基于可进化模型预测控制的含电动汽车多微电网智能发电控制策略

doi:10.19595/j.cnki.1000-6753.tces.222138

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摘要

多微电网中的环境状态、控制资源及偶然事件均具有强不确定性,而电动汽车在参与电网削峰填谷的同时也给发电控制带来了挑战。为此,本文提出了一种基于可进化模型预测控制（Learning-based Model Predictive Control,LBMPC）的含电动汽车多微电网发电控制策略。首先,基于控制器交互的多微电网互联结构,考虑了发电机端电压调节和负荷频率控制（Load frequency control, LFC）之间的耦合关系,建立出含电动汽车多微电网的发电控制模型。其次,设计了一种基于多智能体的控制器参数自适应算法：频率控制器以实时频偏和EV站输出功率边界为状态集,以MPC控制器的可调参数矩阵Q_x作为动作集,以频率偏差为奖励函数指标;而电压控制器同理,从而实现MPC与PI控制器权重参数的自适应调整。最后,仿真结果表明,自动调压（Automatic voltage regulation,AVR）回路增加了有功功率干扰,对LFC控制器提出了更高的要求;与传统控制和MPC算法相比,应用于控制器互联结构的可进化模型预测控制器能够在子微电网之间进行信息交换,并且根据环境状态实时更新控制器参数,显著提高了多微电网频率控制过程的鲁棒性和快速性。同时,与纯深度确定性策略梯度（Deep Deterministic Policy Gradient,DDPG）控制器相比,本文提出的双层控制结构在机器学习智能体出现故障无法正常输出动作时,能更好地保证系统的安全稳定运行。

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关键词 ：多微电网负荷频率控制, 电动汽车, 发电机端电压, 多智能体算法, 模型预测控制

Abstract：

Under the background of the national energy strategy of "carbon peaking" and "carbon neutrality", conventional generators driven by fossil energy are gradually replaced by renewable energy units such as wind power and photovoltaics. The microgrid with the characteristics of development and extension can fully promote the large-scale access of such distributed power sources with strong randomness, thus achieving rapid development and construction. At the same time, the development of electric vehicles is a trend to ensure low-carbon energy. China also regards the development of electric vehicles as a strategic emerging industry. The development of microgrids has also prompted electric vehicles to be widely used in power grid shaving peaks and valleys, and curb power fluctuations. However, when large-scale electric vehicles are connected to the microgrid at the same time, it may also lead to the degradation of the power quality of the islanded microgrid, and even the instability of the entire microgrid. To this end, a multi-microgrid power generation control strategy with electric vehicles based on evolvable model predictive control is proposed in this paper.
Firstly, based on the multi-microgrid interconnection structure of controller interaction, considering the coupling relationship between generator terminal voltage regulation and system frequency control, a power generation control model with multiple microgrids with electric vehicles is established. Secondly, an adaptive algorithm of controller parameters based on MA-DDPG is designed: the frequency controller takes the real-time frequency offset and EV station output power boundary as the state set, and the adjustable parameter matrix Q_x of the MPC controller as the action set, and the frequency deviation is used as the reward function index, and the voltage controller takes the real-time voltage as the state set, the proportional-integral coefficient of the PI controller as the action set, and the voltage offset as the reward function index; so as to realize the adaptive adjustment of the weight parameters of the MPC and the PI controller. Meanwhile, under the architecture of "centralized training and distributed execution", the intelligent agent group can realize the cooperative control between the sub-microgrids according to the real-time operating status information.
The simulation results show that, the AVR loop increases the active power disturbance, which puts forward higher requirements for the LFC controller. Under the load disturbance and wind power disturbance, the microgrid frequency control effect under the learning-based MPC controller is significantly better than that of the traditional controller. When various extreme faults occur in the system, the proposed controller can still control the frequency fluctuation of the microgrid within 0.01Hz through coordinated control and parameter self-adaptation, the control excellence rate can still reach 100%, and the recovery time is still less than 1s, the robustness of the multi-microgrid performance is significantly enhanced, and the performance is better than the traditional MPC controller in all aspects. In addition, when the machine learning controller fails, the proposed two-layer controller structure can still ensure that the frequency fluctuation of the microgrid is controlled within 0.01Hz, and the control excellence rate can reach 100%, which is significantly better than the DDPG controller.
The following conclusions can be drawn from the simulation analysis: (1) Compared with PID and fuzzy control, the evolvable MPC controller can transform the frequency control process into solving an optimization problem, and thus well adapt to the stochastic scene in the multi-microgrid system. (2) Compared with the traditional MPC, the DDPG agent can adjust the MPC and PI control parameters according to the real-time operating environment state, so as to better adapt to the complex working conditions where the system parameters and structure change with time. (3) Compared with the DDPG controller, the proposed double-layer protection structure has stronger security and stability. When the machine learning agent fails and cannot output actions normally, the MPC controller can use the preset parameters to complete the frequency control process until the machine learning controller returns to normal.

Key words： Multi-microgrid load frequency control electric vehicle generator terminal voltage MA-DDPG algorithm model predictive control

收稿日期: 2022-11-13

PACS:

TM727

基金资助:

国家自然科学基金资助项目（51977154）

通讯作者: 杨军男,1977年生,教授,博士生导师,研究方向为电动汽车、电力系统运行安全与稳定等。E-mail：JYang@whu.edu.cn

作者简介: 范培潇男,1999年生,硕士研究生,研究方向为微电网智能控制。E-mail：whufpx0408@163.com

引用本文:

范培潇, 杨军, 温裕鑫, 柯松, 谢黎龙. 基于可进化模型预测控制的含电动汽车多微电网智能发电控制策略[J]. 电工技术学报, 0, (): 126-126. Fan Peixiao, Yang Jun, Wen Yuxin, Ke Song, Xie Lilong. A Multi Microgrid Intelligent Generation Control Strategy with Electric Vehicles Based on Evolutionary Model Predictive Control. Transactions of China Electrotechnical Society, 0, (): 126-126.

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