Economic Dispatch in Microgrids with Renewable Energy Using Interior Point Algorithm and Lineal Constrainst
Main Article Content
Keywords
Renewable energy, optimization, economic dispatch, heuristic optimization, MATPOWER
Abstract
Throughout this article simulations of possible financial firms for a system with renewable energy penetration shown when there are variations in wind speed and solar radiation for different times of day. For this test and a valid methodology to minimize the total cost of the system from the use of the interior point method used by the function fmincon MatLab. One of the contributions of this article, is that an adaptation of the restrictions of the power system to function syntax that requires these proposed restrictions are linear.
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References
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[3] J. Hetzer, D. C. Yu and K. Bhattarai, An Economic Dispatch Model Incorporating Wind Power, IEEE Transactions on Energy Conversion, vol. 23, no. 2, 2008.
[4] E. Mojica, S. Rivera and N. Quijano (in press), Game-theoretic dispatch control in microgrids considering network losses and renewable distributed energy resources integration, IET Generation, Transmission & Distribution, 2017.
[5] R. D. Zimmerman, C. E. Murillo-Sánchez, and R. J. Thomas, MATPOWER: Steady-State Operations, Planning and Analysis Tools for Power Systems Research and Education, IEEE Transactions on Power Systems, vol. 26, no. 1, pp. 12-19, 2011.
[6] E. Hill. and W. Stevenson, An improved method of determining incemental loss factors from power systems admittances and voltages, IEEE Transactions on Power Apparatus and Systems, vol. 87, no. 6, 1968.
[7] E. Hill. and W. Stevenson, A new method of determining loss coefficients, IEEE Transactions on Power Apparatus and Systems, vol. 87, no. 7, 1968.
[8] M. Anumaka, Analysis of technical losses in electrical power system (Nigerian 330kV Network as a case of study), IJRRAS, vol. 21, no. 8, 2012.
[9] Y. Chang, W. Yang and C. Liu, A new method for calculating loss coefficients [of power systems], IEEE Transactions on Power Systems, vol. 9, no. 3, 1994.
[10] D. Arango, R. Urrego and S. Rivera, (in press) Robust Loss coeficients, Application to Power Systems with Solar and Wind Energy, International Journal of Power and Energy Conversion (2017).
[11] A. Stefek, Benchmarking of heuristic optimization methods, MECHATRONIKA, 2011 14th International Symposium, Trencianske Teplice, 2011.
[12] V. Miranda and R. Alves, Differential evolutionary particle swarm optimization (deepso): A successful hybrid, 11th Brazilian Congress on Computational Intelligence, 2013.
[13] L. Carvalho, F. Loureiro, J. Sumaili, H. Keko, V. Miranda, C. Marcelino, E. Wanner, Statistical tuning of DEEPSO soft constraints in the Security Constrained Optimal Power Flow problem, 2015 18th
International Conference on Intelligent System Application to Power Systems (ISAP), Porto, 2015.
[14] J. H. Zhao, F. Wen, Z. Y. Dong, Y. Xue, and K. P. Wong, Optimal Dispatch of Electric Vehicles and Wind Power using Enhanced Particle Swarm Optimization, IEEE Transactions on Industrial Informatics,
vol. 8, Nov. 2012.
[15] J. Chen., X. Yang, L. Zhu and M. Zhang, Microgrid economic operation and research on dispatch strategy, IEEE Power engineering and automation conference (PEAM), 2012.
[16] L. Xiaoping, D. Ming, H. Jianghong, H. Pingping and P. Yali, Dynamic Economic Dispatch for Microgrids Including Battery Energy Storage, IEEE International Symposium on Power Electronics for Distributed Generation Systems, 2010.
[17] W. Gu, Z. Wu, and X. Yuan, Microgrid economic operation of the combined heat and power system with renewable energy, IEEE Power and Energy Society General Meeting, 2010.
[18] A. Ghods, F. Al Farid, S. Barman, E. Sheiki and U. Salma, Optimizing Economical Dispatch of a Microgrid in Islanding Mode, IEEE 8th International Conference on Electrical and Computer Engineering, 2014.
[19] J. Arevalo, F. Santos and S. Rivera, (in press) Uncertainty Cost Functions for Solar Photovoltaic Generation, Wind Energy Generation, and Plug-In Electric Vehicles: Mathematical Expected Value and Verification by Monte Carlo Simulation, International Journal of Power and Energy Conversion (2017).
[20] F. Molina, S. Pérez and S. Rivera, Uncertainty Cost Function Formulation in Small Hydropower Plants Inside a Microgrid, Ingenierías USBMed, vol. 8, no. 1, Ene-Jun 2017.
[21] Windiest day yet for ECN Cairngorms [online]. England: UK Environmental change network, 2016 Disponible en: http://www.ecn.ac.uk/news/windiest-day-yet-for-ecn-cairngorms, (2016).
[22] C. Tian P. Investigation on Frequency Distribution of Global Radiation Using Different Probability Density Functions, International Journal of Applied Science and Engineering. Chaoyang University of Technology, Taiwan 2010.