Abstract
The efficient utilization of distributed generation resources (DGs) and demand side management (DSM) in large-scale power systems play a crucial role in satisfying and controlling electricity demand through an economically viable and environmentally friendly way. However, uncertainties in power generation from DGs, variations in load demand, and conflicts in objectives (emission, cost, etc.) pose major challenges to determine the optimal operation planning of microgrids. In this chapter, we propose a dynamic data-driven multi-objective optimization model for a day-ahead operation planning for microgrids, integrating interruption load management (ILM) as a DSM program, while collectively considering the total cost and emissions as objective functions. The proposed model includes three modules that interact with each other: (1) a simulation module that captures the behavior of operating components such as solar panels, wind turbines, etc. and provides the data for the optimization model; (2) an optimization module that determines the optimal operational plan, which includes utilization of diesel generators, purchased electricity from utility and interrupted load, considering cost and emissions objective functions using 𝜖-constraint method; and (3) a rule-based real-time decision making module that adapts the operation plan from the optimization model based on dynamic data from the microgrid and sends the revised plan back to the microgrid. The capabilities and performance of the proposed dynamic data-driven optimization framework are demonstrated through a case study of a typical electrical power system. The resultant operation plan is quite promising regarding total cost and CO2 emission.
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References
M. Abido, Multiobjective particle swarm optimization for environmental/economic dispatch problem. Electr. Power Syst. Res. 79(7), 1105–1113 (2009)
Y. Atwa, E. El-Saadany, M. Salama, R. Seethapathy, Optimal renewable resources mix for distribution system energy loss minimization. IEEE Trans. Power Syst. 25(1), 360–370 (2010)
E. Blasch, Y. Al-Nashif, S. Hariri, Static versus dynamic data information fusion analysis using dddas for cyber security trust. Proc. Comput. Sci. 29, 1299–1313 (2014)
N. Celik, S. Lee, K. Vasudevan, Y.J. Son, DDDAS-based multi-fidelity simulation framework for supply chain systems. IIE Trans. 42(5), 325–341 (2010)
F. Darema, Dynamic data driven application systems, in Process coordination and ubiquitous computing, ed. by D. C. Marinescu, C. Lee (Eds), (CRC Press, Boca Raton, 2002), p. 149
F. Darema, Dynamic data driven applications systems: a new paradigm for application simulations and measurements, in International Conference on Computational Science (Springer, Berlin, 2004), pp. 662–669
FAWN, Ftp: Yearly csv data (2014). http://agrofawn-prod01.osg.ufl.edu/fawnpub/data/hourly_summaries
R. Fourer, D.M. Gay, B. Kernighan, AMPL, vol. 117 (Boyd & Fraser, Danvers, 1993)
R. Fujimoto, R. Guensler, M. Hunter, H.K. Kim, J. Lee, J. Leonard II, M. Palekar, K. Schwan, B. Seshasayee, Dynamic data driven application simulation of surface transportation systems, in International Conference on Computational Science (Springer, Berlin/New York, 2006), pp. 425–432
R.M. Fujimoto, N. Celik, H. Damgacioglu, M. Hunter, D. Jin, Y.J. Son, J. Xu, Dynamic data driven application systems for smart cities and urban infrastructures, in Winter Simulation Conference (WSC) (IEEE, Piscataway, 2016), pp. 1143–1157
R.E. Kass, A.E. Raftery, Bayes factors. J. Am. Stat. Assoc. 90(430), 773–795 (1995)
F. Katiraei, M.R. Iravani, Power management strategies for a microgrid with multiple distributed generation units. IEEE Trans. Power Syst. 21(4), 1821–1831 (2006)
A.M. Khaleghi, D. Xu, Z. Wang, M. Li, A. Lobos, J. Liu, Y.J. Son, A DDDAMS-based planning and control framework for surveillance and crowd control via UAVs and UGVs. Expert Syst. Appl. 40(18), 7168–7183 (2013)
H.L. Li, C.T. Chang, J.F. Tsai, Approximately global optimization for assortment problems using piecewise linearization techniques. Eur. J. Oper. Res. 140(3), 584–589 (2002)
G. Mavrotas, Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Appl. Math. Comput. 213(2), 455–465 (2009)
A.H. Mohsenian-Rad, V.W. Wong, J. Jatskevich, R. Schober, A. Leon-Garcia, Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Trans. Smart Grid 1(3), 320–331 (2010)
National Grid, Hourly electric supply charges (2015). https://www.nationalgridus.com/niagaramohawk/business/rates/5_hour_charge.asp
D.S. Parker, Research highlights from a large scale residential monitoring study in a hot climate. Energy Build. 35(9), 863–876 (2003)
A.E. Raftery, Choosing models for cross-classifications. Am. Soc. Rev. 51(1), 145–146 (1986)
A. Setämaa-Kärkkäinen, K. Miettinen, J. Vuori, Best compromise solution for a new multiobjective scheduling problem. Comput. Oper. Res. 33(8), 2353–2368 (2006)
X. Shi, H. Damgacioglu, N. Celik, A dynamic data-driven approach for operation planning of microgrids. Proc. Comput. Sci. 51, 2543–2552 (2015)
A.E. Thanos, X. Shi, J.P. Sáenz, N. Celik, A DDDAMS framework for real-time load dispatching in power networks, in Proceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World (IEEE Press, Piscataway, 2013), pp. 1893–1904
A.E. Thanos, D.E. Moore, X. Shi, N. Celik, System of systems modeling and simulation for microgrids using DDDAMS, in Modeling and simulation support for system of systems engineering applications, ed. by L. B. Rainey, A. Tolk (Eds), (Wiley, Hoboken, 2015), p. 337
A.E. Thanos, M. Bastani, N. Celik, C.H. Chen, Dynamic data driven adaptive simulation framework for automated control in microgrids. IEEE Trans. Smart Grid 8(1), 209–218 (2017)
C. Wu, H. Mohsenian-Rad, J. Huang, A.Y. Wang, Demand side management for wind power integration in microgrid using dynamic potential game theory, in 2011 IEEE GLOBECOM Workshops (GC Wkshps) (IEEE, Piscataway, 2011), pp. 1199–1204
D.C. Yu, T.C. Nguyen, P. Haddawy (1999) Bayesian network model for reliability assessment of power systems. IEEE Trans. Power Syst. 14(2), 426–432
Acknowledgements
This work was supported in part by the Air Force Office of Scientific Research under award FA9550-18-1-0075, and National Science Foundation under award number ECCS-1462409.
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Damgacioglu, H., Bastani, M., Celik, N. (2018). A Dynamic Data-Driven Optimization Framework for Demand Side Management in Microgrids. In: Blasch, E., Ravela, S., Aved, A. (eds) Handbook of Dynamic Data Driven Applications Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-95504-9_21
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DOI: https://doi.org/10.1007/978-3-319-95504-9_21
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