Abstract
In order to mitigate the extra cost and to reduce the energy consumption, distributive power system are widely accepted in recent years. The reason of adaptation of distributive power system is the scalability of power supply and demand which helps in reliable power supply and optimizes the annual expenditures. Moreover, the integration of power distributive systems with renewable energy sources enabled the optimal utilization of photovoltaic arrays for effective and cost efficient power supply. To exploit the integration of distributive power and renewable sources, we solve the power dispatch problem with heuristic optimization techniques. We have performed scheduling for supply side management. For this purpose, we have formulate our problem using chance constrained optimization and transformed the problem into mixed integer linear programming. Finally, simulation results demonstrate that the proposed scheduling method for microgrid performs efficiently and effectively.
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References
Motevasel, M., Seifi, A.R.: Expert energy management of a micro-grid considering wind energy uncertainty. Energy Convers. Manage. 83, 58–72 (2014)
Davidović, T.: Bee colony optimization Part I: the algorithm overview. Yugoslav J. Oper. Res. 25(1) (2016)
Mohanty, B., Tripathy, S.: A teaching learning based optimization technique for optimal location and size of DG in distribution network. J. Electr. Syst. Inf. Technol. 3(1), 33–44 (2016)
Hasanpor Divshali, P., Choi, B.J.: Electrical market management considering power system constraints in smart distribution grids. Energies 9(6), 405 (2016)
Varela Souto, A.: Optimization and Energy Management of a Microgrid Based on Frequency Communications (2016)
Naderipour, A., Mohd Zin, A.A., Habibuddin, M.H., Moradi, M., Miveh, M., Afrouzi, H.N.: A new compensation control strategy for grid-connected wind turbine and fuel cell inverters in a microgrid. Int. J. Power Electron. Drive Syst. (IJPEDS) 8(1), 272–278 (2017)
Trivedi, I.N., Thesiya, D.K., Esmat, A., Jangir, P.: A multiple environment dispatch problem solution using ant colony optimization for micro-grids. In: International Conference on Power and Advanced Control Engineering (ICPACE), 2015, pp. 109–115. IEEE (2015)
Olivas, F., Valdez, F., Castillo, O., Gonzalez, C.I., Martinez, G., Melin, P.: Ant colony optimization with dynamic parameter adaptation based on interval type-2 fuzzy logic systems. Appl. Soft Comput. 53, 74–87 (2017)
Li, X., Wang, Y., Wang, Z., Shu, X., Zhang, Y.: The open electrical and electronic engineering journal. Open Electr. Electron. Eng. J. 10, 46–57 (2016)
Thirumalaisamy, B., Jegannathan, K.: A novel energy management scheme using ANFIS for independent microgrid. Int. J. Renew. Energy Res. (IJRER) 6(3), 735–746 (2016)
Lin, W.-M., Tu, C.-S., Tsai, M.-T.: Energy management strategy for MG by using enhanced bee colony optimization. Energies 9(1), 5 (2015)
Huld, T., Moner-Girona, M., Kriston, A.: Geospatial analysis of photovoltaic mini-grid system performance. Energies 10(2), 218 (2017)
Dehghanpour, E., Karegar, H., Kheirollahi, R., Soleymani, T.: Optimal coordination of directional overcurrent relays in microgrids by using cuckoo-linear optimization algorithm and fault current limiter. IEEE Trans. Smart Grid (2016)
Wang, J., Li, Y., Zhou, Y.: Interval number optimization for household load scheduling with uncertainty. Energy Buildings 130, 613–624 (2016)
Moon, S., Lee, J.-W.: Multi-residential demand response scheduling with multi-class appliances in smart grid. IEEE Trans. Smart Grid (2016)
Dong, W., Li, Y., Xiang, J.: Optimal sizing of a stand-alone hybrid power system based on battery/hydrogen with an improved ant colony optimization. Energies 9(10), 785 (2016)
https://www.wbdg.org/resources/microturbines. Accessed 21 Oct 2017
http://www.dieselserviceandsupply.com/why_use_diesel.aspx. Accessed 21 Oct 2017
World’s Largest Carbon Neutral Fuel Cell Power Plant, 16 October 2012. Accessed 21 Oct 2017
Zachar, M., Daoutidis, P.: Microgrid/Macrogrid energy exchange: a novel market structure and stochastic scheduling. IEEE Trans. Smart Grid 8(1), 178–189 (2017)
https://optimization.mccormick.northwestern.edu/index.php/Chance-constraint_method. Accessed 24 Oct 2017
https://en.wikipedia.org/wiki/Branch_and_bound. Accessed 24 Oct 2017
Roy, K., Mandal, K.K., Mandal, A.C.: Modeling and managing of MG connected system using improved artificial bee colony algorithm. Int. J. Electr. Power Energy Syst. 75, 50–58 (2016)
Cheng, Y.-S., Chuang, M.-T., Liu, Y.-H., Wang, S.-C., Yang, Z.-Z.: A particle swarm optimization based power dispatch algorithm with roulette wheel re-distribution mechanism for equality constraint. Renew. Energy 88, 58–72 (2016)
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Fatima, I., Javaid, N., Wahid, A., Nadeem, Z., Naz, M., Khan, Z.A. (2018). Stochastic Power Management in Microgrid with Efficient Energy Storage. In: Barolli, L., Xhafa, F., Javaid, N., Spaho, E., Kolici, V. (eds) Advances in Internet, Data & Web Technologies. EIDWT 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-75928-9_18
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DOI: https://doi.org/10.1007/978-3-319-75928-9_18
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