Optimal dispatch of microgrid with demand response and an improved bat algorithm

被引：0

作者：

Shen Y. ^{[1
]}

Yang B. ^{[1
]}

机构：

[1] College of Electrical Engineering & New Energy, China Three Gorges University, Yichang, 443002, Hubei

来源：

Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2020年 / 48卷 / 02期

关键词：

Demand response; Economic dispatch; Improved bat algorithm; Microgrid; Renewable energy;

D O I：

10.13245/j.hust.200221

中图分类号：

学科分类号：

摘要：

An economic optimal scheduling model for grid-connected microgrid based on demand response was proposed.The model not only takes into account the minimum cost of the generation side, but also considers that the user's participation in the demand response is the most profitable.Aiming at the characteristics of nonlinear multi-constraints, a modified bat algorithm based on horizontal crossover strategy, two-way learning mechanism and conversion adjustment mechanism was proposed.Finally, the effectiveness and feasibility of the proposed model and algorithm were verified by a simulation example.The results show that the proposed model can effectively reduce the user's demand for electrical energy and reduce the cost of the power generation side.The superiority of the improved bat algorithm is proved by comparison with other algorithms. © 2020, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.

引用

页码：120 / 125

页数：5

共 16 条

[1]

Yao D.L., Choi S.S., Tseng K.J., Et al., A statistical approach to the design of a dispatchable wind power- battery energy storage system, IEEE Transactions on Energy Conversion, 24, 4, pp. 916-925, (2009)

[2]

Liang H., Liu Y., Shen Y., Et al., A hybrid bat algorithm for economic dispatch with random wind power, IEEE Transactions on Power Systems, 33, 5, pp. 5052-5061, (2018)

[3]

Marzbang M., Yousefnejad E., Sumper A., Et al., Real time experimental implementation of optimum energy management system in standalone Microgrid by using multi-layer ant colony optimization, International Journal of Electrical Power & Energy Systems, 75, 2, pp. 265-274, (2016)

[4]

Soumitra M., Aniruddha B., Dey N., Et al., Multi-objective economic emission load dispatch solution using gravitational search algorithm and considering wind power penetration, International Journal of Electrical Power & Energy Systems, 44, 1, pp. 282-292, (2013)

[5]

Yao F., Dong Z.Y., Meng K., Et al., Quantum-inspired particle swarm optimization for power system operations considering wind power uncertainty and carbon tax in Australia, IEEE Transactions on Industrial Informatics, 8, 4, pp. 880-888, (2012)

[6]

Zakariazadeh A., Jadid S., Siano P., Smart microgrid energy and reserve scheduling with demand response using stochastic optimization, International Journal of Electrical Power & Energy Systems, 63, 12, pp. 523-533, (2014)

[7]

Mostafa S., Masoud E., Stochastic multi-objective economic-environmental energy and reserve scheduling of microgrids considering battery energy storage system, International Journal of Electrical Power & Energy Systems, 106, 3, pp. 1-16, (2019)

[8]

Talari S., Yazdaninejad M., Haghifam M.R., Stochastic-based scheduling of the microgrid operation including wind turbines, photovoltaic cells, energy storages and responsive loads, Generation Transmission & Distribution Iet, 9, 12, pp. 1498-1509, (2015)

[9]

Abdi H., Beigvand S.D., Scala M.L., A review of optimal power flow studies applied to smart grids and microgrids, Renewable and Sustainable Energy Reviews, 71, 5, pp. 742-766, (2017)

[10]

Zhang J., Wu Y., Guo T., Et al., A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints, Applied Energy, 183, 23, pp. 791-804, (2016)

← 1 2 →