Coordinated AGC Algorithm for Distributed Multi-region Multi-energy Micro-network Group
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摘要: 综合能源多区域协同是电网发展趋势, 而核心问题是采用何种方法对多区域进行协同. 本文基于Q (
$\sigma $ )融入了资格迹及双重Q学习, 提出一种面向多区域多能微网群的多智能体协同控制算法, 即DQ ($\sigma ,\lambda $ ), 避免传统强化学习动作探索值高估的同时, 来获取分布式多区域的协同. 通过对改进的IEEE两区域负荷频率控制模型及三区域多能微网群自动发电控制(Automatic generation control, AGC)模型仿真, 结果表明, 与传统方法相比, 所提算法具有快速收敛性和更优动态性能, 能获得分布式多区域多能微网群的协同.Abstract: Comprehensive energy multi-region coordination is the development trend of the power grid, and the core question is what method to use for multi-region coordination. Based on the integration of the qualification trace and dual Q-learning in Q ($\sigma $ ), this paper proposes a multi-agent collaborative control algorithm for multi-region and multi-energy micro-grid group, named DQ ($\sigma ,\lambda$ ), to avoid high exploration value of traditional reinforcement learning actions. At the same time of evaluation, the distributed multi-region collaboration is obtained. Simulations of the improved IEEE two area load frequency control model and the three area multi-energy microgrid group automatic generation control (AGC) model show that the proposed algorithm has fast convergence and better dynamic performance than traditional methods, and can achieve distributed Synergy of regional multi-energy microgrid groups. -
图 1 多能微网群多区域协同控制架构
Fig. 1 Multi-energy microgrid group multi-region cooperative control architecture
图 4 改进的IEEE标准两区域负荷频率控制模型
Fig. 4 Improved IEEE standard two-area load frequency control model
图 6 阶跃负荷扰动下不同算法的性能指标
Fig. 6 Performance index of different algorithms under step load disturbance
图 7 随机白噪声扰动下不同算法的控制性能
Fig. 7 Control performance of different algorithms under stochastic white noise disturbance
图 8 分布式3区域多能微网群协同AGC模型
Fig. 8 Coordinated AGC model of a distributed three-area multi-energy microgrid group
表 1 模型传递函数的参数
Table 1 Parameters of the model transfer function
机组 参数 数值 小水电机组 二次时延TSH 3 伺机电动机时间常数TP 0.04 伺机增益KS 5 永态转差系数RP 1 复位时间TR 0.3 暂态转差系数RT 1 闸门最大开启率Rmaxopen/(pu/s) 0.16 闸门最大关闭率Rmaxclose/(pu/s) 0.16 机组启动时间TWH 1 生物发电机组 二次时延TSB 10 调速器的时间常数TGB 0.08 蒸汽启动时间TWB 5 机械启动时间TMB 0.3 微型燃气轮机机组 二次时延TSM 5 燃油系统滞后时间常数T1 0.8 燃油系统滞后时间常数T2 0.3 负荷限制时间常数T3 3 温度控制环路增益KT 1 负荷限制Lmax 1.2 燃料电池机组 二次时延TSF 2 调速器的时间常数TF 10.056 逆变器增益KF 9.205 柴油发电储能机组 二次时延TSD 7 调速器的时间常数TGD 2 蒸汽启动时间TWF 1 机械启动时间TMD 3 
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表 2 AGC机组参数
Table 2 AGC unit parameters
区域 类型 机组序号 $\Delta P_{\rm{in}}^{\max }$
(kW/s)$\Delta P_{\rm{in}}^{\min }$
(kW/s)$\Delta P_{\rm{in}}^{\rm{rate }+ }$
(kW/s)$\Delta P_{\rm{in}}^{\rm{rate} - }$
(kW/s)区域1和区域3 小水电 G1 250 − 250 15 − 15 G2 250 − 250 15 − 15 G3 150 − 150 8 − 8 G4 150 − 150 8 − 8 G5 150 − 150 8 − 8 G6 100 − 100 7 − 7 G7 100 − 100 7 − 7 微型燃气轮机 G8 100 − 100 1.2 − 1.2 G9 100 − 100 1.2 − 1.2 G10 150 − 150 1.8 − 1.8 G11 150 − 150 1.8 − 1.8 燃料电池 G12 200 − 200 7 − 7 G13 200 − 200 7 − 7 G14 150 − 150 6 − 6 G15 150 − 150 6 − 6 区域2 小水电 G1 250 − 250 15 − 15 G2 250 − 250 15 − 15 G3 150 − 150 8 − 8 G4 150 − 150 8 − 8 G5 150 − 150 8 − 8 G6 100 − 100 7 − 7 柴油发电机储 G7 250 − 250 2 − 2 G8 250 − 250 2 − 2 G9 120 − 120 1 − 1 G10 120 − 120 1 − 1 生物质能 G11 200 − 200 3 − 3 G12 200 − 200 3 − 3 G13 200 − 200 3 − 3 G14 200 − 200 3 − 3
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