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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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 (EV) 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 (MPC) 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 automatic voltage regulation loop increases the active power disturbance, which puts forward higher requirements for the load frequency 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 0.01 Hz through coordinated control and parameter self-adaptation, the control excellence rate can still reach 100%, and the recovery time is still less than 1 s, 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 0.01 Hz, 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

Received: 13 November 2022

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TM727

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	Fan Peixiao
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Fan Peixiao,Yang Jun,Wen Yuxin等. A Multi Microgrid Intelligent Generation Control Strategy with Electric Vehicles Based on Evolutionary Model Predictive Control[J]. Transactions of China Electrotechnical Society, 2024, 39(3): 699-713.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.222138 OR https://dgjsxb.ces-transaction.com/EN/Y2024/V39/I3/699

[1] 何晨可, 朱继忠, 刘云, 等. 计及碳减排的电动汽车充换储一体站与主动配电网协调规划[J]. 电工技术学报, 2022, 37(1): 92-111.
He Chenke, Zhu Jizhong, Liu Yun, et al.Coordinated planning of electric vehicle charging-swapping-storage integrated station and active distribution network considering carbon reduction[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 92-111.
[2] Pascual J, Arcos-Aviles D, Ursúa A, et al.Energy management for an electro-thermal renewable-based residential microgrid with energy balance forecasting and demand side management[J]. Applied Energy, 2021, 295: 117062.
[3] 周玮, 蓝嘉豪, 麦瑞坤, 等. 无线充电电动汽车V2G模式下光储直流微电网能量管理策略[J]. 电工技术学报, 2022, 37(1): 82-91.
Zhou Wei, Lan Jiahao, Mai Ruikun, et al.Research on power management strategy of DC microgrid with photovoltaic, energy storage and EV-wireless power transfer in V2G mode[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 82-91.
[4] 随权, 魏繁荣, 林湘宁, 等. 一种基于可控负荷效率控制的孤岛微网新型调度策略[J]. 中国电机工程学报, 2019, 39(24): 7168-7178, 7490.
Sui Quan, Wei Fanrong, Lin Xiangning, et al.A novel dispatching strategy for isolated microgrid based on controllable load efficiency control[J]. Proceedings of the CSEE, 2019, 39(24): 7168-7178, 7490.
[5] 李长云, 徐敏灵, 蔡淑媛. 计及电动汽车违约不确定性的微电网两段式优化调度策略[J]. 电工技术学报, 2023, 38(7): 1838-1851.
Li Changyun, Xu Minling, Cai Shuyuan.Two-stage optimal Scheduling strategy for micro-grid considering EV default uncertainty[J]. Journal of Electrotechnical Technology, 2023, 38(7): 1838-1851.
[6] 刘迎澍, 陈曦, 李斌, 等. 多微网系统关键技术综述[J]. 电网技术, 2020, 44(10): 3804-3820.
Liu Yingshu, Chen Xi, Li Bin, et al.State of art of the key technologies of multiple microgrids system[J]. Power System Technology, 2020, 44(10): 3804-3820.
[7] Bevrani H, Feizi M R, Ataee S.Robust frequency control in an islanded microgrid: H∞ and μ-synthesis approaches[J]. IEEE Transactions on Smart Grid, 2016, 7(2): 706-717.
[8] 张释中, 裴玮, 杨艳红, 等. 基于柔性直流互联的多微网集成聚合运行优化及分析[J]. 电工技术学报, 2019, 34(5): 1025-1037.
Zhang Shizhong, Pei Wei, Yang Yanhong, et al.Optimization and analysis of multi-microgrids integration and aggregation operation based on flexible DC interconnection[J]. Transactions of China Electrotechnical Society, 2019, 34(5): 1025-1037.
[9] Khokhar B, Dahiya S, Singh Parmar K P. Load frequency control of a microgrid employing a 2D Sine Logistic map based chaotic sine cosine algorithm[J]. Applied Soft Computing, 2021, 109: 107564.
[10] Esmaeili Karkevandi A, Daryani M J, Usta O.ANFIS-based intelligent PI controller for secondary frequency and voltage control of microgrid[C]//2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Sarajevo, Bosnia and Herzegovina, 2018: 1-6.
[11] 崔明勇, 曹朋, 朱大伟, 等. 基于FOPI+FOPD控制器的单区域电力系统频率控制及电压调节[J]. 燕山大学学报, 2022, 46(2): 157-165, 176.
Cui Mingyong, Cao Peng, Zhu Dawei, et al.Frequency control and voltage regulation of single area power system based on FOPI+FOPD controller[J]. Journal of Yanshan University, 2022, 46(2): 157-165, 176.
[12] Jan M U, Ai Xin, Abdelbaky M A, et al.Adaptive and fuzzy PI controllers design for frequency regulation of isolated microgrid integrated with electric vehicles[J]. IEEE Access, 2020, 8: 87621-87632.
[13] Mohammadzadeh A, Kayacan E.A novel fractional-order type-2 fuzzy control method for online frequency regulation in ac microgrid[J]. Engineering Applications of Artificial Intelligence, 2020, 90: 103483.
[14] Mahdi M M, Ahmad A Z.Load frequency control in microgrid using fuzzy logic table control[C]//2017 11th IEEE International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG), Cadiz, Spain, 2017: 318-323.
[15] 项雷军, 陈昊, 郭新华, 等. 基于模糊分数阶PID的含电动汽车的多能源微电网二次频率控制[J]. 电力自动化设备, 2021, 41(11): 74-80.
Xiang Leijun, Chen Hao, Guo Xinhua, et al.Secondary frequency control of multi-energy microgrid with electric vehicles based on fuzzy fractional-order PID[J]. Electric Power Automation Equipment, 2021, 41(11): 74-80.
[16] Yang Jun, Zeng Zhili, Tang Yufei, et al.Load frequency control in isolated micro-grids with electrical vehicles based on multivariable generalized predictive theory[J]. Energies, 2015, 8(3): 2145-2164.
[17] Fan Peixiao, Ke Song, Kamel S, et al.A frequency and voltage coordinated control strategy of island microgrid including electric vehicles[J]. Electronics, 2021, 11(1): 17.
[18] Mounce R, Nelson J D.On the potential for one-way electric vehicle car-sharing in future mobility systems[J]. Transportation Research Part A: Policy and Practice, 2019, 120: 17-30.
[19] Chen Lei, Lu Xiaomin, Min Yong, et al.Optimization of governor parameters to prevent frequency oscillations in power systems[J]. IEEE Transactions on Power Systems, 2018, 33(4): 4466-4474.
[20] 王敏, 李想, 张程飞. 基于多重逆变器复杂控制策略的微电网运行控制[J]. 现代电力, 2016, 33(5): 24-29.
Wang Min, Li Xiang, Zhang Chengfei.The operation and control of microgrid based on the complex control strategy of multiple inverters[J]. Modern Electric Power, 2016, 33(5): 24-29.
[21] Hu Jianchen, Ding Baocang.Output feedback robust MPC for linear systems with norm-bounded model uncertainty and disturbance[J]. Automatica, 2019, 108: 108489.
[22] Rao Yingqing, Yang Jun, Xiao Jinxing, et al.A frequency control strategy for multimicrogrids with V2G based on the improved robust model predictive control[J]. Energy, 2021, 222: 119963.
[23] Khokhar B, Singh Parmar K P. A novel adaptive intelligent MPC scheme for frequency stabilization of a microgrid considering SoC control of EVs[J]. Applied Energy, 2022, 309: 118423.
[24] 刘全, 翟建伟, 章宗长, 等. 深度强化学习综述[J]. 计算机学报, 2018, 41(1): 1-27.
Liu Quan, Zhai Jianwei, Zhang Zongchang, et al.A survey on deep reinforcement learning[J]. Chinese Journal of Computers, 2018, 41(1): 1-27.
[25] 余涛, 梁海华, 周斌. 基于R(λ)学习的孤岛微电网智能发电控制[J]. 电力系统保护与控制, 2012, 40(13): 7-13.
Yu Tao, Liang Haihua, Zhou Bin.Smart power generation control for microgrids islanded operation based on R(λ) learning[J]. Power System Protection and Control, 2012, 40(13): 7-13.
[26] 范培潇, 杨军, 肖金星, 等. 基于深度Q学习的含电动汽车孤岛微电网负荷频率控制策略[J]. 电力建设, 2022, 43(4): 91-99.
Fan Peixiao, Yang Jun, Xiao Jinxing, et al.Load frequency control strategy based on deep Q learning for island microgrid with electric vehicles[J]. Electric Power Construction, 2022, 43(4): 91-99.
[27] 赵星宇, 丁世飞. 深度强化学习研究综述[J]. 计算机科学, 2018, 45(7): 1-6.
Zhao Xingyu, Ding Shifei.Research on deep reinforcement learning[J]. Computer Science, 2018, 45(7): 1-6.
[28] 范培潇, 柯松, 杨军, 等. 基于改进多智能体深度确定性策略梯度的多微网负荷频率协同控制策略[J]. 电网技术, 2022, 46(9): 3504-3515.
Fan Peixiao, Ke Song, Yang Jun, et al.Load frequency coordinated control strategy of multi-microgrid based on improved MA-DDPG[J]. Power System Technology, 2022, 46(9): 3504-3515.
[29] Fan Peixiao, Ke Song, Yang Jun, et al.A load frequency coordinated control strategy for multimicrogrids with V2G based on improved MA-DDPG[J]. International Journal of Electrical Power & Energy Systems, 2023, 146: 108765.
[30] 李捷, 余涛, 潘振宁. 基于强化学习的增量配电网实时随机调度方法[J]. 电网技术, 2020, 44(9): 3321-3332.
Li Jie, Yu Tao, Pan Zhenning.Real-time stochastic dispatch method for incremental distribution network based on reinforcement learning[J]. Power System Technology, 2020, 44(9): 3321-3332.