Intelligent load frequency controller using GrADP for island smart grid with electric vehicles and renewable resources

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Increasing deployment of intermittent power generation from renewable resources in the smart grid, such as photovoltaic (PV) or wind farm, will cause large system frequency fluctuation when the load-frequency control (LFC) capacity is not enough to compensate the unbalance of generation and load demand. Even worse, the system inertia will decrease when the smart grid is in island operating mode, which would degrade system damping and cause system instability. Meanwhile, electric vehicles (EVs) will be widely used by customers in the near future, where the EV station could be treated as dispersed battery energy storage. Therefore, the vehicle-to-grid (V2G) technology can be employed to compensate for inadequate LFC capacity, thus improving the island smart grid frequency stability. In this paper, an on-line reinforcement learning (RL) based method, called goal representation adaptive dynamic programming (GrADP), is employed to adaptive control of units in an island smart grid. In the controller design, adaptive supplementary control signals are provided to proportional-integral-derivative (PID) controller by GrADP in a real-time manner. Comparative simulation studies on a benchmark smart grid with micro-turbine (MT), EVs, PV array and wind power are carried out among the GrADP controller, the original PID controller and the particle swarm optimization (PSO) based fuzzy logic controller. Simulation results demonstrate competitive performance and satisfied learning ability of the GrADP based coordinate controller. Moreover, the impact of signal transmission delay on the control performance is also considered, and suggestions to address this issue are given in the paper.

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