Optimizing power-to-H2 participation in the Nord Pool electricity market: Effects of different bidding strategies on plant operation

RENEWABLE ENERGY

卷:156

Article

Janke, Leandro¹  McDonagh, Shane²  Weinrich, Soren³  Murphy, Jerry²  Nilsson, Daniel¹  Hansson, Per-Anders¹  Nordberg, Ake¹ 

[1] Swedish Univ Agr Sci, Dept Energy & Technol, Uppsala, Sweden

[2] Univ Coll Cork, MaREI Ctr, Environm Res Inst, Cork, Ireland

[3] Deutsch Biomasseforschungszentrum Gemeinnutzige G, Dept Biochem Convers, Leipzig, Germany

关键词: Variable renewable electricity;    Electrolysis;    Neural networks;    Hydrogen production;    Bidding strategy;    Economic indicators;   

DOI  :  10.1016/j.renene.2020.04.080

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

Free