绿色能源互补智能电厂云控制系统研究

针对现代电力系统中设施庞杂、多源异构海量数据难以有效处理、“信息孤岛”长期存在以及整体优化调度管理能力不足等问题, 基于云控制系统理论, 以智能电厂为研究对象, 本文提出了智能电厂云控制系统(Intelligent power plant cloud control system, IPPCCS)解决方案. 基于智能电厂云控制系统, 针对绿色能源发电波动性强、抗扰能力差的问题, 利用机器学习算法对采集到的风电、光伏输出功率进行短时预测, 获知未来风、光机组功率输出情况. 在云端使用经济模型预测控制(Economic model predictive control, EMPC)算法, 通过实时滚动优化得到水轮机组的功率预测调度策略, 保证绿色能源互补发电的鲁棒性, 充分消纳风、光两种能源, 减少水轮机组启停和穿越振动区次数, 在为用户清洁、稳定供电的同时降低了机组寿命损耗. 最后, 一个区域云数据中心的供电算例表明了本文方法的有效性.

Green Energy Complementary Based on Intelligent Power Plant Cloud Control System

Based on the theory of cloud control system, an intelligent power plant cloud control system (IPPCCS) is designed to overcome problems of complex objects, multi-sources heterogenous data, “information island” and the poor ability of overall optimization scheduling in modern electric power enterprise. To solve problems of strong fluctuation and poor disturbance resistance of green power generation, a machine learning method is used to obtain the short-term prediction value of wind and solar power based on their history data. Then in the cloud, the economic model predictive control (EMPC) algorithm is applied to provide the power predictive scheduling strategy of water turbines by real-time rolling optimization, to ensure the robustness of green energy complementary power generation, consume wind and solar power fully and reduce the frequency of starting/stopping and crossing the vibration zones of the turbines, which both provides clear and stable energy support for the users and protects the devices. The simulations show the effectiveness of the proposed method in an example of regional cloud data center.