Energy flow estimation-control of two interconnected microgrids




Being dependent on weather, photovoltaic and wind system energy contributions fluctuate and are not continuously available, and sometimes not in the desired quantity. To avoid load shedding or blackout in this situation, the estimation-control of energy can be useful to ensure continuity of supply and assist the planning operation of the power system. This paper proposes the estimation-control of the flow of energy between two microgrids interconnected via two alternating current tie-lines. Two sources of power generation depending on weather behaviours have been considered. The effectiveness of the proposed estimation-control model was shown using the Extended Kalman filter combined with the fmincon algorithm.


Download data is not yet available.

Author Biography

Ramesh C. Bansal

Prof. Ramesh Bansal,

FIET (UK), FIE (India), FIEAust, SM IEEE (USA), CPEngg (UK)

Professor & Group Head (Power)

Department of Electrical, Electronic and Computer Engineering,

Room 14-27, Eng. Building 1, University of Pretoria, Hatfield Campus, Pretoria 0002, South Africa


Dubey, R., Joshi, D. and Bansal, R.C.2016. Opti-mization of solar photovoltaic plant and economic analysis. Electric Power Components and Sys-tems, 44 (18), 2025-2035.

Zobaa, A.F. and Bansal, R.C. (eds). 2011. Hand-book of renewable energy technology. World Sci-entific Publishers, Singapore.

Bansal, R.C. and Bhatti, T.S. 2008. Small signal analysis of isolated hybrid power systems: Reac-tive power and frequency control analysis. Alpha Science International, Oxford, U.K.

Wan C., Song, Y., Xu, Z., Yang, G. and Nielsen, A.H. 2016. Probabilistic wind forecasting with hy-brid artificial neural networks. Electrical Power Components and Systems, 44 (15), 1656-1668.

Singh, R. and Banerjee, R. 2015. Estimation of rooftop photovoltaic potential of a city. Solar En-ergy, 115, 589-602.

Cherif, H., and Belhadj, J. 2013. Energy estima-tion on large-scale time of stand-alone wind tur-bine. In Proc. of IEEE Electrical Engineering and Software Applications, Hammamet, Tunisia, 21-23 March, 2013: 1-6.

Mabel, M.C. and Fernandez, E. 2009. Estimation of energy yield from wind farms using artificial neural networks. IEEE Transactions on Energy Conversion, 24 (2), 459-464.

Xie, K., Zhou, J. and Li, W. 2009. Analytical mod-el and algorithm for tracing active power flow based on extended incidence matrix. Electric Pow-er System Research, 79, 399-405.

Beltran, H., Perez, E., Aparicio, N. and Rodriguez, P. 2013. Daily solar energy estimation for minimis-ing energy storage requirements in PV power plants. IEEE Transactions on Sustainable Energy, 4 (2), 474-481.

Ayodele, T.R., Jimoh, A.A., Munda, J.L. and Agee, J.T., 2012. Wind distribution and capacity factor estimation for wind turbines in coastal region of South Africa. Energy Conversion and Manage-ment, 64, 614-625.

Foley, A.M., Leahy, P.G., Marvuglia, A. and Mckeogh, E.J. 2012. Currents methods and ad-vances in forecasting of wind power generation. Renewable Energy, 37, 1-8.

Monteiro, R.V.A., Guimaraes, G.C., Moura, F.A.M., Albertini, M.R.M.C. and Albertini, M.K. 2017. Es-timating photovoltaic power generation: perfor-mance analysis of artificial neural networks, sup-port vector machine and Kalman filter. Electric Power Systems Research, 143, 643-656.

Chanda, S., Shariatzadeh, F., Srivastava, A., Lee, E., Stone, W. and Ham, J. 2015. Implementation of non-intrusive energy saving estimation for Volt/Var control of smart distribution system. Elec-tric Power Systems Research, 120, 39-46.

Lydia, M., Kumar, S.S., Selvakumar, A.I. and Ku-mar, G.E.P. 2015. Wind resource estimation using wind speed and power curve models. Renewable Energy, 83, 425-434.

Saxena, N. and Ganuli, S. 2015. Solar and wind power estimation and economic load dispatch us-ing firefly algorithm. Procedia Computer Sciences, 70, 699-700.

Hoseinzadeh, B., Da Silva, F.F. and Bak, C.L. 2015. Active power deficit estimation in presence of renewable energy sources. In Proc. IEEE Power and Energy Society General Meeting, Denver, Colorado, 26-30 July, 2015: 1-5.

Beltran, H., Perez, E., Aparicio, N. and Rodriguez, P. 2013. Daily solar energy estimation for minimis-ing energy storage requirements in PV power plants. IEEE Trans. Sustainable Energy, 4 (2), 474-481.

Marin, M.R., Sumper, A., Robles, A.V. and Jane, J.B. 2014. Active power estimation of photovoltaic generators for distribution network planning based on correlation models. Energy, 64, 758-770.

Song, D., Yang, J., Cai, Z., Dong, M., Su, M. and Wang, Y. 2017. Wind estimation with a non-standard extended Kalman filter and its application on maximum power extraction for variable speed wind turbines. Applied Energy, 190, 670-685.

Wang, S., Fernandez, C., Shang, L., Li, Z. and Li, J. 2017. On-line state of charge estimation for aer-ial lithium-ion battery packs based on the im-proved extended Kalman filter method. Journal of Energy Storage, 9, 69-83.

Sepasi, S., Ghorbani, R. and Liaw, B.Y. 2014. Improved extended Kalman filter for state of charge estimation of battery pack. Journal of Pow-er Sources, 255, 368-376.

Li, Y., Wang, C. and Gong, J. 2016. A Combina-tion Kalman filter approach for state of charge es-timation of lithium ion battery considering model uncertainty. Energy, 109, 933-946.

Han, J., Kim, D. and Sunwoo, M. 2009. State of charge estimation of lead acid batteries using an adaptive extended Kalman filter. Journal of Power Sources, 188, 606-612.

Mastali, M., Arenas, J.V., Fraser, R., Fowler, M., Afshar, S. and Stevens, M. .2013. Battery state of the charge estimation using Kalman faltering. Journal of Power Sources, 239, 294-307.

Zhang, W., Shi, W. and Ma, Z. 2015. Adaptive unscented Kalman filter based state of energy and power capability estimation approach for lithium-ion battery. Journal of Power Sources, 289, 50-62.

Long, X., Junping, W. and Quanshi, C. 2012. Kalman filtering state of charge estimation for bat-tery management system based on a stochastic fuzzy neural network battery model. Energy Con-version Management, 53, 33-39.

Tungadio, D.H., Bansal, R.C. and Siti, M.W. 2017. Optimal control of active power of two microgrids interconnected with two ac tie-lines. Electric Power Components and Systems, 45(19), 2188-2199.

Tungadio, D.H., Numbi, B.P., Siti, M.W. and Jimoh, A.A. 2015. Particle swarm optimization for power system state estimation. Neurocomputing, 148, 175-180.

Tungadio, D.H., Jordaan, J.A. and Siti, M.W. 2016). Power system state estimation solution us-ing modified models of PSO algorithm: Compara-tive study. Measurement, 92, 508-523.

Exposito, A.G., Quiles, C.G. and Dzafic, I. 2015. State estimation in two time scales for smart distri-bution systems. IEEE Transactions on Smart Grid, 6 (1), 421-430.

Tungadio, D.H., Numbi, B.P., Siti, M.W. and Jor-daan J.A. 2013. Weighted least squares and itera-tively reweighted least squares comparison using particle swarm optimization algorithm in solving power system state estimation. In Proc. IEEE Afri-con, Point-aux-Piments, Mauritius, 9-12 Septem-ber, 2013: 1264-1269.

Tebianian, H. and Jeyasurya, B. 2015. Dynamic state estimation in power systems: Modelling, and challenges. Electric Power System Research, 121, 109-114.

Cao Z., Lu J., Zhang R. and Gao F. 2016. Iterative learning Kalman filter for repetitive processes. Journal of Process Control, 46, 92-104.

Beides, H.M. and Heydt, G.T. 2017. A robust iter-ated extended Kalman filter for power system dy-namic state estimation. IEEE Transactions on Power Systems, 32 (4), 3205-3216.

Tungadio, D.H. and Bansal, R.C. 2017. Active power reserve estimation of two interconnected microgrids. Energy Procedia, 105, 3909-3914.

Tungadio, D.H., Bansal, R.C, Siti, M.W., and Mbungu, N.T. 2018. Predictive active power con-trol of two interconnected microgrids. Technol Econ Smart Grids Sustain Energy, 3 (3), 1-15

Efficient microgrid power control model




How to Cite

Tungadio, D. H., Bansal, R. C., & Siti, M. W. (2018). Energy flow estimation-control of two interconnected microgrids. Journal of Energy in Southern Africa, 29(4), 69–80.