METHODS ON CONVERGENCE
AND
SEARCHING.........................................................................................................60
4.15 NICHING METHOD AND SIMILARITY FUNCTION
...............................................................
61
4.16 FITNESS FUNCTION ASSIGNMENT
................................................................................
63
4.17 OPTIMAL SELECTION / SETTING OF GA MODULES AND OPERATORS
................................
64
4.18 STOP CRITERIA
.................
65
4.19 SMART (SELECTIVE) NETWORK CONSIDERATION
4.20 SUMMARY OF THE
CHAPTER........................................................................................
68
5 MILP-BASED OPTIMIZER FOR MICROGRID EMS
.......................................................
71
5.1 FEATURES AND UTILIZATION OF MILP
...........................................................................
71
5.2 STRUCTURE OF THE OPTIMIZATION
MODULES..................................................................72
5.3 DEFINITION OF LINEAR OPTIMIZER
CORE.......................................................................
73
5.3.1 FORMULATION OF THE PROBLEM FOR
MILP...............................................................73
5.3.2 MILP SOLVER AND RELATED TECHNIQUES
............................................................
76
5.4 POWER PLOW OPTIMIZATION IN NETWORK CALCULATION TOOL
........................................
76
5.4.1 INITIAL POWER FLOW CALCULATION MODULE
(PFC)..................................................79
5.4.2 VOLTAGE SET POINT OPTIMIZATION (VSO) AND Q-REDISPATCHING
........................
79
5.4.3 P - REDISPATCHING MODULE
(PR).......................................................................
81
5.4.4 UNIT RECOMMITMENT
(URC)...............................................................................84
5.4.5 RES CURTAILMENT (RESC) BY
OVERVOLTAGES.....................................................84
5.4.6 CURRENT CORRECTION MODULE FOR
OVERLOADINGS..................................................85
5.5 SUMMARY OF THE
CHAPTER........................................................................................
85
6 ANALYSIS OF EMS PERFORMANCE FOR A TEST MICROGRID
.......................................
87
6.1
INTRODUCTION.............................................................................................................
87
6.2 DESCRIPTION OF ANALYSIS PROCESS
............................................................................
87
6.3 TEST MICROGRID FOR EMS CASE
STUDIES...................................................................88
6.3.1 OPERATION COST CURVE OF DISPATCHABLE GENERATORS
.........................................
89
6.3.2 EXCHANGE ENERGY PRICE OF THE MAIN
GRID........................................................ 90
6.3.3 SETTING OF
ALGORITHMS........................................................................................
91
6.4 MICROGRID OPERATION IN REFERENCE CASE
STUDY.....................................................91
6.5 SENSITIVITY ANALYSIS OF BSS AGING IN
MOP1......................................................... 93
6.6 CONSIDERATION OF NETWORK CONSTRAINTS UNDER MOP2
............................................
96
6.6.1 ANALYSIS OF GA PERFORMANCE WITH VOLTAGE AND CURRENT CONSTRAINTS
.............
96
6.6.2 EVALUATION OF MILP MODULES WITH NETWORK CONSTRAINT
VIOLATIONS..................99
6.7 MICROGRID OPERATION IN DIFFERENT ISLANDING DEGREES
(MOP3).............................103
6.7.1 CONGESTION MANAGEMENT OF MICROGRID FOR UPPER GRID
..................................
103
6.7.2 MICROGRID OPERATION UNDER ISLANDED
MODE....................................................104
6.7.3 MICROGRID WITH SCHEDULED / FIXED EXCHANGE POWER WITH MACRO-GRID
........
105
6.7.4 SENSITIVITY ANALYSIS OF EXCHANGE POWER TO ENERGY PRICE
............................
106
6.7.5 SENSITIVITY ANALYSIS OF DDG ACTIVATION TO UNIFORM ENERGY PRICING
...............
108
6.8 REDUCTION OF GREENHOUSE GASES PRODUCTION UNDER
MOP4...............................110
6.9 PERFORMANCE OF GA IN HIGHLY CONSTRAINED MULTI-OBJECTIVE PROBLEM OF
MOP5.113
6.10 ALGORITHM PERFORMANCE AND SPEED IN SMART PFC MODE
..................................
116
6.11 CONCLUSION OF THE CASE STUDIES
..........................................................................
116
7 SUMMARY AND
OUTLOOK......................................................................................
121
7.1
SUMMARY...............................................................................................................121
7.2
OUTLOOK..................................................................................................................
125
8
APPENDIX..............................................................................................................127
8.1 DIESEL GENERATOR DATA SHEET OF CUMMINS DG C90 D5
.....................................
127
8.2 MACRO / MICRO-CYCLES AND BSS LIFE LOSS CALCULATION
.......................................
126
8.3 THE GA SETTING AND MICROGRID INPUTS FOR CASE STUDY OF CHAPTER 4
..................
129
8.4 PERFORMED ANALYSIS, GA SETTING AND MICROGRID INPUTS AND POLICIES IN
CHAPTER 6
..............................................................................................................131
8.5 EXAMPLES OF DEFINED SPM MUTATION
PROCESS.................................................... 134
6.6 EXTRA RESULTS FOR CONSIDERING NETWORK CONSTRAINTS IN EMS (RELATED TO
CHAPTER
6.6).........................................................................................................
136
8.6.1 SMALL PV-FARM POWER CURTAILMENT IN CASE OF OVERLOADINGS
......................
136
8.6.2 PEAK SHAVING PERFORMANCE OF BSS IN CASE OF OVERLOADINGS
......................
137
8.7 CATEGORIZATION AND COMPARISON OF THE OPTIMIZATION METHODS
.........................
136
6.7.1
INTRODUCTION.....................................................................................................
136
8.7.2 EVOLUTIONARY ALGORITHMS
................................................................................
140
8.7.3 MATHEMATICAL
METHODS...................................................................................143
6.7.4 ARTIFICIAL INTELLIGENCE
TECHNIQUES...................................................................145
6.7.5 CONCLUSION AND SELECTION OF THE
METHODS..................................................... 146
6.8 PARAMETRIZATION OF THE G A
..................................................................................148
6.9 SUMMARY OF ADVANTAGES AND DRAWBACKS OF THE DEVELOPED MILP-BASED
OPTIMIZATION METHOD IN CHAPTER 5
......................................................................
150
6.10 AUTHOR*S
PUBLICATIONS............................................................................................
152
8.11 SUPERVISED BACHELOR AND MASTER
THESES............................................................153
6.12
BIBLIOGRAPHY.........................................................................................................
156
|
| adam_txt |
CONTENT
ACKNOWLEDGEMENTS.
I
ABSTRACT.II
KURZFASSUNG.
IV
CONTENT.
VI
LIST OF
ABBREVIATIONS.
X
1 INTRODUCTION. 1
1.1 BACKGROUND AND MOTIVATION
.
1
1.2 OBJECTIVES AND CONTRIBUTION OF THE
THESIS.3
1.3 OUTLINE OF THE
WORK.4
2 ENERGY MANAGEMENT SYSTEM OF
MICROGRIDS. 6
2.1
INTRODUCTION.
6
2.1.1 TRADITIONAL CONCEPT OF ENERGY SYSTEMS
.
6
2.1.2 DRIVERS OF DISTRIBUTED GENERATION DEVELOPMENT
.
6
2.2 MICROGRID
CONCEPT.7
2.3 MICROGRID
BENEFITS.9
2.4 ANCILLARY SERVICES IN / FROM THE MICROGRIDS
.
10
2.5 CHALLENGES AND BARRIERS OF
MICROGRIDS.11
2.6 MARKET MODELS FOR
MICROGRIDS.11
2.6.1 MICROGRIDS INTERNAL MARKET
MODELS.12
2.6.2 MICROGRIDS EXTERNAL
MARKET.14
2.7 ENERGY MANAGEMENT SYSTEM
FRAMEWORK.17
3 MODELS AND OPERATION POLICIES IN MICROGRID*S EMS
.
18
3.1 COMPONENT MODELS AND ASSUMPTIONS IN
EMS. 18
3.1.1 SEMI-CONTROLLABLE INTERMITTENT
RES.18
3.1.2 DISTRIBUTED DISPATCHABLE GENERATORS
(DDG).21
3.1.3 ENERGY STORAGE
SYSTEM.26
3.2 MICROGRID OPERATION
POLICIES.31
3.2.1 COST-EFFECTIVE OPERATION (MOP
1).32
3.2.2 GRID SUPPORTING MODE (MOP 2
).32
3.2.3 MAXIMUM ISLANDING DEGREE (MOP 3)
.
33
3.2.4 ECO-FRIENDLY OPERATION (MOP 4 )
.33
3.2.5 MULTIFUNCTIONAL POLICY (MOP
5).34
3.2.6 REFERENCE MODE
(REF).34
4 OPTIMIZATION OF THE MICROGRIDS* UC AND ED WITH GENETIC
ALGORITHM.35
4.1 BACKGROUND AND
BASICS.
36
4.2 MAIN FEATURES OF THE GA RELATED TO THE W
ORK.37
4.3 BINARY CODING VS. REAL
ENCODING.
38
4.4 GENETIC ALGORITHM PROCESS FOR UC &
ED.39
4.5 COMPONENT MODELS AND
INPUTS.
41
4.6 DATA TRANSFORMATION AND ENCODING IN P.U. VALUES
.
41
4.7 REFERENCE ANALYSIS
CASE.43
4.8 FIRST POPULATION INITIALIZATION
.
43
4.8.1 RANDOM
INITIALIZATION.44
4.8.2 INITIALIZATION BASED ON PRIORITY LIST
METHOD.44
4.8.3 EVALUATION OF DIFFERENT INITIALIZATIONS METHODS
.
46
4.9 RANKING OF THE
INDIVIDUALS.47
4.10 SELECTION OF THE BEST
SOLUTIONS.
48
4.10.1 TOURNAMENT
SELECTION.46
4.10.2 ELITISM
SELECTION.
48
4.10.3 THE INFLUENCE OF ELITISM SELECTION ON THE OPTIMIZATION
RESULTS.49
4.11 CROSSOVER
METHODS.
49
4.11.1 SINGLE POINT CROSSOVER
(SPX).50
4.11.2 TWO POINT CROSSOVER (TPX) AND MULTI POINT CROSSOVER (MPX)
.
50
4.11.3 SIMULATED BINARY CROSSOVER (SBX)
.
50
4.11.4 DYNAMIC-TYPE CROSSOVER
(DTX):.52
4.11.5 COMPARISON OF DIFFERENT CROSSOVER
METHODS.52
4.12 MUTATION
PROCESS.53
4.12.1 RANDOM MUTATION METHOD (RM
).53
4.12.2 SEMI-PROBABILISTIC MUTATION METHOD
(SPM).54
4.12.3 ADOPTIVE SEMI PROBABILISTIC MUTATION (ASPM)
.
56
4.12.4 PERFORMANCE OF DIFFERENT MUTATION
METHODS.56
4.13 NETWORK ANALYSIS
TYPES.57
4.14 CONSTRAINTS HANDLING AND PENALTY
FUNCTION.58
4.14.1 PENALTY
FUNCTION.
58
4.14.2 INFLUENCE OF DIFFERENT PENALTY ASSIGNMENT METHODS ON CONVERGENCE
AND
SEARCHING.60
4.15 NICHING METHOD AND SIMILARITY FUNCTION
.
61
4.16 FITNESS FUNCTION ASSIGNMENT
.
63
4.17 OPTIMAL SELECTION / SETTING OF GA MODULES AND OPERATORS
.
64
4.18 STOP CRITERIA
.
65
4.19 SMART (SELECTIVE) NETWORK CONSIDERATION
4.20 SUMMARY OF THE
CHAPTER.
68
5 MILP-BASED OPTIMIZER FOR MICROGRID EMS
.
71
5.1 FEATURES AND UTILIZATION OF MILP
.
71
5.2 STRUCTURE OF THE OPTIMIZATION
MODULES.72
5.3 DEFINITION OF LINEAR OPTIMIZER
CORE.
73
5.3.1 FORMULATION OF THE PROBLEM FOR
MILP.73
5.3.2 MILP SOLVER AND RELATED TECHNIQUES
.
76
5.4 POWER PLOW OPTIMIZATION IN NETWORK CALCULATION TOOL
.
76
5.4.1 INITIAL POWER FLOW CALCULATION MODULE
(PFC).79
5.4.2 VOLTAGE SET POINT OPTIMIZATION (VSO) AND Q-REDISPATCHING
.
79
5.4.3 P - REDISPATCHING MODULE
(PR).
81
5.4.4 UNIT RECOMMITMENT
(URC).84
5.4.5 RES CURTAILMENT (RESC) BY
OVERVOLTAGES.84
5.4.6 CURRENT CORRECTION MODULE FOR
OVERLOADINGS.85
5.5 SUMMARY OF THE
CHAPTER.
85
6 ANALYSIS OF EMS PERFORMANCE FOR A TEST MICROGRID
.
87
6.1
INTRODUCTION.
87
6.2 DESCRIPTION OF ANALYSIS PROCESS
.
87
6.3 TEST MICROGRID FOR EMS CASE
STUDIES.88
6.3.1 OPERATION COST CURVE OF DISPATCHABLE GENERATORS
.
89
6.3.2 EXCHANGE ENERGY PRICE OF THE MAIN
GRID. 90
6.3.3 SETTING OF
ALGORITHMS.
91
6.4 MICROGRID OPERATION IN REFERENCE CASE
STUDY.91
6.5 SENSITIVITY ANALYSIS OF BSS AGING IN
MOP1. 93
6.6 CONSIDERATION OF NETWORK CONSTRAINTS UNDER MOP2
.
96
6.6.1 ANALYSIS OF GA PERFORMANCE WITH VOLTAGE AND CURRENT CONSTRAINTS
.
96
6.6.2 EVALUATION OF MILP MODULES WITH NETWORK CONSTRAINT
VIOLATIONS.99
6.7 MICROGRID OPERATION IN DIFFERENT ISLANDING DEGREES
(MOP3).103
6.7.1 CONGESTION MANAGEMENT OF MICROGRID FOR UPPER GRID
.
103
6.7.2 MICROGRID OPERATION UNDER ISLANDED
MODE.104
6.7.3 MICROGRID WITH SCHEDULED / FIXED EXCHANGE POWER WITH MACRO-GRID
.
105
6.7.4 SENSITIVITY ANALYSIS OF EXCHANGE POWER TO ENERGY PRICE
.
106
6.7.5 SENSITIVITY ANALYSIS OF DDG ACTIVATION TO UNIFORM ENERGY PRICING
.
108
6.8 REDUCTION OF GREENHOUSE GASES PRODUCTION UNDER
MOP4.110
6.9 PERFORMANCE OF GA IN HIGHLY CONSTRAINED MULTI-OBJECTIVE PROBLEM OF
MOP5.113
6.10 ALGORITHM PERFORMANCE AND SPEED IN SMART PFC MODE
.
116
6.11 CONCLUSION OF THE CASE STUDIES
.
116
7 SUMMARY AND
OUTLOOK.
121
7.1
SUMMARY.121
7.2
OUTLOOK.
125
8
APPENDIX.127
8.1 DIESEL GENERATOR DATA SHEET OF CUMMINS DG C90 D5
.
127
8.2 MACRO / MICRO-CYCLES AND BSS LIFE LOSS CALCULATION
.
126
8.3 THE GA SETTING AND MICROGRID INPUTS FOR CASE STUDY OF CHAPTER 4
.
129
8.4 PERFORMED ANALYSIS, GA SETTING AND MICROGRID INPUTS AND POLICIES IN
CHAPTER 6
.131
8.5 EXAMPLES OF DEFINED SPM MUTATION
PROCESS. 134
6.6 EXTRA RESULTS FOR CONSIDERING NETWORK CONSTRAINTS IN EMS (RELATED TO
CHAPTER
6.6).
136
8.6.1 SMALL PV-FARM POWER CURTAILMENT IN CASE OF OVERLOADINGS
.
136
8.6.2 PEAK SHAVING PERFORMANCE OF BSS IN CASE OF OVERLOADINGS
.
137
8.7 CATEGORIZATION AND COMPARISON OF THE OPTIMIZATION METHODS
.
136
6.7.1
INTRODUCTION.
136
8.7.2 EVOLUTIONARY ALGORITHMS
.
140
8.7.3 MATHEMATICAL
METHODS.143
6.7.4 ARTIFICIAL INTELLIGENCE
TECHNIQUES.145
6.7.5 CONCLUSION AND SELECTION OF THE
METHODS. 146
6.8 PARAMETRIZATION OF THE G A
.148
6.9 SUMMARY OF ADVANTAGES AND DRAWBACKS OF THE DEVELOPED MILP-BASED
OPTIMIZATION METHOD IN CHAPTER 5
.
150
6.10 AUTHOR*S
PUBLICATIONS.
152
8.11 SUPERVISED BACHELOR AND MASTER
THESES.153
6.12
BIBLIOGRAPHY.
156 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Nemati, Mohsen Shiralizadeh |
| author_GND | (DE-588)1161478809 |
| author_facet | Nemati, Mohsen Shiralizadeh |
| author_role | aut |
| author_sort | Nemati, Mohsen Shiralizadeh |
| author_variant | m s n ms msn |
| building | Verbundindex |
| bvnumber | BV046757931 |
| classification_rvk | ZP 4400 |
| ctrlnum | (OCoLC)1164661159 (DE-599)DNB1155778677 |
| dewey-full | 621.3192 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 621 - Applied physics |
| dewey-raw | 621.3192 |
| dewey-search | 621.3192 |
| dewey-sort | 3621.3192 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Elektrotechnik / Elektronik / Nachrichtentechnik Energietechnik |
| discipline_str_mv | Elektrotechnik / Elektronik / Nachrichtentechnik Energietechnik |
| format | Thesis Book |
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV046757931 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T14:43:42Z |
| indexdate | 2024-07-10T08:52:59Z |
| institution | BVB |
| institution_GND | (DE-588)1066127425 |
| isbn | 9783737604642 3737604649 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032167538 |
| oclc_num | 1164661159 |
| open_access_boolean | |
| owner | DE-29T DE-92 DE-B768 |
| owner_facet | DE-29T DE-92 DE-B768 |
| physical | xii, 169 Seiten Diagramme 21 cm, 250 g |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Kassel University Press |
| record_format | marc |
| series | Energy management and power system operation |
| series2 | Energy management and power system operation |
| spelling | Nemati, Mohsen Shiralizadeh Verfasser (DE-588)1161478809 aut Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming Mohsen Shiralizadeh Nemati Kassel Kassel University Press [2018] xii, 169 Seiten Diagramme 21 cm, 250 g txt rdacontent n rdamedia nc rdacarrier Energy management and power system operation Vol. 3 Dissertation Universität Stuttgart 2017 Genetischer Algorithmus (DE-588)4265092-6 gnd rswk-swf Gemischt-ganzzahlige Optimierung (DE-588)4156566-6 gnd rswk-swf Microgrid Energietechnik (DE-588)1121110479 gnd rswk-swf Blindleistungsregelung (DE-588)4313844-5 gnd rswk-swf Mehrkriterielle Optimierung (DE-588)4610682-0 gnd rswk-swf Wirkleistungsregelung (DE-588)4424462-9 gnd rswk-swf Leistungsbewertung (DE-588)4167271-9 gnd rswk-swf Kosten-Nutzen-Analyse (DE-588)4032589-1 gnd rswk-swf Netzbetrieb (DE-588)4171506-8 gnd rswk-swf Energiemanagement (DE-588)4124833-8 gnd rswk-swf Economic dispatch Genetic algorithm Microgrids Mixed integer linear programming Unit commitment (DE-588)4113937-9 Hochschulschrift gnd-content Microgrid Energietechnik (DE-588)1121110479 s Energiemanagement (DE-588)4124833-8 s Wirkleistungsregelung (DE-588)4424462-9 s Blindleistungsregelung (DE-588)4313844-5 s Mehrkriterielle Optimierung (DE-588)4610682-0 s Genetischer Algorithmus (DE-588)4265092-6 s Gemischt-ganzzahlige Optimierung (DE-588)4156566-6 s Netzbetrieb (DE-588)4171506-8 s Kosten-Nutzen-Analyse (DE-588)4032589-1 s Leistungsbewertung (DE-588)4167271-9 s DE-604 kassel university press (DE-588)1066127425 pbl Erscheint auch als 978-3-7376-0465-9 Online-Ausgabe Nemati, Mohsen Shiralizadeh Optimization of Unit Commitment and Economic Dispatch in Microgrids Based on Genetic Algorithm and Mixed Integer Linear Programming Kassel : Kassel University Press, 2018 Online-Ressource Energy management and power system operation Vol. 3 (DE-604)BV043191667 3 X:MVB text/html http://www.upress.uni-kassel.de/katalog/abstract.php?978-3-7376-0464-2 Inhaltstext B:DE-101 application/pdf https://d-nb.info/1155778677/04 Inhaltsverzeichnis DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032167538&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Nemati, Mohsen Shiralizadeh Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming Energy management and power system operation Genetischer Algorithmus (DE-588)4265092-6 gnd Gemischt-ganzzahlige Optimierung (DE-588)4156566-6 gnd Microgrid Energietechnik (DE-588)1121110479 gnd Blindleistungsregelung (DE-588)4313844-5 gnd Mehrkriterielle Optimierung (DE-588)4610682-0 gnd Wirkleistungsregelung (DE-588)4424462-9 gnd Leistungsbewertung (DE-588)4167271-9 gnd Kosten-Nutzen-Analyse (DE-588)4032589-1 gnd Netzbetrieb (DE-588)4171506-8 gnd Energiemanagement (DE-588)4124833-8 gnd |
| subject_GND | (DE-588)4265092-6 (DE-588)4156566-6 (DE-588)1121110479 (DE-588)4313844-5 (DE-588)4610682-0 (DE-588)4424462-9 (DE-588)4167271-9 (DE-588)4032589-1 (DE-588)4171506-8 (DE-588)4124833-8 (DE-588)4113937-9 |
| title | Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming |
| title_auth | Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming |
| title_exact_search | Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming |
| title_exact_search_txtP | Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming |
| title_full | Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming Mohsen Shiralizadeh Nemati |
| title_fullStr | Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming Mohsen Shiralizadeh Nemati |
| title_full_unstemmed | Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming Mohsen Shiralizadeh Nemati |
| title_short | Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming |
| title_sort | optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming |
| topic | Genetischer Algorithmus (DE-588)4265092-6 gnd Gemischt-ganzzahlige Optimierung (DE-588)4156566-6 gnd Microgrid Energietechnik (DE-588)1121110479 gnd Blindleistungsregelung (DE-588)4313844-5 gnd Mehrkriterielle Optimierung (DE-588)4610682-0 gnd Wirkleistungsregelung (DE-588)4424462-9 gnd Leistungsbewertung (DE-588)4167271-9 gnd Kosten-Nutzen-Analyse (DE-588)4032589-1 gnd Netzbetrieb (DE-588)4171506-8 gnd Energiemanagement (DE-588)4124833-8 gnd |
| topic_facet | Genetischer Algorithmus Gemischt-ganzzahlige Optimierung Microgrid Energietechnik Blindleistungsregelung Mehrkriterielle Optimierung Wirkleistungsregelung Leistungsbewertung Kosten-Nutzen-Analyse Netzbetrieb Energiemanagement Hochschulschrift |
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| volume_link | (DE-604)BV043191667 |
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