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基于深度确定策略梯度算法的主动配电网协调优化
作者:
作者单位:

1.上海电力大学电气工程学院, 上海市 200090;2.国网上海市电力公司, 上海市 200438

作者简介:

龚锦霞(1984—),女,通信作者,博士,讲师,硕士生导师,主要研究方向 :新能源并网 、智能电网调度。E-mail: jxgong2015@163.com
刘艳敏(1983—),女,硕士,高级工程师,主要研究方向:电力系统运行和检修。

通讯作者:

基金项目:

国家自然科学基金青年科学基金资助项目 (51607112)。


Coordinated Optimization of Active Distribution Network Based on Deep Deterministic Policy Gradient Algorithm
Author:
Affiliation:

1.College of Electric Power Engineering, Shanghai University of Electric Power, Shanghai 200090, China;2.State Grid Shanghai Municipal Electric Power Company, Shanghai 200438, China

Fund Project:

This work is supported by National Natural Science Foundation of China (No. 51607112).

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    摘要:

    将新一代人工智能在智能电网和能源互联网中进行应用,实现高比例可再生能源及时有效接入电网,文中基于深度学习中的深度确定策略梯度(DDPG)算法实现主动配电网的优化运行。首先,构造了含多微电网的主动配电网优化模型的DDPG回报函数,使主动配电网的节点电压总偏差和线损最小,最大限度地降低微电网功率调节量的变化以减小对微电网运行的影响,同时维持联络线功率平衡以减小对配电网的影响。然后,分析了主动配电网优化控制的DDPG样本数据处理、回报函数设计、模型训练和学习过程。最后,通过改进IEEE 14节点算例仿真验证了DDPG算法的有效性。

    Abstract:

    Applying the new generation of artificial intelligence in smart grid and Energy Internet, to achieve high proportion renewable energy access to the power grid in a timely and effective manner, the deep deterministic policy gradient (DDPG) algorithm based on deep learning is applied in the optimized operation of active distribution network (ADN). Firstly, DDPG return function of optimization model for ADN with multiple microgrids is constructed, which can minimize the total node voltage deviation and line loss of ADN. The proposed function can also minimize the variation of the power regulation of microgrid to reduce the impact on operation of the microgrid, and maintain the balance of tie-line power blance to reduce the impact on the distribution network. Secondly, DDPG sample data processing, design of return function, model training and learning process of optimization control for ADN are analyzed. Finally, the effectiveness of the algorithm is verified by the improved IEEE 14-bus example simulation.

    表 1 Table 1
    图1 主动配电网的优化系统结构Fig.1 Optimization system structure of active distribution network
    图2 主动配电网的DDPG优化学习过程Fig.2 DDPG optimal learning process of active distribution network
    图3 DDPG算法训练过程中的回报值Fig.3 Return value in training process of DDPG algorithm
    图4 DDPG算法训练过程中的损失函数值Fig.4 Loss function value in training process of DDPG algorithm
    图5 S1至S4的优化目标值Fig.5 Optimization target values of S1 to S4
    图6 S1至S4模式下的平均计算时间Fig.6 Average calculation times of mode S1 to mode S4
    图 14节点系统图Fig. System Structure of 14-bus distribution network
    图 光伏和风电发电量的百分比Fig. Percentage of photovoltaic and wind power generation
    图 4个微网的等效输出负荷Fig. Equivalent output load of four microgrids
    图 配电网的系统总负荷Fig. System total load of distribution network
    图 4种模式的配电网成本Fig. 4 modes of distribution network costs
    图 4种模式的配电网联络线功率Fig. Tie-line power of distribution network in 4 modes
    图 4种模式的节点1电压Fig. Voltage of bus 1 in 4 modes
    图 4种模式的节点5电压Fig. Voltage of bus 5 in 4 modes
    图 4种模式的配电网网损Fig. Power loss of distribution network in 4 modes
    图 4种模式的配电网等效负荷Fig. Equivalent load of distribution network in 4 modes
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引用本文

龚锦霞,刘艳敏.基于深度确定策略梯度算法的主动配电网协调优化[J].电力系统自动化,2020,44(6):113-120. DOI:10.7500/AEPS20190321010.
GONG Jinxia,LIU Yanmin.Coordinated Optimization of Active Distribution Network Based on Deep Deterministic Policy Gradient Algorithm[J].Automation of Electric Power Systems,2020,44(6):113-120. DOI:10.7500/AEPS20190321010.

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  • 收稿日期:2019-03-21
  • 最后修改日期:2019-11-13
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  • 在线发布日期: 2020-03-21
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