Intelligent load frequency controller using GrADP for island smart grid with electric vehicles and renewable resources
Intelligent load frequency controller using GrADP for island smart grid with electric vehicles and renewable resources
复制标题
使用 GrADP 的智能负载频率控制器用于具有电动汽车和可再生资源的岛屿智能电网
DOI：
10.1016/j.neucom.2015.04.092
复制
发表时间：
2015-12
期刊：
影响因子：
6
通讯作者：
Haibo He
中科院分区：
文献类型：
--
作者：
Yufei Tang;Jun Yang;Jun Yan;Haibo He
在智能电网中增加光伏、风电等可再生能源的间歇性发电，当负荷频率控制能力不足以补偿发电量与负荷需求的不平衡时，会引起系统频率波动。更糟糕的是，当智能电网处于孤岛运行模式时，系统惯性将减小，这将降低系统阻尼并导致系统不稳定。同时，电动汽车（EV）将在不久的将来被广泛使用，其中EV站可以被视为分散的电池能量存储。因此，车辆到电网（V2 G）技术可以用来补偿不足的LFC容量，从而提高岛屿智能电网的频率稳定性。提出了一种基于在线强化学习的目标表示自适应动态规划方法（GrADP），用于孤岛智能电网中机组的自适应控制。在控制器设计中，自适应补充控制信号提供给比例-积分-微分（PID）控制器的GrADP以实时的方式。在一个包含微型燃气轮机（MT）、电动汽车（EV）、光伏阵列和风力发电的基准智能电网上，对GrADP控制器、原始PID控制器和基于粒子群优化（PSO）的模糊逻辑控制器进行了对比仿真研究。仿真结果表明，基于GrADP的协调控制器具有较好的性能和较好的学习能力.此外，还考虑了信号传输延迟对控制性能的影响，并给出了解决该问题的建议。
Increasing deployment of intermittent power generation from renewable resources in the smart grid, such as photovoltaic (PV) or wind farm, will cause large system frequency fluctuation when the load-frequency control (LFC) capacity is not enough to compensate the unbalance of generation and load demand. Even worse, the system inertia will decrease when the smart grid is in island operating mode, which would degrade system damping and cause system instability. Meanwhile, electric vehicles (EVs) will be widely used by customers in the near future, where the EV station could be treated as dispersed battery energy storage. Therefore, the vehicle-to-grid (V2G) technology can be employed to compensate for inadequate LFC capacity, thus improving the island smart grid frequency stability. In this paper, an on-line reinforcement learning (RL) based method, called goal representation adaptive dynamic programming (GrADP), is employed to adaptive control of units in an island smart grid. In the controller design, adaptive supplementary control signals are provided to proportional-integral-derivative (PID) controller by GrADP in a real-time manner. Comparative simulation studies on a benchmark smart grid with micro-turbine (MT), EVs, PV array and wind power are carried out among the GrADP controller, the original PID controller and the particle swarm optimization (PSO) based fuzzy logic controller. Simulation results demonstrate competitive performance and satisfied learning ability of the GrADP based coordinate controller. Moreover, the impact of signal transmission delay on the control performance is also considered, and suggestions to address this issue are given in the paper.
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DOI：
10.1109/icicip.2012.6391478
发表时间：
2012-07
期刊：
2012 Third International Conference on Intelligent Control and Information Processing
影响因子：
--
作者：
Xiaonan Fang;Haibo He;Zhen Ni;Yufei Tang
通讯作者：
Xiaonan Fang;Haibo He;Zhen Ni;Yufei Tang
DOI：
10.1109/ciasg.2014.7011567
发表时间：
2014-12
期刊：
2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG)
影响因子：
--
作者：
Yufei Tang;Xiangnan Zhong;Zhen Ni;Jun Yan;Haibo He
通讯作者：
Yufei Tang;Xiangnan Zhong;Zhen Ni;Jun Yan;Haibo He
DOI：
--
发表时间：
2011-08
期刊：
--
影响因子：
--
作者：
Haibo He
通讯作者：
Haibo He
DOI：
10.1109/ciasg.2013.6611499
发表时间：
2013-04
期刊：
2013 IEEE Computational Intelligence Applications in Smart Grid (CIASG)
影响因子：
--
作者：
Yufei Tang;Haibo He;J. Wen
通讯作者：
Yufei Tang;Haibo He;J. Wen
影响因子：
6.6
作者：
Xianchao Sui;Yufei Tang;Haibo He;J. Wen
通讯作者：
Xianchao Sui;Yufei Tang;Haibo He;J. Wen