Strategy Based on Neural Network for Microgrid Participating in Real-Time Optimal Scheduling of Upper Grid

doi:10.12204/j.issn.1000-7229.2020.10.002

Abstract

Abstract:

In addition to solving the problems of on-site photovoltaic and wind power and its stable operation, the optimization scheduling strategy for microgrid should also have flexible resources such as distributed power and loads with demand-response capabilities to provide auxiliary services to the grid and capabilities to participate in real-time scheduling of the upper-level grid. This paper proposes a microgrid resource optimization scheduling strategy based on BP neural network. This paper combines the microgrid operating costs and demand-response capacity gains to establish an economic optimal scheduling strategy for the day-to-day stage. The intra-day simulation stage simulates the forecast of power fluctuations and the real-time demand of the upper-level grid, and learns through the neural network to obtain the intra-day scheduling model to prepare for intra-day scheduling. In the daytime phase, through the demand-response signal of the upper-layer power grid, the power of the contact line is input into the training model of neural network, and the real-time power of each distributed power source in the daytime phase is obtained. The strategy proposed in this paper can not only guarantee the economic operation of the microgrid, but also meet the real-time dispatching requirements of the upper-level grid. Finally, the economics and effectiveness of the strategy are verified by the results of day-after optimal scheduling examples.

Key words: microgrid, economic dispatch, BP neural network, coordinated control, flexible resources

CLC Number:

TM73

ZHU Yunjie, QIN Wenping, YU Hao, YAO Hongmin, YIN Qilin, HAN Xiaoqing. Strategy Based on Neural Network for Microgrid Participating in Real-Time Optimal Scheduling of Upper Grid[J]. ELECTRIC POWER CONSTRUCTION, 2020, 41(10): 9-11.

Figures/Tables 20

Fig.1 Flowchart of intra-day simulation phase

Fig.2 Structure of BP neural network

Fig.3 Intra-day scheduling flowchart

Table 1 Time-of-use price

Table 2 Parameters of distributed power

Fig.4 Forecast data of PV, wind and conventional load power in day ahead phase

Fig.5 Load power with demand-response capability

Fig.6 Power of tie line

Fig.7 Power of controllable distribution generations

Fig.8 Power of micro-turbines under the planned power condition of the tie line

Fig.9 Power of lithium battery under the planned power condition of the tie line

Fig.10 Power of fuel cell under the planned power condition of the tie line

Fig.11 Load power with corresponding capacity under the planned power condition of the tie line required

Table 3 Scheduling cost under the planned power condition of the tie line

Fig.12 Power of micro-turbines under the real time power condition of the tie line

Fig.13 Power of lithium battery under the real time power condition of the tie line

Fig.14 Power of fuel cell under the real time power condition of the tie line

Fig.15 Load power with corresponding capacity required under the real time power condition of the tie line

Table 4 Analysis of data errors

Table 5 Scheduling cost under the real-time power condition of the tie line

References 24

[1]	王成山, 李鹏 . 分布式发电、微网与智能配电网的发展与挑战[J]. 电力系统自动化, 2010,34(2):10-14, 23.
	WANG Chengshan, LI Peng . Development and challenges of distributed generation, the micro-grid and smart distribution network[J]. Automation of Electric Power Systems, 2010,34(2):10-14, 23.
[2]	李霞林, 郭力, 王成山 , 等. 直流微电网关键技术研究综述[J]. 中国电机工程学报, 2016,36(1):2-17. doi: 10.13334/j.0258-8013.pcsee.2016.01.001 URL
	LI Xialin, GUO Li, WANG Chengshan , et al. Key technologies of DC microgrids: An overview[J]. Proceedings of the CSEE, 2016,36(1):2-17.
[3]	吉立航 . 新电改下灵活性资源的市场交易模式分析与选择研究[D]. 北京: 华北电力大学, 2017.
	JI Lihang . Reaearch on the analysis and choice of market transaction mode of flexibility resource in the context of the new electric power system reform[D]. Beijing: North China Electric Power University, 2017.
[4]	梅坚, 杨立兵, 李晓刚 , 等. 调峰市场效用分析与低谷调峰市场设计[J]. 电力系统自动化, 2013,37(21):134-138, 175. doi: 10.7500/AEPS20130425013 URL
	MEI Jian, YANG Libing, LI Xiaogang , et al. Utility analysis for peak regulation market and trading market design for peak regulation capacity in valley time[J]. Automation of Electric Power Systems, 2013,37(21):134-138, 175. doi: 10.7500/AEPS20130425013 URL
[5]	王皓, 艾芊, 吴俊宏 , 等. 基于交替方向乘子法的微电网群双层分布式调度方法[J]. 电网技术, 2018,42(6):1718-1725.
	WANG Hao, AI Qian, WU Junhong , et al. Bi-level distributed optimization for microgrid clusters based on alternating direction method of multipliers[J]. Power System Technology, 2018,42(6):1718-1725.
[6]	吕朋蓬, 赵晋泉, 苏大威 , 等. 基于一致性理论的独立微电网分布式动态经济调度[J]. 电力系统自动化, 2019,43(5):22-44.
	LÜ Pengpeng, ZHAO Jinquan, SU Dawei , et al. Consensus-based distributed dynamic economic dispatching for islanded microgrids[J]. Automation of Electric Power Systems, 2019,43(5):22-44.
[7]	刘博, 姜秉梁, 郝宁 , 等. 分时阶梯电价-微电网联合优化调度的不确定二层规划方法[J]. 电力系统保护与控制, 2019,47(7):75-83.
	LIU Bo, JIANG Bingliang, HAO Ning , et al. Coordinated optimization of TOU & tiered pricing and optimal scheduling model combining microgrid under uncertain bilevel programming[J]. Power System Protection and Control, 2019,47(7):75-83.
[8]	侯慧, 薛梦雅, 陈国炎 , 等. 计及电动汽车充放电的微电网多目标分级经济调度[J]. 电力系统自动化, 2019,43(17):55-62.
	HOU Hui, XUE Mengya, CHEN Guoyan , et al. Multi-objective hierarchical economic dispatch for microgrid considering charging and discharging of electric vehicles[J]. Automation of Electric Power Systems, 2019,43(17):55-62.
[9]	杨晓东, 张有兵, 蒋杨昌 , 等. 微电网下考虑分布式电源消纳的电动汽车互动响应控制策略[J]. 电工技术学报, 2018,33(2):390-400.
	YANG Xiaodong, ZHANG Youbing, JIANG Yangchang , et al. Renewable energy accommodation-based strategy for electric vehicle considering dynamic interaction in microgrid[J]. Transactions of China Electrotechnical Society, 2018,33(2):390-400.
[10]	王佳颖, 史俊祎, 文福拴 , 等. 计及需求响应的光热电站热电联供型微网的优化运行[J]. 电力系统自动化, 2019,43(1):176-185.
	WANG Jiaying, SHI Junyi, WEN Fushuan , et al. Optimal operation of CHP microgrid with concentrating solar power plants considering demand response[J]. Automation of Electric Power Systems, 2019,43(1):176-185.
[11]	程杉, 黄天力, 魏荣宗 . 含冰蓄冷空调的冷热电联供型微网多时间尺度优化调度[J]. 电力系统自动化, 2019,43(5):30-40.
	CHENG Shan, HUANG Tianli, WAI Rongzong . Multi-time-scale optimal scheduling of CCHP microgrid with ice-storage air-conditioning[J]. Automation of Electric Power Systems, 2019,43(5):30-40.
[12]	王建学, 王锡凡, 王秀丽 . 电力市场可中断负荷合同模型研究[J]. 中国电机工程学报, 2005,25(9):11-16.
	WANG Jianxue, WANG Xifan, WANG Xiuli . Study on model of interruptible load contract in power market[J]. Proceedings of the CSEE, 2005,25(9):11-16.
[13]	NAN S B, ZHOU M, LI G Y . Optimal residential community demand response scheduling in smart grid[J]. Applied Energy, 2018,210:1280-1289.
[14]	于浩, 秦文萍, 魏斌 , 等. 考虑预测误差的交直流混合微电网经济调度策略[J]. 电网技术, 2019,43(11):3987-3996.
	YU Hao, QIN Wenping, WEI Bin , et al. Economic dispatch of hybrid AC/DC microgrid considering prediction error[J]. Power System Technology, 2019,43(11):3987-3996.
[15]	黄予春, 曹成涛, 顾海 . 基于IKFCM与多模态SSO优化SVR的光伏发电短期预测[J]. 电力系统保护与控制, 2018,46(24):96-103.
	HUANG Yuchun, CAO Chengtao, GU Hai . Short-term photovoltaic power generation forecasting scheme based on IKFCM and multi-mode social spider optimization SVR[J]. Power System Protection and Control, 2018,46(24):96-103.
[16]	肖浩, 裴玮, 孔力 . 基于模型预测控制的微电网多时间尺度协调优化调度[J]. 电力系统自动化, 2016,40(18):7-14.
	XIAO Hao, PEI Wei, KONG Li . Multi-time scale coordinated optimal dispatch of microgrid based on model predictive control[J]. Automation of Electric Power Systems, 2016,40(18):7-14.
[17]	张海涛, 秦文萍, 韩肖清 , 等. 多时间尺度微电网能量管理优化调度方案[J]. 电网技术, 2017,41(5):1533-1540.
	ZHANG Haitao, QIN Wenping, HAN Xiaoqing , et al. Multi-time scale optimization scheduling scheme of microgrid energy management[J]. Power System Technology, 2017,41(5):1533-1540.
[18]	陈磊, 胡伟, 徐飞 , 等. 互联电网联络线随机功率波动统计特性与评价标准分析[J]. 电网技术, 2013,37(11):3137-3143.
	CHEN Lei, HU Wei, XU Fei , et al. Analysis on statistic characteristic and evaluation criteria of tie-line stochastic power fluctuation in interconnected power systems[J]. Power System Technology, 2013,37(11):3137-3143.
[19]	HECHT-NIELSEN R . Theory of the backpropagation neural network [C]//International 1989 Joint Conference on Neural Networks. Washington, DC, USA: IEEE, 1989.
[20]	周念成, 肖舒严, 虞殷树 , 等. 基于质心频率和BP神经网络的配网故障测距[J]. 电工技术学报, 2018,33(17):4154-4166.
	ZHOU Niancheng, XIAO Shuyan, YU Yinshu , et al. Fault location in distribution networks based on centroid frequency and BP neural networks[J]. Transactions of China Electrotechnical Society, 2018,33(17):4154-4166.
[21]	王元章, 李智华, 吴春华 , 等. 基于BP神经网络的光伏组件在线故障诊断[J]. 电网技术, 2013,37(8):2094-2100.
	WANG Yuanzhang, LI Zhihua, WU Chunhua , et al. A survey of online fault diagnosis for PV module based on BP neural network[J]. Power System Technology, 2013,37(8):2094-2100.
[22]	李奎, 李晓倍, 郑淑梅 , 等. 基于BP神经网络的交流接触器剩余电寿命预测[J]. 电工技术学报, 2017,32(15):120-127.
	LI Kui, LI Xiaobei, ZHENG Shumei , et al. Residual electrical life prediction for AC contactor based on BP neural network[J]. Transactions of China Electrotechnical Society, 2017,32(15):120-127.
[23]	马燕峰, 范振亚, 刘伟东 , 等. 考虑碳权交易和风荷预测误差随机性的环境经济调度[J]. 电网技术, 2016,40(2):412-418.
	MA Yanfeng, FAN Zhenya, LIU Weidong , et al. Environmental and economic dispatch considering carbon trading credit and randomicity of wind power and load forecast error[J]. Power System Technology, 2016,40(2):412-418.
[24]	薛禹胜, 郁琛, 赵俊华 , 等. 关于短期及超短期风电功率预测的评述[J]. 电力系统自动化, 2015,39(6):141-151.
	XUE Yusheng, YU Chen, ZHAO Junhua , et al. A review on short-term and ultra-short-term wind power prediction[J]. Automation of Electric Power Systems, 2015,39(6):141-151.