基于改进双延迟深度确定性策略梯度算法的电网有功安全校正控制

doi:10.19595/j.cnki.1000-6753.tces.221073

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摘要

新型电力系统中,由于源荷不确定性的影响,发生线路过载事故的风险增大,传统的有功安全校正方法无法有效兼顾计算速度、效果等。基于此,该文提出一种基于改进双延迟深度确定性策略梯度算法的电网有功安全校正控制方法。首先,在满足系统静态安全约束条件下,以可调元件出力调整量最小且保证系统整体运行安全性最高为目标,建立有功安全校正控制模型。其次,构建有功安全校正的深度强化学习框架,定义了计及目标与约束的奖励函数、反映电力系统运行的观测状态、可改变系统状态的调节动作以及基于改进双延迟深度确定性策略梯度算法的智能体。最后,构造考虑源荷不确定性的历史系统过载场景,借助深度强化学习模型对智能体进行持续交互训练以获得良好的决策效果;之后进行在线应用,计及源荷未来可能的取值,快速得到最优的元件调整方案,消除过载线路。IEEE 39节点系统和IEEE 118节点系统算例结果表明所提方法能够有效消除系统中的线路过载且避免短时间内再次越限,在计算速度、校正效果等方面,与传统方法相比具有明显的优势。

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	顾雪平
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	杨晓东

关键词 ：新型电力系统, 有功安全校正, 深度强化学习, 改进双延迟深度确定性策略, 最优调整方案

Abstract：

With the construction and development of the novel power system, the probability of line overload caused by component faults or source-load fluctuations has been significantly increased. If the system cannot be corrected timely and effectively, the propagation speed and range of cascading faults may be aggravated, and then lead to blackout accident. Therefore, the timely and effective implementation of safety correction measures to eliminate power flow over limit is of great significance to ensure the safe operation of the system.
An active power safety correction control method based on twin delayed deep deterministic policy gradient algorithm (TD3) algorithm is proposed. Firstly, an active power safety correction model is established, and one of the objectives is to minimize the sum of the absolute values of the adjustable elements, and the other is to ensure the maximum safety of the system.
Secondly, a deep reinforcement learning framework for active power safety correction is established, as shown in Fig.A1. State expresses the characteristics of power system. Action is the output of adjustable components. The reward function is composed of the objective function and constraint conditions of the active power safety correction model. The agent selects the TD3 algorithm.
Finally, the active power safety correction control is carried out based on the improved TD3 algorithm. The historical overload scenario is constructed to pre-train the active power safety correction model based on the improved TD3 algorithm. Considering the influence of source-load fluctuation on the correction results during the correction process, the possible fluctuation value of the source-load output is calculated for each operation condition. During online application, the predicted value of source and load in the next 5 minutes plus the prediction error value are used as the output value of new energy and the load value at the current time, which are input into the actor network together with the states of other system components. The improved TD3 algorithm with sufficient pre-learning can make the optimal decision quickly according to the system state.

An operation state of IEEE 39-bus system is taken as an example to verify the effectiveness and feasibility of the proposed method. In this state, line 23 is suddenly disconnected then lead to line 13 overload. The correction result is shown in Fig.A2.

100 groups of source and load prediction error values are selected, and the predicted values are added to evaluate the system security after correction of the proposed method. At the same time, the correction results without considering the change of new energy output and load are used for comparison. The results show that the correction effect based on the proposed method considering the fluctuation of source-load, system uniformity and the line highest load rate is the best, and it can withstand relatively more uncertainties to ensure that the system will not appear overload in a short time.
The same historical overload scenario is used to train and test deep deterministic policy gradient (DDPG), TD3 and improved TD3 deep reinforcement learning algorithms. The results show that the proposed improved TD3 method is better than the other two algorithms in terms of training time, testing time and calculation results.
The results of the proposed method are compared with those of the traditional sensitivity method and optimization method. The results show that the calculation time of sensitivity method is shorter, but the total adjustment amount is more. The optimization method has the least amount of adjustment, but the calculation time is longer, about 10 times that of the sensitive method. In the calculation results of this paper method, the number of adjustment components and the time are small, and the system uniformity after correction is the highest, but the total adjustment amount is slightly greater than that of the optimization method.
In conclusion, the calculation results of the active power safety correction model established by the proposed method are more consistent with the actual operation scenario of the power grid. In addition, compared with the traditional methods, the proposed method has certain advantages and is more suitable for the current novel power system.

Key words： Novel power systems active power security correction deep reinforcement learning improved twin delayed deep deterministic policy gradient optimal adjustment scheme

收稿日期: 2022-06-08

PACS:

TM732

基金资助:

国家电网公司科技项目（SGTYHT/17-JS-199）

通讯作者: 李少岩男,1989年生,副教授,研究方向为电力系统安全防御与恢复控制、人工智能技术及其在电力系统中的应用等。E-mail: shaoyan.li@ncepu.edu.cn

作者简介: 顾雪平男,1964年生,教授,博士生导师,研究方向为电力系统安全稳定评估与控制、电力系统安全防御与恢复控制、人工智能技术及其在电力系统中的应用等。E-mail: xpgu@ncepu.edu.cn

引用本文:

顾雪平, 刘彤, 李少岩, 王铁强, 杨晓东. 基于改进双延迟深度确定性策略梯度算法的电网有功安全校正控制[J]. 电工技术学报, 0, (): 32-32. Gu Xueping, Liu Tong, Li Shaoyan, Wang Tieqiang, Yang Xiaodong. Active Power Correction Control of Power Grid Based on Improved Twin Delayed Deep Deterministic Policy Gradient Algorithm. Transactions of China Electrotechnical Society, 0, (): 32-32.

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