冷热电多能联供(combined cooling heating and power,CCHP)型微网具有节能、环保、经济等特点,有良好的发展前景和应用价值。为应对可再生能源的大规模接入及负荷的不确定性,该文提出基于模型预测控制的CCHP型微网动态优化调度策略。...冷热电多能联供(combined cooling heating and power,CCHP)型微网具有节能、环保、经济等特点,有良好的发展前景和应用价值。为应对可再生能源的大规模接入及负荷的不确定性,该文提出基于模型预测控制的CCHP型微网动态优化调度策略。该优化策略首先对设备效率曲线模型进行分段线性化处理,以日前计划联供设备的出力值为参考值,在日内调度下建立可再生能源及负荷预测模型,基于多步滚动优化求解出各联供设备的平滑出力。仿真结果表明:分段线性效率曲线模型不但能更好模拟系统实际运行工况,且计算时间满足在线调度需求;滚动时长选取4h,不仅满足鲁棒性也能满足快速性;所提模型能够有效应对系统不确定性对系统经济调度的影响,实现其经济及安全运行。展开更多
This paper presents a gain-scheduling model predictive control(MPC) for linear parameter varying(LPV) systems subject to actuator saturation. The proposed gain-scheduling MPC algorithm is then applied to the lateral c...This paper presents a gain-scheduling model predictive control(MPC) for linear parameter varying(LPV) systems subject to actuator saturation. The proposed gain-scheduling MPC algorithm is then applied to the lateral control of unmanned airship.The unmanned airship is modeled by an LPV-type system and transformed into a polytopic uncertain description with actuator saturation. By introducing a parameter-dependent state feedback law, the set invariance condition of the polytopic uncertain system is identified. Based on the invariant set, the gain-scheduling MPC controller is presented by solving a linear matrix inequality(LMI) optimization problem. The proposed gain-scheduling MPC algorithm is demonstrated by simulating on the unmanned airship system.展开更多
文摘冷热电多能联供(combined cooling heating and power,CCHP)型微网具有节能、环保、经济等特点,有良好的发展前景和应用价值。为应对可再生能源的大规模接入及负荷的不确定性,该文提出基于模型预测控制的CCHP型微网动态优化调度策略。该优化策略首先对设备效率曲线模型进行分段线性化处理,以日前计划联供设备的出力值为参考值,在日内调度下建立可再生能源及负荷预测模型,基于多步滚动优化求解出各联供设备的平滑出力。仿真结果表明:分段线性效率曲线模型不但能更好模拟系统实际运行工况,且计算时间满足在线调度需求;滚动时长选取4h,不仅满足鲁棒性也能满足快速性;所提模型能够有效应对系统不确定性对系统经济调度的影响,实现其经济及安全运行。
基金supported by the National Natural Science Fundation of China(6117507411272205)
文摘This paper presents a gain-scheduling model predictive control(MPC) for linear parameter varying(LPV) systems subject to actuator saturation. The proposed gain-scheduling MPC algorithm is then applied to the lateral control of unmanned airship.The unmanned airship is modeled by an LPV-type system and transformed into a polytopic uncertain description with actuator saturation. By introducing a parameter-dependent state feedback law, the set invariance condition of the polytopic uncertain system is identified. Based on the invariant set, the gain-scheduling MPC controller is presented by solving a linear matrix inequality(LMI) optimization problem. The proposed gain-scheduling MPC algorithm is demonstrated by simulating on the unmanned airship system.