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Economic Dispatch for on-line operation of grid-connected microgrids

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, a control strategy for real-time operation of a master-slave controlled microgrid is developed. The basic idea of this control strategy is to schedule all dispatchable energy sources available into a microgrid to minimize its operational costs. Control actions are centrally evaluated by solving a two-stage optimization problem formulated to take place on two different time-scales: in the day-ahead and in the real-time. The first one provides a 24-hour plan in advance. It mainly draws up the active power levels that Distributed Energy Resources (DERs) should provide for each quarter hour of the next day by taking into account energy prices of the day-ahead energy market, the forecasted energy production of non-dispatchable renewables and loads. The real-time optimization problem updates the active power set-points of DERs in order to minimize as much as possible the real-time deviations between the actual power exchanged with the utility grid and its scheduled value. The effectiveness of the proposed methodology has been experimentally tested on an actual microgrid.
Rocznik
Strony
651--659
Opis fizyczny
Bibliogr. 37 poz., rys., tab.
Twórcy
autor
  • Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Re David, 200, 70125, Bari, Italy
autor
  • Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Re David, 200, 70125, Bari, Italy
autor
  • Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Re David, 200, 70125, Bari, Italy
autor
  • Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Re David, 200, 70125, Bari, Italy
  • Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warsaw, Poland
Bibliografia
  • [1] N.W.A. Lidula, and A.D. Rajapakse, “Microgrids research: A review of experimental microgrids and test systems”, Renewable Sustainable Energy Rev. 15 (1), 186–202 (2011).
  • [2] T.S. Ustun, C. Ozansoy, and A. Zayegh, “Recent developments in microgrids and example cases around the world – A review”, Renewable Sustainable Energy Rev. 15 (8), 4030–4041 (2011).
  • [3] I. Patrao, E. Figueres, G. Garcerá, and R. González-Medina, “Microgrid architectures for low voltage distributed generation”, Renewable Sustainable Energy Rev. 43, 415–424 (2015).
  • [4] A. Cagnano, E. De Tuglie, and P. Mancarella, “Microgrids: Overview and guidelines for practical implementations and operation”, Appl. Energy 258, 114039, (2020).
  • [5] R. Hari Kumar, N. Mayadevi, V.P. Mini, and S. Ushakumari, “Transforming distribution system into a sustainable isolated microgrid considering contingency”, Bull. Pol. Ac.: Tech. 67 (5), 871–881 (2019).
  • [6] L. Mariam, M. Basu, and M.F. Conlon, “A review of existing microgrid architectures”, J. Eng. 2013, 937614 (2013).
  • [7] C. Huang and S. Sarkar, “Dynamic pricing for distributed generation in smart grid”, 2013 IEEE Green Technologies Conference (GreenTech), Denver, USA, 2013, pp. 422–429.
  • [8] P. Qaderi-Baban M. B. Menhaj, M. Dosaranian-Moghadam, and A. Fakharian, “Intelligent multi-agent system for DC microgrid energy coordination control”, Bull. Pol. Ac.: Tech. 67 (4), 741–748 (2019).
  • [9] M. Parol, Ł. Rokicki, R. Parol, “Towards optimal operation control in rural low voltage microgrids”, Bull. Pol. Ac.: Tech. 67 (4), 799–812 (2019).
  • [10] G. Benysek, M.P. Kazmierkowski, J. Popczyk, and R. Strzelecki, “Power electronic systems as a crucial part of Smart Grid infrastructure – a survey”, Bull. Pol. Ac.: Tech. 59 (4), 455–473 (2011).
  • [11] H. Shayeghi, E. Shahryari, M. Moradzadeh, and P. Siano, “A Survey on Microgrid Energy Management Considering Flexible Energy Sources”, Energies 12 (11), 2156 (2019).
  • [12] A.G. Tsikalakis, and N.D. Hatziargyriou, “Centralized control for optimizing microgrids operation”, 2011 IEEE Power and Energy Society General Meeting, Detroit, USA, 2011, pp. 1–8.
  • [13] M. Marzband, R.R. Ardeshiri, M. Moafi, and H. Uppal, “Distributed generation for economic benefit maximization through coalition formation–based game theory concept”, Int. Trans. Electr. Energy Syst. 27 (6), 1–16 (2017).
  • [14] A. Belgana, B.P. Rimal, and M. Maier, “Open energy market strategies in microgrids: A Stackelberg game approach based on a hybrid multiobjective evolutionary algorithm”, IEEE Trans. Smart Grid 6 (3), 1243–1252 (2015).
  • [15] N.I. Nwulu and X. Xia, “Optimal dispatch for a microgrid incorporating renewables and demand response”, Renewable Energy, 101, 16–28 (2017).
  • [16] L. Fu, W. Zhang, Z. Dong, and K. Meng, “A mixed logical dynamical model for optimal energy scheduling in microgrids”, In Proceedings of 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), Beijing, China, 2018.
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  • [18] A. Sobu, and G. Wu, “Dynamic optimal schedule management method for microgrid system considering forecast errors of renewable power generations”, 2012 IEEE International Conference on Power System Technology (POWERCON), Auckland, Australia, 2012, pp. 1–6.
  • [19] A.C. Luna, N.L. Diaz, F. Andrade, M. Graells, J.M. Guerrero, and J.C. Vasquez, “Economic power dispatch of distributed generators in a grid-connected microgrid”, In Proc. of 2015 9th International Conference on Power Electronics and ECCE Asia (ICPE-ECCE Asia), Seoul, South Korea, 2015, pp. 1161–1168.
  • [20] B. Analui, and A. Scaglione, “A dynamic multistage stochastic unit commitment formulation for intraday markets”, IEEE Trans. Power Syst. 33 (4), 3653–3663, (2017).
  • [21] X. Zhaoxia, N. Jiakai, J.M. Guerrero, and F. Hongwei, “Multiple time-scale optimization scheduling for islanded microgrids including PV, wind turbine, diesel generator and batteries”, In Proc. of IECON 2017-43rd Annual Conference of the IEEE Industrial Electronics Society, Beijing, China, 2017, pp. 2594–2599.
  • [22] A. Borghetti, M. Bosetti, S. Grillo, A. Morini, M. Paolone, and F. Silvestro, “A two-stage scheduler of distributed energy resources”, In Proc. of 2007 IEEE Lausanne Power Tech, Lausanne, Switzerland, 2007, pp. 2168–2173.
  • [23] I. Blanco and J.M. Morales, “An efficient robust solution to the two-stage stochastic unit commitment problem”, IEEE Trans. Power Syst., 32 (6), 4477–4488 (2017).
  • [24] M. Zachar and P. Daoutidis, “Microgrid/macrogrid energy exchange: A novel market structure and stochastic scheduling”, IEEE Trans. Smart Grid 8 (1), 178–189 (2017).
  • [25] A. Cagnano, E. De Tuglie, and L. Cicognani, “Prince—Electrical Energy Systems Lab: A pilot project for smart microgrids”, Electr. Power Syst. Res. 148, 10–17, (2017).
  • [26] K.B. Song, Y.S. Baek, D.H. Hong, and J. Jang, “Short-term load forecasting for the holidays using fuzzy linear regression method”, IEEE Trans. Power Syst. 20 (1), 96–101 (2005).
  • [27] J.P. González, A.M. San RoqueGonzalez, and E.A. Perez, “Forecasting functional time series with a new Hilbertian ARMAX model: Application to electricity price forecasting”, IEEE Trans. Power Syst. 33 (1), 545–556 (2018).
  • [28] I. Shafi, J. Ahmad, S.I. Shah, and F.M. Kashif, “Evolutionary timefrequency distributions using Bayesian regularised neural network model”, IET Signal Proc. 1 (2), 97–106 (2007).
  • [29] A. Deihimi, O. Orang, and H. Showkati, “Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction”, Energy 57, 382–401 (2013).
  • [30] N. Amjady, A. Daraeepour, and F. Keynia, “Day-ahead electricity price forecasting by modified relief algorithm and hybrid neural network”, IET Gener. Transm. Distrib. 4 (3), 432–444 (2010).
  • [31] P.H. Kuo and C.J. Huang, “An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks”, Sustainability 10, 10 (2018).
  • [32] P. Gibilisco, G. Ieva, F. Marcone, G. Porro, and E. De Tuglie, “Day-ahead operation planning for microgrids embedding Battery Energy Storage Systems. A case study on the PrInCE Lab microgrid”, 2018 AEIT International Annual Conference. Bari, Italy, 2018, pp. 1–6.
  • [33] A. Cagnano, A. Caldarulo Bugliari, and E. De Tuglie, “A co-operative control for the reserve management of isolated microgrids”, Appl. Energy 218, 256–265 (2018).
  • [34] A. Cagnano, E. De Tuglie, M. Trovato, L. Cicognani, and V. Vona, “A simple circuit model for the islanding transition of microgrids”, 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), Bologna, Italy, 2016, pp. 1–6.
  • [35] A. Cagnano, E. De Tuglie, R. Turri, A. Cervi, and A. Vian, “On-line identification of simplified CHP models”, In 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe) 2019, pp. 1–6.
  • [36] A. Cagnano and E. De Tuglie, “ On-line identification of simplified dynamic models: Simulations and experimental tests on the Capstone C30 microturbine”, Electr. Power Syst. Res. 157, 145–156 (2018).
  • [37] A. Cagnano and E. De Tuglie, “Time domain identification of a simplified model of So–Nick BESS: A methodology validated with field experiments”, Electr. Power Syst. Res. 165, 229–237 (2018).
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-489153c1-eac0-485f-ada3-22320f55eebf
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