Journal
RENEWABLE ENERGY
Volume 156, Issue -, Pages 820-836Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2020.04.080
Keywords
Variable renewable electricity; Electrolysis; Neural networks; Hydrogen production; Bidding strategy; Economic indicators
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The operation of power-to-X systems requires measures to control the cost and sustainability of electricity purchased from spot markets. This study investigated different bidding strategies for the day-ahead market with a special focus on Sweden. A price independent order (PIO) strategy was developed assisted by forecasting electricity prices with an artificial neural network. For comparison, a price dependent order (PDO) with fixed bid price was used. The bidding strategies were used to simulate H-2 production with both alkaline and proton exchange membrane electrolysers in different years and technological scenarios. Results showed that using PIO to control H-2 production helped to avoid the purchase of expensive and carbon intense electricity during peak loads, but it also reduced the total number of operating hours compared to PDO. For this reason, under optimal conditions for both bidding strategies, PDO resulted in an average of 10.9% lower levelised cost of H-2, and more attractive cash flows and net present values than PIO. Nevertheless, PIO showed to be a useful strategy to control costs in years with unexpected hourly price behaviour such as 2018. Furthermore, PIO could be successfully demonstrated in a practical case study to fulfil the on-demand requirement of an industrial captive customer. (C) 2020 The Authors. Published by Elsevier Ltd.
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