The residential energy scheduling of solar energy is an important research area of smart grid. On the demand side, factors such as household loads, storage batteries, the outside public utility grid and renewable ener...The residential energy scheduling of solar energy is an important research area of smart grid. On the demand side, factors such as household loads, storage batteries, the outside public utility grid and renewable energy resources, are combined together as a nonlinear, time-varying, indefinite and complex system, which is difficult to manage or optimize. Many nations have already applied the residential real-time pricing to balance the burden on their grid. In order to enhance electricity efficiency of the residential micro grid, this paper presents an action dependent heuristic dynamic programming(ADHDP) method to solve the residential energy scheduling problem. The highlights of this paper are listed below. First,the weather-type classification is adopted to establish three types of programming models based on the features of the solar energy. In addition, the priorities of different energy resources are set to reduce the loss of electrical energy transmissions.Second, three ADHDP-based neural networks, which can update themselves during applications, are designed to manage the flows of electricity. Third, simulation results show that the proposed scheduling method has effectively reduced the total electricity cost and improved load balancing process. The comparison with the particle swarm optimization algorithm further proves that the present method has a promising effect on energy management to save cost.展开更多
电力系统发生故障时需要快速平抑振荡以保障系统稳定性.超导磁储能装置(Superconducting Magnetic Energy Storage Device,SMES)能够迅速跟踪功率波动,进行4象限功率补偿,有效提高系统暂态稳定性.针对电压源型变流器的超导磁储能装置,...电力系统发生故障时需要快速平抑振荡以保障系统稳定性.超导磁储能装置(Superconducting Magnetic Energy Storage Device,SMES)能够迅速跟踪功率波动,进行4象限功率补偿,有效提高系统暂态稳定性.针对电压源型变流器的超导磁储能装置,提出了一种基于执行依赖启发式动态规划(Action Dependent Heuristic Dynamic Programming,ADHDP)智能算法的超导储能外环控制方法.该方法通过强化学习,自适应调整结构参数,实现系统的最优控制.在MATLAB/Simulink环境下建立了单机无穷大系统和2机系统仿真模型,分别对传统的PI控制、固定学习率ADHDP控制器和自适应学习率ADHDP控制器的控制效果进行对比分析.仿真结果表明,自适应学习率ADHDP具有较明显的优势;另外,在系统连续故障情况下,ADHDP表现出了较好的学习功能,能够获得比上次更好的控制效果.展开更多
基金supported in part by the National Natural Science Foundation of China(61533017,U1501251,61374105,61722312)
文摘The residential energy scheduling of solar energy is an important research area of smart grid. On the demand side, factors such as household loads, storage batteries, the outside public utility grid and renewable energy resources, are combined together as a nonlinear, time-varying, indefinite and complex system, which is difficult to manage or optimize. Many nations have already applied the residential real-time pricing to balance the burden on their grid. In order to enhance electricity efficiency of the residential micro grid, this paper presents an action dependent heuristic dynamic programming(ADHDP) method to solve the residential energy scheduling problem. The highlights of this paper are listed below. First,the weather-type classification is adopted to establish three types of programming models based on the features of the solar energy. In addition, the priorities of different energy resources are set to reduce the loss of electrical energy transmissions.Second, three ADHDP-based neural networks, which can update themselves during applications, are designed to manage the flows of electricity. Third, simulation results show that the proposed scheduling method has effectively reduced the total electricity cost and improved load balancing process. The comparison with the particle swarm optimization algorithm further proves that the present method has a promising effect on energy management to save cost.
文摘电力系统发生故障时需要快速平抑振荡以保障系统稳定性.超导磁储能装置(Superconducting Magnetic Energy Storage Device,SMES)能够迅速跟踪功率波动,进行4象限功率补偿,有效提高系统暂态稳定性.针对电压源型变流器的超导磁储能装置,提出了一种基于执行依赖启发式动态规划(Action Dependent Heuristic Dynamic Programming,ADHDP)智能算法的超导储能外环控制方法.该方法通过强化学习,自适应调整结构参数,实现系统的最优控制.在MATLAB/Simulink环境下建立了单机无穷大系统和2机系统仿真模型,分别对传统的PI控制、固定学习率ADHDP控制器和自适应学习率ADHDP控制器的控制效果进行对比分析.仿真结果表明,自适应学习率ADHDP具有较明显的优势;另外,在系统连续故障情况下,ADHDP表现出了较好的学习功能,能够获得比上次更好的控制效果.
