Fast learning optimiser for real‐time optimal energy management of a grid‐connected microgrid

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.