Fast learning optimiser for real-time optimal energy management of a grid-connected microgrid
Fast learning optimiser for real-time optimal energy management of a grid-connected microgrid
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快速学习优化器，用于并网微电网的实时优化能源管理
DOI：
10.1049/iet-gtd.2017.1983
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发表时间：
2018-04
影响因子：
2.5
通讯作者：
Yu Tao
中科院分区：
文献类型：
--
作者：
Tan Zhukui;Zhang Xiaoshun;Xie Baiming;Wang Dezhi;Liu Bin;Yu Tao
提出了一种新的快速学习优化器(Flo)，用于实时优化并网微电网的能量管理。为了降低优化难度，将非凸实时OEM分解为两层优化。非凸顶层优化负责确定联络线功率的方向，以及热电联产机组的热能输出。然后，底层优化是严格凸的，其余的变量是可控的，用经典的内点法求解。顶层优化采用无模型Q学习进行知识学习和决策，底层优化的反馈回报可以有效地实现两者之间的协调。为了更有效地优化连续可控变量，提出了实数编码联想记忆。为了大大减少执行时间，通过抽象预测源任务的最优知识矩阵，采用知识转移的方法逼近实时新任务的最优知识矩阵。仿真结果表明，该算法能快速搜索到高质量的实时OEM最优解，其计算速度是8种经典启发式算法的2.75~29.23倍。
This study proposes a novel fast learning optimiser (FLO) for real-time optimal energy management (OEM) of a grid-connected microgrid. To reduce the optimisation difficulty, the non-convex real-time OEM is decomposed into a two-layer optimisation. The non-convex top-layer optimisation is responsible to determine the direction of tie-line power, and the heat energy outputs of combined heat and power units. Then bottom-layer optimisation is strictly convex with the rest controllable variables, which is solved by the classical interior point method. The model-free Q-learning is employed for knowledge learning and decision making in the top-layer optimisation, thus the feedback reward from the bottom-layer optimisation can effectively realise a coordination between them. The real-coded associative memory is presented for a more efficient optimisation of continuous controllable variables. In order to dramatically reduce the execution time, the knowledge transfer is adopted for approximating the optimal knowledge matrices of a real-time new task by abstracting the optimal knowledge matrices of the predictive source tasks. Simulation results demonstrates that the proposed FLO can rapidly search a high-quality optimum of real-time OEM, in which the computation rate is about 2.75-29.23 times faster than that of eight classical heuristic algorithms.
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