|
|
|
| Power Demanding Control for Energy-intensive Enterprises based on Load Transfer Dispatch |
| Xing Wen1, 2, Duan Bin1, 2, 3 |
1. College of Information Engineering, Xiangtan University, Xiangtan, Hu’nan 411105;
2. Cooperative Innovation Center of Wind Power Equipment and Energy Conversion, Xiangtan, Hu’nan 411105;
3. Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education, Xiangtan, Hu’nan 411105 |
|
|
|
|
Abstract According to the problem of energy-intensive enterprises for using electrical response under demand control and then considering the cost of basic tariff in the production under the premise of the existing two-part tariff , therefore, a load transfer dispatch in energy-intensive enterprises approach is proposed for power demand control. We adopted the load forecasting by Elman neural network on this basis of minimizing the cost of basic tariff for motivation and established model of day-ahead load transfer dispatch in energy-intensive enterprises production. It could increase the range of maximum demand controlling for energy-intensive enterprises demand control system. The example shows that the daily basic tariff ratio is about 2.7% when implementing model of load transfer dispatch compared with no implementing demanding control, the daily basic tariff ratio is control about 1.04% compared against when only owned smart device real-time demanding.
|
|
Received: 22 September 2016
Published: 22 September 2016
|
|
|
|
| Cite this article: |
|
Xing Wen,Duan Bin. Power Demanding Control for Energy-intensive Enterprises based on Load Transfer Dispatch[J]. Electrical Engineering, 2016, 17(9): 70-76.
|
|
|
|
| URL: |
|
http://dqjs.cesmedia.cn/EN/Y2016/V17/I9/70
|
[1] 《企业•行业•产业 转型升级科学发展政策法规汇编》课题研究组. 工业节能“十二五”规划[G]. 浙江工商大学出版社, 2013, 693-703.
[2] Paulus M, Borggrefe F. The potential of demand-side management in energy-intensive industries for elec- tricity markets in Germany[J]. Applied Energy, 2011, 88(2, SI): 432-441.
[3] 谭貌, 段斌, 苏永新, 等. 分时电价下面向经济负荷调度的热轧批量计划两阶段优化[J]. 中南大学学报(自然科学版), 2014, 10(10): 3456-3462.
[4] 陈庆. 两部制电价中约定最大需量的选择[J]. 电力需求侧管理, 2012, 14(4): 53-54.
[5] 肖欣, 周渝慧, 郑凯中, 等. 台湾实施可中断电价进行削峰填谷的需求响应策略及其成本效益分析[J]. 中国电机工程学报, 2014, 34(22): 3615-3622.
[6] Hadera H, Harjunkoski I, Sand G, et al. Optimization of steel production scheduling with complex time- sensitive electricity cost[J]. Computers & Chemical Engineering, 2015, 76(8): 117-136.
[7] Godoy-Alcantar J M. Cruz-Maya J A. optimal sche- duling and self-generation for load management in the Mexican power sector[J]. Electric Power Systems Research, 2011, 81(7): 1357-1362.
[8] Wang Z, Gao F, Zhai Q, et al. An integrated optimization model for generation and batch pro- duction load scheduling in energy intensive enterprise[C]// Power and Energy Society General Meeting, 2012 IEEE.IEEE, 2012(10): 1-8.
[9] 刘荣. 基于Elman神经网络的短期负荷预测[D]. 杭州: 浙江大学, 2013.
[10] 刘晓琳, 王兆杰, 高峰, 等. 分时电价下的高耗能企业发用电响应[J]. 电力系统自动化, 2014, 8(8): 41-49.
[11] 马丁, 等. 电力负荷控制(DSM)节能装置在大型钢厂的研究与实践[J]. 金属世界, 2006, 6(3): 17-22, 25.
[12] 陈翔, 熊嘉, 陈朋. 智能最大需量控制系统在某钢铁厂的应用[J]. 科技与企业, 2013, 14(14): 336-337, 339.
[13] 王思彤, 周晖, 袁瑞铭, 等. 智能电表的概念及应用[J]. 电网技术, 2010, 4(4): 17-23.
[14] 曾利军, 陈敏, 罗细飞. 改进蚁群化学聚类算法在短期负荷预测中的应用[J]. 电力系统保护与控制, 2012, 40(4): 59-62, 70.
[15] 毛力, 王运涛, 刘兴阳, 等. 基于改进极限学习机的短期电力负荷预测方法[J]. 电力系统保护与控制, 2012, 40(20): 140-144.
[16] 刘荣, 方鸽飞. 改进Elman神经网络的综合气象短期负荷预测[J]. 电力系统保护与控制, 2012, 40(22): 113-117.
[17] 黄毅成, 杨洪耕. 改进遗传K均值算法在负荷特性分类的应用[J]. 电力系统及其自动化学报, 2014, 26(7): 70-75. |
|
|
|
2016, Vol. 17 
