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access icon free Fast learning optimiser for real-time optimal energy management of a grid-connected microgrid

© The Institution of Engineering and Technology
Received 14/12/2017, Accepted 29/03/2018, Revised 06/03/2018, Published 04/04/2018

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.

Inspec keywords: cogeneration; content-addressable storage; learning (artificial intelligence); decision making; energy management systems; distributed power generation; concave programming; power engineering computing; power grids; convex programming; matrix algebra

Other keywords: combined heat-power units; predictive source tasks; classical heuristic algorithms; two-layer optimisation; continuous controllable variable optimisation; tie-line power; real-coded associative memory; knowledge learning; real-time optimal energy management; fast learning optimiser; classical interior point method; grid-connected microgrid; bottom-layer optimisation; nonconvex top-layer optimisation; FLO; decision making; heat energy outputs; optimal knowledge matrices; nonconvex real-time OEM; feedback reward; model-free Q-learning; convex optimization

Subjects: Distributed power generation; Algebra; Optimisation techniques; Optimisation techniques; Power system management, operation and economics; Power engineering computing; Knowledge engineering techniques; Algebra

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