Optimal Operation of Distributed Generation in Microgrids in the Presence of Electrical and Thermal Loads Using the Improved Suffeled Frog Leaping Algorithm

Document Type : Original Article

Authors

1 Faculty of Electrical Engineering, Islamic Azad University, Saveh branch, Saveh, Iran

2 دانشکده فنی و مهندسی، دانشگاه آزاد اسلامی واحد ساوه، ساوه، ایران

Abstract

In traditional generation systems, about 25% of energy is wasted, and the presence of distributed energy resources (DER) such as photovoltaic, wind turbines, fuel cell, and the combined heat and power can reduce fuel consumption, pollution, and transmission losses. In this paper, a complete energy management framework in a microgrid is proposed by considering the constraints using Improved Shuffled Frog Leaping Algorithm, in which the exact share of energy production for different units is determined. The proposed scheme is used to select the best arrangement of DERs in the microgrid, the output of which is to determine the number and optimal location of DERs in several bus-bars. Then, the independent system operator determines the quantity of energy exchange and consumption by considering load distribution constraints. Boilers and CHPs have also been used to maintain the balance between the production of thermal power by energy sources and thermal demands. In addition, the Demand Response Program has been used with the aim of smoothing the load curve and of reducing the operating costs. Finally, the proposed method has been implemented and simulated using MATLAB software on a two standards 69-and- 118-bus IEEE system. The comparison of the results with other algorithms showed the accuracy and superiority of the proposed method from the point of view of reducing operating costs.

Keywords

Main Subjects


[1] Kasaei, M. J., Gandomkar, M., and Nikoukar, J., "Optimal management of renewable energy sources by virtual power plant", Renewable Energy, Vol. 114, pp. 1180–1188, 2017, https://doi.org/10.1016/j.renene.2017.08.010.
[2] Nikoukar, J., "Unit commitment considering the emergency demand response programs and interruptible/curtailable loads", Turkish Journal Of Electrical Engineering & Computer Sciences, Vol. 26, No. 2, pp. 1069–1080, 2018, https://doi.org/10.3906/elk-1706-66.
[3] Heydari, H., Nikoukar, J., and Gandomkar, M., "Optimal operation of virtual power plant with considering the demand response and electric vehicles", Journal of Electrical Engineering & Technology, Vol. 16, No. 5, pp. 2407-2419, 2021, https://doi.org/10.1007/s42835-021-00784-8.
[4] Morales, J. M., Conejo, A. J., and Ruiz, J. P., "Economic valuation of reserves in power systems with high penetration of wind power", IEEE Transactions on Power Systems, Vol. 24, No. 2, pp. 900-910, 2009, https://doi.org/10.1109/TPWRS.2009.2016598.
[5] Ma, K., Hu, G., and Spanos, C. J., "A cooperative demand response scheme using punishment mechanism and application to industrial refrigerated warehouses", IEEE Transactions on Industrial Informatics, Vol. 11, No. 6, pp. 1520-1531, 2015, https://doi.org/10.1109/TII.2015.2431219.
[6] Amir, V., Azimian, M., Javadifar, M., "Optimal design of a microgrid with multiple energies considering reliability", Energy Engineering and Management,Vol. 11, No. 2, pp 56-69, 2021.(In Persian) https://doi.org/10.22052/11.2.7
[7] Parvania, M., Fotuhi-Firuzabad, M., "Demand response scheduling by stochastic SCUC", IEEE Transactions on Smart Grid, Vol. 1, No. 1, 2010, https://doi.org/10.1109/TSG.2010.2046430.
[8] Ioakimidis, C. S, Oliveira, L. J., and Genikomsakis, K. N, "Wind power forecasting in a residential location as part of the energy box management decision tool", IEEE Trans on Ind Informat, Vol. 10, No. 4, pp. 2103-2111, 2014, https://doi.org/10.1109/TII.2014.2334056.
[9] Nguyen, D. T., Negnevitsky, M., and Groot, M. D., "Walrasian market clearing for demand response exchange", IEEE Trans. Power Syst, Vol. 27, No. 1, pp. 535-544, 2012, https://doi.org/10.1109/TPWRS.2011.2161497.
[10] Moreno, M., Bueno, M., and Usaola, J., "Evaluating risk-constrained bidding strategies in adjustment spot markets for wind power producers", Int. J. Electr. Power Energy Syst., Vol. 43, No, 1, pp. 703-711, 2012, https://doi.org/10.1016/j.ijepes.2012.05.059.
[11] Roshandel, R., Ahmadi, S. , Rezaie Mirghaed, M., "Modeling of a combined wind turbine-fuel cell hybrid power system and contribution of fuel cell electrical supply demand in case study", Energy Engineering and Management,Vol. 2, No. 3, pp 12-19, 2012. (In Persian)
[12] Baringo, L., and Conejo, A. J., "Strategic offering for a wind power producer", IEEE Trans. Power Syst, , Vol. 28, No. 4, pp. 4645-4654, 2013, https://doi.org/10.3390/en11051239.
[13] Zugno, M., Morales, J. M., Pinson, P., and Madsen, H., "Pool strategy of a price-maker wind power producer", IEEE Trans. Power Syst.,Vol. 28, No. 3, pp. 3440-3450, 2013, https://doi.org/10.1109/TPWRS.2013.2252633.
[14] Nieta, A. A, Contreras, J, Munoz, J. I., and O’Malley, M., "Modeling the impact of a wind power producer as a price-maker", IEEE Trans. Power Syst., Vol. 29, No. 6, pp. 2723-2732, 2014, https://doi.org/10.1109/TPWRS.2014.2313960.
[15] Heydarian-Forushani, E., Moghaddam, M., Sheikh-El-Eslami, M., Shafie-khah, M., and Catalao, J., "Risk-constrained offering strategy of wind power producers considering intraday demand response exchange", IEEE Trans. Sustain. Energy, Vol. 5, No. 4, pp. 1036-1047, 2014, https://doi.org/10.1109/TSTE.2014.2324035.
[16] Chua-Liang, S., and Kirschen, D., "Quantifying the effect of demand response on electricity markets", IEEE Trans. Power Syst., Vol. 24, No. 3, pp. 1199-1207, 2009, https://doi.org/10.1109/TPWRS.2009.2023259.
 [17] Momen, S., Nikoukar, J., and Gandomkar, M., "Multi‑objective optimization of energy consumption in microgrids considering CHPs and renewables using improved shufed frog", Journal of Electrical Engineering & Technology, Vol. 18, No. 2, pp. 1-17, 2022, https://doi.org/10.1007/s42835-022-01289-8.
[18] Alaee, S., Hooshmand, R., and Hemmati, R., "Stochastic transmission expansion planning incorporating reliability solved using SFLA meta-heuristic optimization technique", Journal of Power and Energy Systems, Vol. 2, No. 2, pp. 79-86, 2016, https://doi.org/10.17775/CSEEJPES.2016.00025.
[19] Ding, W., Sun, Y., Ren, L., Ju, H., Feng, Z., and Li, M., "Multiple lesions detection of fundus images based on convolution neural network algorithm with improved SFLA", IEEE Access, Vol. 8, pp. 97618-97631, 2020, https://doi.org/10.1109/ACCESS.2020.2996569.
[20] Shafie-khah, M, Moghaddam, M and Sheikh-El-Eslami, M. K., "Unified solution of a non-convex SCUC problem using combination of modified branch-and-bound method with quadratic programming", Energy Convers. Manage., Vol. 52, No. 11, pp. 3425-3432, 2011, https://doi.org/10.1016/j.enconman.2011.07.012.
[21] Karki, R., Hu, P., and Billinton, R., "A simplified wind power generation model for reliability evaluation", IEEE Trans. Energy Convers, Vol. 21, No. 2, pp. 533-540, 2006, https://doi.org/10.1109/TEC.2006.874233.
[22] Li, J., Fu, Y., Li, C., Xing, Z., and Ma, T., "Improving wind power integration by regenerative electric boiler and battery energy storage device", International Journal of Electrical Power & Energy Systems, Vol. 131, pp. 314-325, 2021, https://doi.org/10.1016/j.ijepes.2021.107039.
[23] Guimãraes, J. A., Pinto, L. M., and Maculan, N., "What will be the proxy value for a Brazilian utility company triggering its demand side management in the light of price elasticity of demand?", IEEE Latin America Transactions, Vol. 14, No. 8, pp. 3746-3754, 2016, https://doi.org/10.1109/TLA.2016.7786359.
[24] Mnatsakanyan, A. and Kennedy, S. W., "A novel demand response model with an application for a virtual power Plant", IEEE Transactions on Smart Grid, Vol. 6, No. 1, pp. 230-237, 2015, https://doi.org/10.1109/TSG.2014.2339213.
[25] Kadhema, A. A, Abdul Wahaba, N. I., Arisa, I., Jasnia, J. and Abdalla, A. N., "Computational techniques for assessing the reliability and sustainability of electrical power systems: A review", Renewable and Sustainable Energy Reviews, Vol. 80, pp. 1175-1186, 2017, https://doi.org/10.1016/j.rser.2017.05.276.
[26] Eusuff, M., and Lansey, K., "Optimization of water distribution network design using the shuffled frog leaping algorithm", Journal of Water Resources planning and management. Vol. 129, No. 3, pp. 210-225, 2003, https://doi.org/10.1061/(ASCE)0733-9496(2003)129:3(210).
[27] Kia, M., Setayeshnazar M., Sepasian, M.S., "Simultaneous Implementation of Controllable Load Management and Combined Heat and Power Commitment in Presence of Electriacal Storage System" Energy Engineering and Management, Vol. 7, No. 2, pp. 2-13, 2017. (In Persian)
[28] Toy, G., Acakpovi, A., Adjei, P., and Rey, G. K., "Optimal sizing and techno economic of a hybrid solar PV/wind/diesel generator system", International Conference on Alternative Fuels and Electric Vehicles, Vol. 1042, pp. 1-16, 2022, https://doi.org/10.1088/1755-1315/1042/1/012014.
[29] Dinakara, P., Veera, V. R., and Gowri, T., "Optimal renewable resources placement in distribution networks by combined power loss index and whale optimization algorithms", Journal of Electrical Systems and Information Technology, Vol. 5, No. 2, pp. 175-191, 2018, https://doi.org/10.1016/j.jesit.2017.05.006.