Optimization Of Hydrogen Filling Station
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Due to the price hike of the fossil fuel and the concern of the global warming, the hydrogen economy can be envisioned to address those problems. After the Energy Bill was announced in 2005, a transition of national energy system from hydrocarbon to hydrogen had been recognized. However, safe and convenient refueling and widespread availability of hydrogen from various energy sources are the prerequisite for the development of hydrogen economy. This research focuses on the hydrogen filling station with on-site hydrogen production by electrolysis of water. The virtual wind farm and on-site installed photovoltaic (PV) supply the main and renewable energy for electrolyzers in the station. The utility electricity is served as an auxiliary energy resource to compensate the intermittence of wind and solar. Since the market clearing price (MCP) can change dramatically in the deregulated power market, the station can participate in the power market with a hydrogen storage tank as energy storage devices. Based on the forecasting of wind power with artificial neural network (ANN) model and MCP with the statistical model, this research proposes an optimization algorithm for the hydrogen production schedule and the strategy of power trading to optimize the production costs of the station.