Hybrid Data-Model Predictive Control for Enabling Participation of Renewables in Regulating Reserves Service

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A long-horizon hybrid data-model predictive direct power control (HD-MPDPC) scheme is proposed. The hybrid control scheme is for grid-side power electronic converters (PEC) in grid-connected renewable energy sources (RES). Computational burden of conventional MPDPC is alleviated by reducing the number of candidate voltage vectors to be examined in the cost function. This is achieved by employing data-driven forecast of RES output power to identify and eliminate the voltage vectors that do not counteract active and reactive power errors appropriately. RES power is forecasted using recurrent neural networks. The method for eliminating voltage vector candidates is inspired from direct power control, where output of active and reactive power hysteresis controllers along with the sector in which grid voltage vector lies are used to determine the switching states of the grid-side PEC. Thanks to reduced computational complexity, the hybrid predictive control scheme dispatches RES power more reliably over long horizons. This in turn enables RES as a regulating reserves service provider in power systems. Hardware-in-the-loop studies of a grid-connected wave energy conversion system verify reduced computational complexity of HD-MPDPC and its effectiveness in controlling RES output power over long horizons.

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IEEE Transactions on Industrial Electronics

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