Receding-horizon optimization for microgrid energy management

被引：0

作者：

He, Shun ^{[1
]}

Zheng, Yi ^{[1
]}

Cai, Xu ^{[1
]}

Wu, Xiaodong ^{[2
]}

Shi, Shanshan ^{[3
]}

机构：

[1] Wind Power Research Center(Shanghai Jiao Tong University), Minhang District, Shanghai

[2] SEARI, Shanghai Electrical Apparatus Research Institute(Group) Co., Ltd., Putuo District, Shanghai

[3] Electric Power Research Institute, State Grid Shanghai Municipal Electric Power Company, Yangpu District, Shanghai

来源：

Dianwang Jishu/Power System Technology | 2014年 / 38卷 / 09期

关键词：

DG; Energy management system; Microgrid; PSO; Receding horizon optimization; Shiftable load; Storage;

D O I：

10.13335/j.1000-3673.pst.2014.09.006

中图分类号：

学科分类号：

摘要：

Taking a certain Sci-tech Park's microgrid in Shanghai as example, an energy source management optimization method, in which three electric power resources such as distributed generation, energy storage system and shiftable load are included, is proposed. In the proposed method firstly the output of renewable energy sources are fully consumed by the load in the park; then the controllable DGs, energy storage system and shiftable load are utilized to perform the second round optimization for the load that has been reduced. Considering the fact that there are a lot of non-linear programmings in the optimization problem, a decomposition iteration algorithm, which independently solves three kinds of controllable resources such as DG, load and energy storage by particle swarm optimization (PSO) to make the solutions of the three kinds of controllable resources closed to globally optimal solution through iterations, is put forward. Besides, in allusion to the difficulty in load prediction due to the randomness of the load in microgrid, a receding horizon optimization method is given to improve the accuracy and real-time of global optimization. The effectiveness of the given method is validated by the results of case calculation.

引用

页码：2349 / 2355

页数：6

共 24 条

[1]

Yu Y., Luan W., Smart grid and its implementations, Proceedings of the CSEE, 29, 34, pp. 1-8, (2009)

[2]

Vojdani A., Smart integration, IEEE Power and Energy Magazine, 6, 6, pp. 71-79, (2008)

[3]

Ledik H.R., How green is the smart grid, The Electricity Journal, 22, 3, pp. 29-41, (2009)

[4]

Huang J., Wang H., Qian Y., Et al., Priority-based traffic scheduling and utility optimization for cognitive radio communication infrastructure-based smart grid, IEEE Transactions on Smart Grid, 4, 1, pp. 78-86, (2013)

[5]

Wang M., Smart grid and smart energy resource grid, Power System Technology, 24, 10, pp. 1-5, (2010)

[6]

Niu M., Huang W., Guo J., Et al., Research on economic operation of grid-connected microgrid, Power System Technology, 34, 11, pp. 38-42, (2010)

[7]

He J., Cheng H., Comprehensive power quality assessment on distribution network planning containing micro-grid, Power System Technology, 36, 8, pp. 209-214, (2012)

[8]

Lasseter R.H., Eto J.H., Schenkman B., Et al., CERTS microgrid laboratory test bed, IEEE Transactions on Power Delivery, 26, 1, pp. 325-332, (2011)

[9]

Zhang Q., Wang X., Fu M., Et al., Smart grid from the perspective of demand response, Automation of Electric Power Systems, 33, 17, pp. 49-55, (2009)

[10]

Colson C.M., Nehrir M.H., Comprehensive real-time microgrid power management and control with distributed agents, IEEE Transactions on Smart Grid, 4, 1, pp. 617-627, (2013)

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