CLC number: TP273
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2018-11-27
Cited: 0
Clicked: 7955
Ke-yong Hu, Wen-juan Li, Li-dong Wang, Shi-hua Cao, Fang-ming Zhu, Zhou-xiang Shou. Energy management for multi-microgrid system based on model predictive control[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(11): 1340-1351.
@article{title="Energy management for multi-microgrid system based on model predictive control",
author="Ke-yong Hu, Wen-juan Li, Li-dong Wang, Shi-hua Cao, Fang-ming Zhu, Zhou-xiang Shou",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="19",
number="11",
pages="1340-1351",
year="2018",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601826"
}
%0 Journal Article
%T Energy management for multi-microgrid system based on model predictive control
%A Ke-yong Hu
%A Wen-juan Li
%A Li-dong Wang
%A Shi-hua Cao
%A Fang-ming Zhu
%A Zhou-xiang Shou
%J Frontiers of Information Technology & Electronic Engineering
%V 19
%N 11
%P 1340-1351
%@ 2095-9184
%D 2018
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601826
TY - JOUR
T1 - Energy management for multi-microgrid system based on model predictive control
A1 - Ke-yong Hu
A1 - Wen-juan Li
A1 - Li-dong Wang
A1 - Shi-hua Cao
A1 - Fang-ming Zhu
A1 - Zhou-xiang Shou
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
IS - 11
SP - 1340
EP - 1351
%@ 2095-9184
Y1 - 2018
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601826
Abstract: To reduce the computation complexity of the optimization algorithm used in energy management of a multi-microgrid system, an energy optimization management method based on model predictive control is presented. The idea of decomposition and coordination is adopted to achieve the balance between power supply and user demand, and the power supply cost is minimized by coordinating surplus energy in the multi-microgrid system. The energy management model and energy optimization problem are established according to the power flow characteristics of microgrids. A dual decomposition approach is imposed to decompose the optimization problem into two parts, and a distributed predictive control algorithm based on global optimization is introduced to achieve the optimal solution by iteration and coordination. The proposed method has been verified by simulation, and simulation results show that the proposed method provides the demanded energy to consumers in real time, and improves renewable energy efficiency. In addition, the proposed algorithm has been compared with the particle swarm optimization (PSO) algorithm. The results show that compared with PSO, the proposed method has better performance, faster convergence, and significantly higher efficiency.
[1]Alharbi W, Raahemifar K, 2015. Probabilistic coordination of microgrid energy resources operation considering uncertainties. Electr Power Syst Res, 128:1-10.
[2]Balasubramaniam K, Saraf P, Hadidi R, et al., 2016. Energy management system for enhanced resiliency of microgrids during islanded operation. Electr Power Syst Res, 137:133-141.
[3]Bie ZH, Zhang P, Li GF, et al., 2012. Reliability evaluation of active distribution systems including microgrids. IEEE Trans Power Syst, 27(4):2342-2350.
[4]Jiang H, Lin J, Song YH, et al., 2015. MPC-based frequency control with demand-side participation: a case study in an isolated wind-aluminum power system. IEEE Trans Power Syst, 30(6):3327-3337.
[5]Kassem AM, Abdelaziz AY, 2014. Reactive power control for voltage stability of standalone hybrid wind-diesel power system based on functional model predictive control. IET Renew Power Gener, 8(8):887-899.
[6]Katiraei F, Iravani MR, 2006. Power management strategies for a microgrid with multiple distributed generation units. IEEE Trans Power Syst, 21(4):1821-1831.
[7]Khorsandi A, Ashourloo M, Mokhtari H, et al., 2016. Automatic droop control for a low voltage DC microgrid. IET Gener Transm Distrib, 10(1):41-47.
[8]Lasseter RH, 2011. Smart distribution: coupled microgrids. Proc IEEE, 99(6):1074-1082.
[9]Mahmood H, Michaelson D, Jiang J, 2015. Accurate reactive power sharing in an islanded microgrid using adaptive virtual impedances. IEEE Trans Power Electron, 30(3): 1605-1617.
[10]Marzband M, Sumper A, Ruiz-Álvarez A, et al., 2013. Experimental evaluation of a real time energy management system for stand-alone microgrids in day-ahead markets. Appl Energy, 106:365-376.
[11]Marzband M, Ghadimi M, Sumper A, et al., 2014. Experimental validation of a real-time energy management system using multi-period gravitational search algorithm for microgrids in islanded mode. Appl Energy, 128:164- 174.
[12]Marzband M, Parhizi N, Savaghebi M, et al., 2016a. Distributed smart decision-making for a multimicrogrid system based on a hierarchical interactive architecture. IEEE Trans Energy Conv, 31(2):637-648.
[13]Marzband M, Javadi M, Domínguez-García JL, et al., 2016b. Non-cooperative game theory based energy management systems for energy district in the retail market considering DER uncertainties. IET Gener Transm Distrib, 10(12): 2999-3009.
[14]Marzband M, Parhizi N, Adabi J, 2016c. Optimal energy management for stand-alone microgrids based on multi- period imperialist competition algorithm considering uncertainties: experimental validation. Int Trans Electr Energy Syst, 26(6):1358-1372.
[15]Marzband M, Yousefnejad E, Sumper A, et al., 2016d. Real time experimental implementation of optimum energy management system in standalone microgrid by using multi-layer ant colony optimization. Int J Electr Power Energy Syst, 75:265-274.
[16]Marzband M, Ghazimirsaeid SS, Uppal H, et al., 2017. A real-time evaluation of energy management systems for smart hybrid home Microgrids. Electr Power Syst Res, 143:624-633.
[17]Moghaddam AA, Seifi A, Niknam T, et al., 2011. Multi- objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source. Energy, 36(11):6490-6507.
[18]Nunna HSVSK, Doolla S, 2012. Demand response in smart distribution system with multiple microgrids. IEEE Trans Smart Grid, 3(4):1641-1649.
[19]Nunna HSVSK, Doolla S, 2013. Multiagent-based distributed- energy-resource management for intelligent microgrids. IEEE Trans Ind Electron, 60(4):1678-1687.
[20]Pahasa J, Ngamroo I, 2016. Coordinated control of wind turbine blade pitch angle and PHEVs using MPCs for load frequency control of microgrid. IEEE Syst J, 10(1):97- 105.
[21]Rahbar K, Xu J, Zhang R, 2015. Real-time energy storage management for renewable integration in microgrid: an off-line optimization approach. IEEE Trans Smart Grid, 6(1):124-134.
[22]Saad W, Han Z, Poor HV, et al., 2012. Game-theoretic methods for the smart grid: an overview of microgrid systems, demand-side management, and smart grid communications. IEEE Signal Process Mag, 29(5):86-105.
[23]Scattolini R, 2009. Architectures for distributed and hierarchical model predictive control—a review. J Process Contr, 19(5):723-731.
[24]Sortomme E, El-Sharkawi MA, 2009. Optimal power flow for a system of microgrids with controllable loads and battery storage. IEEE/PES Power Systems Conf and Exposition, p.1-5.
[25]Tenfen D, Finardi EC, 2015. A mixed integer linear programming model for the energy management problem of microgrids. Electr Power Syst Res, 122:19-28.
[26]Vasiljevska J, Lopes JAP, Matos MA, 2012. Evaluating the impacts of the multi-microgrid concept using multicriteria decision aid. Electr Power Syst Res, 91:44-51.
[27]Wei C, Fadlullah ZM, Kato N, et al., 2014. GT-CFS: a game theoretic coalition formulation strategy for reducing power loss in micro grids. IEEE Trans Parall Distrib Syst, 25(9):2307-2317.
[28]Yuen C, Oudalov A, Timbus A, 2011. The provision of frequency control reserves from multiple microgrids. IEEE Trans Ind Electron, 58(1):173-183.
Open peer comments: Debate/Discuss/Question/Opinion
<1>