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The increasing energy production from variable renewable energy sources such as wind and solar has resulted in several challenges related to the system reliability and efficiency. In order to ensure the supply-demand balance under the conditions of higher variability the micro-grid concept of active distribution networks arising as a promising one. However, to achieve all the potential benefits that micro-gird concept offer, it is important to determine optimal operating strategies for micro-grids. The present paper compares three energy management strategies, aimed at ensuring economical micro-grid operation, to find a compromise between the complexity of strategy and its efficiency. The first strategy combines optimization technique and an additional rule while the second strategy is based on the pure optimization approach. The third strategy uses model based predictive control scheme to take into account uncertainties in renewable generation and energy consumption. In order to compare the strategies with respect to cost effectiveness, a residential micro-grid comprising photovoltaic modules, thermal energy storage system, thermal loads, electrical loads as well as combined heat and power plant, is considered.
PAPER REVISED: 2016-02-29
PAPER ACCEPTED: 2016-02-29
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THERMAL SCIENCE YEAR 2016, VOLUME 20, ISSUE Issue 4, PAGES [1091 - 1103]
  1. Minchala-Avila, L. I., et al., A rewiev of optimal control techniques applied to the energy management and control of microgrids, Procedia Computer Science, 52 (2015), pp. 780-787
  2. Popović, Ž.N., et al., Smart Grid Concept in Electrical Distribution System, Thermal Science, 16 (2012), Suppl. 1, pp. S205-S213
  3. Gamarra, C., Guerrero J. M., Computational optimization techniques applied to microgrids planning: A review, Renewable and Sustainable Energy Reviews, 48(2015), pp. 413-424
  4. Ilić, S.A., et al., Hybrid Artifical Neural Network System for Short-Term Load Forecasting, Thermal Science, 16 (2012), Suppl 1., pp. S215-S224
  5. Tenfen, D., Finardi E.C., A mixed integer linear programming model for the energy management problem of microgrids, Electric Power Systems Research, 122(2015), pp. 19-28
  6. Holjevac N., et al, Adaptive control for evaluation of flexibility benefits in microgrid system, Energy, 92 (2015), 3, pp. 487-504
  7. Kriett, P.O., Salani, M., Optimal control of a residential microgrid , Energy, 42 (2012), pp. 321-330
  8. Perković, L., et al., Receding horizon model predictive control for smart management of microgrids under day-ahead electricity market, Proceedings of the 8th Conference on Sustainable Development of Energy, Water and Environment Systems - SDEWES, Dubrovnik, Croatia, 2013
  9. Prodan, I. Zio E., A model predictive control framework for reliable microgrid energy management, Electrical Power and Energy Systems, 61(2014), pp. 399-409
  10. Parisio, A., Stohastic Model Predictive Control for Economic/Environmental Operation Management of Microgrids, 2013 European Control Conference (ECC), Zürich, Switzerland, 2013, pp. 2014-2019
  11. Silvente, J., et al., A rolling horizon optimization framework for the simultaneous energy supply and demand planning in microgrids, Applied Energy, 155(2015), pp. 485.501
  12. Zhang, Y., et al., An Intelligent Control Strategy of Battery Energy Storage System for Microgrid Energy Management under Forecast Uncertainties, International Journal of Electrochemical Science, 9 (2014), pp. 4190-4204
  13. Gambino, G., et al., Model predictive control for optimization of combined heat and electrical power microgrid, Preprints of the 19th World Congress The International Federation of Automatic Control, Cape Town, South Africa, 2014
  14. Chen, Y.H., et al., Economic analysis and optimal energy management models for microgrid systems: A case study in Taiwan, Applied Energy, 103(2013), pp. 145-155
  15. ***, VDI 4655, Reference load profiles of single-family and multy- familiy houses for the use of CHP systems, 2008
  16. Ridjan, I., Micro CHP plant operation control in multi-apartment house, Final project of the Undergraduate study, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Zagreb, Croatia, 2010
  17. Powell, K. M., Edgar, F.T., An adaptive-grid model for dynamic simulation of thermocline thermal energy storage systems, Energy Conversion and Management, 76(2013), pp. 865-873
  18. Streckiene, G., Andersen, A.N., Analyzing the optimal size of a CHP-unit and thermal store when a German CHP-plant is selling at the Spot market, V 1.2(2008-05-28), EMD International A/S, Denmark, Aalborg, Denmark, 2008
  19. Notton, G. D., et al., Design and Techno-Economical Optimization for Hybrid PV/Wind System under Various Meteorological Conditions, Applied Energy, 85(2008), 10, pp 968 - 987
  20. Singiresu S. Rao, Linear programming I: Simplex Method, in: Engineering Optimization, Theory and Practice, Fourth edition, John Wiley & Sons, Inc., Hoboken, New Yersey, USA, 2009, pp. 119-176
  21. ***, Nord pool spot.htm,

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