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Multi-microgrids with a Frequency Regulation-Based V2G Technology: Systems Analysis, Modeling, and Control Read chapter Read first chapter

2021 | OriginalPaper | Chapter

4. Hierarchical and Distributed Dispatching of Microgrids Considering Uncertainty

Authors : Xiangyu Kong, Dehong Liu, Wenqi Lu, Chengshan Wang, Yu Shen, Wei Hu, Mehdi Rahmani-Andebili

Published in: Design, Control, and Operation of Microgrids in Smart Grids

Publisher: Springer International Publishing

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Abstract

The interconnected microgrid system (IMS) is a promising solution for the problem of growing penetration of renewable-based microgrids into the power system. To optimally coordinate the operation of microgrids owned by different owners while considering uncertainties in market environment, a bi-level distributed optimized operation method for IMS with uncertainties is proposed in this chapter. A hierarchical and distributed operational communication architecture of IMS is first established. A bi-level distributed optimization model was built for IMS, where at the upper level, the IMS operates purchase-sale mode or demand response mode with the distribution network operator and optimizes the trading power with microgrids to maximize revenue. At the lower level, the chance constraint programming is used to describe and deal with the uncertainty of renewable energy and loads and optimize the output and energy storage of distributed energy with the goal of minimum cost. The analytical target cascading and augmented Lagrange method are combined to decouple and reconstruct the bi-level model for distributed solution and establish a fair price mechanism. The optimal solutions of the problem are obtained through parallel iteration, in which the price signal plays a coordinated role in the distributed iterative optimization process. Abundant case studies verify the advantages of the model and the performance of the proposed method.

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Literature
1.
Z. Xu, P. Yang, C. Zheng, Y. Zhang, J. Peng, Z. Zeng, Analysis on the organization and development of multi-microgrids. Renew. Sust. Energ. Rev. 81, 2204–2216 (2018)CrossRef
2.
E. Bullich-Massagué, F. Díaz-González, M. Aragüés-Peñalba, F. Girbau-Llistuella, P. Olivella-Rosell, A. Sumper, Microgrid clustering architectures. Appl. Energy 212, 340–361 (2018)CrossRef
3.
L. Ren, Y. Qin, Y. Li, P. Zhang, B. Wang, P.B. Luh, S. Han, T. Orekan, T. Gong, Enabling resilient distributed power sharing in networked microgrids through software defined networking. Appl. Energy 210, 1251–1265 (2017)CrossRef
4.
L. Wang, B. Zhang, Q. Li, W. Song, G. Li, Robust distributed optimization for energy dispatch of multi-stakeholder multiple microgrids under uncertainty. Appl. Energy 255, 113845 (2019)CrossRef
5.
N. Nikmehr, S. Najafi Ravadanegh, Optimal power dispatch of multi-microgrids at future smart distribution grids. IEEE Trans. Smart Grid 6, 1648–1657 (2015)CrossRef
6.
A. Ouammi, H. Dagdougui, R. Sacile, Optimal control of power flows and energy local storages in a network of microgrids modeled as a system of systems. IEEE Trans. Control Syst. Technol. 23, 128–138 (2015)CrossRef
7.
A. Ouammi, Optimal power scheduling for a cooperative network of smart residential buildings. IEEE Trans. Sustain. Energy 7, 1317–1326 (2016)CrossRef
8.
S.A. Arefifar, M. Ordonez, Y. Mohamed, Energy management in multi-microgrid systems – Development and assessment. IEEE Trans. Power Syst. 2, 910–922 (2017)
9.
L. Ma, N. Liu, L. Wang, J. Zhang, J. Lei, Z. Zeng, C. Wang, M. Cheng, Multi-party energy management for smart building cluster with PV systems using automatic demand response. Energ. Buildings 121, 11–21 (2016)CrossRef
10.
W. Liu, W. Gu, J. Wang, W. Yu, X. Xi, Game theoretic non-cooperative distributed coordination control for multi-microgrids. IEEE Trans. Smart Grid, 1–1 (2017)
11.
A. Naebi, S. SeyedShenava, J. Contreras, C. Ruiz, A. Akbarimajd, EPEC approach for finding optimal day-ahead bidding strategy equilibria of multi-microgrids in active distribution networks. Int. J. Electr. Power Energy Syst. 117, 105702 (2020)CrossRef
12.
M. Jalali, K. Zare, H. Seyedi, Strategic decision-making of distribution network operator with multi-microgrids considering demand response program. Energy 141, 1059–1071 (2017)CrossRef
13.
K. Dehghanpour, H. Nehrir, An agent-based hierarchical bargaining framework for power management of multiple cooperative microgrids. IEEE Trans. Smart Grid 10, 514–522 (2019)CrossRef
14.
Y. Yang, W. Pei, Q. Huo, J. Sun, F. Xu, Coordinated planning method of multiple micro-grids and distribution network with flexible interconnection. Appl. Energy 228, 2361–2374 (2018)CrossRef
15.
B. Zhao, X. Wang, D. Lin, M.M. Calvin, J.C. Morgan, R. Qin, C. Wang, Energy management of multiple microgrids based on a system of systems architecture. IEEE Trans. Power Syst. 33, 6410–6421 (2018)CrossRef
16.
V. Bui, A. Hussain, H. Kim, A multiagent-based hierarchical energy management strategy for multi-microgrids considering adjustable power and demand response. IEEE Trans. Smart Grid 9, 1323–1333 (2018)CrossRef
17.
Y. Liu, L. Guo, C. Wang, A robust operation-based scheduling optimization for smart distribution networks with multi-microgrids. Appl. Energy 228, 130–140 (2018)CrossRef
18.
P. Wu, W. Huang, N. Tai, S. Liang, A novel design of architecture and control for multiple microgrids with hybrid AC/DC connection. Appl. Energy 210, 1002–1016 (2018)CrossRef
19.
Z. Li, Y. Xu, Temporally-coordinated optimal operation of a multi-energy microgrid under diverse uncertainties. Appl. Energy 240, 719–729 (2019)CrossRef
20.
D. Bertsimas, E. Litvinov, X.A. Sun, J. Zhao, T. Zheng, Adaptive robust optimization for the security constrained unit commitment problem. IEEE Trans. Power Syst. 28, 52–63 (2013)CrossRef
21.
B. Zhang, Q. Li, L. Wang, W. Feng, Robust optimization for energy transactions in multi-microgrids under uncertainty. Appl. Energy 217, 346–360 (2018)CrossRef
22.
H. Qiu, W. Gu, Y. Xu, Z. Wu, S. Zhou, G. Pan, Robustly multi-microgrid scheduling: Stakeholder-parallelizing distributed optimization. IEEE Trans. Sustain. Energy 11, 988–1001 (2020)CrossRef
23.
W. Ma, J. Wang, V. Gupta, C. Chen, Distributed energy management for networked microgrids using online ADMM with regret. IEEE Trans. Smart Grid 9, 847–856 (2018)CrossRef
24.
Z. Wang, B. Chen, J. Wang, M.M. Begovic, C. Chen, Coordinated energy management of networked microgrids in distribution systems. IEEE Trans. Smart Grid 6, 45–53. 2015-01-01 2015
25.
G. Liu, Y. Xu, K. Tomsovic, Bidding strategy for microgrid in day-ahead market based on hybrid stochastic/robust optimization. IEEE Trans. Smart Grid 7, 227–237 (2016)CrossRef
26.
J. Wei, Y. Zhang, J. Wang, X. Cao, M.A. Khan, Multi-period planning of multi-energy microgrid with multi-type uncertainties using chance constrained information gap decision method. Appl. Energy 260, 114188 (2020)CrossRef
27.
M. Rahmani-Andebili, Analyzing the effects of problem parameters on the operation cost of the networked microgrids, in 2020 IEEE Kansas Power and Energy Conference (KPEC), 2020
28.
Chapter 4: Robust operation of a reconfigurable electrical distribution system by optimal charging management of plug-in electric vehicles considering the technical, social, and geographical aspects, in Planning and Operation of Plug-In Electric Vehicles: Technical, Geographical, and Social Aspects (Springer, Cham, 2019)
29.
An adaptive approach for PEVs charging management and reconfiguration of electrical distribution system penetrated by renewables. IEEE Trans. Ind. Inf. 14(5) (2018)
30.
Dynamic and adaptive reconfiguration of electrical distribution system including renewables applying stochastic model predictive control. IET Gener. Transm. Distrib. 11(16), 3912–3921 (2017)
31.
Stochastic, adaptive, and dynamic control of energy storage systems integrated with renewable energy sources for power loss minimization. Renew. Energy. 113, 1462–1471, (2017)
32.
X. Zhou, Q. Ai, M. Yousif, Two kinds of decentralized robust economic dispatch framework combined distribution network and multi-microgrids. Appl. Energy 253, 113588 (2019)CrossRef
33.
I.I. JTC, Internet of Things (IoT) (Cham, International Organization for Standardization, 2014)
34.
Z. Yan, L. Zeng, C. Xi, X. Li, J. Liu, S. Li, Architecture of power internet of things. Electricity 22, 10–15 (2011)
35.
X. Jiang, Y. Liu, F. U. Xiaofei, X. U. Peng, S. Wang, G. Sheng, Construction ideas and development trends of transmission and distribution equipment of the ubiquitous Power Internet of Things. High Voltage Eng. (2019)
36.
T. Dragicevic, J.M. Guerrero, J.C. Vasquez, D. Skrlec, Supervisory control of an adaptive-droop regulated DC microgrid with battery management capability. IEEE Trans. Power Electron. 29, 695–706 (2013)CrossRef
37.
S. Adhikari, F. Li, Coordinated V-f and P-Q control of solar photovoltaic generators with MPPT and battery storage in microgrids. IEEE Trans. Smart Grid 5, 1270–1281 (2014)CrossRef
38.
J. Kirtley, H.H. Zeineldin, A.H.K. Alaboudy, Simple control strategy for inverter-based distributed generator to enhance microgrid stability in the presence of induction motor loads. Iet Gener. Transm. Distrib. 7, 1155–1162 (2013)CrossRef
39.
J. Wang, An introduction to stochastic programming. Chinese J Oper Res 1, 22–27 (1984)MathSciNet
40.
J. Zhu, P. Duan, M. Liu, Electric real-time balance dispatch via bi-level coordination of source-grid-load of power system with risk. Proc. CSEE 35, 3239–3247 (2015)
41.
B. Liu, Y. Kai, Uncertain Multilevel Programming (Berlin/Heidelberg, Springer, 2015)
42.
Y. Li, Z. Lu, J. Michalek, Diagonal quadratic approximation for parallelization of analytical target cascading. J. Mech. Des. 130, 680–682 (2008)
43.
A.R. Malekpour, A. Pahwa, Stochastic networked microgrid energy management with correlated wind generators. IEEE Trans. Power Syst. 32, 3681–3693 (2017)CrossRef
44.
N. Michelena, H. Park, P. Papalambros, Convergence properties of analytical target cascading. AIAA J. (2003)
45.
N. Kang, M. Kokkolaras, P.Y. Papalambros, S. Yoo, W. Na, J. Park, D. Featherman, Optimal design of commercial vehicle systems using analytical target cascading. Struct. Multidiscip. Optim. 50, 1103–1114 (2014)CrossRef
46.
X. Guan, P.B. Luh, H. Yan, J.A. Amalfi, An optimization-based method for unit commitment. Int. J. Electr. Power Energy Syst. 14, 9–17 (1992)CrossRef
47.
J.B. Lassiter, M.M. Wiecek, K.R. Andrighetti, Lagrangian coordination and analytical target cascading: solving ATC-decomposed problems with Lagrangian duality. Optim. Eng. 6, 361–381. 2005-01-01 2005
48.
S. Tosserams, L.F.P. Etman, P.Y. Papalambros, J.E. Rooda, An augmented Lagrangian relaxation for analytical target cascading using the alternating direction method of multipliers. Struct. Multidisc. Optim. 31, 176–189 (2006)MathSciNetMATHCrossRef
49.
C. Zhang, X. Lin, Analytical target cascading based on the quadratic exterior penalty method for complex system design. Adv. Mater. Res. 544, 164–169 (2012)CrossRef
50.
Ruszczyński, Andrzej, On convergence of an augmented Lagrangian decomposition method for sparse convex optimization. Math. Oper. Res 20, 634–656 (1995)MathSciNetMATHCrossRef
51.
F.S. Gazijahani, S.N. Ravadanegh, J. Salehi, Stochastic multi-objective model for optimal energy exchange optimization of networked microgrids with presence of renewable generation under risk-based strategies. ISA Trans. 73, 100–111 (2018)CrossRef
52.
H. Haddadian, R. Noroozian, Multi-microgrid-based operation of active distribution networks considering demand response programs. IEEE Trans. Sustain. Energy 10(4), 1804–1812 (2019)CrossRef
Metadata
Title
Hierarchical and Distributed Dispatching of Microgrids Considering Uncertainty
Authors
Xiangyu Kong
Dehong Liu
Wenqi Lu
Chengshan Wang
Yu Shen
Wei Hu
Mehdi Rahmani-Andebili
Copyright Year
2021
Book
Design, Control, and Operation of Microgrids in Smart Grids

Print ISBN: 978-3-030-64630-1

Electronic ISBN: 978-3-030-64631-8

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-64631-8_4