Atjaunināt sīkdatņu piekrišanu

E-grāmata: Coordination of Distributed Energy Resources in Microgrids: Optimisation, control, and hardware-in-the-loop validation

4.00/5 (2 ratings by Goodreads)
(Rolls-Royce-NTU Corporate Lab, Nanyang Technological University, Singapore), (University of New South Wales, School of Electrical Engineeri), (Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore),
  • Formāts: PDF+DRM
  • Sērija : Energy Engineering
  • Izdošanas datums: 03-Feb-2022
  • Izdevniecība: Institution of Engineering and Technology
  • Valoda: eng
  • ISBN-13: 9781839532696
Citas grāmatas par šo tēmu:
  • Formāts - PDF+DRM
  • Cena: 212,89 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
Coordination of Distributed Energy Resources in Microgrids: Optimisation, control, and hardware-in-the-loop validationPalielināt
  • Formāts: PDF+DRM
  • Sērija : Energy Engineering
  • Izdošanas datums: 03-Feb-2022
  • Izdevniecība: Institution of Engineering and Technology
  • Valoda: eng
  • ISBN-13: 9781839532696
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

Coordination of Distributed Energy Resources in Microgrids: Optimisation, control, and hardware-in-the-loop validation provides a structured overview of research into techniques for managing microgrids with distributed energy resources (DERs). The DERs including distributed generators, energy storage systems, and flexible loads are posing both challenges and opportunities to microgrids' security, planning, operation, and control. Advanced operation and control techniques are needed to coordinate these components in the microgrids and maintain power quality, as well as keeping the system economically feasible.

This book is for researchers and students in the area of smart grids, power engineering, and control engineering, as well as for advanced students, transmission network and grid operators. It focuses on cutting-edge techniques for secure, economic, and robust operation and control of microgrids. Effective coordination of DERs on both temporal and spatial scales are introduced in detail. Topics covered include comprehensive mathematical models of DERs and microgrids, sizing and siting of DERs under uncertainties, stochastic and robust optimisation for active and reactive power dispatch of DERs in microgrids, distributed coordinated control, and hardware-in-the-loop tests for validation of control algorithms.



A structured research overview of techniques to manage microgrids with distributed energy resources (DERs). The focus is on coordination on both temporal and spatial scales. Chapters cover mathematical models, sizing and siting of DERs, robust optimisation, distributed coordinated control, and hardware-in-the-loop tests.

About the authors xix
Foreword xxi
Preface xxiii
Part I Distributed Energy Resources and Microgrids: Preliminaries
1(34)
1 Distributed energy resources: introduction and classification
3(12)
1.1 Background
3(1)
1.2 Definition and classification
4(11)
1.2.1 Distributed generator
5(2)
1.2.2 Energy storage system
7(6)
1.2.3 Flexible load 10 References
13(2)
2 Microgrids: introduction and research problem descriptions
15(20)
2.1 Definition
15(1)
2.2 Microgrid architecture and classification
16(5)
2.2.1 Electric microgrid
17(3)
2.2.2 Multi-energy microgrid
20(1)
2.3 Planning of DER units in microgrid
21(2)
2.4 Microgrid operation
23(3)
2.5 Microgrid control
26(3)
2.6 Microgrid stability
29(6)
References
32(3)
Part II Coordinated Planning of DERs in Micogrids: Optimal Sizing and Siting
35(100)
3 Composite sensitivity factor-based method for DG planning
37(20)
Nomenclature
37(2)
3.1 Introduction
39(2)
3.2 Sensitivity factors
41(2)
3.2.1 Power loss sensitivity factor
41(1)
3.2.2 Voltage sensitivity factor
42(1)
3.2.3 Composite sensitivity factor
43(1)
3.3 Power loss and voltage stability assessment indices
43(2)
3.3.1 Line loss reduction index
43(1)
3.3.2 Voltage collapse proximity indicator reduction index
44(1)
3.4 Composite sensitivity factor-based method
45(3)
3.4.1 Distributed generation and load modelling
45(1)
3.4.2 Composite sensitivity factor-based method
46(2)
3.5 Case study
48(5)
3.5.1 Test system description
48(1)
3.5.2 Power loss sensitivity factor analysis
49(2)
3.5.3 Single-stage planning results
51(1)
3.5.4 Multi-stage planning with load growth results
51(2)
3.6 Conclusion
53(4)
References
53(4)
4 Probability-weighted robust optimisation method for DG planning
57(20)
Nomenclature
57(2)
4.1 Introduction
59(2)
4.2 Mathematical formulation
61(3)
4.3 Probability-weighted robust optimisation
64(5)
4.3.1 Probabilistic modelling of uncertainties
64(1)
4.3.2 Probability-weighted uncertainty sets
65(1)
4.3.3 Probability-weighted robust optimisation model
66(1)
4.3.4 Solution algorithm
67(2)
4.4 Case study
69(4)
4.4.1 Test system description
69(1)
4.4.2 Probability-weighted uncertainty sets
70(1)
4.4.3 Distributed generation planning results
71(1)
4.4.4 Operation results with Monte Carlo simulation
72(1)
4.5 Conclusion
73(4)
References
74(3)
5 Multi-stage stochastic programming method for multi-energy DG planning
77(28)
Nomenclature
77(3)
5.1 Introduction
80(2)
5.2 MEMG modelling
82(2)
5.2.1 System configuration
82(1)
5.2.2 DG classification
82(1)
5.2.3 Multi-energy conversion
83(1)
5.3 Mathematical modelling for DG placement
84(4)
5.3.1 Two-stage DG placement framework
84(1)
5.3.2 Two-stage mathematical modelling
84(4)
5.4 Solution method
88(1)
5.4.1 Typical seasonal day selection
88(1)
5.4.2 Stochastic optimisation model
88(1)
5.5 Test system set-up and case study
89(4)
5.5.1 Test system set-up
89(1)
5.5.2 Methods comparison
90(3)
5.5.3 Assumptions for case study
93(1)
5.6 Simulation results and discussions
93(6)
5.6.1 Investment-stage simulation results and discussions
94(1)
5.6.2 Operation-stage simulation results and discussions
95(2)
5.6.3 Methods comparison results
97(2)
5.7 Conclusion
99(6)
Appendix A
100(2)
References
102(3)
6 Stochastic planning of heterogeneous energy storage (HES) in residential MEMG
105(30)
Nomenclature
105(3)
6.1 Introduction
108(3)
6.2 Modelling of the residential MEMG
111(5)
6.2.1 Basic structure of the residential MEMG
111(1)
6.2.2 Battery storage
112(1)
6.2.3 Thermal storage tank
112(1)
6.2.4 Price-based demand response (PBDR) for the electricity
113(1)
6.2.5 Thermal loads modelling with thermal inertia
114(2)
6.2.6 The demand side management (DSM) for thermal energy
116(1)
6.3 Mathematic modelling for HES deployment
116(3)
6.3.1 Two-stage HES deployment framework
116(1)
6.3.2 Mathematical formulation
116(3)
6.4 Solution method
119(2)
6.4.1 Typical seasonal day selection approach
119(1)
6.4.2 Two-substage stochastic operation model
120(1)
6.5 Simulation results
121(8)
6.5.1 Set-up of the system
121(3)
6.5.2 Investment-stage simulation results and discussion
124(1)
6.5.3 Operation-stage simulation results and discussions
125(1)
6.5.4 Case comparison
125(4)
6.6 Conclusion and future work
129(6)
Appendix A
130(1)
References
131(4)
Part III Coordinated Operation of DERs in Microgrids: Energy Management and Voltage Regulation
135(170)
7 Hourly coordination of energy storage and direct load control
137(24)
Nomenclature
137(2)
7.1 Introduction
139(2)
7.2 Two-stage coordination of ES operation and DLC
141(2)
7.2.1 Principle of multi-stage coordination
141(1)
7.2.2 First stage-hourly energy storage operation
142(1)
7.2.3 Second stage-direct load control
142(1)
7.3 Mathematical formulation
143(5)
7.3.1 Energy storage models
143(2)
7.3.2 Direct load control model
145(1)
7.3.3 Coordination optimisation model
146(2)
7.4 Two-stage robust optimisation method
148(4)
7.4.1 Principle of two-stage robust optimisation
148(1)
7.4.2 Uncertainty set modelling
148(2)
7.4.3 Two-stage robust optimisation model
150(1)
7.4.4 Solution algorithm
151(1)
7.5 Case study
152(6)
7.5.1 Test system description
152(1)
7.5.2 Initial tests
153(3)
7.5.3 Comprehensive tests
156(2)
7.6 Conclusion
158(3)
References
158(3)
8 Daily coordination of microturbines and demand response
161(26)
Nomenclature
161(2)
8.1 Introduction
163(2)
8.2 Two-stage coordination of day-ahead demand response and microturbine dispatch
165(2)
8.2.1 First stage-day-ahead PBDR
165(1)
8.2.2 Second stage-hourly microturbine dispatch
166(1)
8.3 Mathematical formulation
167(3)
8.3.1 Price-based demand response model
167(1)
8.3.2 Coordination optimisation model
167(3)
8.4 Two-stage robust optimisation method
170(4)
8.4.1 Uncertainty set modelling
170(1)
8.4.2 Two-stage robust optimisation model
171(1)
8.4.3 Solution algorithm
172(2)
8.5 Case study
174(10)
8.5.1 Test system description
174(1)
8.5.2 Uncertainty sets
175(2)
8.5.3 First-stage price-based demand response results
177(1)
8.5.4 Second-stage implementation results
178(3)
8.5.5 Monte Carlo simulation check
181(3)
8.6 Conclusion
184(3)
References
184(3)
9 Optimal dispatch of MEMGs
187(24)
Nomenclature
187(2)
9.1 Introduction
189(2)
9.2 Multi-energy microgrid modelling
191(2)
9.2.1 Combined cooling, heat and power (CCHP) plant
191(1)
9.2.2 Energy storage
192(1)
9.2.3 Electric boiler (EB) and Electric chiller (EC)
193(1)
9.2.4 Fuel cells
193(1)
9.3 Coordinated optimal dispatch
193(4)
9.3.1 Grid-connected mode
193(2)
9.3.2 Islanded mode
195(1)
9.3.3 Model linearisation
196(1)
9.4 Case studies
197(9)
9.4.1 Data input
197(2)
9.4.2 Remarks
199(1)
9.4.3 Grid-connected mode
200(4)
9.4.4 Islanded mode
204(2)
9.5 Conclusions
206(5)
References
207(4)
10 Temporally coordinated dispatch of MEMGs under diverse uncertainties
211(24)
Nomenclature
211(2)
10.1 Introduction
213(3)
10.1.1 Background and motivation
213(1)
10.1.2 Literature survey
213(2)
10.1.3 Contribution of this chapter
215(1)
10.2 Multi-energy microgrid modelling
216(2)
10.2.1 Structure of the microgrid
216(1)
10.2.2 Components modelling
216(2)
10.3 Proposed operation method
218(1)
10.3.1 Timescale decomposition
218(1)
10.3.2 Temporally coordinated operation framework
218(1)
10.4 Mathematical formulation
219(3)
10.4.1 Multi-energy microgrid coordination model
219(2)
10.4.2 Model linearisation
221(1)
10.5 Solution method
222(2)
10.5.1 Uncertainty modelling
222(1)
10.5.2 Stochastic programming model
222(1)
10.5.3 Deterministic equivalence
223(1)
10.5.4 Scenario generation and reduction
224(1)
10.6 Simulation results
224(8)
10.6.1 Test system
224(2)
10.6.2 Day-ahead operation results and discussion
226(1)
10.6.3 Intraday online operation results and discussion
227(3)
10.6.4 Performance check and comparison
230(2)
10.7 Conclusion and future work
232(3)
References
232(3)
11 Robustly optimal dispatch of MEMGs with flexible loads
235(24)
Nomenclature
235(2)
11.1 Introduction
237(2)
11.2 Two-stage coordinated operation of multi-energy microgrid
239(3)
11.2.1 Multi-energy microgrid with flexible thermal and electric loads
239(1)
11.2.2 First stage-day-ahead PBDR and thermal energy storage scheduling
240(1)
11.2.3 Second stage-hourly CCHP dispatch
241(1)
11.3 Mathematical formulation
242(5)
11.3.1 Price-based demand response model
242(1)
11.3.2 Indoor temperature control model
242(1)
11.3.3 Coordination optimisation model
243(4)
11.4 Two-stage robust optimisation method
247(2)
11.4.1 Uncertainty set modelling
247(1)
11.4.2 Two-stage robust optimisation model
248(1)
11.4.3 Solution algorithm
248(1)
11.5 Case study
249(6)
11.5.1 Test system description
249(2)
11.5.2 Day-ahead scheduling results
251(2)
11.5.3 Intraday dispatch results
253(1)
11.5.4 Operation robustness check
253(2)
11.6 Conclusion
255(4)
References
255(4)
12 Multi-timescale coordinated voltage/var control optimisation
259(22)
12.1 Introduction
259(2)
12.2 Multi-timescale coordinated voltage/var regulation
261(2)
12.2.1 Timescale decomposition
261(1)
12.2.2 Proposed coordination framework
262(1)
12.2.3 Load and RES generation forecasting
263(1)
12.3 Mathematical formulation
263(2)
12.3.1 Distribution network power flow model
263(1)
12.3.2 Optimisation model for volt/var control
264(1)
12.4 Two-stage stochastic programming model
265(4)
12.4.1 Stochastic programming model
265(1)
12.4.2 Deterministic equivalent
266(1)
12.4.3 Scenario construction and reduction
267(1)
12.4.4 Mapping relationship between VVC and programming model
268(1)
12.5 Simulation test results
269(9)
12.5.1 Test system and parameter settings
269(1)
12.5.2 Initial case
270(1)
12.5.3 Deterministic VVC
270(2)
12.5.4 Stochastic VVC
272(4)
12.5.5 Time-series test
276(2)
12.6 Conclusions
278(3)
References
279(2)
13 Three-stage robust inverter-based voltage/var control optimisation
281(24)
Nomenclature
281(2)
13.1 Introduction
283(2)
13.2 Three-stage robust inverter-based voltage/var control
285(2)
13.2.1 First stage-capacitor bank and on-load tap changer (OLTC) scheduling in a rolling horizon
285(2)
13.2.2 Second stage-inverter output dispatch
287(1)
13.2.3 Third stage-inverter droop control
287(1)
13.3 Mathematical formulation
287(5)
13.3.1 Photovoltaic (PV) inverter operation model
287(2)
13.3.2 Coordination optimisation model
289(3)
13.4 Two-stage robust optimisation method
292(2)
13.4.1 Uncertainty set modelling
292(1)
13.4.2 Two-stage robust optimisation model
293(1)
13.4.3 Solution algorithm
294(1)
13.5 Case study
294(8)
13.5.1 Test system description
294(1)
13.5.2 One-hour simulation
295(3)
13.5.3 24-hour time-series simulation
298(3)
13.5.4 TRI-VVC vs. Single-Stage Centralised VVC
301(1)
13.6 Conclusion
302(3)
References
303(2)
Part IV Coordinated real-time control of DERs: distributed controller design and hardware-in-the-loop tests
305(140)
14 Power system frequency control by aggregated energy storage systems
307(26)
14.1 Introduction
307(2)
14.2 Proposed frequency control scheme
309(1)
14.2.1 System overview
309(1)
14.2.2 Multi-area microgrids
309(1)
14.3 Proposed disturbance observer
310(2)
14.3.1 System disturbance observer
311(1)
14.3.2 Band-pass filter
312(1)
14.4 Distributed finite-time control of ESA
312(5)
14.4.1 Communication graph
313(1)
14.4.2 Finite-time consensus control
313(2)
14.4.3 Numerical illustrations
315(2)
14.5 Results and discussions
317(10)
14.5.1 Case 1: system contingency
318(2)
14.5.2 Case 2: normal operation
320(2)
14.5.3 Case 3: multi-area microgrids
322(1)
14.5.4 Case 4: comparison with linear control algorithm
323(4)
14.6 Conclusion
327(6)
Appendix A
327(3)
References
330(3)
15 Power system frequency support by grid-interactive smart buildings
333(26)
15.1 Introduction
333(2)
15.2 System modelling
335(4)
15.2.1 Multi-area microgrids with GISBs
335(1)
15.2.2 Modelling of GISBs
336(2)
15.2.3 Communication network of GISBs
338(1)
15.3 Proposed control framework for GISBs
339(4)
15.3.1 Control objectives
339(1)
15.3.2 Leader control
340(2)
15.3.3 Design of distributed sliding mode control
342(1)
15.4 Results and discussions
343(10)
15.4.1 Case 1: system contingency
344(3)
15.4.2 Case 2: normal operation
347(1)
15.4.3 Case 3: comparison with linear consensus control
348(2)
15.4.4 Case 4: impact of communication time delay
350(3)
15.5 Conclusions
353(6)
Appendix
353(2)
References
355(4)
16 Decentralised-distributed hybrid voltage control by inverter-based DERs
359(18)
16.1 Introduction
359(1)
16.2 Voltage control in distribution networks
360(3)
16.2.1 Problem description
360(1)
16.2.2 Var capacity from power inverters
361(2)
16.3 Proposed hybrid voltage control
363(5)
16.3.1 Decentralised voltage control
364(1)
16.3.2 Distributed voltage control
365(3)
16.3.3 Supplementary voltage control
368(1)
16.4 Simulation studies
368(7)
16.4.1 Test system
368(3)
16.4.2 Scenario 1
371(1)
16.4.3 Scenario 2
372(1)
16.4.4 Scenario 3
373(2)
16.5 Conclusion
375(2)
References
376(1)
17 Two-level distributed voltage/var control by aggregated PV inverters
377(22)
17.1 Introduction
377(1)
17.2 Proposed VVC architecture
378(2)
17.2.1 System overview
378(1)
17.2.2 Architecture of proposed VVC
379(1)
17.3 Lower-level VVC
380(3)
17.3.1 Real-time VVC by droop control
380(1)
17.3.2 Distributed aggregation of PV inverters
381(2)
17.3.3 Var capacity of PV aggregator
383(1)
17.4 Upper-level VVC
383(4)
17.4.1 Power flow in MV distributed networks
384(1)
17.4.2 Distributed solution
385(2)
17.5 Simulation results
387(10)
17.5.1 Test system and parameter settings
387(1)
17.5.2 Convergence
388(2)
17.5.3 Case A: 33-bus networks
390(4)
17.5.4 Case B: 69-bus networks
394(3)
17.6 Conclusion
397(2)
Appendix A
397(1)
References
398(1)
18 Event-triggered control of DERs and controller hardware-in-the-loop validation
399(24)
18.1 Introduction
399(1)
18.2 Cyber-physical Microgrids
400(3)
18.2.1 Physical system
400(2)
18.2.2 Cyber system
402(1)
18.3 Distributed event-triggered secondary control
403(6)
18.3.1 Problem formation and control objectives
403(2)
18.3.2 Controller design
405(4)
18.4 Controller hardware-in-the-loop implementation
409(1)
18.5 Experimental test results
410(10)
18.5.1 Case 1: step response
412(2)
18.5.2 Effectiveness of the event-triggered control
414(1)
18.5.3 Case 2: Communication failures and topology change
415(2)
18.5.4 Case 3: scalability test
417(3)
18.6 Conclusion
420(3)
References
420(3)
19 Three-level coordinated voltage control of DERs and power hardware-in-the-loop validation
423(22)
19.1 Introduction
423(1)
19.2 Preliminaries
424(2)
19.2.1 Power distribution networks
424(1)
19.2.2 PV inverters
425(1)
19.2.3 Communication network
425(1)
19.3 Three-level coordinated voltage control
426(4)
19.3.1 Level I: ramp-rate control
426(2)
19.3.2 Level II: droop control
428(1)
19.3.3 Level III: distributed control
428(2)
19.4 Stability analysis
430(2)
19.5 Power hardware-in-the-loop experimental tests
432(11)
19.5.1 Test system
432(3)
19.5.2 Eigenvalue analysis
435(1)
19.5.3 Test Case 1: voltage drop with step load change
436(1)
19.5.4 Test Case 2: communication delays
437(1)
19.5.5 Test Case 3: voltage rise with real PV data
438(2)
19.5.6 Test Case 4: comparison study
440(3)
19.6 Conclusion
443(2)
References
443(2)
Index 445
Yan Xu is an associate professor at School of Electrical and Electronic Engineering and a cluster director at Energy Research Institute at NTU (ERI@N). Dr Xu has published 1 book, 86 IEEE Transactions papers and 30 IET journal papers. He has 13 'Web-of-Science highly cited papers' and received 10 IEEE/IET best paper awards. Dr Xu is serving as an editor for IEEE Transactions (TSG and TPWRS) and IET journals (GTD and ECE).



Yu Wang is a research fellow in the Rolls-Royce-NTU Corporate Lab, Nanyang Technological University, Singapore. He was a recipient of the EU Marie Skodowska-Curie Action Individual Fellowship 2020. His research interests include distributed control and optimisation of energy storage systems, microgrids, and cyber-physical power systems. He has published 50 peer-reviewed journal and conference papers, including 3 'web-of-science highly cited papers.'



Cuo Zhang is a research fellow with the University of New South Wales, Australia. He is also a chief investigator of ARC Research Hub for Integrated Energy Storage Solutions. His research interests include power system planning and operation, voltage stability and control, microgrids, multi-energy systems, and applications of optimisation theory in these areas. He has published 10 IEEE Transactions journal papers including 2 'web-of-science highly cited papers' and awarded 3 IEEE/IET conference best papers.



Zhengmao Li is a research fellow at Nanyang Technological University under the NTU-ETH future resilience system project. He was previously a research fellow with the Stevens Institute of Technology, Hoboken, NJ, USA. His research interests include renewable energy integration, multi-energy systems such as multi-energy microgrid, multi-energy ship, optimisation and reinforcement learning techniques such as approximate dynamic programming, robust and stochastic optimisation method, resilience of multi-energy systems.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97818395326962e.html
Keywords: