Modeling an electric power microgrid by model predictive control for analyzing its characteristics from reliability,controllability and topological perspectives
Restricted accessResearch articleFirst published online April, 2018
Modeling an electric power microgrid by model predictive control for analyzing its characteristics from reliability,controllability and topological perspectives
Microgrids can be a key solution for integrating renewable and distributed energy resources. This article analyzes microgrids’ characteristics adopting model predictive control. We study the microgrid performance under two operation modes: grid-connected and stand-alone. For each mode, we consider different faulty scenarios, and by dynamic simulations, we investigate the importance of the microgrid components from different perspectives: topological, reliability and controllability. This analysis enables evaluation of the microgrid performance and quantification of the importance of each component with respect to the different perspectives considered. The findings provide information for the design and operation of a microgrid, seeking the right balance of multiple characteristics.
ZioEGoleaLR. Analyzing the topological, electrical and reliability characteristics of a power transmission system for identifying its critical elements. Reliab Eng Syst Safe2012; 101: 67–74.
8.
LiJDueñas-OsorioLChenC. Connectivity reliability and topological controllability of infrastructure networks: A comparative assessment. Reliab Eng Syst Safe2016, 156: 24-33.
9.
ZhangLLiDQinP. Reliability analysis of interdependent lattices. Physica A2016; 452: 120–125.
10.
LasseterRHPaigiP. Microgrid: a conceptual solution. In: 2004 IEEE 35th annual power electronics specialists conference (PESC ‘04), Aachen, 20–25 June 2004, pp.4285–4290. New York: IEEE.
11.
HanFProdanIZioE. A framework of model predictive control for the safety analysis of an electric power microgrid. In: ESREL 2015, 25th European safety and reliability conference, Zurich, 7–10 September 2015. London: CRC Press.
12.
ZamoraRSrivastavaAK. Controls for microgrids with storage: review, challenges, and research needs. Renew Sustain Energ Rev2009; 147: 2009–2018.
13.
JimenoJAnduagaJOyarzabalJ. Architecture of a microgrid energy management system. Eur T Electr Power2011; 21(2): 1142–1158.
14.
KuznetsovaECulverKZioE. Complexity and vulnerability of Smartgrid systems. In: Proceedings of the European safety and reliability conference, Troyes, pp.2474–2482. London: CRC Press.
15.
KrauseTBeckEVCherkaouiR. A comparison of Nash equilibria analysis and agent-based modelling for power markets. Int J Elec Power2006; 28(9): 599–607.
16.
WeidlichAVeitD. A critical survey of agent-based wholesale electricity market models. Energ Econ2008; 30(4): 1728–1759.
17.
ProdanIZioE. A model predictive control framework for reliable microgrid energy management. Int J Elec Power2014; 61: 399–409.
18.
ProdanIZioE. On the microgrid energy management under a predictive control framework. In: IEEE conference on control applications (CCA), Juan Les Antibes, 8–10 October 2014, pp.861–866. New York: IEEE.
19.
OlivaresDEMehrizi-SaniAEtemadiAH. Trends in microgrid control. IEEE T Smart Grid2014; 5(4): 1905–1919.
20.
WangSLiZWuL. New metrics for assessing the reliability and economics of microgrids in distribution system. IEEE T Power Syst2013; 28(3): 2852–2861.
21.
RawlingsJBMayneDQ. Postface to model predictive control: theory and design, 2011. USA: Nob Hill Publishing.
GoodwinGCSeronMDe DonaJ. Constrained control and estimation: an optimisation approach. London: Springer-Verlag, 2005.
24.
HovgaardTGLarsenLFJorgensenJB. Robust economic MPC for a power management scenario with uncertainties. In: Proceedings of the 50th IEEE conference on decision and control and European control conference (CDC-ECC), Orlando, FL, 12–15 December 2011, pp.1515–1520. New York: IEEE.
25.
HalvgaardRPoulsenNKMadsenH. Economic model predictive control for building climate control in a smart grid. In: IEEE PES innovative smart grid technologies (ISGT), Washington, DC, 16–20 January 2012, pp.1–6. New York: IEEE.
26.
EdlundKBendtsenJDJørgensenJB. Hierarchical model-based predictive control of a power plant portfolio. Control Eng Pract2011; 19(10): 1126–1136.
27.
NegenbornRRDe SchutterBHellendoornH. Multi-agent model predictive control of transportation networks. In: Proceedings of the IEEE international conference on networking, sensing and control, Fort Lauderdale, FL, 23–25 April 2006, pp.296–301. New York: IEEE.
28.
FangYZioE. Hierarchical modeling by recursive unsupervised spectral clustering and network extended importance measures to analyze the reliability characteristics of complex network systems. Am J Oper Res2013; 3(1A): 101–112.
29.
LombardiAHörnquistM. Controllability analysis of networks. Phys Rev E2007; 75: 056110.
30.
LiuYYSlotineJJBarabásiAL. Controllability of complex networks. Nature2011; 473: 167–173.
31.
BakolasASalehJH. Augmenting defense-in-depth with the concepts of observability and diagnosability from control theory and discrete event systems. Reliab Eng Syst Safe2011; 96(1): 184–193.
32.
KalmanRE. Mathematical description of linear dynamical systems. J Soc Ind Appl Math1963; 1(2): 152–192.
33.
GriggCWongPAlbrechtP. The IEEE reliability test system-1996. A report prepared by the reliability test system task force of the application of probability methods subcommittee. IEEE T Power Syst1999; 143: 1010–1020.
34.
JustusCGHargravesWRYalcinA. Nationwide assessment of potential output from wind-powered generators. J Appl Meteorol1976; 15(7): 673–678.
35.
ProdanIZioEStoicanF. Fault tolerant predictive control design for reliable microgrid energy management under uncertainties. Energy2015; 91: 20–34.
